rafmacalaba/data-use-mentions-extended huggingface dataset:
sharp decline in the first half of the 2000s, and, although the differences narrowed again after 2010, the level recorded in 2014 was virtually the same as the one in 1992. Second, the relatively high _β_ -convergence coefficients across municipalities in border states seem to have reduced income dispersion along the U.S. border at a rate of 22 percent in 1992–2014.

# **5 Testing for poverty convergence**

Have poorer, converging municipalities been able to translate their relative income gains into poverty reduction? If per capita income follows a log-normal distribution, then any change in the poverty headcount ratio is determined, in a magnitude _η_ , by two components: one that is attributable to changes in income and one that is attributable to changes in the distribution of income. The relationship between each component and changes in poverty is illustrated in figure 4 over 1992–2014. As expected, those municipalities that experienced relatively higher rates of poverty reduction, according to the food poverty line, were those that experienced higher growth rates in mean per capita income (panel a), but also experienced progressive changes in the distribution of income (panel b), because such changes imply transferring resources from richer to poorer populations, thus stimulating poverty reduction.<sup>13</sup> Focusing on the first component, for now, let be the partial elasticity of poverty to growth in the mean per capita income of municipalities, representing the percent change in the poverty headcount ratio as a result of a 1 percent increase in income, holding the income distribution constant. _gi_ ( _Pit_ ) is the annualized change in poverty rates, calculated as in (2); _η_ is the elasticity parameter, with the expectation that _η <_ 0; _δ_ is a municipality-specific effect; and, _νit_ is a stochastic term.<sup>14</sup>

> 13The bulk of the following analysis focuses on extreme poverty as measured by the food poverty line. This is deliberate because the narrative is consistent,
tax rate (right axis). Panel (a) presents the counterfactual revenue absent the 2016 enforcement initiatives, while Panel (b) plots the forgone wealth tax revenue absent evasion, as detected through the 2016 enforcement initiatives. Argentina’s policy package more than doubled the wealth tax revenue by 165–180% from 2016 to 2018. Moreover, the wealth tax revenue increased more than sevenfold from Arg$4.9 billion in 2018 to Arg$35.4 billion in 2019, after the 2019 reform increased the wealth tax rates. Relative to the counterfactual revenue in 2019, Argentina raised tax revenue more than threefold thanks to the prominent disclosures of offshore wealth it induced.

_Source:_ authors’ calculations using data from AFIP statistical yearbooks.

33
# **2.3 Subjective Assessments of Public Officials**

We used the public official’s survey to ask each official direct questions on the socio-economic conditions of the citizens they serve and on civil service rules that govern their workplace. Respondents had to answer based on their existing knowledge of their constituencies and workplace rather than by referring to external reference documents. The questions we asked are listed in Table A2 and relate to both district characteristics and civil service rules. Those relating to socio-economic characteristics of citizens correspond to the core variables of interest in the national ‘Growth and Transformation Plan’, and sectorspecific plans, such as the ‘Education Sector Development Program IV: 2010 - 2015’. Those relating to the public service correspond to the rules outlined in the ‘Ethiopian Federal Civil Servants Proclamation No. 515/2007’. Thus, all the variables we asked about are fundamental quantities relating to the core work of the public officials we interviewed. For example, the core mission of education officers is to implement the education sector development programs, focusing on the topics it prioritizes.

There are three sets of questions. The first set asks basic demographic questions about districts such as population and the proportion of the population that lives in rural areas. These questions are asked to respondents across all sectors. The second part asks sector-specific questions enumerated to sector-officials only. For example, respondents working in the education sector were asked the following questions about the citizens in their constituency: “What do you think the primary (1-8) enrolment numbers are for [district name] according to official administrative data?”; “What do you think the primary (1-8) pupil-teacher ratio is for [district name] according to official administrative data?”; “What do you think the primary (1-8) pupil-class ratio is for [district name] according to official administrative data?”; and, “What do you think the primary (1-8) pupils-per-school ratio is for [district name] according to official administrative data?”
# **Figure 8. Epidemiological designs of studies compiled, by country.  Andean Region; 2009**

