rafmacalaba/data-use-mentions-extended huggingface dataset:
assessing the quality of the data, conducted a Post-Enumeration Check Survey (PECS) that found that there was under coverage both in urban and rural areas, but this was more prevalent in urban areas. BBS thus used a two-step approach to adjust the 2011 Census of Population and Housing estimates. First, it reclassified urban and rural areas using the concepts of: (i) growth centers, (ii) urban agglomerations, and (iii) other urban areas. Second, it inflated all urban and rural counts from the 2011 Census of Population Areas to align with the PECS results. These two adjustments estimated the share of the urban population at 28 percent, which is the number that BBS has been using since then in order to produce official population projections and statistics.<sup>30</sup> To address this mismatch between the original HIES 2016/17 sampling and the official population statistics, we adjust our _ex-ante_ sampling weights in two ways. First, we re-classify all selected urban/rural PSUs using the new “growth centers” categorizations, which affects 24 out of the 64 _zilas_ in the sample. This adjustment brings the urban share from 23 percent to 26.6 percent. Second, we use the official 2016 population projections estimated at the _zila_ level to inflate the adjusted sampling weights; that is, for each _zila_ we divide the official 2016 population projections by the adjusted 2011 population projections used for the original sampling design.<sup>31</sup> For this exercise, we use the number of households in the original 2011 census frame (i.e. enumerated households) instead of the adjusted number of households (i.e. the number adjusted after the PECS). After all these adjustments, the final sampling weights for the HIES 2016/17 yield an urban share of 28 percent, and the sum of the sampling weights by _zila_ match the official BBS 2016 population statistics.

> 30 <mark>The BBS official population projections are available at the following link:
**_<u>________________________________________________________</u>_**

- <u>Module 6:</u> (PDF, 600KB) provides a guide to the regulation of port services, and defines the reasons for regulating such services (based on public interest) and the regulatory means for doing so.

- <u>Module 7:</u> (PDF, 1.2MB) analyzes the PortReform issues that may emerge during reform and ways to address them.

- <u>Module 8:</u> (PDF, 354KB) introduces practical steps for implementing port reform.

- <u>The Annex:</u> (PDF, 78KB) contains a glossary of port and shipping terms.

# _<u>The Containerization International Yearbook</u>_

The Containerization International Yearbook is a port-by-port guide to container facilities, handling equipment and traffic statistics (over 450 port/terminal throughputs) for sea and river ports and terminals worldwide. It is an easy way to obtain the information, and is available on a commercial basis.

_<u>Clark, Dollar, Micco (2002) Working Paper on Maritime Transport Costs and Port Efficiency</u>_ Form various sources such as Transport Unit of the World Bank (19 countries), Camara maritime y Portuaria de Chile A.G (12 countries), LSU-National Ports and Waterways Institute (4 countries), this study compiles _Container Handling Charges_ (in US$/TEU). Handling costs are the costs of moving containers through ports. They are determined by port efficiency – countries with more efficient seaports have lower handling costs. An acceptable benchmark for container cargo handling in optimal conditions is about $US 100 per container.

Very often, higher efficiency and thus lower shipping costs can offset the handling charges. Another indicator of efficiency is the turnaround time of ships at ports which means the average container movements per crane or ship hour within a port. Such costs are incurred by
Chart 16 Contributions of Shocks to Interest Rates: Easing Phases (Average across G5 economies, percentage points) <!-- Start of picture text --> By global and domestic shocks<br>Global Domestic<br>0<br>-3<br>-6<br>-9<br>-12<br>1970s 1980s 1990s 2000s 2010s 2020s<br>By structural shocks<br>OP GS GD GMP DS DD DMP<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>1970s 1980s 1990s 2000s 2010s 2020s<br>By types of shocks<br>Oil and supply Demand MP<br>0<br>-3<br>-6<br>-9<br>-12<br>1970s 1980s 1990s 2000s 2010s 2020s<br><!-- End of picture text --> Sources: Authors’ calculations for data from January 1970 through December 2023 for the G5 economies (Canada, the euro area, Japan, the United Kingdom, and the United States). Notes: Historical decompositions of domestic policy interest rates around the easing phases defined in Section II. See notes to Chart 15 for definitions and details.

