rafmacalaba/datause-encoder-data huggingface dataset:
Mattoo (2004) and Baller (2007) focus on transnational mandatory standards (EC Directives). Only Czubala et al. (2007) look at the role played by transnational voluntary standards, such as those issued by the European Committee for Standardization (CEN).

CEN is a transnational association established in 1961 by national standards bodies from across Europe. Its standards must be adopted by all EU countries, and override any conflicting or inconsistent national standards. In addition to its work complementing EU Harmonization Directives, CEN is also active in independently developing standards in consultation with industry and national bodies. As noted above, CEN’s output to date is substantial: 12,357 standards and approved documents, with 3,510 more in preparation. By contrast, the European Commission has issued less than two dozen Harmonization Directives under its "New Approach". (See Pelkmans, 1987, for a review of the New Approach.) The World Bank’s EU Standards Database (EUSDB) provides the first catalogue of CEN European standards in the agriculture, textiles, clothing, and footwear sectors, with mapping to a standard trade classification (HS 2000). This paper focuses exclusively on the three manufactured goods sectors.!° From a development point of view, these sectors are particularly important since they are associated with the early stages of industrialization in many countries.

For a full overview of EUSDB’s methodology, see Czubala et al. (2007). The general approach is similar to that of Swann et al. (1996) and Moenius (2000, 2004), although those authors both consider national standards in EU member states rather than CEN’s transnational standards.'! The primary information source for EUSDB is Perinorm (www.perinorm.com), a bibliographic database maintained jointly by the British, French, and German national standards bodies. It contains over 1.1 million records from 22 (mostly OECD) countries. Each record corresponds to a single national, regional, or international standard. For each standard, EUSDB contains data including the dates of entry into force and withdrawal, and a 1-0 dummy variable indicating whether or not <u>~ 107 exclude agriculture because the measure</u> of international harmonization recorded by EUSDB (equivalence with an ISO standard) is arguably less relevant to that sector. Standards promulgated by organizations such as the Codex Alimentarius are likely to be of greater importance. 'l An anternative approach is taken in recent papers by Fontagné et al. (2005) and Disdier et al. (2007). They use WTO notifications under the SPS and TBT Agreements to build databases of mandatory national standards. This is a promising approach, but one which currently suffers from the inconsistent reporting behavior of WTO Members.

14
conditions drive banks’ risk appetite and their cost of funding which in turn co-determines their supply of credit.

2. <u>Banking sector characteristics:</u> Banking sector features such as the structure of assets and liabilities, the prevalence of floating versus fixed rate and foreign currency loans, market and pricing power, business models, economies of scale, and capacity to diversify revenue sources are all relevant supply factors.

3. <u>Business environment: The business environment mainly comprises implicit and explicit</u> taxes (e.g., (unremunerated) reserve requirements, directed lending and interest subsidy programs, interest rate restrictions, counter-cyclical capital buffers), the information environment (e.g., credit bureaus, collateral registries), and contracting and enforcement costs (e.g., (unremunerated) reserve requirements, directed lending and interest subsidy programs, interest rate restrictions, counter-cyclical capital buffers), the information environment (e.g., credit bureaus, collateral registries), and contracting and enforcement costs which determine the expected loss to the bank when a borrower defaults (e.g., the insolvency and creditor rights framework).

Bank intermediation spreads can be decomposed into four basic components:

1. <u>Operational costs: Staff and administrative costs are transferred to borrowers and</u> depositors and typically form the largest component in lending rates and spreads (De la Torre et al, 2006; Poghosyan, 2012; Calice and Zhou, 2018). Operational costs are typically lower in larger banking systems, which initially have more capacity to generate economies of scale, and in a favorable business environment which, inter alia, allows for more accurate borrower screening, better contract enforcement, and higher collateral recovery.

2. <u>Risk premia: Banks need to make allowances against expected and unexpected losses</u> arising from credit and other risks in their operations for economic and regulatory reasons.<sup>1</sup> A key determinant is the composition of the credit portfolio. The associated

> 1 Many jurisdictions are moving to using expected credit loss (ECL) models for accounting provisioning purposes which recognize losses earlier compared to incurred loss models (e.g., the adoption IFRS 9). For a regulatory perspective, see the Basel III framework which governs minimum (capital) requirements for credit, market, liquidity and other risks.

