rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4.2. Physical and human capital**

The reform scenarios assume increases in private investment above the 17.7% of GDP in the baseline, based on reforms such as an improved business environment and greater access to credit. In the moderate reform scenario, we assume that private investment will increase to the 75<sup>th</sup> percentile of private investment among high income countries (20.4% of GDP over 2015-19) in the span of 10 years. While private and public investment are perfectly substitutable in the LTGM-NR, the investment rate is formed by combining an increase in private investment with an unchanged 4.7% of GDP rate for public investment (so total investment is 25.1% of GDP). The ambitious reform scenario is similarly calibrated but targets a private investment rate at the 90<sup>th</sup> percentage of HI countries, which results in private investment increasing to 25% of GDP by 2033 (Figure 12) (so total investment at 29.7% of GDP) **.** Increases in investment bring a large boost to growth. Figures 16 and 17 show that improvements in investment are projected to spur GDP growth by 0.5 and 1.1 percentage points on average in the moderate and ambitious reform scenario respectively in the entire period to 2050.

For human capital, we raise levels to match those seen in the 75<sup>th</sup> percentile of high-income countries (HIC). In the moderate scenario, we set targets based on median values from available data for HIC countries, resulting in Expected Years of Schooling at 13.6, Quality at 0.83, Adult Survival Rate at 0.94, and Fraction of Children Under 5 Not Stunted at 0.94.<sup>14</sup> In the ambitious scenario, we aim for even higher targets, aligning with the best values observed in HIC countries. Both moderate and ambitious reforms
If the number of cross-sections available for the same country is large enough, and their time span covers a sufficient number of years, one could interpret them as a pseudo-panel, in order to get as close as possible to model (3). In such a case the relevant model becomes where Y��� is the income of individual i born in year τ and sampled in survey t. In such a case, EOp can be repeatedly measured along three dimensions: in a specific year of survey t, repeated observations refer to different birth cohorts τ’s; for a specific birth cohort τ, repeated observations refer to different dates of survey t’s; for a specific age cohort �t�τ�, repeated observations refer to different life cycles. Section 2.2 initially adopts the approach described by model (5). It uses repeated cross-section surveys of the population of a specific country and estimates, for each year, the relevant parameters of the model. An extension which uses the cohort structure of model (6) is also considered. Both these _dynamic_ approaches provide interesting and distinct insights on the evolution of EOp.

# **2.2 Our empirical model**

This section presents a decomposition of measured inequality of opportunity into its constituting components in the same vein as what Solon (2004) did for intergenerational mobility of incomes. In the empirical literature (Ferreira and Peragine 2015), circumstances have included gender, age, ethnicity, region of birth, parental background (in terms of educational attainment and occupational status). For simplicity of exposition, let us consider circumstances as consisting of a single variable, parental education, indicated with E��� where θ denote generations.<sup>4</sup> We assume that parental background affects the income opportunity of the child through two main channels: _educational investment_ and _family networking_ .<sup>5</sup> The first channel can be simply described by the intergenerational persistence of educational attainment (Black and
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again the cases where distance was used for the "transport cost" variable and thc cases where total imports or primary imports from developing countries were the dependent variable.

30. Barbone (1988) was dissatisfied with the procedure chosen by Balassa (1986b) to correct for the endogeneity of transport costs. The problem is the following: When actual cif-fob data are used to derive the measure of transport costs, that transport cost measure is endogeneos in the sense that it is already influenced by the structure of the country's imports, which in turn is presumed to be "determined" by transport cost differefnces. Therefore, Balassa's measure of transport costs would tend to underestimate the influence of Japan's especially long distance from her major trading partners.

31. Another significant point raised by Barbone was that the country dummies used in the one-at-a-time fashion as done by Balassa (1986b) would result in biased estimation of remaining variables. Barbone indeed found that dummies for several other countries were significant (see below).

V. Sato (1986) 32. Kazuo Sato (1986) found that the level of Japanese imports was too low when compared to the norm developed on a sample of 15 industrial countries, allowing for certain economic factors presumed to affect the import performance.~ The approach taken was similar to those of Bergsten/Cline (1985) and Balassa (1986b). Data covered the years 1960, 1970 and 1980.

33. Sato focused on the ratio of manufactured imports to GDP originating in the manufacturing sector, or more precisely, he compared
0 _._ 92SD (or 2 months of schooling) but with strong implications for distribution, with greater impacts on 9<sup>_th_</sup> grade students from low quantiles and significantly reduced impacts for 5<sup>_th_</sup> grade students.

