rafmacalaba/datause-encoder-data huggingface dataset:
The AMFm has been approved by the Board of the Roll Back Malaria Partnership (RMP). The Board of the Global Fund to Fight AIDS, Tuberculosis and Malaria approved a decision to host and manage the AMFm in November 2007. It is projected to be operational in 2009.

Further information: <u>http://rbm.who.int</u> Reading: AMFm Task Force of the Roll Back Malaria Partnership 2007

# **Arms Trade Tax**

The Arms Trade Tax is a proposal for a global tax to reduce trade in arms and raise money for development, disarmament, or compensation of victims. A number of objections have been raised against such a tax, including difficulties in achieving compliance; tax evasion; creating an incentive for increased illicit trade in arms; and the likelihood that developing countries, as the purchasers of armaments, would have to pay a substantial part of the tax.<sup>77</sup> Reading: Brzoska 2004 See also: Global taxes

# **Auctioning/Sales of Emission Permits**

One source of funds from cap and trade mechanisms is the auctioning or sales of emission permits. For example, EU Allowances (EUA) under the European Unions European Trading System may be auctioned or otherwise sold rather than being distributed to emitters. The proceeds could be directed to financing international development.

An EU Directive recommends that at least half the revenue should be used to fight and adapt to climate change and lists a number of purposes, mainly within the EU, but also in developing countries.<sup>78</sup> Between January 1, 2008, and November 14, 2008, Germany sold a total of 40 million EU allowances with an overall value of €933.3 million.<sup>79</sup> Germany decided a “share of the income, Euros 120 million in 2008, will be invested in international climate protection measures in developing countries. This share will be and must be increased in subsequent years.”<sup>80</sup> Also, the United Kingdom and Austria have auctioned emission permits.<sup>81</sup> In general, under the EU Emissions Trading Scheme, for the first years the majority of the emissions permits are supposed to be allocated for free. However, the latest EU Commission proposal suggests that from 2013 full auctioning is to be the rule for the power sector and allocating for free is to be phased out for industry over the 2013–20 period.<sup>82</sup> Further information: <u>http://www.bmz.de/en/approaches/bilateral_development_cooperation/</u> - <u>approaches/joint financing/innovative_funding_instruments/index.html http://ec.europa.eu/environment/climat/emission/auctioning_en.htm</u> See also: Cap and Trade

> 77 Brzoska 2004.

> 78 http://ec.europa.eu/environment/climat/emission/auctioning_en.htm.

> 79 http://www.bmu.de/files/pdfs/allgemein/application/pdf/jahresbericht_kwf_08_en.pdf.

> 80 http://www.bmz.de/en/approaches/bilateral_development_cooperation/approaches/jointfinancing/innovative_funding_instruments/index.html.

> 81 http://ec.europa.eu/environment/climat/emission/auctioning_en.htm.

> http://www.defra.gov.uk/environment/climatechange/trading/eu/operators/auctioning.htm.

> 82 http://ec.europa.eu/environment/climat/emission/auctioning_en.htm.

59
# _5.2.2 Nigeria_

In Nigeria (Table 8), while there are no significant gender gaps in education, among the poor or the wider population, the deprivation levels do vary by displacement status. This may be because many IDP children have been out of school for at least three years since initial displacement (Pape et. al 2018). For more than half of displaced households, lack of financial resources is a main reason for not sending children to school, despite the provision for universal basic education in Nigeria and free primary schooling in the Northeast region.

_Table 8: Proportion of deprived individuals across displacement status by gender, Nigeria_

|||IDPs<br>|||Hosts<br>||
|---|---|---|---|---|---|---|
||Male|Female|Difference|Male|Female|Difference|
|_All households_|||||||
|Years of schooling|0.54|0.56|-0.02|0.39|0.45|-0.06<sup>*</sup>|
|School attendance|0.31|0.34|-0.03|0.20|0.21|-0.01|
|Unemployment|0.05|0.11|-0.06<sup>***</sup>|0.05|0.11|-0.06<sup>***</sup>|
|Legal identification|0.70|0.74|-0.05<sup>***</sup>|0.54|0.59|-0.05<sup>***</sup>|
|Early marriage|0.13|0.59|-0.46<sup>***</sup>|0.10|0.53|-0.43<sup>***</sup>|
|_MPI poor households_|||||||
|Years of schooling|0.81|0.86|-0.05|0.85|0.88|-0.03|
|School attendance|0.64|0.71|-0.06<sup>*</sup>|0.68|0.62|0.06|
|Unemployment|0.10|0.21|-0.11<sup>***</sup>|0.14|0.16|-0.02|
|Legal identification|0.87|0.89|-0.01|0.83|0.86|-0.03|
|Early marriage|0.21|0.67|-0.46<sup>***</sup>|0.22|0.64|-0.42<sup>***</sup>|

