rafmacalaba/data-use-mentions-extended huggingface dataset:
>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> 30
>60%<br>50%<br>40%<br>0% 50% 100% 150% 200%<br>Subdistrict average yield, as percentage of average historical yield...<br>positive<br>claim payment is<br>Probility that WBCIS<br><!-- End of picture text --> Note: The lines show the point estimate and 95% confidence intervals for an Epanechnikov kernel with a bandwidth of 0.8.

However, the interpretation of this analysis requires care due to three data limitations. First, the yield data are based on crop cutting experiments which may not be wholly reliable. In past years the results from crop cutting experiments determined the claim payment due to farmers under the NAIS, and there are concerns both about measurement error and that some yield figures may have been underreported to trigger claim payments (Mahul et al. 2011). However, there is limited 10
adequate employment and 44% are employed informally. Last, average monthly earnings were $331 and average hourly wages stood at $2.6 (at current prices).

The table also reports our estimates of the number of Venezuelan migrants in each canton as of the summer of 2019. The average across _cantons_ is 2,312 individuals, with a wide range of variation. While it is estimated that some _cantons_ had zero Venezuelan migrants, in Quito the estimated count is 87,505 individuals. As a percent of the population in the canton, the average across _cantons_ is 3.8% but also displays large variability.<sup>22</sup> As a percent of the population in Ecuador, our mobile records data imply that Venezuelan migrants make up 2.8% of the overall population and 3.7% of the working-age population.

Our analysis relies on the comparison of the changes in labor market outcomes (for different socio-demographic groups) across _cantons_ that differ in the _density_ of Venezuelan migrants relative to the canton’s population. More specifically, we classify all _cantons_ into three groups: low density ( _<_ 2 _._ 5%), medium density (between 2.5% and 5%) and high density of Venezuelans ( _>_ 5%). Our regression analysis will compare the evolution of several labor market indicators in the medium and high density groups relative to the low-density group before and after 2016, which we take as the first year with important inflows of Venezuelan migrants.<sup>23</sup> The cantons of Quito and Guayaquil together make up 32% of the working-age population in Ecuador. The low-density group contains 96 cantons, the medium density group contains 51 cantons and the high density group contains 31 cantons. In terms of working-age population, the low, medium and high groups account for 33%, 53% and 14%, respectively.

# **6.2 Estimation Labor-Market**

Our main analysis is
the same time, and as is noted further below, our simulations are highly stylized and predicated on simplifying assumptions, and as such can at best provide suggestive guidance to the design of policy.

The paper is structured as follows. The second and third sections introduce the estimation method and data sources used in this paper, respectively. The fourth section presents the empirical findings on the geographical distribution of poverty. The fifth section presents inequality and wealthy maps. Next, the change in poverty during the period 19992009 at the disaggregated levels is discussed in the sixth section. The simulation of the impact of spatial targeting of transfers on poverty reduction is presented in the seventh section. Finally, the eighth section concludes.

# **2. Methodology**

To estimate the poverty rate in small areas such as districts, we employ the small area estimation method developed by Elbers, Lanjouw and Lanjouw (2002, 2003). The method involves three broad steps. In the first step, we select a set of variables that are common to both the household survey and the population census. The common variables include household characteristics, as well as characteristics of small areas such as villages and/or communes via area mean variables computed from the census. Area mean variables include, for example, the average household size at the commune level as calculated from the census, or the total population of the commune. These variables are then “inserted” into the household survey so that they can be included as candidate variables in the modeling stage. All variables common to the survey and census are subject to careful comparison; they need to be similar in terms of their means and distribution and in terms of the framing of the question.

In the second step, we regress observed expenditure in the household survey on the selected common variables. More specifically, we estimate the following model: 5
# 2. Sampling strategy

Wave 2 of the SHFS employed a multi‐stage stratified random sample, ensuring a sample representative of all sub‐populations of interest, while optimally balancing cost and precision of estimates. Strata were defined along two dimensions – administrative location (pre‐war regions and emerging states) and population type (urban areas, rural settlements, IDP settlements, and nomadic population), leading to a total of 57 strata (Table A.2). Sub‐populations in the urban centers of Mogadishu, Baidoa, and Kismaayo, in fisheries livelihood zones in coastal areas (Figure A.1), and IDP host communities were of particular interest and therefore deliberately oversampled.

