rafmacalaba/datause-encoder-data huggingface dataset:
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|---|---|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|HN Water and Sanitation Sector Modernization Project|Marco Antonio Aguero|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|West Beka'a EmergencyWater SupplyProject|Claire Kfouri|
|IDF-EnhancingInstitutional and Legal Framework for Environ|mentalElena Klochan|
|IDF-EnhancingInstitutional and Legal Framework for Environ|mentalElena Klochan|
|IDF-EnhancingInstitutional and Legal Framework for Environ|mentalElena Klochan|
|IDF-EnhancingInstitutional and Legal Framework for Environ|mentalElena Klochan|
|IDF-EnhancingInstitutional and Legal Framework for Environ|mentalElena Klochan|
|IDF- Implementation of IPSAS Strategy|Arman Vatyan|
|IDF- Implementation of IPSAS Strategy|Arman Vatyan|
|IDF- Implementation of IPSAS Strategy|Arman Vatyan|
|GW-Donor Harmonization & PRSP(LICUS TF)|Fernando Cossio|
|GW-Donor Harmonization & PRSP(LICUS TF)|Fernando Cossio|
|GW-Donor Harmonization & PRSP(LICUS TF)|Fernando Cossio|
|GW-Donor Harmonization & PRSP(LICUS TF)|Fernando Cossio|
|GW-Donor Harmonization & PRSP(LICUS TF)|Fernando Cossio|
|GW-Donor Harmonization & PRSP(LICUS TF)|Fernando Cossio|
|Agro-biodiversityand Adaptation|GarryCharlier|
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

# **I. Introduction**

The goal of this paper is to provide an update on the state of skills, wages, and productivity in the manufacturing sector in Sub-Saharan Africa (SSA). Although employment in the manufacturing sector has been growing at a steady pace in the last years, manufacturing still accounts for less than 10 percent of overall value added in SSA.<sup>1</sup> As Table 1 shows, the share of manufacturing in GDP between 2002 and 2006 in 17 countries does not exceed 14 percent. (An exception is Swaziland, where it reaches 37 percent.) The contribution of manufacturing to total employment is also very small, accounting for less than 10 percent on average.<sup>2</sup>

# INSERT TABLE 1

One reason cited for the slow development of the manufacturing sector is the low education level of the African workforce. A number of studies have highlighted the importance of education to economic growth, and the dampening effect on growth of the failure to expand education in the colonial period.<sup>3</sup> African countries attained independence with a huge education deficit relative to the rest of the world. This massive skill shortage hurt African economies in every sector, public and private (see Figure 1).

<!-- Start of picture text --> Figure 1<br>Literates as % of Adult Population<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Sub-Saharan Africa Eastern Asia/Oceania Latin America/Caribbean Southern Asia<br>1980 1990<br>Source: SPESSA (1999)<br><!-- End of picture text --> <!-- Start of picture text --> 1980<br><!-- End of picture text --> Since the 1960s, African countries, jointly with donors and other partners, have made a huge effort to expand access to education, with astonishing levels of success. While the workforce in SSA

> 1 We exclude South Africa from our analysis.

> 2 For example, the share of industry employment in total employment is about 7 percent in Madagascar and Uganda (World Bank, 2007a).

> 3 See Bosworth and Collins (2003), and Ndulu _et al_ . (2007).

2
Understanding these gender differences in tax burdens and the roles of tax compliance, tax enforcement measures and tax structure in either exacerbating or mitigating gender bias is key to bridging the knowledge gap surrounding gender and taxation. Tax compliance and administration are critical for a robust tax system and have been areas of concern in developing countries (see Slemrod, 2019). The discussion above underscores the importance of data collection to monitor patterns of gender differences in tax compliance and administration. This data can inform efforts to correct biases within the tax system and promote a more equitable distribution of the tax burden.

Prior research has shown that women tend to exhibit higher levels of tax compliance compared to men (see Bruner et al., 2017; Cabral et al., 2021; D’Attoma et al., 2017; D’Attoma et al., 2020; Kastlunger et al., 2010). Grosch and Rau (2017) argue that these gender differences in honesty are influenced by the prosocial behavior of women. Their primary finding suggests a correlation between concern for others and honesty, with women generally displaying greater prosocial tendencies (i.e., showing concern for others) and honesty. Expanding on this, D’Attoma et al. (2020) conducted a laboratory experiment to investigate the role of social values in explaining gender disparities in tax compliance. They found that, while women tend to be more tax-compliant than men, the impact of social values on tax compliance varies from one country to another. They also posited that differences in risk aversion between men and women might be another contributing factor to these gender disparities in tax compliance.<sup>1</sup> Most studies use laboratory experiments to study gender differences in tax compliance. Laboratory experiments are well-suited for designing complex scenarios and are therefore undertaken in controlled environments. However, Kangave et al. (2021) argue that it is important to complement the results of laboratory experiments

> 1D’Attoma et al. (2020) found that women, on average, report 73% of their income whereas men report only 48%.