51
. Nevertheless, to analyze poverty and multiple deprivations among the Roma community, we used the latest available national SILC data from Bulgaria (2021), leveraging the AROPE framework and the longitudinal components for a nuanced understanding of persistent socio-economic challenges among this group over time.

**Our results show that the incidence of inequality in outcomes across a range of social and economic indicators is still high, with wide disparities between national averages and specific population groups (including Roma and other vulnerable and marginalized communities).** This is important as it can potentially affect social cohesion and societal well-being (Vergolini, 2011; Wilkinson & Pickett, 2009) and reshape national and European policies and institutions.

**While the European Union-27 (EU-27) witnessed a marginal decrease in absolute multidimensional poverty recently, different rates of progress are observed across EU subregions** . Among the EU subregions, Southern Europe (SE) experienced the largest decline in the AROPE rate, dropping from 26.0 percent in 2020 to 24.2 percent in 2021, marking a decrease of 1.8 percentage points. Conversely, Central Eastern Europe (CEE) exhibited the lowest decline, with its AROPE rate decreasing only slightly from 17.4 percent to 16.8 percent, reflecting a small

36
frequently cited as a barrier. For example, 24 percent and 19 percent of the unbanked in Iraq and Morocco, respectively, cited religion as a barrier. There are also gender differences in citing religion as a barrier to account ownership. For example, in Morocco, 14 percent of unbanked men compared to 23 percent of unbanked women cite religion as a barrier. Sharia-compliant savings and credit products can help address this barrier (Karlan et al. 2021). However, prices on Sharia-compliant products are typically more expensive (Hussain et al. 2016), stressing the importance that users understand the often more complicated terms and fees.

# **_5.2 Lack of access to technology is a leading barrier to using a mobile money account_**

Account ownership has increased in certain countries due to the adoption of digital technologies, including mobile phones. Since 2014, Sub-Saharan Africa has been the global leader in mobile money account ownership. As of 2021, 33 percent of adults reported having a mobile money account in the region, compared with only 10 percent of adults globally. In this way, mobile money has played a foundational role in driving increased account ownership in Sub-Saharan Africa.

Given such high mobile money adoption in Sub-Saharan Africa, the Global Findex 2021 survey included a dedicated module that asked unbanked adults in that region (i.e., adults with neither a mobile money account nor an account at a traditional financial institution, like a bank) specifically why they did not have a mobile money account. As with the general set of questions about financial institution accounts, respondents could give more than one reason for not having a mobile money account, though most gave fewer than two.

18
benefits from exporting? This is a particularly relevant question given evidence which suggests that individual countries often protect their own "poor" (or declining sectors) by raising tariffs and/or non-tariff barriers on the goods these households/individuals (or declining sectors) produce or are employed in the production of. But, when all countries protect the sectors where the poor work and if the poor are employed in similar kind of sectors in different countries, a “coordination problem” arises: the goods (and services) produced by the poor will face higher barriers to trade than those provided by the non-poor, and the resulting decline in the global demand will lower the price of goods and services that the poor produce.

Tariff and non-tariff barriers faced by the poor, if relatively higher, may therefore impair income distribution by keeping poor workers disconnected from global markets. The potential problem is then not too much globalization, but too little "inclusive" globalization. This view is in stark contrast to the rhetoric that is usually seen and heard.

In light of the above, the objective of the paper is to assess the obstacles that “poor” households in a given country face when trying to export goods to the rest of the world. In doing so, we investigate the overall tariff profile on exports from India. Tariff data are matched with consumption/income data by the industrial classification of each household member’s sector

> 1 For the impact of high domestic transport costs see Nicita (2009), Emran and Hou (2013), and Atkin and Donaldson (2012). For the impact of market frictions see Campa and Goldberg (2002), Atkin and Donaldson (2012), Ural Marchnad (2012) and Han et al. (2016).

> 2 See, for example, Porto (2010) and McCaig (2011).