We use detailed data on teachers, principals and public spending to analyze several mechanisms for these findings. First, there are significant reductions in principals “ _Designated_ " to schools and replaced by those going through formal selection processes. Second, there is a strong indication that both principals and teachers in _Ceará_ are more likely to be involved in training and report it as useful for their daily work compared to similar principals and teachers in the immediate municipalities outside _Ceará_ ’s border. Fourth, after the RBF reform, teachers in _Ceará_ are more likely to be hired on 40-hour contracts (both grades), check school homework more often (5<sup>_th_</sup> grade), and cover at least 80% of the curriculum (both grades). Finally, schools in _Ceará_ report significantly less frequent lack of pedagogical materials at the same time that significantly more teachers consider these books to be at least “good”.

Exploring different formats of the RBF model in _Ceará_ in time provides a clear contribution to public policy design. Particularly, introducing the RBF in combination with technical assistance boosts learning impacts in at least two _−_ fold relative to RBF alone. Elaborating textbooks and providing training to teachers, based on student test scores, is the main source of these impacts. Another important contribution to policy design is that redistributing fiscal resources exclusively based on education performance tends to widen learning gaps. Such inequalities are significantly reduced, when penalizing municipalities where there is a high percentage of students below certain thresholds of performance.

All these gains in student learning in _Ceará_ were obtained without increasing public spending on education. This means that the large gains in student learning and education quality followed the efficient allocation of public spending to municipalities in _Ceará_
recession was well targeted, and of an appropriate magnitude. This work also fits within the growing body of research on how households cope in times of recession; UNESC (2009) presents a recent example.

An important reason for focusing on Thailand is the availability of excellent and timely household survey data. The National Statistics Office conducts its Socio-Economic Survey on an on-going basis, interviewing about 3,800 households nationwide every month, and collecting detailed information on consumption as well as many other variables. In principle this allows one to track the actual effects of global recession on different groups in Thailand, with a lag (to allow for data cleaning and processing) of just a month or two. We are also interested in Losers and Winners in Recession: Thailand 2008-09 4
severity of the pandemic across countries and over time is captured using data from Google mobility reports around transit stations (Google, 2021). For countries without available data, we impute the severity based on the Oxford Government Response Tracker index (Hale et al., 2021). Following Cirera et al. (2021a), an indicator of the severity of the crisis is constructed as a weighted average of the severity over 30-day periods since the start of the pandemic until the date of the survey.

For sectoral heterogeneity analysis, we classify sectors according to six attributes pertaining to the flexibility for remote work, prevalence of durable goods in output, GVC intensity, contract intensity, external financial dependence, and reliance on letters of credit. We identify sectors for which an attribute is high through an indicator variable that takes the value of 1 for the fourth quartile of the given attribute as defined in Appendix A.3. For country heterogeneity analysis, we rely on the google mobility data to understand the initial severity of the lockdown, the income classification from the World Bank, and a GVC taxonomy developed in World Bank (2020) based on the extent of GVC participation, the goods and services exported, and measures of innovation, that differentiates

> 2For the BPS round 1 and 2 questionnaires, see Apedo-Amah et al. (2020) and Cirera et al. (2021a), for the trade module introduced in wave 3 see Appendix D. For most countries, the sampling frame was based on censuses from Statistics Agencies, Ministries of Finance or Economy, or business listings from Business Associations, and typically only included businesses that could be found in some registers or listings. The WBES COVID-19 follow-up surveys, by design, cover only formal firms.

> 3The panel of firms surveyed both in the first and the latest round includes 27,110 firms. Due to the smaller coverage, this panel is used only in robustness checks.

> 4A set of seven countries have customs data and BPS data: El Salvador, Georgia,
A. Sanghi and D. Johnson

# **3.2 Chinese companies in Kenya: From large state companies to small private ones**

Kenya currently hosts around 400 Chinese firms spread across every sector. In February 2014, the Sino-African Centre of Excellence (SACE) foundation launched the Business Perception Index (BPI) survey to learn the views and experiences of Chinese companies in Kenya. The BPI surveyed 75 companies: 25 state owned and 50 privately owned enterprises. Many firms in the sample are in the manufacturing, construction, and resources sectors; Chinese firms are also involved in trade, tourism, hotels, and restaurants. Chinese electrical companies are working with Kenya Power, the government-owned power company, to establish 33 power lines in five Kenyan towns: Kitale, Awendo, Konza and Kutus (Business Daily 2015). Oil companies are sure to be attracted to Kenya’s new oil discoveries in Turkana and the northwest part of the Rift Valley, but the UK based Tullow Oil Plc has rights for drilling.

