rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 2: Count of children and childcare hours in the last 7 days, by provider of care and school closure status <!-- Start of picture text --> 80 A. Providers of household childcareJanuary-March 2021 80 B. Change in respondent childcarehours with school closures<br>70 All household adults 70<br>60 60<br>During school closures, women<br>50 50<br>Total hours per child<br>Before closures, women<br>40 40<br>Respondent During school closures, men<br>30 30 After full reopening, women Before closures, men<br>After full reopening, men<br>20 All household children 20<br>10 10<br>Non-household members<br>0 0<br>1 2 3 4+ 1 2 3 4+<br>Number of household children Number of household children<br>Hours of childcare in last 7 days<br><!-- End of picture text --> The figures show mean childcare hours in the last 7 days by number of household children (ages 0-17). Panel A presents data from RRPS round 4 (January-March 2021) which asks about childcare hours for each household adult, for all children in total, and for all non-household members in total. Previous rounds only ask about childcare hours for the respondent. The hours for ’all household adults’ include the respondent’s hours. Total hours per child is the sum of all childcare hours divided by the number of children.

Panel B presents data for female (black) and male (gray) _respondent_ childcare hours before school closures (dashed lines, recall data from RRPS round 4 about January-early March 2020), during school closures (solid lines, RRPS rounds 1-3 covering May-November 2020), and after schools fully reopened (dash-dot lines, RRPS round 4 covering January-March 2021). Data on childcare hours before and during the school closures period for other care providers are
Control Affects Labor Supply and Gender Norms,” NBER Working Papers 26294, National Bureau of Economic Research 2019.

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# **1. Introduction**

Understanding the implications of large-scale forced displacement events such as refugee influxes or population movements due to natural disasters requires representative and comparable information about the demographic and socio-economic characteristics of the displaced and host populations. This is particularly important when trying to shed light on aspects related to the selectivity of those who are displaced compared to host communities, or the impacts of displacement. Representativeness is central to minimize drawing biased conclusions as decisions to relocate and where to locate in host areas are likely not random. In addition, comparability is key as contrasts between displaced and hosts are a core element of these types of analysis. National household surveys typically exclude displaced populations, particularly at the onset of these events. Humanitarian data collects information on those displaced but these are not designed to answer questions related to selectivity or impacts as they are developed to inform the humanitarian response. One drawback of these data is that they typically exclude representative information on the host populations.

Using the same representative survey to cover both hosts and displaced arises as a desirable strategy, however it is not a straightforward effort. One key challenge is having complete or updated sampling frames to draw the samples of both hosts and arriving migrants. Sampling frames of host populations are typically available but, in many cases, exclude forcibly displaced populations. This is certainly the case when the focus is collecting information right after a mass displacement event as it is unlikely that the sampling frame for that area will be updated to reflect the most recent population. On the other hand, population counts on the recently displaced usually collected by humanitarian agencies may face challenges in providing complete or accurate counts, particularly when there is significant movement of populations within the affected area or when the displaced spread across geographic areas. Additional challenges arise in identifying and counting the displaced in cases where they may not be willing to self-identify.

This paper describes a sampling approach that triangulates different sources of data to design a representative survey in the context of the recent Rohingya displacement from Myanmar to the
� � _μFi_ . Notice that, for those fishermen who adopted before _t_ 0 + 5, _γ_ 1 _Di_ (5) has been added � to _μFi_ . The diagram thus gives the long-run expected gains from technology adoption, which will also be used throughout the rest of this paper. The straight line depicts the 45<sup>_◦_</sup> line. According to these results, three fishermen suffered a loss in sales of more than 1%, 2 experienced virtually no change (less than 1% change), while 20 enjoyed increases in average sales between 3.5 and 158%. The average change equals 40.2% with a standard deviation of 46.8%.

# **5.2 Determinants of the Timing of Adoption**

Since adoption in the context of this study can be interpreted as a one-time transition from one state, catamaran fishing, to another state, fibre boat fishing, the timing of the individual adoption decision is most suitably modelled using methods from the statistical analysis of survival data. In typical applications of these methods, the outcome variable is the time of a ”failure”, e.g. of a light bulb, conditional on certain characteristics of the item considered (e.g. the manufacturer of the bulb). In our application the ”failure” event is the adoption of the new technology.

