rafmacalaba/data-use-mentions-extended huggingface dataset:
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE leading operators has reportedly been established for developing the connection to undersea cables, which presumably will enhance open access ( _TelecomPaper_ , June 5, 2009).

Internet penetration in Angola is average for the southern African region (figure 20). There are no official data on the number of Internet users in Angola. But according to the government, there were some 300,000 subscribers in 2008; assuming 3 users to a subscription, this amounts to about 900,000 users or 5.4 percent of the total population.<sup>7</sup>

**Figure 20. Angola’s Internet market benchmarked against southern African peers**

<!-- Start of picture text --> a. Internet service trends, Angola, 2000–08 b. Internet service trends, southern Africa, 2008<br>20 350<br>6.0 25<br>18<br>300<br>5.0 16<br>20<br>14 250<br>4.0<br>12<br>200<br>15<br>10<br>3.0 150<br>8<br>10<br>6 100<br>2.0<br>4<br>50<br>5<br>1.0 2<br>0 0<br>0.0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per<br>International Internet bandwidth (bits per second per person)<br>person)<br>Internet users<br>Internet users<br>International Internet bandwidth<br>International Internet bandwidth<br>Madagascar Malawi Zambia Lesotho Mozambique Zimbabwe Botswana Angola South Africa Namibia Mauritius<br><!-
on economic growth, income inequality, and poverty across countries and over time.<sup>6</sup> Moreover, according to Pradhan et al. (2019), the growth effects from ICT adoption can be fostered if combined with other factors such as innovation and diffusion.

More recently, ICT adoption together with good telecommunications infrastructure have proven to be crucial at mitigating the economic losses during pandemic times (Katz et al., 2020). According to Ting et al. (2020), during the COVID-19 outbreak, ICT were being used for monitoring the spread of the virus, performing forecasting models of the new cases, educating individuals about the pandemic, engaging in public communication, and the creation of detection and diagnosis tools (e.g., those that use artificial intelligence (AI) tools). Previously, using data reported from 178 countries between 2000 to 2017, Katz et al. (2020) demonstrate that countries with better broadband connectivity minimized the economic losses caused by the 2003 SARS pandemic. They find that the economic effect of increasing connectivity by 1 percent is negligible for a country with a broadband level above 20 percent, but it is linked to a 13 percent offset in countries with a 10 percent broadband penetration. Another important role lies in the health care system through, for example, virtual clinics and medical “chat bots”. Budd et al. (2020) identify the role of ICT in epidemiological surveillance (through machine learning, digital surveys, and data collection and visualization), identification (via sensors for symptoms checking, Wi-Fi-based diagnostic devices, and machine learning), tracing (through smartphone apps and mobile phones’ GPS tracks), public communication (via social media, “chat bots”, and online search engines), and clinical care (e.g., telemedicine).

In conclusion, the findings presented in this section suggest that the adoption of ICT has a positive and significant impact on growth, although the extent and timing of the impact
siblings. This reduces the male sample from 4,139 to 3,271 and the female sample from 4,131 to 3,333. The results are shown in table S.4 and are similar to those in table 4.

Fourth, in an attempt to understand why the effect of the local supply of public schools varies by the level of parental education for basic schools but not for secondary schools, an attempt was made to examine whether parents have an outside option when public schools are absent. In particular, if private schools are available in the vicinity, educated parents are more likely to avail themselves of them, resulting in a less binding public school supply constraint at higher levels of parental education. However, if private schools are not available, the education of the children of even educated parents will be constrained by the absence of public schools, making the local supply of public schools equally binding regardless of parental education. Accordingly, the supply of private basic and secondary schools at the subdistrict level is examined in the 2010 Jordanian school census. It is found that private basic schools are relatively spread out across subdistricts whereas private secondary schools are mostly concentrated in a handful of districts. For example, 79 percent of all subdistricts in 2010 had no private secondary schools and therefore no outside option for parents, as compared to only 45 percent of subdistricts not having private basic schools. Thus an increase in the supply of public secondary schools relieves a constraint for everyone, while an increase in the supply of public basic schools provides relatively more relief for less educated parents.

