rafmacalaba/data-use-mentions-extended huggingface dataset:
# **VI. Conclusion**

Having used the VIIRS data as an economic proxy across modeled flood, earthquake and typhoon damage, the results were not convincing. The analysis in this paper shows a lack of significance across all hazard types and all countries. Furthermore, looking at specific cases for each hazard type only yielded strong results for typhoon Haiyan of the Philippines.

Overall, the cause for this is unknown, as multiple approaches were taken to try to correct the data and see if a connection between the nightlights and the disasters existed. As previously mentioned, the most likely culprit is the noise in the VIIRS data that is due to measurement issues and cloud cover. Another likely factor is that our sample of low‐income countries and the low correlation between monthly VIIRS and GDP is not a good fit for a broad analysis like this. These different factors might also explain why the authors found stronger results using the annual – and in most ways inferior ‐ DMSP data in Skoufias, Strobl and Tveit (2017).

That being said, the data and the methodology have shown earlier that there is merit in their use, with this paper simply demonstrating some of the limits of the data and that when they are used it is important to consider the temporal and spatial scale of analysis and carefully review both the data and the results to account for factors such as noise, cloud cover, light levels and urbanization.

As a follow‐up, it could be worthwhile looking at more specific cases to see if one could find an effect at a more local scale. One could also try different regression types, as this type of analysis could lend itself well to, for example, a difference‐in‐difference approach. Finally, with the addition of secondary data from other sources, such as for example daytime satellite images, vegetation images, national census data or similar, one could use machine learning methods to try to account for or correct some of the noise.

31
**Figure 2: Correlation between the relative intensity of communication versus manual tasks and the share of teleworkable jobs in each of ISCO three-digit occupations in 2018**

<!-- Start of picture text --> 531 232413351 14225 2 262 1 234125111411251133235 313441 3 43 2 231121 23324 2 212 243 41 261<br>122<br>216<br>143431 331264<br>213<br>352<br>333<br>214<br>342<br>341<br>211<br>263<br>422<br>311 421265<br>441 132<br>516<br>622831 612<br>961 732 315 343432511 335<br>226<br>92175263633721728128 98111 39486218833962763283427 3871 613 2 525911753257513711611 1 8237481393359418328 1 6512742731754 5 541524121532 3 312432514314 3 5233 2 325 12 1312524141323522222 223 221 952<br>1 1.5 2 2.5 3 3.5<br>Relative intensity of communication / manual tasks<br>1<br>.8<br>.6<br>.4<br>.2<br>Share of telework jobs in occupation group 0<br><!-- End of picture text --> Source: Own elaboration based on EU-LFS data and O*NET surveys. Note: The size of the circles represents the relative size of each ISCO three-digit occupation in employment. The correlation uses the relative size of each occupation as weights.

# 3.3 Migrant-Native Gaps in Income and Health Risk Exposure to COVID-19

Before illustrating the econometric analysis, we present several descriptive statistics of our sample of working age individuals (ages 25-64) from the EU Labour Force Survey. As of 2018, there were 270 million working-age people residing in Western European countries. Almost one out of five (18.7%) of them were foreign-born (Table 1). These immigrants originally come from various high-income Western European countries (3.1%), new EU
contribution to global progress in terms of both poverty and hunger, while South Asia has done so much more modestly.