In cases where the technology is divisible, like the adoption of new seeds in agriculture, the farmer with several plots can choose on how many of them to try the new technology. In contrast, a fishing boat is by nature an indivisible productive asset for a household. Moreover, switching technologies is expensive, while in agriculture, a farmer can return to the old crop variety in subsequent growing cycles without incurring a cost from switching back. To summarize, in the context of adoption of new crop varieties in agriculture, the adoption decision is typically both divisible and reversible, while in the present setup, neither of these
(Pov. 2)|[0.816]|[0.297]|[0.444]|
|Number of clusters|224|182|224|
|Observations|1045|468|1045|

_Notes_ : This table shows OLS estimates and the associated _p_ -values (in brackets) on student and parental outcomes measured after two academic years of exposure to the API program under the second experiment designed and implemented by the authors in collaboration with the government. For a detailed descriptions of the test scores used in this table, see Appendix A.2. The dependent variables in the first and third columns are standardized with respect to their means and the standard deviations in the control group. The dependent variable in the second column is computed from administrative school records (see Appendix A.1). All _p_ -values account for clustering at the school level.

results reveal limited variation in program impacts along both poverty measures, with effect sizes for the interaction terms with the indicator variables for the program modalities that are not statistically different from zero, and not statistically different from each other.

Taken together, the evidence shown in this section documents that differences across populations (if any) are unlikely to represent a meaningful threat to scalability in this context. There is a very high degree of similarity in observable characteristics between the experimental sample and the overall population of schools in Chiapas. Program impacts are also not heterogeneous along the determinants of the rollout of the program under the government implementation, which is indicative of the fact that the community/school targeting process is unlikely to play a role for the scalability of the program.

23
One of the main roles of guarantee schemes is precisely to compensate for the lack of collateral hindering SME access to finance. However, the complete absence of collateral may generate adverse selection and moral hazard effects and ultimately result in large losses for the scheme. To mitigate this risk, the scheme should be allowed to require whatever collateral is available up to reasonable limits.  For example, in France and in Canada, the schemes are allowed to require personal guarantees but these guarantees are capped respectively at 50% and 25% of the loan value.

_Reviewing Collateral Rules in MENA_

**Most MENA guarantee schemes allow banks to take collateral but do not impose ceilings** (Table 12). This may contradict the objectives of PCGs, **a** lthough there is little information on additional collateral provided by guarantee users in MENA. This information should be collected and disclosed.  Some enterprise surveys conducted by the World Bank indicate that banks in MENA tend to require high levels of collateral even when using

19
‘smaller’ publicly listed exporters, we look at their average sales and compare them to average sales of firms in Pakistan. In this paper, ‘smaller exporters’ are defined as those in the bottom third quintile of the distribution of sales. These exporters employ about 100 fewer employees than larger exporters.<sup>20</sup> They exported on average about 760 million PKR in 2017, which is close to the

> 19 The survey evidence is only anecdotal. A total of 80 firms in the textile and apparel sector were interviewed in early 2019 through an online platform. See World Bank (2019b) for a discussion of the survey results.

> 20 Based on available employees’ data, smaller exporters employ about 330 employees on average versus an average of 480 employees for larger exporters. According to the State Bank of Pakistan, SMEs have up to 250 employees.

20
# **Scenarios**

13. As noted above the model was run for the Business as Usual Scenario, plus six scenarios reflecting a menu of instruments that look at the impacts of reducing PM10 and other particles through different tax instruments (see Figure 2). Details of the different scenarios are given in Table 1.

## **Figure 2:  How the CGE Model Works**

<!-- Start of picture text --> CGE Model Instruments Outputs<br>• Simulate Indian economy • Endogenous energy • GDP<br>under various scenarios efficiency and end of pipe • PM10 Emissions<br>covering 57 sectors technology improvements<br>• Environmental taxes<br>• Transition to cleaner and<br>cost effective production<br>technologies and processes<br><!-- End of picture text --> <!-- Start of picture text --> Inputs<br>• Social Accounting Matrix<br>created for India using GTAP<br>data and inouts from<br>various government sources,<br>academic literature<br>• Estimates on potential<br>emission reduction<br>programs<br><!-- End of picture text --> <!-- Start of picture text --> Outputs<br>• GDP<br>• PM10 Emissions<br><!-- End of picture text --> Two types of taxes are modeled: Domestic fuel tax ( _to_ ) is added to the producer price ( _ps_ ) for coal, oil, natural gas and refined oil to obtain the market price ( _pm_ ):

- (1)  eq. 1

The tax rate increases at a decreasing growth rate starting from 2012. Tax rates (to) used in different scenarios are displayed in Table 4.

- (eq. 2)

Imported fuel tax ( _tm_ ) is applied to the import price ( _pms_ ) of coal, oil, natural gas and refined
%|35.6%|45.8%|28.7%|36.4%|
|N obs(based on total debt)|18|39|149|14|168|

Source: Worldscope Data Quick Ratio is a measure of liquidity, which plays a role of a buffer against shocks to financing and cash flows. It’s defined as the ratio of current assets, minus inventories, over current liabilities. The quick ratio measures a company's ability to meet its short-term obligations with its most liquid assets. The higher the quick ratio, the better the liquidity of the company. Jordan reports a fairly high quick ratio of 1.92, which suggests that companies have nearly twice the amount of liquid assets to liquid liabilities and are well positioned to cover liquid liabilities with liquid assets. This is above Egypt and Turkey, while below Israel.