<!-- Start of picture text --> 100%<br>Other<br>90%<br>Experimental/Clinical<br>trials<br>80%<br>Case series<br>70%<br>Case-control<br>60%<br>Cohort studies<br>50%<br>Other survey studies<br>40%<br>30% KAP survey<br>20%<br>Serological survey<br>with/without KAP<br>10% survey<br>0%<br>Peru n= 57 Ecuador n= 40 Bolivia n= 25<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

**Findings:** As Figure 8 above clearly shows, cross-sectional studies and surveys constituted between 86 to 96% of the total group of studies compiled in each of the three countries assessed. The differences can be attributed to the fact that in Peru research has focused on serological prevalence studies supplemented by demographic and behavior surveys, while in Ecuador and Bolivia research has focused on KAP surveys.  One possible explanation for the difference is that serological survey studies are more expensive and require more logistical support.

It should also be noted that these three countries have demographic and health surveys (DHS) conducted by their respective National Statistics Institutes. These surveys include KAP about HIV; they have national outreach and focus on the general population. Because they are carried out periodically and with consistent methodology, such surveys can be 23
value of the forecast errors. The figure is constructed based on a common sample of 141 countries (largely developing economies) collected in January for each year between 2010 and 2020. The MENA region includes both GCC and non-GCC countries.

MENA also stands out as the region with highest forecast error and absolute forecast error from the private sector’s growth forecasts. Appendix Figure A1 shows average GDP growth forecast errors (Panel

> 8 Forecast errors can be the result of factors other than optimism bias, such as unforeseen shocks.

6
missing 10 percent of the poor.<sup>4</sup> These assumptions are in line with what is suggested by targeting evaluations of transfer programs in the region (Fiszbein and Schady, 2010).

Figure 8 presents the poverty impacts of the various variants. In all countries, the imperfectly targeted program continues to deliver a better poverty impact than the categorical programs. The impact remain in fact closer to the perfectly poverty targeted program, than to the categorical ones. Nevertheless, in countries where differences between targeted and categorical systems were already small, the attractiveness of a categorical program with respect to an imperfectly targeted one has now increased: in Nicaragua, for instance, a categorical program only achieves almost the same poverty reduction than an imperfectly targeted program that costs the same – but does not “miss” 30 percent of the extreme poor. In contrast, in wealthier and more unequal countries, such as Colombia, the need to transfer higher amounts to a fewer pool of poor beneficiaries makes an imperfectly targeted system still an attractive option.

**Figure 8: Imperfect targeting and administrative costs (children only)**

<!-- Start of picture text --> 80%<br>Imperfectly Categorical<br>Categorical<br>60%<br>Imperfectly Targeted<br>Targeted<br>40%<br>20%<br>0%<br>Change in Ext. Poverty<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases

# **Relative targeting**

Some countries do not target beneficiaries using an absolute poverty line, but a relative one, in which case transfers are targeted to people below a certain percentile, rather than those

> 4 To keep the budget at 0.5 percent of GDP, under the categorical variant of the simulation we distribute all the resources of the program to the remaining 90 percent of the population.

16
The Services Trade Restrictions Index (STRI) takes a value between 0 and 100, with higher values indicating more restrictive regimes. Policy regimes are divided into five broad categories: i) completely open, i.e., no restrictions at all (0); ii) completely closed, i.e., no entry allowed at all (100); iii) virtually open but with minor restrictions (25); iv) virtually closed but with very limited opportunities to enter and operate (75); and v) a final residual “middle” category of regimes that allow entry and operations but impose restrictions that are neither trivial nor stringent (50).

The STRIs for Eurasia show that the region maintains virtually open regimes with minor restrictions (see Table 17 and Appendix for more details). The financial and telecommunication sectors are the ones with the highest barriers to trade. The most closed economy is Belarus, with an overall index of 35.1, followed by Ukraine (27.2), Russia (25.7), and Uzbekistan (23.4). Belarus’s restrictions in telecommunications (mode 3) are associated to virtually closed regimes in fixed-line telecommunications. Beltelcom is a state-owned monopoly provider of services. If a foreign investor wishes to acquire a share of the incumbent, a resolution by the President is required. Constraints in telecommunications are also important in Russia and Uzbekistan. In the first case, entrance into fixedline telecommunications must be done through a subsidiary. The radio frequency spectrum may be limited, but licenses are allocated first come, first served or by auction. In the second case, a limit on licenses applies. The number of licenses and the method of allocation are determined by the Cabinet of Ministers.