Source: Authors’ calculations using the Nigeria IDP Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

Among the wider population, women are more likely to be deprived in employment regardless of their displacement status, but among the poor, this gender gap only holds for displaced women. Women are more deprived in legal identification only among the wider population, although it is a serious issue among the poor, too, with eight in ten individuals lacking identification across the board. Furthermore, women report being married early more often than men in all permutations, although host women see smaller gender gaps than displaced women.

19
**Figure 1 Sources of Per Capita GDP Growth, 1964-1993a (Deviation from World Average)**

<!-- Start of picture text --> OECD<br>Latin America and<br>Catin Ameribbeand ETotal ExplainedGrowth Differential.<br>Caribbean r<br>O Policy and Investment<br>South Asia<br>EQuality of Bureaucracy<br>East Asia and Pacific<br>- _ _ Government<br>Middle East and North<br>Comsumption<br>Africa<br>IL _ _ ___ _ ___ __ _ _ . ._ . __ _ _ tiAverage Years of<br>Sub-Saharan Africa<br>Schooling<br>-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0<br><!-- End of picture text --> a~~~~~~~~~ a Based on IV regression in Table 6, column 4. Each bar represents the contribution of each source to the deviation of growth of each region from the world average. Total explained growth differential is the sum of the four listed sources of growth, net of initial per capita GDP, population growth rate, the interaction of government consumption and quality of bureaucracy, and terms of trade changes.

57
economy play a significant role in defining the economic context facing the study participants at each follow-up survey, from a particularly weak labor market at the first follow-up to one that had largely recovered by the third follow-up. Specifically, the employment rates for universityeducated individuals averaged 77% over the four quarters leading up to the first set of lockdowns in March 2020. The labor market softened over the following year, dropping to 67.8% by the time the program was advertised in March 2021 and bottoming out at 67.3% at the time of the first follow-up in August 2021 (Rwandan National Institute of Statistics, 2020-2022). The employment rate ticked up over the following year, increasing to 72.5% by the second follow-up in February 2022 and exceeding the pre-COVID-19 level (78.8%) by our final follow-up in August 2022 (Rwandan National Institute of Statistics, 2020-2022).

# **2.4 Sample Characteristics**

Appendix Table A1 reports the socio-demographic, education, and labor market characteristics of the sample at baseline, together with the p-value of the difference between the treatment and control groups.<sup>19</sup> At baseline, the sample summary statistics describe an educated population in the process of forming families and entering the labor force. In line with the education-eligibility criteria of the program, all participants had recently completed a tertiary education degree: 77% of the sample completed a bachelor’s degree, 21% completed a TVET diploma degree, and 2% completed a master’s degree. Despite an average age of almost 28, only 12% of the sample were married or co-habitating, with a similar proportion having a child. There are important gender differences in these behaviors, with women reporting being almost twice as likely to be married as men (15% vs 8%), and about 2.5 times as likely to have a child (18% vs 7%). On the labor market front, while almost 85% had ever worked, only 40% had worked for pay in the month before the survey, and only 3% report working in a formal job. Across all of the variables reported in Table A1, the only statistically significant difference between the treatment and control groups is the share of respondents from Southern Province. This broad balance is confirmed in Panel D which reports a joint F-test of equality across the treatment and control groups for the variables in Panels A-C.

Recent national surveys demonstrate that our sample is significantly more educated and less likely to have started families than the broader population of Rwandan youth. Specifically, only about 8% of Rwandan women aged 24-30 have attended some tertiary education—one of the eligibility criteria for our program—while 62% are married and 77% have given birth, rates almost 4 times higher than our sample (Rwandan National Institute of Statistics, 2021). Tertiary educated women are generally significantly better off than their less-educated peers, with living

> 19Appendix Tables A6-A7 present balance tables of pre-program measures of soft skills for the treatment and control groups.