4
of GenAI does not bypass those who are most in need of its advantages. Focus on informality in the transition will also be of key importance. We find that workers with formal jobs are more exposed to GenAI automation than their counterparts in the informal sector. Even though formal jobs typically offer some coverage of the social protection systems, eventual automation of these occupations does not guarantee that the same workers would easily find new formal employment. Since workers in the informal sector from developing countries rarely move to better paying and formal jobs (Donovan et al., 2023), allowing automation to slowly erode the existing formal sector would simply expand informality. Policies aimed at reducing the segmentation of the labor market along the formal-informal lines would help improve the chances of displaced workers’ transitioning back to the formal sector. In this context, it is important to recognize that technological transformation can actually offer opportunities for job e-formalization through innovative government services (Chacaltana Janampa et al., 2024).

Finally, while this study provides a detailed overview of the LAC region, it is not without limitations, some of which can be considered as open avenues for future inquiry. The first of those concerns data on the use of computers and internet at work, for which the imputation from PIACC was the best available strategy in our case. Obtaining new survey data for at least the most exposed occupations would surely offer more precision to future estimates in that regard. In the absence of such data, imputation based on the second cycle of OECD’s PIAAC, covering 2022-23, might be a viable option, as soon as such data become publicly available. Second, more could be done to understand how the task content of occupations varies across countries and in systematizing such differences according to income groups and possibly regional characteristics beyond LAC. Third, future studies could try to obtain more fine-grained characteristics of individuals’ internet access, since network latency, reliability or type of devices used may affect the adoption and impacts of GenAI by workers.<sup>42</sup> Accordingly, we will continue our efforts to collect data on firms’ and workers’ adoption of GenAI in developing countries, in order to validate the exposure measures developed in this study and to adapt our policy responses to more detailed findings. In that regard, we would welcome collaboration with institutions, including national administrations, that might be able to assist us in the collection of such data for future research.

> 42 Figure A5 shows a robustness check where exposure to GenAI is split by whether workers have access to internet and a computer (instead of computer only).

33
To evaluate the impact of a policy to replace diesel buses with CNG buses, with an increase in bus fares to cover the cost of the conversions, let<sup>0</sup> ’s denote emissions and passenger kilometers traveled before the program is enacted and<sup>1</sup> ’s denote after-program values. The impact of the program on total emissions from transport is given by (3), where _i_ refers to “bus.” Whether the net effect of the clean bus program is to reduce emissions from transport depends on whether an increase in the bus fare induces substitution to other, dirtier modes (per passenger kilometer), such as cars and twowheelers.

# **_B.  The Contribution of Various Modes to Transport Emissions in Mumbai_**

Calculating the elasticity of emissions from transport with respect to a change in the price of gasoline or a “clean bus” policy requires estimates of the share of emissions attributable to each transport mode ( _eixi/E_ ). In this section we present the stylized facts about the vehicle fleet, vehicle kilometers traveled and the contributions of various types of vehicles to PM10 emissions in Mumbai.

Table 1 presents the 2001 vehicle fleet, estimates of the fraction of VKTs attributable to various segments of the fleet, and the emissions factors used by the National Environmental and Engineering Research Institute (NEERI) in calculating an emissions inventory for Mumbai (NEERI 2004). Two-wheelers and cars constitute three quarters of the vehicle fleet in Mumbai. According to the 2001 Census, 9% of all 7
as the flow of remittances has increased, fees remain at least 3-5 percent of transfers (IMF, 2005),<sup>6</sup> providing a substantial incentive for banks to expand their outreach in order to capture a larger share of the remittance market and the fee revenue associated with these transactions.

To study the impact of remittances on banking sector breadth and depth in Mexico, we combine _municipio_ -level data (obtained from the 2000 Population Census) on the percentage of households that receive remittances with information from the banking regulatory authority on the location of every branch of registered commercial banks in the country as of 2000, as well as data on the number of accounts and the peso value of deposits and loans at each _municipio_ .7 In our estimations, we explore the connection between remittances and the number of branches and deposit accounts per capita, both of which are measures of outreach or breadth, and the volume of deposits and credit to GDP, both traditional indicators of depth.