2
retrospective data are used instead. Information on parental education is obtained for respondents who reside in the same household as their parents – so-called co-residents.<sup>9</sup> Since coresiding adults need not be representative of the target population, estimates derived from this type of data may be subject to co-residence bias (Emran et al., 2017). The magnitude of this bias depends on the share of adults who co-reside with their parents and the extent to which coresiding adults differ from adults who live away from their parents.

To minimize co-residence bias, samples are restricted to co-residents aged 21-25 at the time of the survey. These respondents, who are part of the 1980s cohort, are often old enough to have had a chance to complete their education yet young enough such that a large majority still co-resides with their parents. Since co-resident surveys only allow us to estimate mobility for this cohort, we are unable to track mobility over time (i.e. across cohorts) for these countries. When time trends are analyzed, the same set of countries is used throughout, so these countries are dropped.

In Figure C.1 of Appendix C we use surveys with retrospective data on parental education to test the bias of estimating mobility with and without only relying on co-residents. We find that the size of the bias does not seem to be large enough to generate notable re-rankings.

# **3.2. Global coverage**

In total, the GDIM provides estimates of intergenerational mobility – absolute and relative – for 153 countries representing about 97 percent of the world’s population born in the 1980s (see Table 2). For 114 countries, or about 87 percent of the world’s population, estimates of mobility span four decades: from those born in the 1950s to those born in the 1980s. In all regions but the Middle East and North Africa, the population coverage is greater than 90%. For the Middle East and North Africa, 83% of the population is covered
time is obtained from FAO sources, and the per capita non-rural income is estimated from national account data. Finally, since no data for agricultural labor exist, we use the total rural labor force as a proxy for L. Summary statistics for all the data used are provided in the appendix.

# **V. The Results**

We estimate the reduced-form for per capita agricultural GDP using various methods of estimations and aggregating the data in several alternative ways. Our interest is primarily in probing the robustness of the estimated coefficients. We present estimates using three specifications: (1) regressions using country data aggregated for relatively long periods of time

> 21  The actual cross-country regressions used to estimate the norm included more than 120 countries. Details about the methodology used are available fro m the author.

17
A variety of definitions are useful.<sup>18</sup> First, household membership and the corresponding sampling frame are defined on the basis of residency and registration. Second, income is calculated as the sum of net income (gross revenue less current expenditures) from agriculture, farming sidelines (e.g. animal husbandry and livestock), family-run businesses, plus wage income (earned inside and outside the village), and transfers. We calculate the value of farm output that is not sold, and thus largely consumed by the household, at market prices. We also use household income before taxes, and deflate all nominal values into 1986 prices using the NBS rural consumer price index for each province.

The key outcome in our analysis is “growth” of household per capita income. To mitigate the effect of transitory shocks, or measurement error, on income, we construct two-year averages of household income for each two-year time period. Our initial period is 1987-88, and all household-level variables, and the village statistics calculated from them, are calculated using the average of 1987 and 1988 outcomes for each household. Subsequent two-year endpoints, “ _T_ ”, include: 1990-91, three years after the initial period (1987-88); 1995-96, the next period for which we have adjacent time periods (8 years out); 1997-98; 1999-2000; and finally 2001-02, fourteen years after the initial period.

In Table 1, we report the mean and quantiles of key variables for the panel households. Several things are noteworthy. First, over the entire period per capita income growth averaged 3.0 percent per annum, but there were significant differences in the rates of growth from 1987-88 and our five ending dates. This largely reflects cyclical factors: Marked declines in economic activity accompanied the postTiananmen economic retrenchment and the Asian Financial Crisis. Incomes actually fell between 19871988 and 1990-1991, but recovered significantly by 1995-96 so that over the period between 1987-88 and 1995-96, growth averaged 3.6 percent. Growth in the period
**Table 2: Selected Predetermined Conditions, Definitions, and Summary Statistics**