**3. The Asian Contribution to Turbulent World Food Prices Today... and to Their**

# **Possible Solution**

Much has been said about the challenge of feeding today’s global population of 7 billion, and tomorrow’s 9 billion, while dealing with severe threats to environmental sustainability and, more recently, the rising and volatile cost of food at both the global and national levels (FAO 2009; Godfray 2010; Nelson et al. 2010; USGC 2011; Alexandratos and Bruinsma 2012; EJDR forthcoming). The key question is whether the pace of future agro-technological progress will be sufficient to meet the increasing demand of food from the growing population. Using FAO data, Beddow, Pardey, and Alston (2009) have shown that average annual crop yield growth rates for corn, wheat, rice, and soy declined between1961 and 1989, and again between 1990 and 2007. However, these global rates conceal marked regional differences. Increases in productivity in China went from 2.29 to 4.45 percent per worker and from 2.81 to 4.50 percent per hectare from 1961 to 1989 and from 1990 to 2005 (compared with world levels of 1.12 percent and 2 percent), respectively. However, this spectacular growth in productivity is not a phenomenon that extends to the rest of Asia. When China is excluded, the growth of productivity in Asia actually slowed during the period in question, as it did in the rest of the world, with the exception of Latin America. Interestingly, Alexandratos and Bruinsma’s (2012) projections maintaining current agricultural yield growth suggest that the world would be producing more grain than required by the estimated demand through 2050. Yields would expectedly increase by 44 kg per hectare per 6
seem to have any impact on beneficiaries’ probability of being employed in any activity one year after graduation. On the one hand, this is explained by the fact that the entrepreneurship track is only effective in increasing self-employment for a limited (although significant) number of students. On the other hand, the evidence does not

> 20 These results are comparable with data from a tracer survey of university graduates from the class of 2004, which found that 46 percent of graduates were still unemployed 18 months after graduation (MFPE and World Bank, 2009).

18
Policy Research Working Paper 9075

# **Abstract**

This paper examines how parents’ early childhood exposure to a refugee crisis impacts their children’s health status. Based on Demographic and Health Survey data from Tanzania with the migration history of mothers and fathers, the analysis exploits geographical, time, and cohort variations using shock intensity and distance from refugee camps to instrument treatment. The findings show that children who were born to parents who were living closer to refugee camps during their early childhood have lower height for their age and are more likely to be stunted. The results are robust to alternative functional forms of the distance from camps, alternative specifications of the treatment and control groups, alternative cohorts of mothers, and several placebo tests.

This paper is a product of the Fragility, Conflict and Violence Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at swangsonne@worldbank.org and pverme@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
nighttime lights where the DMSP-OLS data observes no light (see Appendix S3 for a comparison of DMSPOLS data and DMSP-OLS-like data). This difference results from VIIRS’s ability to capture low-level light that DMSP-OLS cannot capture; when simulating DMSP-OLS-like data from VIIRS data, some of the lowlevel light is retained. However, these differences should not have a large impact on results. First, these differences are concentrated in low-lit regions; Appendix S4 shows that average nighttime light trends are relatively consistent across DMSP-OLS and DMSP-OLS-like data. Second, as the empirical strategy section later describes, we control for factors common to all units in a given time period, such as a difference in the satellite used to capture nighttime lights.

While nighttime lights are now a staple of the economic literature on roads programs, including in Africa (Donaldson and Storeygard, 2016), the GlobCover data is a more recent addition to the toolbox. This dataset is produced by the European Space Agency (ESA), and is available annually from 1992 to 2018 at 300m resolution (European Space Agency, 2017, 2019). It classifies each pixel into one of the 36 land cover classes defined in the United Nations Land Cover Classification System. From this dataset, we use two land cover classes. First, we use land classified as urban, defined as 300m pixels covered mostly ( _>_ 50%) by artificial surfaces and associated areas. Second, we create a cropland category, defined as 300m pixels where over 50% of the pixel is cropland (this aggregates three cropland categories in the underlying data).