8
Figure A7: Correlation of Domestic sales concentration with outward FDI concentration in major investing economies <!-- Start of picture text --> Coefficient of Top 3 firms domestic sales share<br>FDI concentration on Domestic sales concentration<br>By industry, conditional on country/industry fixed effects<br>Mining and quarrying<br>Manufacturing<br>Electricity, gas, steam<br>Construction<br>Wholesale and retail trade<br>Transportation and storage<br>Accommodation and food<br>Information and communication<br>Financial and insurance activities<br>-1 -.5 0 .5 1<br><!-- End of picture text --> Note: The share of top 3 companies by industry, country and year is the ratio between the top 3 listed companies in _Worldscope_ and total output from _EU KLEMS_ . Countries in the sample include the United States, France, the United Kingdom and Germany. The share of top 3 outward investing firms is calculated using greenfield FDI capital expenditure values. Each coefficient along with their standard errors refers to a regression of FDI concentration on market sales concentration including fixed country-industry effects, in order to highlight co-variation over time.

Figure A8: FDI Concentration Ratio, Top Receiving Developing Countries <!-- Start of picture text --> 2 China<br>India<br>Vietnam<br>Indonesia<br>Malaysia<br>1.5<br>1<br>.5<br>2010 2012 2014 2016 2018 2020 2022<br>Project year<br>Source: Financial Times fDi Markets<br>Concentration Ratio (5 investing MNEs)<br><!-- End of picture text --> Note: The analysis relies on greenfield FDI investment capital expenditure values. Each line denotes the destination country’s inward FDI concentration ratio calculated as the share of the 5 largest inward investors for each year.

21
two persons engage in collecting consumption data and three persons collect non-consumption data for two weeks in one PSU. Under these assumptions, we have _τ_ = 3 _/_ 5 = 0 _._ 6. Given these examples, we use _τ_ = _{_ 0 _._ 06 _,_ 0 _._ 36 _,_ 0 _._ 66 _}_ as a plausible set of values to consider.

# **Some empirical values of** _c_

Compared with _τ_ , there are more studies that provide us with some empirical values of _c_ . For Demographic and Health Surveys, Aliaga and Ren (2006, Table 3.1) present an estimate of _c_ based on past eight surveys. The value varies from 10 in Cambodia and Uganda to 52 in Togo. For consumption surveys, we are not aware of a study that provides cost comparisons from multiple surveys. Therefore, we derive the estimates of _c_ from existing studies.

Let us start with Pettersson and Sisouphanthong (2005, p.275), who provide the cost ratio, or the ratio of the cost of adding a PSU to the cost of adding a household, for the third Lao Expenditure and Consumption Survey (LECS-3) conducted in 2002-2003. This ratio is nothing but _c_ in our notation. They report _c_ = 3 _._ 9 in urban areas and _c_ = 6 _._ 1 for rural areas. The lower cost of _c_ in urban areas reflects the lower cost of travel to move between primary sampling units in the urban areas.

A few cautions are in order here. First, travel costs and field allowances are included in the calculation of _c_ but the permanent staff salaries are excluded. As Pettersson and Sisouphanthong (2005) claim, the cost ratio may be affected only slightly by the omission of salaries as the omission will have rather similar effects on both the denominator and numerator of the ratio.

Second, as noted by Pettersson and Sisouphanthong (2005), the cost ratios in their study are rather low. This reflects the fact that the survey required considerable time for interview and follow-up per household over the month when the interviewer-supported diary method was used. Therefore, when a simpler consumption module is used, _c_ is likely to be higher.

Next, we also calculated _c_ based on the survey costs reported in Humphreys (1979, p.396) for the Ada Baseline Survey (ABS) conducted in rural Ethiopia. Their first stage sample consists of 87 administrative localities and costs $5,547, whereas 632 observations were in the second stage costs at the cost of $5,899.<sup>16</sup> Therefore, we have an estimate of _c_ as follows: 16In addition, there were fixed costs of $3,589. Humphreys (1979) use a slightly different cost function for their analysis, but their cost model reduces to ours when we ignore the terms involving square roots.

39
- 14 -

decreases with the size of firm. This was found to be true in each country and for each type of infrastructure: power, water, transport, telecommunications and waste disposal. As extensively documented in Report Number 1, smaller manufacturers on the average devote a higher percentage of their resources to each one of these infrastructural needs and pay higher implicit marginal prices in self provision than do larger firms. In the case of power provision in Nigeria, small manufacturers bear marginal costs as much as seven or eight times higher than the marginal costs born by the public sector. These high multiples are evidence that firms attach great value to a high quality power supply.