Finally, two sets of regressions are conducted to further examine the robustness of the results. In particular, there is a concern about possible collinearity between the local supply of basic and secondary schools, so the regressions are re-estimated with one or the other type of school supply. For both males and females, when only the supply of basic schools was retained, there was no appreciable change in the baseline effect or in its interaction with parental 24
# <u>5. Comparing the interventions’ effects on reading skills</u>

The school readiness intervention ultimately aimed to improve the home environment and subsequently improve a child’s ability to learn in a school environment. While its objectives were much broader than improving the specific reading skills assessed in TEGRA, an important question is did it have any effect on them? Two birth cohorts were exposed to both the school readiness intervention prior to starting school and the CLRW intervention once in school. Using data on mappings between communities and schools, we estimate the effect of the school readiness intervention on early grade reading scores. We find a positive effect for children with one year of exposure to the school readiness intervention at the end of two years of schooling in CLRW control schools and for girls with two years of exposure to the school readiness intervention at the end of one year of schooling.

# _Data_

In TEGRA, there are two sources of data on whether a student lived in a treatment or control community prior to starting primary school. First, the February round of the 2016 TEGRA collected data on which community 1<sup>st</sup> grade children lived prior to starting school. However, the information could not be collected for 17 treatment or control schools, and a further review of the data found that it may not have been reliable for all students. Second, 421 students in TEGRA were previously sampled in the 2014 TEHCI data. This source of data of where a child lived prior to primary school was reliable and almost all schools in TEGRA contained some of these students. We use this source to derive for each school the probability that a student was from a school readiness treatment community as opposed to a control community or unassigned community. Unassigned communities were also valid controls as the treatment (and control) communities were selected at random from all communities in Tonga. The indicator was calculated as the proportion of students in a school who came from a CPBA treatment community. This measure was highly
45.2|88.7|39.0|50.3|6.2|4.5|
|||**Develo**<br>|**ping country sam**<br>|**ple(123 countries)**<br>|||
|**Number**|55|108|48|60|7|8|
|**Share(in %)**|44.7|87.8|39.0|48.8|5.7|6.5|
|||**Devel**<br>|**oped country sam**<br>|**ple(54 countries)**<br>|||
|**Number**|25|49|21|29|4|0|
|**Share(in %)**|46.3|90.7|38.9|53.7|7.4|0.0|

**Source: Based on data collection by the authors**

The table shows that, while 88.7% of the countries of this very large sample have some type of PPP, only 45.2% rely on the existence of a sector regulator. The proportions are roughly similar across country groups at this very high level of aggregation. When matching the existence of any PPP and the existence of an IRA, the shares drop to less than 40% and once more there is not much difference between the two country groups. Very roughly, one out of two countries with a PPP has no IRA. As expected somehow given the differences in risk levels to investors, it seems somewhat easier for developed countries to attract PPPs without IRAs. There are also a number of countries in which the sector has not relied on any PPP but has adopted the IRA model, less than 6% for developed economies and less
|0.0|0.5|..|0.0|0.0|0.6|10.4|11.0|..|
|Panama|..|..|..|..|..|..|..|..|33.4|14.4|
|Turkey|..|..|..|..|..|..|..|..|28.7|17.9|
|South Africa|0.7|0.0|0.0|0.1|0.3|2.1|3.1|24.3|27.4|12.4|
|Taiwan, China|65.9|0.7|1.1|0.1|1.2|2.8|71.8|129.4|201.1|92.4|
|**Total**|**106.1**|**2.3**|**8.0**|**0.5**|**4.4**|**5.0**|**126.3**|**388.4**|**514.8**|**233.4**|

Source: Locational Banking Statistics, Bank for International Settlements; team calculations. .. = Data not available.

* Including claims to international organizations for Mexico and Taiwan.

9
# 4. Results

## <mark>4.1 Sources of Bias</mark>

Before examining how to correct for bias, it is important to first understand the sources of bias that enter into the phone survey sample and how that bias affects the composition of the sample. Tables 4.1a to 4.3b present weighted means for a wide array of characteristics<sup>11</sup> from the four LSMS-ISA F2F surveys across several different subsamples ending ultimately with the sample of successfully interviewed households in the HFPS for each country. Comparing the means across these subsamples will indicate (1) at what stage any bias is entering into the samples and (2) across what set of characteristics the samples are biased in relation to the F2F surveys. Figure 4.1 also presents estimated bias (measured by deviation from LSMS-ISA mean) in graphical form for selected characteristics.