The total planned sample size was 6,384 interviews, allowing for high‐precision consumption estimates with less than 10 percent relative standard errors for key sub‐populations and overall. The sample was allocated across strata following optimal (Neyman) allocation, minimizing the global sampling error of the consumption estimates (Neyman, 1934). Optimal allocation is given by where n� is the sample size in stratum _h, n_ is the total sample size, _H_ is the total number of strata, N� is the total population of stratum _h_ , _N_ is the total overall population, and S� is the standard deviation in stratum _h._ Hence, the number of households to be interviewed per stratum is mainly determined by the variability of consumption within the stratum ( S� ). S� was derived from the results of the SHFS Wave 1. The population size only matters for practical purposes in very small strata below 10,000 households. In the absence of a recent population census, the population of each stratum was derived from UNFPA’s 2014 Population Estimation Survey (PESS), which contains detailed estimates for each population type and administrative unit of interest.

The optimal allocation of interviews was subject to the following requirements:

- (i) 500 expected interviews in IDP settlements and 500 in nomadic populations;

- (
firm-level data from the World Bank Enterprise Survey and combine it with PM 2.5 data estimated by van Donkelaar et al. (2016) (based on NASA MODIS and MIRS satellite data). We perform the analysis at the global level and then focus on how these effects are different for African firms.

When compared to the rest of the world, African firms experience different effects in terms of pollution. While a global analysis shows that the association between pollution and labor productivity is positive in the beginning and negative as pollution rises (quadratic), African firms experience a different story. PM 2.5 levels are negatively associated with labor productivity for these firms at low levels of pollution. Between approximately 8 μ and 25 μ , the effects turn positive, showing that as pollution and growth rise, the positive effect from growth /mm<sup>3</sup> /mm<sup>3</sup> offsets the negative effects of pollution. After a not so high level of pollution (an annual average equal to 25 μ ) its effects on employment and labor productivity growth are negative again. The fact that African cities are growing fast but PM 2.5 levels have not yet reached the high /mm<sup>3</sup> levels experienced in places like China and India, poses an opportunity to plan ahead and avoid the mistake of not accounting for the negative effects associated with fast and unplanned growth. PM 2.5 has a negative effect on firm performance at levels that are not as high as the ones that these countries are currently experiencing. Pollution management is crucial to tackle the problem before it gets more serious. African cities could benefit from the examples we see now in cities that have experienced fast growth.

Looking at business environment and the effects on firm performance, we find that the only variable that affects African firms differently is capacity agglomeration. The effect of capacity agglomeration on labor productivity growth is stronger for firms in this region. An increase of 0
# **3.1 Data and descriptive statistics**

Our data are from two nationally-representative surveys in Malawi, namely the Fourth Integrated Household Survey 2016/17 (IHS4), a cross-sectional survey of 12,480 households, and the Integrated Household Panel Survey 2016 (IHPS), a longitudinal survey of 2,508 households that had been followed since 2010.<sup>7</sup> The IHS4 asked the most knowledgeable household member to provide information on household members’ ownership of and rights to agricultural parcels and other assets.<sup>8</sup> On the other hand, the IHPS aimed to conduct private interviews with each adult household member on his/her personal ownership of and rights to the residential and each agricultural parcel, based on a common roster of parcels that is used across all private interviews in each household. In contrast to the IHS4, the IHPS questions on land rights for a parcel were asked only if the respondent identified him or herself as a (co)-owner.<sup>9</sup> The mean values for key variables at the household- and parcel-level from the analysis sample of parcels that were reported to be owned are provided in Table 1 for the IHPS (column 1) and the IHS4, split into the total sample (column 2) and the sample of respondents claiming to be owner or co-owner of the parcel in question (column 3). The average household has about 5 members, with a 44-year old head. Eighty percent of heads of households are Christian; approximately 30 percent are female; 70 percent can read and write Chichewa; and have an average of 5.5 years of education. The mean area for agricultural parcels is 0.4 hectare and the incidence of organic fertilizer application and cash crop cultivation is 23 percent and 6 percent, respectively. The residential parcels are smaller and dwellings on them have been occupied for an average of 7 years. Thirty-nine percent of household residential dwellings have improved roofs,
(Table 2 column 5), a one percent increase in the rate of internet access by Tunisian men increases the total number of recruits in the sample by an estimated 2.6 individuals, which, with a sample size of 468 recruits, yields a .56 elasticity. Our quantitative results thus complement qualitative accounts on the importance of the internet for Daesh recruitment of foreign fighters.