# **3.2 Aggregating Data**

We rely on Kebeles (Ethiopia’s smallest administrative unit) as our primary unit of analysis. We use a dataset that includes the boundaries of Kebeles for all regions in Ethiopia except Somali Region (15,670 Kebeles).<sup>6</sup> For Somali Region, we rely on boundaries of administrative units one higher than Kebeles—Woredas (N=44
tices. These practices were measured using items describing parent-child relationships adapted from the Longitudinal Study of Australian Children (Zubrick, Smith, Nicholson, Sanson, & Jackiewicz, 2008). The questions covered a range of possible practices. Parenting practices are specific behaviors that parents use in their interactions with their child. These include, for example, using reprimands, giving praise, showing physical affection and setting and keeping with rules for behavior (Bornstein & Zlotnik, 2008; L. O. Walker & Kirby, 2010).<sup>11</sup> Following a recent narrative review of parenting, three hierarchical summary constructs are proposed: warmth, consistency, and hostility (Jansen, Daniels, & Nicholson, 2012). We compute these constructs by calculating within each of them the proportion of items the caregiver responds as having undertaken frequently.

Many dimensions of parenting have been shown to influence child development, although different terms are often used to describe overlapping or similar constructs. Generally, children show better developmental outcomes when exposed to parenting that is high on the dimensions of warmth, consistency, inductive reasoning and self-efficacy and low on the dimensions of irritability, hostility and over-protectiveness (Bayer et al., 2011; Bradley et al., 1989; Chang, Schwartz, Dodge, & McBride-Chang, 2003; Chao & Willms, 2002; Paterson & Sanson, 1999; Pettit & Bates, 1989). Parenting styles are multidimensional categories of behaviors and attitudes which classify parents according to where they lie on the distributions of some specific parenting dimensions (Darling & Steinberg, 1993). One of the most well-known classifications of parenting style is that applied by Baumrind and others (Baumrind, 1991; Darling & Steinberg, 1993; Maccoby & Martin, 1983) defining four parenting styles based around levels of over-controlling and responsive parenting: authoritative (high control, high responsiveness), authoritarian (high control, low responsiveness), indulgent/permissive (low control, high responsiveness), and uninvolved/neglectful parenting (low control
to the US, becomes the weight of the estimated function. In practice, the estimates differed some, but not greatly from those previous.

Figure 4 presents the elasticity of migration to the US with respect to FDI above the axis and below the axis, the impact of an investment of $US100 million on the number of people who migrate. The elasticities are quite low-- on average a doubling of FDI would lead only to a 1.5-2% reduction in migration. Further, for many states the impact of a single $US100 million plant opening decreases migration little. However in some areas, such as Veracruz, where there is little FDI and a substantial amount of migration, the decline in outflows is substantialin the first case approaching 4,000 people. Again, these are intended as back of the envelope calculations that rely heavily on the assumption that the US is considered broadly as the 32<sup>rd</sup> destination for a migrant leaving his state. And again, we are only capturing the impact of redirection of existing migration to other Mexican states, not the overall decline in migration so these must be seen as lower bounds.

# **Conclusions**

This paper evaluated the mechanisms through which NAFTA related variables might work to reduce migration to the US using data from within Mexico. We make three contributions to both the literature on migration more generally, and the impact of trade and FDI flows on migration.

First, we offer the first estimates of determinants of migration flows within Mexico and make some advances in the LDC migration literature. In line with recent innovations in the industrialized country literature, we generate proxies for both the level of amenities, costs of living and networks and generally find significance. Contrary to much of the literature, our specification generates intuitive and significant signs on home state labor market variables: a rise in home earnings or employment levels does deter migration. We argue that part of the traditional poor performance of the origin state variables is that they capture a
# **Table S2.6 Estimating the effects of percentage changes in domestic food prices on changes in the $1.25/day poverty headcount for different gaps between successive poverty observations**

||**R**<br>|**obust regression result**<br>|**s**<br>|
|---|---|---|---|
|Regression Number|**1**|**2**|**3**|
|Gap between successive poverty observations|**1-4 years**|**1-3 years**|**1-2 years**|
|Change in log of domestic food prices|-22.36***|-21.59***|-10.85|
||(5.95)|(6.45)|(8.04)|
|Number of observations|273|248|198|
|Observations from low income countries (%)|15.23%|11.27%|8.65%|
|||**IV-GMM results**||
|Regression Number|**4**|**5**|**6**|
|Gap between successive poverty observations|**1-4 years**|**1-3 years**|**1-2 years**|
|Change in log of domestic food prices|-50.02|-63.79|6.70|
||(37.84)|(45.76)|(40.20)|
|Number of observations|273|248|198|
|Observations from low income countries (%)|15.23%|11.27%|8.65%|

Source: Author’s estimates.

