rafmacalaba/datause-encoder-data huggingface dataset:
tax exemptions. Subsidies in the EU are of the same order of magnitude as in the US, with the majority of subsidies having the effect of supporting production (e.g., subsidies for vessel construction and improvements of port and processing facilities) (Mulvad and Thurston, 2009). Overall, subsidies that enhance fishing capacity of developed countries were some $7.8 billion in 2000 (World Bank and FAO, 2009).

The Doha Ministerial Declaration called for negotiations to “clarify and improve WTO disciplines on fisheries subsidies, taking into account the importance of this sector to developing countries.” Although disciplines on the use of production-expanding subsidies must be complemented by improvements in the property rights to fish stocks – a matter that lies outside the purview of the WTO – phasing out fishery subsidies is an important part of the solution, and is one where the WTO can deliver. The draft text of a new WTO agreement on subsidies for fisheries put forward by the Chair of the Negotiating Group on Rules in July 2008 represented a major potential advance in terms of using the trading system to pursue both environmental and development objectives. It suggested prohibiting a wide range of subsidies while recognizing the need for flexibility in the application of subsidy disciplines to small-scale, labor intensive fishing in developing countries. While many specifics remained to be agreed, this element of the DDA is an example of how ignoring the rule-making dimensions of the negotiations results in greatly underestimating the value of what is on the table.

**Figure 1: Environmental goods – Tariff-only and Overall Trade Restrictiveness**

_Note:_ Data pertain to high efficiency and clean coal, efficient lighting, solar photovoltaics, and wind power technologies and products. See World Bank (2007). The overall measure of trade restrictiveness includes the tariff equivalent of nontariff measures. _Source_ : World Bank and IMF (2008).

8
# **7. References**

Al Samarrai, S. _et al_ (2012) _Making Better Use of Teachers: Strengthening Teacher Management to Improve the Efficiency and Equity of Public Spending_ , Jakarta: World Bank.

Amelia R. (2012) ‘Angelina Faces 20 Years in Jail as Bribery Trial Begins’, _Jakarta Globe_ , 7 September.

APINDO (2014) Stengthening Business Competitiveness For a Prosperous Indonesia: Findings from APINDO’s 2014 Survey on Business Competitiveness in ndonesia, Jakarta: APINDO.

Aspinall, E. (2014) ‘Health Care and Democratization in Indonesia’, _Democratization_ , 21 (5), pp.803-823.

Ayuningtias, R. (2012) ‘FSGI Nilai Uji Kompetensi Guru Bentuk Malpraktek’, Tribun News, 1 August, available at: http://www.tribunnews.com/nasional/2012/08/01/fsgi-nilai-uji- <u>kompetensi-guru-bentuk-malpraktek, accessed 14 September 2015.</u> _Balikpapanpos_ (2014) ‘2015, Anggaran Tunjangan Profesi Guru Capai Rp80 Triliun’, 21 October, available at: <u>http://www.balikpapanpos.co.id/berita/detail/138484-2015-anggarantunjangan-profesi-guru-capai-rp80-triliun.html.</u> _Batampos_ (2014) ‘Putri Bupati Bintan Ansar Ahmad Tak Lulus Tes CPNS’, available at: <u>http://batampos.co.id/27-12-2014/putri-bupati-bintan-ansar-ahmad-tak-lulus-tes-cpns/,</u> accessed 13 November 2015.

Beteille, T. (2009) _Absenteeism, Transfers and Patronage: The Political Economy of Teacher Labor Markets in India_ , unpublished PhD dissertation, Stanford University.

Bjork, C. (2003) ‘Local Responses to Decentralization Policy in Indonesia’, _Comparative Education Review_ , 47 (2), pp.184-216.

Boediwardhana, W. (2011) ‘Civil Servant Recruitment Moratorium Opposed’, _Jakarta Post_ , 6 September, p.5.

Buehler M. (2009) ‘Decentralisation and Local Democracy in Indonesia: The Marginalisation of the Public Sphere,’ in E. Aspinall and M. Mietzner (eds.) _Problems of Democratization in Indonesia: Elections, Institutions and Society_ , Singapore: ISEAS.

Chang, M., S. Shaeffer, S. Al-Samarrai, A. Ragatz, J. de Ree, and R. Stevenson (2014) _Teacher Reform in Indonesia: The Role of Politics and Evidence in Policy Making_ , Washington DC: World Bank.Gaynor, C. (1998) _Decentralization of Education: Teacher Management_ , Washington DC: World Bank.

Grindle, M. (2004) _Despite the Odds: The Contentious Politics of Education Reform_ , Princeton: Princeton University Press.