### <mark>4.1.1 Coverage bias</mark>

Table 4.1a presents results comparing characteristics for the full F2F sample (columns 1, 4, 7, and 10) as well as the portion of the F2F sample that has contacted information (columns 2, 5, 8, and 11) for the samples from all four countries. The portion of the sample with contact information serves as the frame of eligible households to be selected for the phone survey sample. Therefore, any differences between these two samples would indicate the presence of coverage bias brought on by exclusion of households who are ineligible (i.e. have no phone number to be contacted on). Columns 3, 6, 9, and 12 present differences between the two samples (with significant differences indicated with “*” and highlighted in red).

Looking down the variety of characteristics contained in Table 4.1a for Nigeria (column 9), it is evident that there are no substantial differences between the full sample and phone survey frame, suggesting coverage bias is not a major concern for Nigeria.
Policy Research Working Paper 9618

# **Abstract**

This paper describes new global evidence—derived from satellite data—for rates and patterns of urban spatial development since 1990 along three margins: horizontal spread (outward extension), infill development (inward additions in the gaps left between earlier structures), and vertical layering (upward construction). The end product of this growth is floor space, the amount and distribution of which are central to understanding how a city becomes livable and sustainable. Over the quarter century between 1990 and 2015, urban built-up area worldwide grew by 30 percent through horizontal spread and infill. While most cities grow through a combination of horizontal spread and infill, the paper provides the first estimates of the relative prominence of each type of expansion at different stages of economic development. In low-income and lower-middle-income countries, 90 percent of urban built-up area expansion occurs as horizontal spread. The study also finds that increasing incomes are a uniquely necessary condition for a rise in floor space per person through vertical layering: the reason is that building tall is capital intensive. The analysis highlights that if a city’s population doubles but incomes stay constant, the city’s floor space per person declines by 40 percent; by contrast, if per capita income doubles but population stays constant, the city’s total floor space per person increases by 29 percent.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. 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 slall1@worldbank.org and mlebrand@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
Complementary feedings for six‐ to eight‐month olds was high in Bangladesh (in the IFPRI sample), Nepal, Zimbabwe and Bolivia, where more than 80% of the children of the age range received complementary foods. In Nepal, only 44% of the six‐ to eight‐month olds received complementary feedings from one of the dietary diversity food groups.

<!-- Start of picture text --> Figure 6a: Components of Adequate Care<br>Components of Adequate Care<br>100 100<br>98<br>96<br>91<br>86<br>83 82 81 80 84 82<br>78 80 80 78<br>74<br>68 69 67 69 69 67<br>63<br>59<br>55<br>51<br>49 51 50 48<br>45 44<br>30<br>BGD (HK, '11) BOL ('08) IDN (RKD, '10) NPL ('11) ZWE ('10)<br>BGD (IFPRI, '11) ETH ('11) KHM ('10) PER ('12)<br>Exclusive breastfeeding Early breastfeeding initiation<br>Complementary feedings Breast-fed up to 24 months<br>Source: Author estimates.<br>Note: (1) Exclusive breastfeeding for first 6 months; (2) Complementary feedings for 6- to 8-month olds;<br>(3) Breastfeeding within 1 hour of birth (100 minutes for Bolivia and Peru);<br>(4) Breast-fed for 24 months or currently breastfeeding if less than 24 months.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text --> Applying stricter standards by requiring simultaneous access to adequate levels
wells, (6) rain water, (7) purchased water, or (8) water from a water tank.