An econometric confirmation of the basic result that the marginal cost of embedded electricity substantially declines with firm size, was done using our econometric models of the blending firms in Nigeria and Indonesia. (There were very few blenders in Thailand where the ptiblic power supply is fairly reliable); The econometric results are included in Report Number 2 [Econometric Analysis].

In addition to confirming the presence of strong scale economies in private provision, our econometric results for Nigeria and Indonesia revealed that the shadow prices of firms for the publicly supplied electricity were much higher than the marginal costs of purchasing such electricity and also much higher than the marginal cost of producing electricity internally. As well, the marginal cost of producing electricity internally, as noted earlier, was generally higher than the marginal tariff price paid to the public sector for purchased electricity.

Since shadow price measures the willingness to pay for marginal quantities of a limited resource, the finding that the shadow price for public electricity exceeds the marginal cost of internally produced electricity means that firms would much prefer to buy their electricity rather
This research also contributes to the literature on the drivers of the skill content of jobs by using the World Bank’s International Income Distribution Dataset (I2D2). I2D2 covers more than 150 countries and several years of household survey data. By applying the skill-intensity measures to each occupation, we estimate cross-country regressions and find that the positive correlation between economic development and the intensity of jobs in non-routine cognitive skills weakens or disappears once other factors are accounted for. ICT adoption is consistently correlated with job de-routinization. The magnitudes of the estimated coefficients are also economically significant: An increase of 50 percentage points in internet penetration – roughly the increase experienced by developing countries since the early 1990s - is associated with a decline in the routine cognitive intensity of jobs equivalent to 42 percent of the decline in this measure experienced by Ghana since the 1990s. Higher levels of exports, in contrast, are accompanied by an increasing routinization of the labor market. These findings are robust to several specifications.

Interaction terms suggest that ICT adoption coupled with high population growth – the _demographic dividend_ - seem to be stronger predictors of the increase in the share of jobs intensive in non-routine cognitive skills. We also find that the change in the demand for skills associated with ICT is linked to labor market disruptions. ICT adoption is followed by lower employment growth in countries with a higher share of routine jobs, which are more susceptible to being replaced by this technology.

The rest of this paper is structured as follows. Section 2 contains a review of the literature and discusses the contribution of this paper. Section 3 describes the data, while Section 4 presents the methodology and the estimated trends in the skill content of jobs. Section 5 investigates the drivers of the skill content of jobs across countries, while Section 6 estimates the impacts of ICT adoption on employment. Finally, Section 7 concludes.

# **2. Literature review**

While the canonical model assumes a one-to-one link between skills and tasks, there is a rising body of literature that emphasizes the distinction between these two concepts. In particular, while a task is a unit of work activity that produces output, skills are the workers’ endowments of capabilities to perform several tasks (Acemoglu and Autor, 2011). Since the seminal work of Autor, Levy, and Murnane (2003), there has been a steady increase in the number of articles 3
16 indicators of student educational aspirations and attitudes.14/ The measure of educational aspirations was tapped by the number of years of additional education the student expected to receive. We have also included four attitudinal factors. In constructing these indices, we first conducted exploratory principal component and varimax rotation factor analyses of a 9-item student survey of perceived parental attitudes and a 46-item student attitude survey. Two factors emerged from the perceived parental attitudes survey and five interpretable factors emerged from the student attitude survey. We then conducted confirmatory factor analyses and computed factor scores for each of the seven factors.

This paper reports results from four of these factors: (a) self-perception of ability, (b) perceived parental support, (c) motivation with respect to mathematics, and (d) gender-stereotypic views about mathematics. "Perceived ability" was constructed from four items (e.g., "I could never be a good mathematician") having factor loadings ranging from .68 to .77, and a higher score on this factor indicates a lower perception of one's ability in mathematics. "Perceived parental support" was constructed from four items (e.g., "My parents are interested in helping me with mathematics") having factor loadings ranging from .64 to .79. "Motivation" was constructed from four items, (e.g., "I want to do well in mathematics") having factor loadings ranging from .68 to .81. "Gender stereotypes" was constructed from three items ("Boys have more natural ability in math than girls"; "Boys need more mathematics than girls"; "Men make better scientists and engineers than women") having factor loadings of .86, .82 and .77 respectively; higher scores indicate views of mathematics being more appropriate for males, which we interpret as a more stereotypic attitude.
**Box 3.1 Air Quality Trends for the South Coast Air Basin of California**