An important measure of corporate ability to repay its debt obligations is Interest Coverage Ratio (ICR), defined as EBITDA – Earnings before Interest, Taxes, Depreciation and Amortization – over interest expense. Interest Coverage shows the ability to cover interest expenses using the current period earnings. The average interest coverage in Jordan is relatively high – 8.96, which is the highest (except Morocco, which contains a number of outlier observations with very low interest payments).

While the average interest coverage is useful for determining how vulnerable the average firm is, it is also important to know how many firms have dangerously low interest coverage, i.e. statistically speaking, what is the low end of the distribution of interest coverage. The interest coverage below one might indicate that the firm will find it difficult to cover interest payments with current earnings, and may fall into arrears. However, the firm might still be able to cover interest by using some liquid assets. Therefore the ratio of interest expense below 0.75 is a more 5
3.3|10|
|grain milling***|7-low|3.3<br>3.3|10|
|processed food|7-low|3.3<br>3.3|10|
|textiles and apparel|13-low|3.3<br>3.3|10|
|petroleum refineries|2-low|3.3<br>3.3|10|
|chemicals|34-medium|1.9<br>10|15|
|fertilizers and pesticides***|34-medium|1.9<br>10|15|
|iron, steel and metal products|7-low|3.3<br>3.3|10|
|glass and cement|0-17-low|3.3<br>3.3|10|
|*Based on average R&D expenditures s f<br>**We evaluate transportation as a mediu<br>These are transportation, chemiicals and<br>multinationals operatingi in the US was<br>TechnologyLinkages," at 'http://www.n|or theyears 2004 and 2005. The averag<br>m R&D sector since three sectrors dom<br>computers and electronics. Moreover,<br>performed in the same three sectors. See<br>sf.gov/statistics/seind04/c4/c4s5.htm.|e for all US industries was 36. See table<br>inate R&D expenditures of US multinati<br>about two-thirds of all R&D expenditure<br>"U.S. and International Research and D|E1 for details.<br>onals operating abroad.<br>s of foreign<br>evelopment: Funds and|
|**SOURCE**: R&D and sales data from N<br>_Development: 2005, Data Tables_. Avai<br>***Food is the proxy for grain mlling; ch
Both the REER index and the share of primary commodities in total exports are retrieved from the UNCTAD database.<sup>13</sup> Labor tax data are from the World Bank, Doing Business project.<sup>14</sup> The proxy variables for governance are from the World Bank, Worldwide Governance Indicators, and the remaining proxy variables are from the World Bank, World Development Indicators database.

# _4.2.2 Results_

The results show that of the eight (8) factors analyzed, only three (3) have positively evolved to support structural change in Guinea. The three factors are: the share of primary commodities in total exports, access to electricity, and control of corruption. The growth rate of the share of primary commodities in total merchandise exports has declined over time. While during the period 2006-10, the share of primary commodities in total merchandise exports has grown by 0.6 percentage point per year on average, this has declined by 1.6 percentage points during the period 2011-15. Thus, between 2006-10 and 2011-15, the dynamics of primary commodity exports should have been favorable to structural change. The average annual growth rate of access to electricity has increased by 0.4 percentage point between 2006-10 and 2011-15.

**Table 3.** Guinea: Dynamics of drivers of structural change

||Averag<br>growth rat|e annual<br>e<br>annual|Variation of<br>growth rate|average<br>Positive<br>(+)/Negative (-) evolution|
|---|---|---|---|---|
||200610|2011-15|||
|Primary<br>commodity’s<br>share<br>of exports|0.6|-1.6|-2.2|+|
|Real effective exchange rate index|8.9|11.7|2.8|
detailed evidence on enterprise lifecycle dynamics, including factors determining their growth trajectories and survival rates (Banerjee and Duflo, 2014). This gap is particularly acute for informal enterprises, which constitute the majority of rural non-farm businesses but are often excluded from standard enterprise surveys. Additionally, the role of technology and digitalization in transforming rural non-farm opportunities remains understudied. While digital technologies are rapidly penetrating rural areas, we have limited understanding of how they affect non-farm employment opportunities and enterprise performance (Foster and Rosenzweig, 2022).