33 Sanitary latrines include flush toilets with septic tank/sewage pipes, suilabh toilets, and double vault compost.

34 The VMAP0 (NIMA 1997) was used to obtain data on three classes of roads: 1) divided highways; 2) primary/secondary roads; 3) paths or trails. For rivers and lakes, the CIA World Data Bank II (CIA 1972) was used. The population centers are from the GRUMP settlement points dataset (CIESIN et al. 2004a) and the World Gazetteer database (Helders 2005). Urban boundaries are from the GRUMP urban extents database (CIESIN et al. 2004b). The international boundaries are from the World Bank mapping office. 35 To maintain confidentiality, the geographic coordinates of each household in the VHLSS are not made publicly available, only the shapefiles for the different communes surveyed.
24

**Figure 7:** _Factor intensities of Turkey’s exports to the EU in 1990, 1995-2004 (in percent)_

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Natural Resources Capital Intensive Unskilled Labor Skilled Labor<br><!-- End of picture text --> Source: Computations based on EU data from UN COMTRADE Statistics.

**Table 12:** _Share of skilled labor and capital intensive products in total exports of EU candidate countries and EU-8 in 1996 and 2004 (in percent)_

||199|6|200|4|Percent P|oint Change|
|---|---|---|---|---|---|---|
|<br>Country/Group|% Share in<br>Total Exports|Of which: % in<br>Skilled Labor|% Share in<br>Total Exports|Of which: % in<br>Skilled Labor|% Share in<br>Total Exports|Of which: % in<br>Skilled Labor|
|Turkey|21|13|44|35|23|22|
|Bulgaria|39|15|31|15|-7|0|
|Croatia|33|10|34|11|1|1|
|Romania|29|14|35|16|6|1|
|EU-8 (simple average)|51|26|69|34|18|9|
|**Memo:**<br>SEE-4(simple average)|20|10|27|16|7|6|

Note: SEE-4: Albania, Bosnia and Herzegovina, FYR Macedonia and Serbia and Montenegro
made no deposit or withdrawal in the past year.<sup>12</sup> In addition, only 29 percent of adults have savings at formal financial institutions and just 17 percent have loans.<sup>13</sup> This data indicate that despite Sri Lanka’s sizeable per capita income and overall level of human capital development, the full potential for financial inclusion remains unfulfilled. If Sri Lanka aims to sustain its recent upper middle-income status, it is more relevant to compare its financial inclusion achievements with those of other countries which have similarly progressed. In this context, we summarize below comparative financial inclusion indicators for Sri Lanka against the South Asian average as well as other middle income and aspirational counterparts.

Table 1: Financial inclusion indicators<sup>14</sup>

||**Sri**<br>**Lanka**|**South**<br>**Asia**|**East**<br>**Asia**|**Upper**<br>**Middle**|**Thailand**|**Malaysia**|
|---|---|---|---|---|---|---|
||**2017**|**2017**|**2017**|**2017**|**2017**|**2017**|
|**GNI Per Capita ($)**|**3850**|**1611**|**6667**|**8176**|**5640**|**9860**|
|**Human Capital Index (0-1)**|**0.58**|**0.46**|**0.61**|**0.58**|**0
**Figure 1: Density estimates of the log of labor productivity for female- and male-owned informal firms**

<!-- Start of picture text --> Kernel density estimate<br>-5 0 5 10 15<br>lnprod<br>Female Largest Owner Male Largest Owner<br>kernel = epanechnikov, bandwidth = 0.2324<br>.4<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text --> Source: Author’s own calculations based on Informal Enterprise Surveys (IES), World Bank.

# _4.2 Differences in returns to drivers of labor productivity_

The mean differences between female and male-owned firms across several factors provide an indication of the differences in adoption or incidence of the factors. However, female-owned firms and male-owned firms could have the same endowments or factors (inputs or technologies), but the returns to factors may differ between them. A difference in returns is indicative of discrimination that women face, but it could also be due to omitted variable bias if a factor is correlated with unobserved determinants of productivity, such as owner’s ability.<sup>8</sup> Differences in returns can be uncovered using regression analysis to estimate how various factors affect labor productivity for the female-owned versus male-owned firms subsamples. These firm-level regressions are presented in table 2.

> 8 Another reason could be diminishing returns to a factor such as years of manager education when female-owned and male-owned firms have different levels of the factor.

16
di↵erent variables and the level of institutional trust. A positive coefficient (represented at the right of the red line) indicates a positive correlation.