7
**Table 8: Distributional effects of participation in the non-farm activities on HHPCE in Malawi (Quantile regression results)**

|VARIABLES|p10|p20|p25|p50|p75|p80|p90|
|---|---|---|---|---|---|---|---|
|**Effects of non-farm wage**<br>**employment participation**|2.611+<br>[0.114]|2.634+<br>[0.138]|4.639***<br>[0.006]|6.198***<br>[0.005]|6.386**<br>[0.016]|6.214*<br>[0.086]|2.709<br>[0.714]|
|**Effects of non-farm self-**<br>**employment participation**|2.347**<br>[0.018]|4.209***<br>[0.001]|4.303***<br>[0.000]|6.503***<br>[0.000]|10.804***<br>[0.000]|13.041***<br>[0.000]|16.227**<br>[0.016]|
|Other controls included|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time averages included|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|5321|5321|5321|5321|5321|5321|5321|

Source: Generated by authors using LSMS data Note: ***, **, *, and + indicate that the corresponding regression coefficient is statistically significant at the 1%, 5%, 10%, and 15% levels, respectively. The table presents quantile partial effects of our main treatment variables from 10<sup>th</sup> to the 90<sup>th</sup> percentile. The full results tables are in appendix 3. P-values between. Also the results without inclusion of time averages of explanatory variables yields the same conclusions. For the sake of conciseness, they are not presented here, but are available from authors upon request.

**Figure 1: Quantile effects of non-farm wage and self-employment on HHPCE in rural Malawi**

Source: Generated by authors using LSMS data Note: The graph presents quantile partial effects of our main treatment variables. Other controls were included in the estimation 35
<!-- Start of picture text --> GIN<br>SWZ<br>CHL<br>AGO<br>MEX<br>BWA TZAMRT UGA ZAF<br>NIC<br>PAN URY PANEGY BWA<br>BRA SWZ COLARGNAMBRA CRI<br>CRI<br>ECU GTM<br>MEX NAM ECU SLV<br>PERGTMMAR<br>BOL AGO<br>SLVPRY RWA PRY<br>ARG COL HND EGY<br>URY CHL KEN NICMRTBOLHND<br>PER<br>ZAF<br>MDG KEN<br>MAR GIN UGAMDGTZARWA<br>.1 .15 .2 .25 0 .02 .04 .06 .08<br>Data Data<br>R−squared=.1407139537033489 R−squared=.4995535949815552<br>(a) Tax evasion (b) GDP per worker<br>.5<br>.6<br>.4<br>.4<br>.3<br>Model Model<br>.2<br>.2<br>.1<br>0 0<br><!-- End of picture text --> Figure 8. Tax evasion and GDP per worker - data vs the model predictions using the same detection probability for all the countries

# **V.3 Alternative calibration**

The analysis so far relied on the model that is calibrated to the United States’ economy. However, the latter can raise criticism the parameters for the United States and those for African and Latina American economies might be different. As a sensitivity check, we recalibrate the model to the Chilean economy, the country with the lowest combination of distortions and tax evasion in our data and one of the largest sample size.

The parameter value for the labor share in Chile is set to the average share of labor income in the GDP from the ILOSTAT.<sup>13</sup> We set labor share to be 0.414 which is the average labor’s share in the GDP.<sup>14</sup> As in the baseline calibration, we assume a decreasing return in the firm-level production function. The extent of the returns is obtained under the assumption

> 13The ILOSTAT is the world leading data source on labor statistics from the International Labour Organization (ILO). The data can be downloaded from the following website: `http://www.ilo.org/ilostat` .

> 14The average labor’s share of the GDP is available for 2009, 2011, and 2013.

35
Pineau, P.O., Hira, A. and froschauer, K. (2004). Measuring International Electricity Integration: A Comparative Study of the Power Systems under the Nordic Council, MERCOSUR, and NAFTA, _Energy Policy_ , Vol. 32, pp. 1457-1475.