## **3.2.1 Chinese invest the most in metals, communications, and automotive original equipment manufacturing (OEM)**

Figure 23a below gives the amount of Chinese investment by sector between 2003 and 2015. Chinese companies invested US $178.9 million in metals, US $150.9 in communications, and US $68 million in automotive original equipment manufacturing (OEM). Investment in metals fits with Kenya’s high exports of titanium ores and copper to China. Companies also want to take advantage of the growing telecommunications market in Kenya: the number of mobile telephone has grown significantly in the last five years, reaching over 33 million subscribers in 2014; Chinese firms such as Huawei and ZTE wish to compete with other major companies such as Nokia or Ericsson (see figure 22). Figure 23b shows the overall distribution of Kenya’s FDI. The top sectors are communications at US $2.51 billion, alternative and renewable energy at US $2
_4.1. Dis-adoption of stoves as of 2016_ At the end of 2016 (about three-and-a-half years after the first survey period), we conducted a visual inspection and survey to identify whether the stoves adopted in 2013 were still in place. Table 1 presents descriptive statistics on variables that are potentially correlated with continued presence of the stove. About 62% of those who received a stove still had it in place 3.5 years after initial distribution, which is high considering that these stoves were approaching the end of their approximately five-year lifespans.

About 83% of the 121 respondents who gave reasons for abandoning the stove said that the reason for abandoning the Mirt stove was breakage. Reasons given by the rest of the respondents included “I do not need it, because I do not bake injera” and “I have a better improved stove.” All the variables listed in Table 1 are from the 2016 data collection, except the last one on frequency of stove use per week in period 4, which is from period 4 of data collection using SUM devices. This variable is included, because it is the most recent indicator of stove usage intensity before the last period of data collection.

14
.pdf (2019).

68. Glossary:Degree of urbanisation. https://ec.europa.eu/eurostat/statisticsexplained/index.php?title=Glossary:Degree_of_urbanisation.

69. Home - Eurostat. https://ec.europa.eu/eurostat.

70. OpenStreetMap Statistics. https://www.openstreetmap.org/stats/data_stats.html.

71. Barrington-Leigh, C. & Millard-Ball, A. The world’s user-generated road map is more than 80% complete. _PLOS ONE_ **12** , e0180698 (2017).

72. Haklay, M. How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets. _Environ Plann B Plann Des_ **37** , 682–703 (2010).

73. Girres, J.-F. & Touya, G. Quality Assessment of the French OpenStreetMap Dataset. _Transactions in GIS_ **14** , 435–459 (2010).

74. Finding a ‘Kneedle’ in a Haystack: Detecting Knee Points in System Behavior. https://ieeexplore.ieee.org/document/5961514/.

75. BRIC. _Wikipedia_ (2021).

76. Bates, P. D., Horritt, M. S. & Fewtrell, T. J. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. _Journal of Hydrology_ **387** , 33–45 (2010).

77. Yamazaki, D. _et al._ Development of the Global Width Database for Large Rivers. _Water Resources Research_ **50** , 3467–3480 (2014).

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actual survey implementation and technical assistance costs, 2) is anchored in the recommendations of the World Bank Household Survey Strategy for Household Surveys regarding optimal questionnaire and sampling design, and 3) is anchored in the World Bank’s experience in supporting the NSOs in the production of multitopic household survey data through the Living Standards Measurement Study program and the Poverty and Equity Global Practice. The budget envelope for delivering on the ambitious agenda laid out by WBG President Jim Yong Kim is estimated at **USD 945 million** , of which USD 692 million is projected to cover the costs of household survey implementation across 78 countries, and USD 253 million is projected to cover the costs of direct technical assistance provided to NSOs. These estimates imply an annual allocation of 46.1 million for survey implementation and 16.9 million for technical assistance.

This is an estimate for overall resource requirements and is not indicative of costs for each specific survey since the cost environment for surveys can differ significantly across countries. Moreover, as mentioned earlier, these cost estimates may be subject to change as the database of countries 15
- Conti, G., Heckman, J. J., and Pinto, R. (2016). The effects of two influential early childhood interventions on health and healthy behaviour. _The Economic Journal_ , 126(596):F28–F65.

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- Dranove, D., Gron, A., and Mazzeo, M.
was assumed for Ethiopia and Lesotho, which is the median bill collection rate for utilities on the continent. Cash collected by the utility in the reference year is assumed to be equivalent to total revenue (including tariff and non-tariff revenue) multiplied by the bill collection rate.

# A1.4 Capital cost estimates

All capital costs were annualized over the economic life of the plant ( _T_ ) at a real discount rate ( _r_ ) of 10 percent to yield annual capex: r Amortized capital cost= Overnight investment× . 1 −(1 + r)<sup>−T</sup>

## Generation capital costs by technology

Generation capital costs were estimated using the assumptions outlined in Table A1.2 and Table A1.3. Installed capacity data used in the reference year calculations are listed in annex 4. New replacement values of generation assets were determined by technology using reference costs from various sources including case studies of 21 IPP projects in SSA from Eberhard and Gratwick (2011), the U.S. Energy Information Administration, International Renewable Energy Agency, and individual projects developed in the region for each technology. In addition, heat rates assumed in Table A1.2 were used to calculate the amount of fuel required, from which fuel expenditures were estimated.