Figure 6: Trust and socio-demographic characteristics: Coefficient plot <!-- Start of picture text --> Geographic and sociodemographic correlates of trust in government<br>Coefficient plot (ordered logit model)<br>Rural<br>Female<br>20 - 24 yo<br>25 - 30 yo<br>31 - 34 yo<br>35 - 40 yo<br>41 - 44 yo<br>45 - 50 yo<br>51 - 54 yo<br>55 - 60 yo<br>61 - 65 yo<br>over 65<br>1500 - 2999 Dhs<br>3000 - 7000 Dhs<br>7001 - 12000 Dhs<br>12001 - 25000 Dhs<br>Over 25000 Dhs<br>University diploma<br>Professional training<br>No diploma<br>-.5 0 .5 1 1.5<br><!-- End of picture text --> Data source: World Bank survey on trust (2021) This graph shows that institutional trust is tied to individual-level socio-demographic determinants, including gender, age and income. First, gender and trust are closely related. Men have considerably higher levels of institutional trust than women (respectively 62 and 48 percent report trusting executive institutions). This is congruent with Moroccan women’s lower levels of civic engagement and political participation and could be a reflection of more systemic patterns of social and political exclusion. Second, there is a positive linear relation between age and institutional trust. Levels of institutional trust increase with age, with about 49.5 percent 18-35-year-old reporting trust in executive institutions, compared to an average of 60 percent of individuals above 35 years old. This is consistent with the evidence that youth are more likely to participate in protests, vote less and emigrate more.
Shaanzi, Sichuan, Guizhou, Yunnan and Tibet.

Table 7 further shows that among those of inter-provincial in-migrants in Zhejiang, majority (89%) came from central and western regions. Thus, many inmigrants in fact crossed provincial lines and traveled a quite long distance to seek economic opportunities in towns in Zhejiang. Whereas in Henan and Sichuan, even though the inter-provincial in-migrants only accounts for less than 10 percent, many of them (61.7% in Henan and 77.2% in Sichuan respectively) were from the western and central regions.

Migration was largely motivated by economic opportunities. Indeed, when census long-form asks about the reasons for migration into towns (Table 8), more than half (60 percent) of in-migrants in Zhejiang responded that purpose of migration into towns was the engagement in production and business, job transfer or assignment. In contrast in Henan and Sichuan, there were only 37 percent and 34 percent of in-migrants who came for the purpose of engaging in production and business, job transfer or assignment.

8
We obtain the global conversion factor, as follows: The conversion factor, _gc_ , is dimensioned as tons of CO2 emissions per 1 part per billion of concentration anomaly.

Fires CO2 emissions for each cell are drawn from Van der Werf et al. (2017). These are gross emissions rather than estimated net emissions from land-use change that incorporate both carbon-emitting conversions (e.g., from forest to cropland) and carbon-absorbing conversions (e.g., from pasture to forest).<sup>18</sup> Using the econometric results in Table 1, Figure 7 maps the predicted global distribution of CO2 emissions using grid cell sums of estimated industry-related emissions ( _gc_ * _ICO2i_ ), population-related emissions ( _gc_ * _PCO2i_ ), and fires emissions from Van der Werf et al. (2017). The figure displays mean annual emissions for 2014–21, highlighting the roles of the United States, Europe, China, and the forested regions of South America, Central Africa, and Southeast Asia.

The map illustrates the potential of satellite-based observations for CO2 emissions monitoring at regional, national, and local scales. Until now, observation-based estimates of CO2 emissions by source from every location on the globe have simply not been available.

# **6.2 Emissions from Functional Urban Areas**

Using the data described in Section 6.1, we estimate emissions from urban areas by aggregating estimates for grid cells within each Functional Urban Area. These can be viewed as expected emissions values for FUAs, given their characteristics. In reality, the impacts of different policies, individual economic decisions and more fine-grained structural factors will cause FUAs to deviate from their expected values. These deviations should be reflected in the residual concentration anomalies from estimation of model (1).

We translate these residuals to emissions using the EDGAR global database of gridded CO2 emissions estimated from local activity measures and standard emissions parameters (Crippa et al. 2020). The current EDGAR database terminates in 2018, so we perform the conversion using