Grindle, M. (2007) ‘Reform Despite the Odds: Improving Quality in Education’, _Rev. Pensiamento Educativo_ , 40 (1), pp.131-152.

52
offered a 100% subsidy to lifeline (low-income) customers who consume 0-50 kWh of electricity a month, while non-lifeline customers (consuming above 50 kWh a month) would enjoy a 50% subsidy (Berkouwer et al., 2022). The subsidies were announced on March 29 and took effect from April 1. Eligibility status was determined based on March consumption. The policy was initially announced to last for three months, however, the subsidy for [only] lifeline customers was eventually extended for 12 months.

# **3 Data**

This paper uses administrative data on electricity billing records from Ghana and Rwanda. The Ghana data comes from the Electricity Company of Ghana (ECG), which is the largest distributor in the country with operations in the southern and middle belts. It accounts for nearly 70% of all electricity customers in the country. We use data on billing records of the universe of electricity customers of the ECG from January 2018 to December 2020. The data identifies customer types based on the tariff applicable: residential (households), non-residential, and heavy industries. For each customer and year-month, it records the amount (kWh) of electricity consumed, the monetary value in Ghana Cedis (GHS), meter type (postpaid vs prepaid), and location (district) of the customer. In all, the data contains 42 million customer-year-month observations.

The Rwanda data comes from the Energy Utility Corporation Limited (EUCL), the main distributor, via the Rwanda Utilities Regulatory Authority (RURA). The dataset contains the billing records of the universe of electricity customers in Rwanda from January 2018 to December 2020. The data identifies customer type based on the tariff applicable: residential, non-residential (commercial, hotels, health centers, and public works ( water storage and pump stations, broadcasters)), and small-and-medium industries. Also, all customers in the dataset use prepaid meters: Rwanda has a universal roll-out of prepaid meters, with large and heavy industries the only exception who are allowed to use postpaid meters. Our data exclude these customers (i.e. large and heavy industries). For each customer, we have monthly records on the amount (kWh) of electricity consumed (purchased), the monetary value in Rwandan Francs (RWF), location (community/district), and rural-urban status. In all, the data contains 21 million customer-year-month observations.

We complement the electricity data with monthly data on temperature and total precipitation from the Copernicus Climate Change Service.<sup>9</sup>

> 9https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset

6
on the gathering and reporting of data to inform the policy debate on emissionreduction options.

# **1. Existing MAC curves**

Measure-explicit MAC curves have been developed since the early 1990s (Rubin et al., 1992), and have recently reached a wide public, when McKinsey and Company published assessments of the cost of abatement potentials in the United States (Enkvist et al., 2007) and at the global scale (McKinsey, 2007). This type of curve is increasingly used to inform policy makers. For instance, McKinsey currently lists MAC curves for 15 different countries or regions on its website. The World Bank also uses MAC curves routinely (ESMAP, 2012), and has recently developed the MACTool to help build such MAC curves (see below). Similar depictions have been used by other institutions (e.g., Climate Works Australia, 2010; NERA and Bloomberg, 2011; CE Delft, 2012) and to analyze other topics such as waste reduction, energy savings and water savings (see Kesicki and Ekins, 2012; Vogt-Schilb and Hallegatte, 2014).

Depending on their implicit definition of the abating potential of a measure, two types of measure-explicit MAC curves should be distinguished.<sup>3</sup>

# _1.1. Full potential MAC curves_

The full-potential approach gives information on how much GHG could be saved if the measure was used at its technical maximum. It is calculated against a reference or baseline technology, as for instance those used in the present (W ̈achter, 2013), taking into account the carbon intensity and imperfect substitutability of different technologies. For instance, this approach assesses what fraction of passenger vehicles can be replaced by electric vehicles (EV), accounting for limited driving range and exiting mobility practices. Given emissions from baseline vehicles (e.g 140g/km today in Europe) and emissions from EVs (e.g. none), one can compute an amount of emissions avoidable using electric vehicles.

Rubin et al. (1992) use this approach. For instance, they assess the potential of nuclear power (in the US) as the quantity of GHG that would be saved if nuclear replaced all the fossil fuel capacity used for base load and intermediate load operation in 1989 (Table 3, footnote j). In this example, the authors assumed that nuclear power is suited for baseload and intermediate load operation, but not for providing peak power.

The main value of full potential MAC curves is descriptive: they highlight to which extent some key measures could reduce emissions in the long-run. As they do not integrate any consideration on speed, they are not fully operational.