Table 15. Services Trade Restrictions Index in Eurasian Countries

|Country|Overall|Financial|Telecomm.|Retail|Transportation|Professional|
|---|---|---|---|---|-
While there are plans to reduce or abolish subsidies, fuel subsidies still amounted to a total of 3.4 USD billion in 2017 or 1.14% of GDP according to IEA data. In the model, subsidies on fuel are represented as negative carbon taxes. In the modeling, it is assumed that the government is not transferring funds to energy producers, and hence it is not a direct expenditure but rather a forgone revenue that it would have collected. The fiscal accounting from a debt and overall balance perspective would have been equivalent if it was accounted for as an expenditure. Mitigation policy can take the form of increasing carbon taxes, from the current negative values to zero and then to positive values. Carbon taxes ( ) are expressed as USD per ton of CO2 (USD/t). As indicated by the subscript , carbon taxes are time-variant. The distinction kk tt ττ SS

> 12 All data used in this model can be traced to the original data through the Excel workbook “Pakistan energy and emission data.xlsx”.

> 13 The Pakistani government provides detailed data on renewable energy prices in the “Indicative Generation Capacity Expansion Plan”. We are planning to update the model with these data.

23
details on DGD. In order to investigate the effect of CEFTA countries adopting the EU common external tariff, we make use of MFN tariff data that come from https://wits.worldbank.org/WITS/ WITS/ Restricted/Login.aspx. The original tariff data are at the HS17 8-digit level for Albania, Bosnia and Herzegovina, Moldova, Montenegro, and Serbia, while the tariff data for Macedonia are at the HS17 6-digit level. We aggregate all tariff data to the HS17 6-digit level, as correspondence tables to HS07 and SITC Reve. 3 & 4 are only available at the 6-digit level. We aggregate the tariff data in two different ways: (i) unweighted by taking simple averages, (ii) weighted by total import shares of the respective sectors. We then merge SITC Rev. 3 & 4 correspondences and HS07 correspondences in order to be able to match with our mining, manufacturing, and agricultural trade data.

# **4 Empirical Analysis and Findings**

This section presents our empirical findings. Subsection 4.1 offers an analysis of our partial equilibrium estimates, while Subsection 4.2 describes the results from our general equilibrium analysis.

## **4.1 On the Uneven Impact of EU and CEFTA Borders**

The estimation results that we report and analyze in this section are based on econometric specification (2). We start with a discussion of our findings across the four main sectors in the sample, including Manufacturing, Agriculture, Mining, and Services. Then, we zoom in on the determinants of trade flows across eleven manufacturing categories, as described in the data section. Following the best estimation practices from the structural gravity literature, which we summarized in Section 2, we obtain our main results with the PPML estimator. Estimates obtained with the OLS estimator are also consistent with our main findings.<sup>17</sup> Several findings stand out from the estimates for the four main sectors in our sample, which appear in Table 1
areas. Specifically, compared to other rural areas, households in rural towns have lower education levels and are more likely to work in agriculture.

Using our microdata approach, we can also analyze the profile of the extreme poor based on location. The comparison of characteristics of poor households across DOU classifications (columns 2 to 5 in Table A2 in Appendix A) reveals some distinct features. For instance, the urban poor tend to have higher levels of education compared to the rural poor. Approximately 29 percent of poor household heads in urban areas have not completed any education, compared to 49 percent among rural poor households. Employment patterns also differ significantly between urban and rural areas. While the majority of poor rural household heads work in agriculture, around half of the poor heads in urban centers are employed in the industry and service sectors. Notably, there are also differences between urban centers and urban clusters. For example, about 61 percent of the poor household heads in urban clusters still work in the agriculture sector, compared to 19 percent in urban centers. In terms of access to basic services, it is not surprising that the rural poor are the most deprived group. The urban poor households have improved access to water at a similar level to non-poor households. However, their access to improved sanitation is extremely low, with only around 32 percent having access even in urban centers. Access to electricity is also limited among the poor households in urban clusters (29 percent). In the case of the DB classifications (columns 6 to 10), the characteristics of the poor present similar results between urban and rural areas. However, it is worth noting that towns appear less educated, with more agricultural workers, and less access to basic services than rural areas.