# **5 Conclusion**

By allowing new forms of social interaction and acquisition of information the internet has fundamentally reshaped society. While it is often a force for development, this paper presents evidence that it has also been accompanied by the rise of violent extremist groups such as Daesh, the epitome of tech-savvy terrorism. After combining administrative data from the international jihadist organization with census data from Tunisia, we document quantitatively that the internet has been, on net, a potent facilitator of international terrorist recruitment. Our analysis suggest a 0.56 elasticity of Daesh recruitment with respect to internet access, attesting to the proclivity of transnational terror organizations to (ab)use cutting-edge information and communication technologies.

# **References**

- **Abadie, Alberto** , “Poverty, Political Freedom, and the Roots of Terrorism,” _American Economic Review_ , May 2006, _96_ (2), 50–56.

- **Andersen, Thomas Barnebeck, Jeanet Bentzen, Carl-Johan Dalgaard, and Pablo Selaya** , “Does the Internet Reduce Corruption? Evidence from US States and Across Countries,” _The World Bank Economic Review_ , 2011, _25_ (3), 387–417.

- **, , , and** , “Lightning, IT Diffusion, and Economic Growth Across US States,” _Review of Economics and Statistics_ , 2012, _94_ (4), 903–924.

- **Autor, David** , “Wiring the Labor Market,” _Journal of Economic Perspectives_ , March 2001, _15_ (1), 25–40.

17
interviewed plus refusals), the survey completion rate was therefore 92 percent.

Participants posted to a state with a Yoruba majority completed the survey at a 47 percent rate compared to 52 percent for those posted to non-Yoruba majority states and this difference is not statistically significant. Appendix Table A1 provides summary statistics from administrative data and survey data.

# **3.2 State Characteristics Data**

There are 36 states (and a Federal Capital Territory) in Nigeria, and ethnicity is a major characteristic of states. There are three major ethnic groups (Hausa, Igbo and Yoruba), alongside over 200 others. Hausas, often grouped together with Fulanis, are based in northern Nigeria. They are predominantly Muslim and have some of the highest poverty rates in the country. Igbos have relatively higher income, are predominantly Christian and are based in the southeast. Yorubas are also relatively of higher income, about evenly split between Christian and Muslim and are located in the southwestern part of Nigeria. The Yoruba region includes Lagos State, the largest commercial hub in Nigeria. Using the Nigeria Demographic and Health Survey (NDHS 2008), which is representative at the state level, I calculate the share of ethnic groups in each state and classify a state as having an ethnic majority if more than 50 percent of the population belongs to one ethnic group.<sup>14</sup> This produces three majority regions (Hausa-Fulani, Igbo and Yoruba majority regions). Following the geopolitical categorization used in Nigeria, I divide the remaining states, those without any majority ethnic group, into two groups: Southern Minorities and Northern Minorities. Figure 1 illustrates the geographic distribution of different ethnic groups across states in Nigeria.

Besides ethnic composition, states of Nigeria differ along other dimensions such as reli-

> 14I use the NDHS because the national census and other national surveys in Nigeria do not collect ethnicity information due to the political sensitivity of ethnicity.