Of course, while remittances may lead to an expansion of banking sector depth and breadth, the causation may also go in the opposite direction. Better access to financial institutions in Mexico may lower the cost of sending remittances, leading to larger and more frequent flows. Or, remittances and banking sector breadth and depth might both be driven by a third factor. Since we are interested in showing a causal link _from_ remittances _to_ banking sector development, we conduct estimations instrumenting for remittances. In particular, following López Córdova (2005) and Woodruff and Zenteno (2007), we use the placement of rail lines before the first wave of migration during the early 1900s as an instrument for remittances.

The distribution within Mexico of the points of origin of early migrants to the United States is closely associated with the location of rail lines which went northward to the Texas border. These rail lines were used by US recruiters under the _Bracero_ –or guest worker- _Program_ to attract Mexican workers to the US. Though railroads are no longer the most important means of transport for US bound migrants, the location of the early rail lines remains

> 6 <u>http://www.imf.org/external/pubs/ft/fandd/2005/12/basics.htm.</u>

> 7 A _municipio_ is the smallest geographic and administrative subdivision in Mexico to have its own democratically elected representatives. It is equivalent to a municipality in Europe, but closer to a county in the US.  A _municipio_ may include multiple communities which may be urban, rural, or a mixture of the two.

4
analyzing PISA and TIMSS results are limited in their inclusion of and application to developing countries. Despite these drawbacks, ISATs provide a strong foundation to obtain globally comparable estimates of education quality.

We build on a literature that aims to produce comparable estimates of cognitive skills across countries and over time, leveraging the emergence and growth of ISATs, and proposing methodological innovations to deal with some of the shortcomings listed above. Our methodology builds on seminal work done by Barro and Lee (1996) and Barro (2001) and provides a global update of previous papers (Altinok and Murseli, 2007, Angrist, Patrinos and Schlotter, 2013, Altinok, Diebolt, de Meulemeester, 2014; Altinok, 2017). We also build on methodologies used by Hanushek and Kimko (2000) as well as extensions by Barro and Lee (2015), Hanushek and Woessmann (2012) and Hanushek and Woessmann (2016).

In a pioneering paper, Barro and Lee (2001) used a simple regression technique to obtain different constants between each test, thus allowing for test differences. Hanushek and Kimko (2000) then created more credible over-time comparisons by adjusting ISATs between 19641995 using the _National Assessment of Educational Progress_ (NAEP) in the United States as an anchor, since the United States participated in both the NAEP and each ISAT. To this end, they use the United States’ performance in NAEP over time to adjust for varying difficulty and scaling across ISATs and construct comparable over-time achievement data. Recent work by Hanushek and Woessmann (2016) aims to address issues of equating _variation_ across ISATs in addition to equating _levels_ . To do this, the authors express performance in terms of standard deviations and project the standard deviation of a relatively homogenous and stable group of OECD countries – termed the “OECD Standardization Group” (OSG) of countries – and then transform these standard deviations into scores using the standardized PISA scale.<sup>12</sup> However, as the authors acknowledge, this does not apply for countries far off the OSG scale since ISATs may be too difficult and irrelevant for this sub-set of countries, distorting the variance equating exercise. This bias is particularly important for analyses focused on developing countries.

Altinok and Murseli (2007) provide the first attempt to include a significant number of developing countries in internationally comparable estimates. Many developing countries do not participate in international tests such as PISA and TIMSS. However, they do participate in

> 1 The criteria chosen for the “OECD Standardization Group (OSG)” includes: the countries have to be member states of the relatively homogenous and economically advanced group of OECD countries over all ISATs observations. Second, the countries should have had a substantial enrollment in secondary education in 1964. 2The OSG countries are: Austria, Belgium, Canada, Denmark, France, Germany, Iceland, Japan, Norway, Sweden, Switzerland, the United Kingdom, and the United States.

3
**Table** **<u>3.</u>** Azerbaijan has extremely low top-bottom consumption ratio

||**Consumption (A**<br>|**zeri manats)**<br>||
|---|---|---|---|
||**2002**|**2003**|**2004**|
|bottom 5% (2003 prices)|81,826|87,207|102,157|
|Top 5% (2003 prices)|345,022|353,932|361,873|
|**95/5 ratio**|**4.2**|**4.1**|**3.5**|
|bottom 10%(2003 prices)|91,293|96,641|110,952|
|Top 10%(2003 prices)|299,803|308,527|315,816|
|**90/10 ratio**|**3.3**|**3.2**|**2.8**|

Source: Azerbaijan HIES data.