# **5.2 Poverty incidence**

_Are poverty rates lower in urban than in rural areas?_ The comparisons of poverty rates across different types of geographic area in 16 SSA countries (Figure 4) reveal that poverty rates tend to be lower
- _(1) Please imagine a ladder with steps numbered from zero at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?_

- _(2) On which step do you think you will stand about five years from now?_

Accordingly, the average life evaluation index is a two-item ordinal scale (Cronbach’s alpha of .91), accounting for both current and expected future life evaluation. The current and expected life satisfaction scores are combined, as it is reasonable to expect that not only current evaluations, but also expectations for the future indicate grievances. This is in line with the modernization theory which posits that improvements in living standards raises people’s expectations about their future well-being. As the answers to the Cantril Ladder questions are ordinal, and hence cannot be treated as continuous, we group respondents into three categories and calculate the percentage of people in each category. _Suffering_<sup>_6_</sup> individuals are those with current and future life satisfaction scores that are equal or less than 4. Individuals are considered _struggling_ when their current life satisfaction scores are 5 or 6 and their future life satisfaction scores are equal or greater than 5 and less than or equal to 7. _Thriving_ individuals are those with current life satisfaction scores of 7 or above and future life satisfaction scores of 8 or above.

The country-level SWB index used as a variable in the regression model represents the share of respondents who report to be _suffering_ . Country-level weights are applied to this calculation and a respondent must have answered both questions to be included in the calculation.<sup>7</sup> People who identify themselves as s _uffering_ are likely to lack the capabilities that allow them to make autonomous decisions and to pursue a fulfilling life (Graham & Nikolova, 2015). The
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE the population was getting access to traditional latrines (figure 15). The lack of sanitary facilities and poor hygiene is a major contributory factor to Sierra Leone‘s exceptionally high maternal, infant, and child mortality rates (UNICEF 2008). Moreover, Sierra Leone has not been able to reduce its reliance on surface water, which is higher than in comparable fragile states. Reliance on open defecation has decreased only marginally, but it is expected to decrease in the upcoming years due to the implementation of WASH programs.

**Table 9. Benchmarking water and sanitation indicators**

||Unit|Fragile states|Sierra Le|one|Low income<br>countries|
|---|---|---|---|---|---|
|||Mid 2000s|2000|2008|Mid 2000s|
|Access to piped water|% pop|17.9|11.3|7.6|9.3|
|Access to standposts|% pop|9.4|16.3|13.7|17.1|
|Access to wells/boreholes|% pop|54.5|44.3|49.8|39.3|
|Access to surface water|% pop|18.1|28.1|28.3|34.2|
|Access to septic tanks|% pop|11.2|6|3|4.7|
|Access to improved latrines|% pop|29|16|8|18.3|
|Access to traditional latrines|% pop|36.8|55|64|38.5|
|Open defecation|% pop|23.1|23|24|38.3|
|<br>|<br>|||2007<br>||
|Domestic water consumption|liter/capita/day|
<!-- Start of picture text --> Figure 17 – Land Cover Classes and Distance from City Center ‐ Dakar<br><!-- End of picture text --> <!-- Start of picture text --> Dakar Land Cover 2015 (Percent) Dakar Land Cover 2015 (Total)<br>100% 140<br>120<br>80%<br>100<br>60% 80<br>60<br>40%<br>40<br>20%<br>20<br>0% 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance to CBD (in Kilometers) Distance to CBD (in Kilometers)<br>Residential (Regular) Residential (Irregular) Residential (Regular) Residential (Irregular)<br>Commercial/Industrial Commercial/Industrial<br>Percent Land cover (sq km) Total Land cover (sq km)<br><!-- End of picture text -->