Poulin, R. and Roth, G. (1994). Midterm Evaluation of the Promotion of Private Infrastructure Project, DAI, Maryland. Accessed in October 2014 from <u>http://pdf.usaid.gov/pdf_docs/PDABK388.pdf</u> PUSCL (2014a). About Us, Public Utilities Commission of Sri Lanka, Sri Lanka. <u>http://www.pucsl.gov.lk/english/about-us/</u> PUSCL (2014b). Legislations and Policies, Information cEntre, Public Utilities Commission of Sri Lanka. <u>http://www.pucsl.gov.lk/english/information-centre/legislations-and-policies/</u>

- Rahman, T. (2008). Electricity Reforms in Bangladesh, Presentation on Capacity Building on Electricity Reforms in Bangladesh, India and Nepal, March 13-14, Kolkata, Centre for Competition, Investment and Economic Regulation, India. <u>http://www.cutsccier.org/RESA/events-archive-mar08.htm</u>

- Rahman, S.H., Khatri, S. and Brunner, H.P. (2012). Regional Integration and Economic Development in South Asia, Edward Elgar, UK.

- Ryan, N. (2013). The Competitive Effects of Transmission Infrastructure in the Indian Electricity Market, Job Market Paper, Massachusetts Institute of Technology, Department of Economics, September.

- Saeed, K. (2013). Pakistan’s Power Crisis: Challenges and the Way Forward, Criterion Quarterly, Vol. 8, No. 4.  http://www.criterion-quarterly.com/pakistan’s-power-crisischallenges-and-the-way-forward/

- Sen, A. and Jamasb, T. (2012). Diversity in Unity: An Empirical Analysis of Electricity Deregulation in Indian States, _The Energy Journal_ , Vol. 33(1), pp. 83-130.

- Singh, A. (2006). Power Sector Reform in India: Current Issues and Prospects, _Energy Policy_ , Vol 34 (16), 2480-2490.

Singh, A. (2007). Policy Environment and Regulatory Reforms for Private and Foreign Investment in Developing Countries: A Case of the Indian Power Sector, ADB Institute Discussion Paper No. 64, Asian Development Bank Institute, Tokyo Accessed from http://www.adbi.org/files/dp64.policy.environment.power.sector.pdf Singh, A. (2010). Towards a Competitive Market for Electricity and Consumer Choice in the Indian Power Sector, _Energy Policy_ , Vol. 38 (8), pp. 4196-4208.

Singh, A., Parikh, J., Agrawal, K.K., Khare, D., Panda, R.R. and Mohla, P. (2013). Prospects for regional Cooperation on Cross-Border Electricity Trade in South Asia, Integrated Research and Action for Development, New Delhi. <u>http://irade.org/Background%20Paper%20revised,%20Write%20Media,%20Oct18.pdf</u> Sinha, S. (2005). Introducing Competition in the Power Sector: Open Access and Cross Subsidies, _Economic and Political Weekly_ , Issues in Power Sector Reforms, February 12.

- Smith, T.B. (2004). Electricity Theft: A Comparative Analysis, _Energy Policy_ , Vol. 32 (18), pp. 2067-2076.

26
# **Measuring intangible assets in an emerging market economy:**

# **An application to Brazil**

Mark A. Dutz, Sérgio Kannebley Jr., Maira Scarpelli and Siddharth Sharma<sup>1</sup>

**Keywords: Economic growth, investment, intangible assets, knowledge-based capital, productivity, innovation.**

**JEL Codes: O47, O31, O54, E22**

.

> 1 PRMED, The World Bank, University of Sao Paulo at Ribeirão Preto (for both Sérgio Kannebley Jr and Maira Scarpelli), and PRMED, The World Bank, respectively. The authors thank Charles Hulten, Daniel Lederman, Thomas Kenyon, and participants at the World Conference on Intellectual Capital, Eighth Edition (Paris, May 31June 1, 2012) for helpful comments.
**15**

2011).<sup>19</sup> Figure 1 shows the different steps of the selection process. As the outcome of each public lottery was shared with those attendance, no promises were made to the control group in terms of future coverage. Yet, during the information campaign, the villages were reassured that further negotiations were on the way to scale-up the program at a later stage (see MCA, 2009, p. 9). To this day, the program has not taken place in the randomly-selected control villages.