**Table A1.2** : **Assumptions about generation parameters**

|**Technology**|**Capex (US$/kW)**|**Economic life (years)**|**Heat rate (gigajoules/MW-hour)**|
|---|---|---|---|
|Biomass|2,500|30|14.25|
|Coal|2,403|30|9.29|
|CoGen|917|30|7.44|
|Diesel|1070|30|11.13|
|Gas CC|917|30|7.44|
|Gas OC|603|30
Diluting the market share of the monopoly in ICT from 100 percent to 45 percent is expected to reduce consumer prices by 25.3 percent (table 2). In addition, the results of implementing the simulation in multiple steps suggest that the welfare impact of reducing the monopoly’s market share would increase at a declining rate as new telecommunication providers enter the market (figure 2).

**Table 2. Price Changes after Diluting the Market Share of the ICT Monopoly**

|MONOPOLY MARKET<br><br>|STEP 1<br><br>|COMPETITIVE MARKET<br><br>|
|---|---|---|
|MMMsMM(%) = 11<br>η= −1. 5<br>|MPMuMuhMPu(%) = 45<br>η= −0.81<br>|MPMuMuhMPu(%) = 0<br>η<sup>∗</sup>= −0.25<br>|
|<br>p1= 3. 1<br>_Source:_Elaboration based on data derive<br>_Note:_The elasticity is interpreted not on|<br>p1 = 2.24<br>d from the WELCOM Stata Tool and t<br>ly as the sensitivity of current consum|p0 = 1.0<br>he 2015/16 HCES; see CSA 2018.<br>ers of the service, but also as the expected|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ The elasticity is interpreted not only as the sensitivity of current consumers of the service, but also as the expected response to the demand of both current and new users. The changes in the value of the elasticity occur because of the simulation of a large price drop for the item of interest, resulting in a larger magnitude in the quantity demanded
Net percentage of banks indicating tightening relative to Net percentage of banks indicating higher demand<br>previous quarter relative to previous quarter<br>70 30<br>60<br>20<br>50<br>40<br>10<br>30<br>20 0<br>10<br>0 -10<br>-10<br>-20<br>-20<br>-30<br>-40<br>-50<br>Long-term loans<br>Short-term loans<br>Long-term loans (Forward looking)<br>Short-term loans (Forward looking) Long-term loans Short-term loans<br>Source: ECB Lending Survey 2013Q1 Source: ECB Lending Survey 2013Q1<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1<br><!-- End of picture text -->

**Banks have used various means to tighten their credit terms including increasing prices, shortening maturities, and demanding more collateral** (Exhibit II.4). By the end of 2008,

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# **2. Profamilia’s Internet-Based Sex Education Course**

The online sexual education course was designed by the local NGO Profamilia.<sup>5</sup> Profamilia is Colombia’s largest organization focused on sexual health and reproductive health. With more than 40 years of presence and over 1,800 employees nationwide, Profamilia is well known and used by the local population for sexual health products and services such as contraceptives, HIV testing, and pregnancy tests.<sup>6</sup> Motivated by the stubbornly high level of some important adolescent sexual health indicators nationwide, such as teenage pregnancy rates (Demographic and Health Survey 2005), as well as legal changes, which mandated the introduction of a sexual health curriculum in Colombian public schools, Profamilia embarked on the design of a comprehensive online sexual education course designed for adolescents.

The curriculum aims to shape adolescents’ understanding and perceptions of sexuality, risks, reproductive health, sexual rights, and dating violence. The overarching theme is a human rights approach to pregnancy and teen sexuality. The course focuses on helping the students recognize themselves as endowed with rights, such as the right to say no to sex, to access basic health services, to access family planning services, and to live without sexual violence.<sup>7</sup> Profamilia’s course takes full advantage of Internet connectivity to provide an interactive experience and responsive, anonymous counseling. The modules can be potentially accessed any time of day using a password-protected account, and there is a remote tutor available to answer questions via messages and support the learning process. These features aim to create a safe social environment for adolescents to discuss sensitive topics.

Treatment consisted of five modules. Students worked on the course for a total of 11 weeks. Each group of treated students was initially given three weeks to become acquainted with the platform and complete activities in the first module. After the first three weeks, each group was given two weeks per
**Table 2: Extent of large land acquisitions in select countries, 2004–09**

|**Country**|**Projects**|**Area(1,000 ha)**|**Median size(ha)**|**Domestic share area**|
|---|---|---|---|---|
|Cambodia|61|958|8,985|70|
|Ethiopia|406|1,190|700|49|
|Liberia|17|1,602|59,374|7|
|Mozambique|405|2,670|2,225|53|
|Nigeria|115|793|1,500|97|
|Sudan|132|3,965|7,980|78|

_Source_ : Country inventories from Deininger _et al._ 2011.

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