# **Box 2– Manual digitizing vs semi‐automated approach in Dakar**

Traditionally planners have relied upon manual digitizing satellite imagery to create acurate city level maps. While usually this produced very precise maps, this approach remains tedious and time intensive. A semi‐automated approach presented in this study could be a time saving alternative. The screenshots below highlight the discrepencies and similaries between a land classification generated from a semi‐automated approach and a land cover map that was manually digitized. On the left hand side are the layers generated by a semi‐automated classification. The layer has been given a slight transparecy so that the 2015 image beneath it can be seen. On the right hand side is that same area, but with a classified layer that has been manually digitized from a 2008 image. In all the maps,
# **2 Empirical strategy**

Our main empirical specification identifies the conditional correlation between radicalization into violent extremism and internet access at the delegation-education-age level (see section 3 for a discussion of how the data are constructed). The dependent variable is the inverse hyperbolic sine (ihs) transformation of _Rdea_ , the number of recruits in district _d_ who joined Daesh in Syria and Iraq that have education level _e_ (primary or below, secondary, and tertiary and above) and belong to age group _a_ (where individuals have been grouped in 5-year age categories).<sup>2</sup> We estimate where _Internetdea_ is the share of men in delegation _d_ with education level _e_ in age group _a_ who have access to the internet, _Populationdea_ is the (log of the) total number of men in a delegation-age-education cell _{d, e, a}_ , and _Xdea_ is a vector of controls. All these independent variables are measured in 2014, the year the data on Daesh recruits are believed to be from (Dodwell et al., 2016a). The coefficient of interest, _α_ , measures the impact of internet access on the propensity to join the terrorist organization.

Theoretically, the sign of _α_ is ambiguous. On the one hand, the internet enables Daesh to spread propaganda and thus increase its “labor supply” (Bloom et al., 2017). It also reduces transaction costs associated with recruiting, similar to the effect internet has on licit recruitment efforts by regular firms (Autor, 2001). On the other hand, the internet facilitates law enforcement, repression of dissent (Greenberg, 2016; Conway et al., 2019; Gohdes, 2020) and can reduce grievances against the government by positively (re-)shaping citizens’ views (Gunitsky, 2015; Rød and Weidmann, 2015; King et al., 2017; Qin et al., 2017; Guriev and Treisman, 2019). Which channel dominates is, therefore, an empirical question;
district to fit pre-established field protocols used by BBS in the HIES 2010, and to facilitate field work and survey implementation management. More specifically, 720 households is divisible by 36 (i.e. the number of PSUs ultimately allocated to each district), which allows easy allocation of 20 households per PSUs. Also, two enumeration teams can easily cover the 36 PSUs in each district over 12 months without having to move to a different district (each team of two enumerators will visit 18 PSUs each over one year). Lastly, the number PSUs, 36, allocated to each district is a multiple of 4, which allows dividing into quarters.

> 15 To estimate _DEFFh_ , for each district, we use the Stata `‘svy: mean, over(district)’` command combined with the ‘ `estat effects, srssubpop` ’ and ‘ `estat sd, srssubpop` ’ routine. An important note to mention here is that Stata uses a linear approximation to estimate survey design effects, which opens the possibility that for some district-stratum pairs we might observe _DEFF_ values that are lower than 1. When this happens, the strategy adopted was to replace the estimated _DEFF_ values with _DEFF_ =1.

15
**Figure 1. Share of Principals Reporting the Issue as a Moderate or Serious Problem in His/Her School**

<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Syria* Morocco Yemen* Tunisia* Egypt Turkey Palestine* Lebanon Oman Jordan Kuwait Saudi Qatar Bahrain United Iran All MENA Other<br>Arabia Arab countries<br>Emirates<br>Classroom disturbance Profanity Vandalism Theft Fights Staff intimdation<br><!-- End of picture text --> Source: Analysis using TIMSS 2015 and 2011 (marked with *) (4<sup>th</sup> and 8<sup>th</sup> grades) 4