# _1.2. Achievable potential MAC curves_

Achievable-potential MAC curves have a prospective dimension, as they are built for a date in the future. This approach fully acknowledges that large-scale diffusion of new technologies can take up to decades (Gr ̈ubler and Messner, 1998; Gr ̈ubler et al., 1999; Wilson et al., 2013). In this context, the abating potential

> 3This classification was first proposed by Vogt-Schilb and Hallegatte (2014)

4
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**Table 1: Total public and private funding of knowledge capital, 1990-2012 (in MM 2012 USD)**

|**Type and Funding Source**|**Public**<br>**Funding**|**Co-financing by**<br>**beneficiaries**|
|---|---|---|
|**_R&D_**|**_59.87_**|**_19.45_**|
|_INNOVA CHILE_|21.77|12.18|
|_FONDEF_|16.31|-|
|_FIA_|5.87|-|
|_FONTEC_|5.62|4.66|
|_FDI_|4.16|2.61|
|_FONDECYT_|3.90|-|
|_SAG_|2.25|-|
|**_Training_**|**_16.06_**|**_4.82_**|
|_SENCE/ Vinos de Chile - OTIC_|9.42|-|
|_CORFO_|3.72|2.97|
|_INNOVA CHILE_|2.92|1.85|
|**_Joint Marketing Support_**|**_6.14_**|**_5.81_**|
|_CORFO_|5.38|4.61|
|_INNOVA CHILE_|0.77|1.19|
|**_Global Collaboration_**|**_4.57_**|**_1.91_**|
|_CORFO_|2.45|-|
|_INNOVA CHILE_|2.12|1.91|
|**_Market Research_**|**_0.21_**|**_0.14_**|
|_CORFO/ INNOVA CHILE_|0.21|0.14|
|**_Business Process Improvement_**|**_0.98_**|**_0.75_**|
|_CORFO_|0.73|0.46|
|_INNOVA CHILE_|0.25|0.29|
|**_Total_**|**_87.83_**|**_32.88_**|

The other main types of knowledge capital receiving public support were funded either directly by CORFO or through INNOVA CHILE. Joint marketing support projects benefited from over $6 million in public funding, with matching private sector funding of almost as much ($5.8 million). Over half of these support projects (74 of 133 projects) were listed as PROFOs or “Associative Promotion Projects”, a program created in 1992 managed by CORFO which promoted associations among groups of small and medium enterprises (SMEs), requiring the participation of at least five SMEs to improve technical, financial, managerial and marketing capabilities. In the wine industry, most PROFOs were directed at marketing challenges such as conquering new international markets. Rivas (2012) describes the case of SME wineries that initially produced and sold bulk wine to larger wine firms, and as a result of the program learned to work together to upgrade quality and jointly market own exports directly to global markets.

9
19 We compute, for each country, time paths of childhood stunting and cognition without the nutrition program, to which we apply the program effects dS��t�∂D⁄ ��t� and dC��t�∂D⁄ ��t�. We get the counterfactual childhood stunting time path by setting the 2015 childhood stunting rate equal to the latest JME childhood stunting rate for the country in question, and then assuming that before and after 2015 stunting falls at an annual rate of 1.5% (the rate we computed in section 2 above). For the counterfactual cognition time path, we assume zero change in the absence of the nutrition program, and assume the z-score is initially zero. As in a recent World Bank report (World Bank 2016), we assume the program goes to scale gradually over a 10-year period between 2016 and 2025, achieving Bhutta et al.’s estimated 20% reduction (compared to the counterfactual) in 2025. This gives us, after 10 years, a value of dS��t�∂D⁄ ��t� (in terms of year-to-year changes) equal to -3.7%. We assume the program is maintained at the same scale thereafter, so the stunting rate remains at 20% below the counterfactual rate for all periods after 2025.

We estimate the change in the cognition z-score attributable to the nutrition program, dC��t�dD⁄ ��t�, by multiplying, for each intervention in the Bhutta et al. package, the estimated cognition effect of the intervention (obtained from Table 3) by 0.9 minus the fraction of children currently covered by the intervention. (The effect size is relevant for going from 0% to 100%, whereas the program takes intervention coverage from its current rate to 90%.) We use the estimated mean cognition effects from the meta-analyses summarized in Table 3. The current intervention coverage rates are from various sources. We assume the effects are achieved over a 10year period, in line with the assumption that the program is scaled up gradually over a 10-year period.

To get ∂S��t�τ�∂S⁄ ��t� and ∂C��t�τ�∂C⁄ ��t� we need to make assumptions about the number of years before a child starts working (we assume 15 given that stunting rates apply to
The Mozambican enterprises present relatively small differences in firm-level skills, which include workers and managers education and access to training (Table 4). Larger firms are more likely to employ more skilled workers and managers and/or provide additional training. Firms that use computers, rely on the Internet, use licensed technology, and use foreign materials have higher skills. But there are no significant differences for younger firms, across sectors, regions, for exporters, and by business ownership.