|**Variable**|**Definition**|**Mean**|**SD**|**Min**|**Max**|**Obs**|**Years**|**Source**|
|---|---|---|---|---|---|---|---|---|
|Population|All residents regardless of legal status or<br>citizenship in millions.|32|128|0.01|1360|2,127|2005-<br>2014|Penn World Table|
|Population<br>density|Total population divided by land area in<br>square kilometers.|391|1850|0.14|19073|2,120|2005-<br>2014|World Development<br>Indicators|
|Offshore dummy|1 if a country is an offshore center, where the<br>bulk of financial sector activity is offshore on<br>both sides of the balance sheet, where the<br>transactions are initiated elsewhere, and<br>where the majority of the institutions<br>involved are controlled by non-residents.|0.2|0.4|0|1|2,130|2005-<br>2014|IMF|
|Small states<br>dummy|1 if a country has less than 1,500,000<br>inhabitants.|0.3|0.5|0|1|2,130|2005-<br>2014|World Development<br>Indicators|
|Landlocked<br>countries|1 if the country is entirely enclosed by land,<br>or whose only coastlines lie on closed seas.|0.2|0.4
Figure B3: Changes in APA and Household Survey indices (2016-2018) <!-- Start of picture text --> Urban planning Fiduciary responsibility<br>0 1 2 -2 0 2<br>Changes in DLI Changes in DLI<br>(a) Urban planning (b) Fiduciary responsibility<br>Infrastructure Accountability<br>-4 -2 0 2 -2 0 2 4<br>Changes in DLI Changes in DLI<br>(c) Infrastructure (d) Accountability<br>1<br>.6<br>.8<br>.4<br>.6 .2<br>.4 0<br>Changes in Survey Index Changes in Survey Index<br>.2 -.2<br>1 1.5<br>1<br>.5<br>.5<br>0<br>0<br>Changes in Survey Index Changes in Survey Index<br>-.5<br>-.5 -1<br><!-- End of picture text --> _Note:_ This figure presents a scatter of changes in DLI subcomponent scores (x-axis) and survey indices (x-axis) by LGA. DLI scores are normalized so that changes can be interpreted as standard deviations in the distribution of the 4th APA scores.

Figure B4: Correlation between APA and household survey measures <!-- Start of picture text --> Planning Baseline<br>Fiduciary Baseline<br>Infrastructure Baseline<br>Accountability Baseline<br>Taxation Baseline<br>Planning Endline<br>Fiduciary Endline<br>Infrastructure Endline<br>Accountability Endline<br>Planning Change<br>Fiduciary Change<br>Infrastructure Change<br>Accountability Change<br>-.5 0 .5<br>Correlation<br><!-- End of picture text --> _Note:_ This figure presents correlations across
the 40% poorest population, according to a targeting index devised by _Secretaría Técnica del Frente Social_ [Technical Social Front Secretariat].

However, the constitutional amendment in year 2008 ordered a gradual expansion in social protection. In order to comply with this, in 2009 a revised policy was established in order to consider people age 65 or older living under poverty income threshold eligible. However, in 2012 this policy became more flexible to allow introducing the whole population in the social records.<sup>5</sup> The original transfer amount was really low (USD11.50, approximately 7% of minimum wages), which was then updated, and from 2013 on, it is USD50 (16% of the unified basic wages).

In terms of coverage, social pension implementation achieved a significant percentage increase in older people getting this social security benefit. In this sense, as discussed above, while in 2016 the contributory system covered 25.8% of older people, with the implementation of the non-contributory pension, the percentage of older people protected by a transfer scheme climbed up to 60%. In other words, the non-contributory pension managed to narrow the gap reaching out to 34% of older people (Figure 8). However, there is still a significant group of older people excluded from the pension system.

Figure 8. Percentage of Older people over 65 Years of Age Receiving a Social Security Benefit per Kind of Scheme. Years 2003-2016 <!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>20032004200520062007200820092010201120122013201420152016<br>Contributory Social Pension Total<br><!-- End of picture text --> Source: author elaboration based on ENEMDU

> 5 At the same time, the government has implemented a non-contributory pension within the framework of program _Mis Mejores Años_ [My Best Years]
and off ramps that is, add and drop points) that feed smaller class roads (access fiber, wireline, and wireless networks). In the motorway scenario, even if a household is located close to the motorway, it may be a long drive to the nearest junction. The same applies to fiber-optic networks, in which the speed of fixed broadband Internet is determined by proximity to the transmission nodes rather than the network lines connecting the nodes. The transmission nodes are categorized by operational status: operational, under construction, and planned or proposed.