|Year|Ref|Ozone||_||Ref|PM1o||||
|---|---|---|---|---|---|---|---|---|---|---|
||Year|1hour max<br>oDrn|Stndrzd<br>toWO|8hourmax<br>DOM|Stndrzd<br>toYO|Year|24hourmax<br>ua/m3|Stndrzd<br>toYO|AA<br>ua/m3|Stndrzd<br>toYO|
|1980|0|0.49|1.00|0.34|1.00||||||
|1981|1|0.39|0.80|0.28|0.84||||||
|1982|2|0.40|0.82|0.27|0.79||||||
|1983|3|0.39|0.80|0.26|0.77||||||
|1984|4|0.34|0.69|0.25|0.74||||||
|1985|5|0.39|0.80|0.29|0.86||||||
|1986|6|0.35|0.71|0.25|0.75||||||
|1987|7|0.33|0.67|0.21|0.62|0|219|1.00|73|1.00|
|1988|8|0.35|0.71|0.26|0.77|1|289|1.32|82|1.11|
|1989|9|0.34|0.69|0.25|0.75|2|271|1.24|81|1.11|
|1990|10|0.33|0.67|0.19|0.58|3|475|2.17|67|0.91|
|1991|11|0.32|0.65|0.20|0.60|4|179|0.82|65|0.89|
|1992|12|0.30|0.61|0.22|0.65|5|649|2.96|62|0.85|
|1993|13|0.28|0.57|0.20|0.58|6|231|1.05|58|0.79|
|1994|14|0.30|0.61|0.21|0.62|7|161|0.74|56|0.76|
|1995|15|0.26|0.52|0.20|0.60|8|219|1.00|52|0.71|
|1996|16|0.24|0.49|0.17|0.52|9|162|0.74|52|0.71|
|1997|17|0.21|0.42|0.17|0.51|10|227|1.04|56|0.77|
|Parame|ter|Ozone|||||PM10||||
||||1hourmax||8hourmax|||24hourmax||AA|
|RSquar|e||0.87||0.78|||0.06||0.81|
|Obs|||18||18|||11||11|
|Intercep|t||0.88||0.88|||1.53||1.07|
|P-value|||>0.001||>0.001|||0.004||>0.001|
|m|||-0.0238||-0.0216|||-0.0498||-0.0400|
|P-value|||>0.001||>0.001|||0.475||>0.001|
|Annualr|ed.||-2.4%||-2.2%|||-5.0%||-4.0%|
|Decade|red.||-24%||-22%|||-50%||-40%/o|

_Source:_ California Air Resources Board **(p.** 90, 1999).

Although the definition of baseline air quality in 2010 is highly uncertain, the benefit analyses that follow are valid for the reductions in ambient air pollution associated with each scenario. This is because the dose-response functions used to quantify health benefits and the economic values applied to these benefits are independent of baseline levels of air pollution-they depend only on _changes_ in ambient pollutant concentrations.

# **4. Exposure Modelling and Contingency Estimates**

This section describes both the modelling of exposure and the estimation of environmental contingencies (that is, alerts). First, exposure of the population to pollution is modelled by combining the air quality maps (section 3) with information on population distribution. This model will be used to estimate the health impacts of air pollution in the next sections. Second, we estimate the number of environmental contingencies declared to value the economic cost of these alerts (in section 6).

_12_
33 bank entry was on cutting entrenched relationships between politically connected enterprises and the banking system (Gianetti and Ongena, 2009). Foreign bank entry was a critical element of the disciplining framework that countries in Central Europe put in place in the mid to late 1990s and set them on a path to financial deepening.

The increase in financial depth, including Private Credit to GDP was accompanied by increase in the share of enterprises that finance their investment with bank credit, as gauged by the World Bank Group’s Enterprise Surveys. Specifically, this share increased from 14 percent in 2002 to 28 percent in 2005 to 36 percent in 2008/9. This increase in access to financial services has been confirmed by in-depth studies and has also been linked to specific institutional upgrades (Brown, Jappelli and Pagano, 2009, Gianetti and Ongena, 2009).

# **5.3. Moving beyond the frontier**

However, the benchmarking exercise might have also given first indications of an overheating in the mid-2000s. Specifically, while both actual credit and deposit to GDP ratios moved beyond the predicted levels, the credit increase was faster than the deposit increase, ultimately resulting in an “intermediation efficiency” ratio of credit to deposit above one. The ratio of credit to deposits moved beyond its predicted level after 2003 (Figure 6). Much of this additional credit was allocated to households rather than enterprises, especially in the form of mortgage credit for longer maturities in some cases in foreign currency (Figures 7 and 8).

33