The human capital dimension of RNFE presents several interconnected challenges. Our understanding of skill requirements and returns to different types of skills in rural nonfarm employment remains incomplete. While education’s importance is well-documented, we lack detailed evidence on which specific skills are most valuable for different types of non-farm employment and how these skill requirements are evolving (Klasen, 2019). This knowledge gap extends to gender-specific challenges. While women’s lower participation in non-farm employment is well-documented, we lack comprehensive understanding of how household responsibilities, social norms, and local economic conditions interact to influence women’s participation decisions (Chatterjee et al., 2015). The broader economic context of RNFE also requires deeper examination, particularly regarding its relationship with agricultural transformation and migration patterns. While theoretical links between agricultural transformation and non-farm employment are well-established, empirical evidence on how changes in agricultural productivity and commercialization affect local non-farm employment opportunities remains limited (Barrett et al., 2017). Similarly, while rural-urban migration is extensively studied, we lack detailed understanding of how temporary migration affects household decisions regarding non-farm enterprise creation and employment (de Janvry and Sadoulet, 2019). Emerging challenges and policy effectiveness represent the final frontier in RNFE research. The impact of climate change on rural non-farm employment is particularly concerning. While climate change’s effects on agriculture are well-studied, its implications for rural non-farm activities and their role in climate adaptation strategies remain poorly understood (Carleton and Hsiang, 2016). Furthermore, while numerous programs aim to promote rural non-farm
construction services and hotels and restaurants are characterized by “low” skill intensity but also by “low” or “medium” productivity-enhancing traits: formal worker training programs, use of foreign technology, exports (direct and indirect), introduction of new products and new processes, and R&D spending. Such non-traded service sectors could also be constrained by the pace of expansion in domestic demand. For instance, the productivity-enhancing structural change in Africa has been attributed to an expansion in low-end services, but it appears that this expansion might be unsustainable, owing to limited demand beyond the domestic market (McMillan, Rodrik, and Sepulveda 2017).<sup>6</sup> Among service sectors, tourism and wholesale and retail trade are perhaps exceptions in that they are both tradable and create jobs for unskilled labor. Based on the analysis of firm-level data across manufacturing and service sectors from a sample of six LMICs in Nayyar, Cruz and Zhu (2018), wholesale and retail trade was classified as “low” skill-intensive but “medium” in tradability, linkage effects, use of foreign technology, and on-the-job learning programs. Similarly, many lowincome countries have used tourism services to diversify their exports away from volatile primary sectors. In Uganda, for instance, services account for just over half of total exports, with 45 percent of that figure made up of tourism. Furthermore, technology has the potential to transform some low-productivity services such as construction and tourism (for example, through e-commerce platforms), as it allows services to be produced and traded just like goods and hence generate greater employment opportunities. Given that barriers to international trade are higher for services than for goods (Miroudot, Sauvage, and Shepherd 2012), there is potential for exports of these services to gather speed if appropriate policy actions are taken.

The issue of the _quality_ of employment among lower-end service activities, which are the large employment creators for unskilled labor, is particularly relevant in light of the labor market polarization resulting from changes in technology and
moderate or severe food insecurity. Severe food insecurity alone affects an estimated 38 million adults, or 23% of the adult population (Table S14). Using the pre-COVID-19 LSMS-ISA survey data, we can calculate household annual per capita income to generate consumption quintiles (see Supplementary Materials - Methods). Examining heterogeneity across consumption quintiles, poorer households (in lower quintiles) suffer significantly greater prevalence of food insecurity (Figure 2C). Further, there is significant heterogeneity in the prevalence of food insecurity, across countries (Table S15). Nigeria suffers from the greatest prevalence food insecurity where an estimated 76% of adults (63 million) are moderately or severely food insecure. This is followed by Malawi with 68% (6.2 million), Ethiopia with 47% (24 million) and Uganda with 33% (6.9 million). As with loss of income, there is a high degree of heterogeneity based on the gender of the household head (Figure S1B). Since the global pandemic began, female-headed households have significantly higher prevalence of moderate and/or severe food insecurity than male-headed households, though food insecurity is decreasing over time at an equal rate for adults in both types of households (Table S16).

Levels of concern are high across all four countries: an estimated 257 million individuals (78%) are concerned that someone will fall ill with COVID-19 and 292 million individuals (88%) are concerned about COVID-19-related financial threats (Table S17). Higher prevalence of food insecurity is associated with concerns about household health and financial status, especially in Malawi (Figure 2D). There is substantial heterogeneity in the degree of concern over health and finances based on food insecurity, initial wealth, and the gender of the household head. Those concerned about the financial threat of COVID-19 have a significantly higher prevalence of food insecurity than those unconcerned about financial threats (Table S18). At the same time concerns about household finances do not vary by the gender of the household head nor across pre-COVID19 consumption