The unusually low level of inequality is not just limited to the national aggregate data; it equally applies at the regional level. For example, the average per capita consumption in Baku is only 7 percent higher than the country-wide average (Table 4) in 2002. In 2004, it was about 12 percent higher than the national average. The average per capita consumption in the poorest region in 2002 and 2003 (i.e., Nakhchivan) is only about 12 percent lower than the national average. The regional Ginis are, similar to the national-wide Gini, uniformly low. Baku’s inequality of close to only 20 percent would make it one of the most (perhaps the most) equal city in the world.

**Table 4.** <u>Similar to the national inequality, regional inequalities are extremely low</u>

|||2002<br>|||2003<br>|||2004<br>||
|---|---|---|---|---|---|---|---|---|---|
||Gini|Consumption<br>(% of<br>national<br>average)|Income<br>(% of<br>national<br>average)|Gini|Consumption<br>(% of<br>national<br>average)|Income<br>(% of<br>national<br>average)|Gini|Consumption<br>(% of<br>national<br>average)|Income<br>(% of<br>national<br>average)|
|Azerbaijan|18.4|100|100|18.2|100|100|16.3|100|100|
|Baku|21.4|107|114|20.0|107|115|17.9|112|117|
|Non-Baku urban|17.4|95|100|18.5|94|101|15.7|94|98|
|Rural|16.8|99|92|16.7|100|91|14.9|98|92|
|Nakhchivan|14.9|88|89|16.7|87|89|15.5|95|92|
|Absheron-Guba|19.4|97|105|19.3|97|103|15.3|98|99|
|Mughan_Salyan|15.9|96|94|15.5|95|94|15.5|89|93|
|Ganja-Gazakh|16.7|97|92|18.7|99|92|15.8|97|94|
|Sheki-Zagatala|14|102|93|14.7|99|93|12|99|95|
|Lankaran_Astara|15|99|94|13.8|101|93|14.5|101|94|
|Shirvan|17.7|95|96|17.4|96|96|16.6|91|93|
|Qarabagh-Mil|18.2|103|95|18.1|102|96|14.9|96|93|

Source: Azerbaijan HIES data.

# **Did poor supervision lead to low inequality?**

To investigate whether poor supervision of the data collection process led to low inequality, an ANOVA and cluster analysis techniques on the inequality estimates at the primary sampling unit level were employed. As each interviewer is permanently assigned to a certain primary sampling 5
-8S -

# B. THE PRACTICE

# B.1 Prolect Samnie

11. Our sample consisted of 10 projects: six oil (in Yugoslavia, Hungary, Colombia, India, China and Benin), two natural gas (Argentina and Bangladesh), one with both oil and **gas** (Pakistan), and one a refinery restructuring (Thailand). Coverage of pricing issues was good, with all 10 SARs providing details of retail and border prices of petroleum products, and eight discussing natural **gas** prices. Six SARs included an analysis of producer pricing arrangements and contracts. The sample accounted for 91 percent of the oil and gas projects approved by the Bank during FY84 and FY85. Excluded from these figures are 13 technical assistance and exploration promotion projects for which pricing issues **are** less relevant and SARs are not prepared.

# 5.2 Calculation and Discussion of Efficiency Prices

12. With respect to oil products, comparisons were routinely made between domestic and border prices. Over the project sample, the consumption-weighted average domestic price was $285/ton while the average border price **was** $237/ton. Retail prices as a percent of border prices ranged from 91 to 174, with an average across the ten countries of 111. The relationship by petroleum product is shown below.

||Retail Price as<br>aof Border Price|Ran|
|---|---|---|
|Gasoline|165|106-351|
|Diesel|103|92-163|
|Kerosene|81|62-127|
|Fuel Oil|82|43-141|

13. For natural gas, eight of **the** projects reported on the relationship between actual prices and efficiency prices. The former ranged fron $0.79 to $3.04/WCF with an average of $1.43/MCF. Average efficiency prices were nearly 90% higher, ranging from $1.15 to $4.59/MCF, with an average of $2.70/MCF. In five of the eight cases, the efficiency price of gas **was** defined in **terms** of its fuel oil substitute at the margin. In two other cases (Bangladesh and Colombia) it was the LRKC plus depletion premium. In the final case (Yugoslavia) there is a border price since gas is imported **at** the margin. Domestic prices of natural gas ranged from 38% to 70% of efficiency prices in these sample countries. Clearly there is scope for price increases on efficiency grounds.

14. Questions of price structure were dealt with in the oil sector, but not generally in gas. Most reports comented on the differential taxation of competing oil products (such as gasoline and diesel) where this was perceived to be a problem, and the differential subsidization of kerosene and diesel.