12
, 2004. Interpersonally Comparable Utility, in: Barberà, S., Hammond, P.J., Seidl, C. (Eds.), Handbook of Utility Theory. Springer US, pp. 1179–1285.

- G7, 2015. Leadersʼ Declara�on G7 Summit Germany [WWW Document]. URL

- http://www.international.gc.ca/g8/g7_germany_declaration‐

- g7_allemagne_declaration.aspx?lang=eng (accessed 6.14.15).

- Geographic Information Science and Technology, 2015. Landscan Population Data. Oak Ridge National Laboratory.

- Guha‐Sapir, D., Santos, I., Borde, A., 2013. The economic impacts of natural disasters. Oxford University Press.

- Hallegatte, S., 2012a. A Cost Effective Solution to Reduce Disaster Losses in Developing Countries: Hydro‐Meteorological Services, Early Warning, and Evacuation. World Bank Policy Res. Work. Pap. No 6058.

- Hallegatte, S., 2012b. A Cost Effective Solution to Reduce Disaster Losses in Developing Countries: Hydro‐Meteorological Services, Early Warning, and Evacuation. World Bank Policy Res. Work. Pap. No 6058.

- Hallegatte, S., 2014. Economic Resilience: definition and measurement.

- Hallegatte, S., 2014. Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters. Risk Anal. 34, 152–167.

- Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt‐Schilb, A., 2016a. Shock Waves: Managing the Impacts of Climate Change on Poverty, Climate Change and Development Series. World Bank, Washington, DC.

- Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch,
’, _Journal of Labor Economics_ , 32(1):95–121.

- Johnson, H. & Mejia, M. C. (2014), ‘Online learning and student outcomes in California’s community colleges’, Public Policy Institute of California, viewed June 18 2021, https://www.ppic.org/wpcontent/uploads/R_514HJR.pdf

- Kane, T. J. & Staiger, D. O. (2012), ‘Gathering Feedback for Teaching: Combining High-Quality Observations with Student Surveys and Achievement Gains’, Research Paper, Measures of Effective Teaching Project, Bill & Melinda Gates Foundation.

- Kane, T. J., Boatman, A., Kozakowski, W., Bennett, C., Hitch, R. & Weisenfeld, D. (2021), ‘Is college remediation a barrier or a boost? Evidence from the Tennessee SAILS program’ _, Journal of Policy Analysis and Management_ , 40(3), p1–31.

39
‐ <u>Table 3. Share of TBT measures imposed on Egyptian exports (by product HS2 level)</u>

|Labels|Share|
|---|---|
|Inorganic chemicals; organic or inorganic compounds of precious metals, of rare‐earth|24.9%|
|Organic chemicals|20.5%|
|Miscellaneous chemical products|12.2%|
|Tanning or dyeing extracts; tannins and their derivatives; dyes, pigments and other coloring|7.3%|
|Essential oils and resinoids; perfumery, cosmetic or toilet preparations|3.4%|
|Soap, organic surface‐active agents, washing preparations, lubricating preparations|3.4%|
|Albuminoidal substances; modified starches; glues; enzymes|3.4%|
|Photographic or cinematographic goods|3.4%|
|Pharmaceutical products|2.9%|
|Explosives; pyrotechnic products; matches; pyrophoric alloys; certain combustible prep.|2.9%|
|Fertilizers|2.4%|
|Animal or vegetable fats and oils and their cleavage products; prepared edible fats; animal ...|1.3%|
|Fish and crustaceans and other aquatic invertebrates|1.2%|
|Meat and edible meat offal|1.1%|
|Others|9.2%|
|Total|100%|

_Source: Own construction using WTO’s TBT STCs database._ iii.Sanitary and Phyto‐sanitary (SPS) Measures The distinction between SPS and technical measures is not always clear, in particular because both apply to agricultural and industrial products. Yet, SPS measures refer to requirements imposed to protect life or health of animal, plant or human from pertinent health risks (such as additives, contaminants, toxins or disease‐causing organisms in foods, beverages or feedstuffs), whereas technical barriers – as described above – refer to the products regulations and standards.