Most firms consider technical skills when contracting technicians and support services (e.g., plant and machine operators and assemblers, clerical support, service and sales, etc.). Few firms consider other type of skills relevant when deciding to hire technicians (Figure 7). Even when hiring professionals, only 11% of firms consider interpersonal and communication skills as important for deciding who to hire, and 14% of companies consider problem solving/critical thinking as important in that decision.

In addition, only 20% of formal enterprises have formal training programs for their permanent, full-time employees. About 55% of the firms with completed trainings, only do them in-house. Of the remaining, 25% of the firms do both in-house and external training, and 20% do only external training. Just 12% of the permanent, full-time employees, in formal companies have received formal training. The majority of the training has been conducted on job-specific technical skills, with just over 10% of it on managerial and leadership skills, and in numeracy/math skills.

# **Figure 7. Percentage of firms who consider the skill important in deciding to hire technicians**

<!-- Start of picture text --> 51%<br>18%<br>10%<br>7% 5% 3% 2%<br>Other<br>skills Problem thinking<br>skills<br>Interpersonal and Numeracy or math solving/critical Managerial and leadership skills Foreign languages<br>Job-specific technical communication skills<br><!-- End of picture text --> Source: Enterprise Survey data, authors 21
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|---|---|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|Agricultural Diversification and Market Development Project|Nicolas Ahouissoussi|
|China: Taiyuan Urban Transport Project|Xiaoke Zhai|
|China: Taiyuan Urban Transport Project|Xiaoke Zhai|
|China: Taiyuan Urban Transport Project|Xiaoke Zhai|
|China: Taiyuan Urban Transport Project|Xiaoke Zhai|
|China: Taiyuan Urban Transport Project|Xiaoke Zhai|
|China: Taiyuan Urban Transport Project|Xiaoke Zhai|
|China: Taiyuan Urban Transport Project|Xiaoke Zhai|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Financial Services Development Project|Michael Brent Edwards|
|Programmatic Adjustment Loan 2(PAL 2)|RosalindaQuintanilla|
|Programmatic Adjustment Loan 2(PAL 2)|RosalindaQuintanilla|
|CGAP-Microinsurance(Mali)|Syed Mesbahuddin Hashemi|
|CGAP-Microinsurance(Mali)|Syed Mesbahuddin Hashemi|
|CGAP-Microinsurance(Mali)|Syed Mesbahuddin Hashemi|
|CGAP-Microinsurance(Mali)|Syed Mesbahuddin Hashemi|
|CGAP-Microinsurance(Mali)|Syed Mesbahuddin Hashemi|
|CGAP-Microinsurance(Mali)|Syed Mesbahuddin Hashemi|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
financial incentives conditional on the quantity and quality of provided services, including family planning, antenatal and postnatal care, child vaccination and growth monitoring, and hypertension. Up to 70 percent of the quarterly performance payments can be distributed as bonuses to the clinical staff, while at least 30 percent had to be reinvested in the facility. The program included multiple layers of supervision and verification by regional and national agencies and a third-party counter-verification of the performance indicators.

This impact evaluation relies on a difference-in-difference design, which compared changes in outcomes between the districts implementing PBF and control districts within the same regions. Coverage rates were measured at the population level by household surveys conducted before the launch of the program and after three years of implementation. Detailed data on quality of care was collected through extensive facility-based surveys that included general facility assessments, interviews with health providers, and direct clinical observations of patient-provider interactions.

The results provide evidence of substantial positive impacts of the PBF reform on many dimensions of quality of care. It increased the availability of equipment and supplies at the primary health centers. It had positive impacts on infrastructure and infection prevention and control standards, such as the availability of containers for sharps and needles in consultation rooms. We also find positive impacts on provider competency, measured through clinical vignettes. Finally, we find evidence that the improvements in structural quality and provider knowledge also translated into better content of care. For example, providers in the PBF facilities are more likely to perform key physical exams such as to measure the height and weight of children under 5.

The PBF pilot had positive impacts on health providers. Their income increased by about twothirds due to performance bonuses, and they reported higher satisfaction, especially concerning the working conditions in the facilities. Community perceptions of health providers were also positively impacted. During the follow-up survey, individuals living in PBF districts reported significantly higher perceived competency of providers and that the staff work closely with and listens to the community. Additionally, individuals reported that during the three years of PBF implementation, the attitude of health providers improved.

While we find strong evidence of improved quality at the primary level and observe that the communities noticed the change, we find more modest impacts on the utilization of health services by the community. Concerning adult health, we find a positive impact of 3 percentage points on 3