Notes: See text for definitions of the variables. *, ** and
AICD rail operator database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ * With 2.5 passenger-km equivalent to 1 traffic unit, 1 tonne-km equivalent to 1 traffic unit. — = Not available.

Zimbabwe‘s railways are interconnected with other national networks along the North–South Corridor, allowing for through traffic across Zambia, Zimbabwe, Tanzania, and South Africa. But while the rails are physically connected and of compatible gauge, there is a lack of reciprocal access rights between operators that would allow through train service due to the lack of arrangements for servicing other operators‘ locomotives that may experience technical difficulties. This means that locomotives need 34
.00140)|(0.00156)|(0.00133)|
|Distance to market squared|-0.000120|0.000255|-8.74e-05|-0.000130|
||(0.000266)|(0.000422)|(0.000281)|(0.000264)|
|Teff pricet-1||0.231***<br>(0.0378)|||
|Wheat pricet-1|||0.119***<br>(0.0383)||
|Maize pricet-1||||0.126***<br>(0.0430)|
|Constant|-5.753*|1.148|-6.271*|-6.120*|
||(3.331)|(6.376)|(3.503)|(3.311)|
|R-squared|0.022|0.063|0.034|0.031|
|Number of individuals|1,021|962|1,019|1,021|
|Observations|1,923|1,730|1,838|1,913|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The coefficients on each of the crop prices are positive and strongly statistically significant. This suggests that changes in crop prices are associated with improved scores for height for age. For individual characteristics, age is negative and statistically significant. However, the impact of age is non-linear. Proximity of the community to a main road is associated with positive outcomes for height for age. This suggests that measures that increase the connectivity of rural communities will contribute to improving health and nutritional outcomes for children. Consistent with the results of Cintron _et al_ (2016), many of the control variables are not significant since these time-varying dimensions
sup><br>|113941.162|124502.742|
|Prob _> χ_<sup>2</sup>|0.000|0.000|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Each model includes exporter, imp-, orter, industry and year fixed effects. Robust standard errors reported.

# **6 Conclusion**

Although the effects of institutions on the trade pattern are now extensively studied, little is known about the exact mechanisms through which this effect operates. In this paper, we disentangle the influence of institutions on trade into two margins: the probability of exporting (extensive-margin) and the volume of export conditional on exporting (intensive-margin).

Our findings both complement and extend the existing empirical literature on institutions and trade flows. Using highly disaggregated product-level and firm-level trade data, we confirm that institutional quality does have a robust and significant effect on international trade patterns. Specifically, we find that countries with better institutional quality export products that are intensive in contracts while those with poor institutions specialize in institutionally less-intensive products. This finding is robust to the use of alternative measures of institutional quality as well as different estimation techniques. Nonetheless, the role of institutional quality does not supersede that of factor endowments in general and that of human capital in particular.

24
.5|
|**C Ditibti f C**|$|63.8|52.3|49.9|39.2|
|**urrency sruon o ross-**<br>**Border Lending**|£|2.8|3.9|5.9|7.4|
||€|23.1|22.8|31.3|41.2|
||Other|10.3|21.0|12.9|12.2|

Source: Bank of International Settlements.

* Currency distribution of international bond issues data from 1993.

** Because two currencies are involved in each transaction, the sum of the percentage shares of individual currencies totals 200% instead of 100%. Data from Triennial Central Bank Survey. Note: ECU used as internal accounting unit for bond issues and lending in the European Community beginning in 1979.  It was later replaced by the Euro at equal parity in 1999.