The positive impact of lower
- Dercon. S and Krishnan. P (1998) “Changes in Poverty in Rural Ethiopia 1989-1995: Measurement, Robustness Tests and Decomposition”, _University of Oxford WPS/98-7_

- Fafchamps. M and Pender. J (1997) “Precautionary Saving, Credit Constraints, and Irreversible Investment: Theory and Evidence from Semiarid India”, _Journal of Business and Economic Statistics_ , 15(2), pp. 180-194

- Ferreira. F, Fruttero. A, Leite. P, and Lucchetti. L (2011) “Rising Food Prices and Household Welfare: Evidence from Brazil in 2008”, _World Bank Policy Research Working Paper No. 5652_

- Foster. A and Rosenzweig. M (2011) “Are Indian Farms Too Small?  Mechanization, Agency costs, and Farm Efficiency”, mimeo, Brown University

- Friedman. J, Hong. S. Y., and Hou. X (2011) “The Impact of the Food Price Crisis on Consumption and caloric Availability in Pakistan: Evidence from Repeated Cross-sectional and Panel Data”, mimeo, World Bank

- Hayami, Y. and Ruttan, V. (1985) “Agricultural Development: An International Perspective”, Baltimore: Johns Hopkins University Press

- Hornbeck. R (2012) “The Enduring Impact of the American Dust Bowl: Short and Long-run Adjustments to Environmental Catastrophe”, _American Economic Review_ , 102(4), pp. 14771507

- Ivanic. M and Martin. W (2008) “Implications of Higher Global Food Prices for Poverty in Low-income Countries”, _Agricultural Economics_ , 39, pp. 405-416

- Jacoby. H and Skoufias. E (1997) “Risk, Financial Markets, and Human Capital in a Developing Country”, _Review of Economic Studies_ , 64(3), pp. 311-335

- Kazianga. H and Udry. C (2006) “Consumption Smoothing? Livestock, Insurance and Drought in Rural Burkina Faso”, _Journal of Development Economics_ , 79(2), pp. 413-446

26
# _2.2. Performance during the recovery_

Annual GDP data show a large bounce-back effect in economic activity: economies that suffered greater collapses were typically those that enjoyed larger growth recoveries defined as GDP growth in 2010 minus that in 2009 (Figure 1 and bottom panel of Figure 2).<sup>9</sup> Only a few economies suffered a decline in growth rates between 2007 and 2009 and a consecutive decline between 2009 and 2010.<sup>10</sup> Nevertheless, the degree of cross-country heterogeneity observed during the recovery has been greater than that of the downturn. In particular, as the effects of the global systemic shock faded, the relative strengths of individual economies might have started to play a more fundamental role. Heterogeneity in the rebound is observed not just between advanced and emerging economies, but also across emerging regions (Figure 1). Even though advanced economies and economies in the Eastern Europe and Central Asia region suffered large collapses, their recovery was not as strong as that observed in other emerging regions.

This last observation predicts that, by 2010 or 2011, not all economies would recover all the losses derived from the crisis; and that is what we observe. One way of capturing this cost is assessing where economies stand relative to some reference level of GDP. To do this, we consider two reference levels for GDP: (i) the GDP level that was reached in 2008 (i.e., the precrisis annual peak), and (ii) the level that would have been reached in 2010–2011 had economies maintained their average growth rates registered during 2000–2007. The latter reference level is an estimate of trend GDP.

Consider first the expected level of GDP in 2010–2011 relative to that of 2008 (Figure 4).

In 2010, the average levels of GDP in advanced economies and in Eastern Europe and Central

> 9 We use Consensus Forecasts or World Economic Outlook estimates when actual 2010 GDP data are not available. 10 These economies
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<u>Notes: Permission statistics for non-holders were not provided as very few individuals in this category held rights.</u> 25