Mobile coverage data are drawn from two sources: the Mobile Coverage Maps database and mobile operators in the country. Data on mobile phone coverage and mobile broadband Internet coverage compiled in the Mobile Coverage Maps database by Collins Bartholomew are based on voluntary submissions made directly by mobile operators for the purposes of constructing roaming coverage maps for end users. The temporal coverage of the data varies depending on 2G and 3G coverage, respectively, in 2011–17 and 2014–17.<sup>6</sup> To ensure that the findings are not an artifact of potential missing information in the Mobile Coverage Maps database, the analysis also utilizes 2G and 3G coverage information collected directly from the three major mobile operators in Senegal: Expresso, Orange (or its local subsidiary Sonatel), and Tigo. While the coverage maps obtained from Expresso and Tigo are limited to 2016, Orange’s coverage maps are available for 2016–18. Orange-Sonatel dominates the fixed and mobile markets, dwarfing

> 5 See Africa Bandwidth Maps (database), Hamilton Research, Bath, UK, http://www.africabandwidthmaps.com/.

> 6 The Mobile Coverage Maps database contains no information on 4G coverage. Moreover, the coverage maps depend on information shared voluntarily by mobile operators. So, their quality should be checked for completeness against other sources of data. For this, the analysis here relies on data collected directly from major mobile operators.

4
<!-- Start of picture text --> 0 1 2 3 6 7 8 0 1 2 3 4 5 6 7 8 9 10<br>Number of public colleges Number of private colleges<br>Very low household income High household income Very low household income High household income<br>Low household income Very high household income Low household income Very high household income<br>Panel (a) Panel (b)<br>Availability of public colleges and income Availability of private colleges and income<br>0 1 2 3 6 7 8 0 1 2 3 4 5 6 7 8 9 10<br>Number of public colleges Number of private colleges<br>Low ability Median ability High ability Low ability Median ability High ability<br>Panel (c) Panel (d)<br>Availability of public colleges and ability Availability of private colleges and ability<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>Probability of moving for college Probability of moving for college<br>0.0 0.0<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>Probability of moving for college Probability of moving for college<br>0.0 0.0<br><!-- End of picture text --> Figure 3: Marginal effects of college availability college, and declines further with additional public colleges. In contrast, the probability of moving for individuals in the highest income quintile (orange solid line) is only about 0.35 when their high school city has no public college, and the probability declines only slightly (to about 0
the country’s current production sphere.** However, as Hausmann et al. (2008) explain, one can think of a series of tests applicable to actual production data which refer to the degree of sophistication of existing activities; their relative position compared to products closer to the “production frontier”; and the number of new, successful activities. These tests permit to check whether, ex‐post, things are taking place in the country’s innovation arena. If innovation is indeed happening as a widespread phenomenon in the economy, then market failures associated with lack of self‐discovery may not be a major problem. This is, low ex‐ante entrepreneurship (before discovery time) and excess ex‐post entry into the new market (hypothesized under laissez faire conditions) are not a binding constraint in the country. A practical problem that arises in attempting to make these tests is the lack of highly disaggregated production data necessary to pinpoint activities by degree of sophistication and discoveries. That is why it has been suggested (Klinger and Lederman, 2006) the use of export data instead. Having in mind these caveats<sup>49</sup> , a battery of export indicators for diversification, sophistication, proximity to technological frontier and discovery, as defined in works by Hausmann et al. (2005) and Hausmann and Klinger (2006), and Klinger and Lederman (2006), can be used to assess whether new higher quality activities are taking place within a country.

**We have in Section 4 already defined proxies for the degree of sophistication of products (PRODY) and the export basket (EXPY) and concluded that Togo exports low‐income products but is improving.** An additional indicator refers to the “density” of exported products and their position in the “open forest”. Products that are “closer”<sup>50</sup> to other higher income value products (as defined by PRODY) are said to be in a