18
some key features of these data for our study.<sup>2</sup> It is important to first define and characterize the distinction between the organized and unorganized sectors in the Indian economy. These distinctions in the Indian context relate to establishment size. In manufacturing, the organized sector is comprised of establishments with more than ten workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register

> 2 For additional detail on the manufacturing survey data, we refer the reader to Kathuria et al. (2010), Fernandes and Pakes (2010), Hasan and Jandoc (2010), Nataraj (2011), and Ghani et al. (2011b). Dehejia and Panagariya (2010) and Ghani et al. (2011b,c) provide a detailed overview of the services data and its important characteristics.

5
2018). Wiese and Eriksen (2024) investigate linkages between expectations of mobility and willingness to pay for improvements in public education and health care using data from the 2016 round of the LiTS, framing it as a test of the POUM hypothesis with respect to indirect rather than direct redistribution, which was the focus on the majority of earlier studies. However, willingness to pay additional taxes for investments in education or health care might not necessarily be driven by redistribution motives, as respondents can view such investments as benefiting themselves and their families; thus support 4
# **Is Poverty in Africa Mostly Chronic or Transient? Evidence from Synthetic Panel Data**

Hai-Anh H. Dang and Andrew L. Dabalen<sup>*</sup>

# **JEL** : C15, D31, I31, O10, O57

Key words: welfare dynamics, poverty, vulnerability, middle class, pro-poor growth, synthetic panel, household surveys, Africa

> * Hai-Anh Dang <u>(hdang@worldbank.org) and Andrew Dabalen (adabalen@worldbank.org) are respectively</u> Economist with the Survey Unit, Development Data Group, and Manager, Global Poverty Practice, the World Bank. We are grateful to Kathleen Beegle, Luc Christiaensen, Francisco H.G. Ferreira, Tomoki Fuji, Isis Gaddis, Yoseph Getachew, Stephan Klasen, Peter Lanjouw, Michael Lokshin, Jacques Morisset, Erik Thorbecke, and participants at the CSAE conference (Oxford), workshops and seminars at Singapore Management University and the World Bank for helpful discussions on earlier versions. We thank Rose Mungai, Vasco Molini, Shinya Takamatsu, and Ayago Wambile for their help with the data. We would further like to thank the UK Department of International Development for funding assistance through its Strategic Research Programs.
to finance, as emphasized by the G20 Principle for Financial Inclusion _._ Household and enterprise-level survey questions can provide governments and regulators with the data they need for designing reforms and then monitoring the effectiveness of those reform measures. For example, OIC governments can encourage Islamic banks to launch financially inclusive accounts, shifting G2P payments (increased transaction volume) through such accounts to enhance their viability.

# **V.3. Policy Recommendation III - Ensure a Level Playing Field for Islamic Microfinance, SME, and Micro** **_Takaful_**

**The lack of** **_Shariah_ -compliant micro-finance services is constraining financial inclusion to a proportion of the population.** The growth of Islamic microfinance will depend to a large degree on whether financial institutions can develop sufficiently attractive financial products and services, which are competitive with conventional products in terms of pricing, transparency, processing time, and burden on the client. This will be determined in part by their capacity and systems, but regulators can also provide a more level playing field for Islamic microfinance. This extends beyond financial sector regulation to the tax code, as Islamic financing that involves additional transactions such as passing on a property title, may have tax implications such as capital gains tax, which are not applicable in conventional deals. OIC governments should also extend access to a broader range of financial services through existing branch networks of Islamic banks, which can offer a unique network of existing outlets as a delivery mechanism for _Shariah_ -compliant financial services.

**When designing the financial inclusion reform plan, OIC governments should take three points into their consideration specifically** i) allowing banks to expand access through agents and use of technology (e.g. mobile phones), ii) providing a _Shariah_ -compliant finance company model for microfinance and microinsurance, and iii) removing interest rate caps for microcredit and strengthen customer protection laws.

**Competition policy can also contribute to further SME lending.** (Rocha 2010) suggests that private