**Figure 1:** Selection of treated villages within commune

<!-- Start of picture text --> Villages are<br>informed<br>Village is<br>interested<br>Covered by Not covered by<br>EMICoV in 2006 EMICoV in 2006<br>Eligible Not eligible Eligible Not eligible<br>Treated Control Treated Control<br>Lottery pool Lottery pool<br>EMICoV non-EMICoV<br><!-- End of picture text --> <u>Source:</u> Author’s illustration based on information collected during the field survey. <u>Note:</u> See Table A-1 for further details about the number of villages in each category across communes.

According to MCC’s administrative data, land demarcation activities were completed in 283 treated villages of the 300 villages assigned to the PFR intervention at the time of our survey. Land demarcation was still ongoing in an additional eight villages, and had not started in three villages. Six villages refused to cooperate towards the production of an ETF and were dropped by the program.

> 19Each set of two lotteries was structured to allow for villages sampled in the 2006 national household survey ( _enquête modulaire integrée sur les conditions de vie_ , EMICoV) to be over-represented in the program, thus allowing for the EMICoV to be used for this evaluation. Since the EMICoV employs a random sampling strategy at the commune level, this should not affect the validity of our identification. For robustness, we account for this lottery stratification in our econometric analysis.
skilled and female migrants in non-OECD countries are lower. Finally, for completeness, the fifth and sixth rows present the numbers and the proportions of migrants imputed across unobserved corridors. For 2000, imputed values represent 16.7 million migrants in the 90 destination countries for which actual data are not available. Although imputed values account for 15 percent of the total migration stock in 2000, the share is around 8.7 percent for college-educated migrants. In other words, over 90% of college-educated migrants are captured by our raw data and we believe that our imputation strategy should therefore not adversely effect our overall measurement of high-skilled emigration, thereby diluting our conclusions.

Overall, the resulting migration matrices identify 111.9 million migrants (age 25+) in 2000 which represents about 63 percent of the 177.4 million migrants (age 0+) recorded in the United Nations database and 70 percent of the 160.1 million migrants (again age 0+) recorded in OPSW for those 190 countries that appear in our matrices.<sup>3</sup> 28.8 million of this migrant stock have college education and 54.5 million are women. For 1990, we identify 85.3 million migrants (aged 25+), including 16.3 million high-skilled migrants and 40.9 million women. Our data show that the overall migrant stock increased by 31 percent between 1990 and 2000, while the stock of high-skilled migrants increased by 77 percent. As a result, the share of high-skilled in the overall migrant stock increased from 19 percent to 26 percent. The share of women increased from 48 percent to 49 percent, a result in part driven by the increased feminization of migration to non-OECD countries.

> 3There are differences between OPSW and the United Nations database. For example, OPSW remove refugees wherever possible from their data since their primary focus is upon economic migration.

7
energy (Panama), increasing access to electricity in rural areas (Bolivia), enhancing the quality of roads and ports (Costa Rica), and reducing the prices of electricity (Costa Rica).

_Education._ While public education expenditure in the region is on par with the EMDE average, various metrics of the quality of education systems, such as the average student-teacher ratio, fall short of EMDE comparators. Urgent education needs include improved pre-school education and access to early childhood education; better teacher training and quality; and a reorientation of education programs towards employer needs, such as information technology and English language skills (Belize, Bolivia, Costa Rica, El Salvador, Guatemala, Panama).

_Public health._ The region’s public health expenditures are slightly above that of EMDE comparators. Health infrastructure, such as access to improved sanitation and improved water sources, exceeds that of EMDE peers. However, urgent health care investment needs remain (World Bank 2015i, m). These include tackling malnutrition (Guatemala), increasing access to improved sanitation in rural and urban areas, and access to specialized health care services for women and children (Bolivia).

# **IV.3. Which policies can help address investment needs in the LAC region?**

While policy priorities differ across countries, most economies in the region have limited funds to expand public investment spending. The lack of resources places a premium on the efficiency of public investment, which may be enhanced by leveraging public funds with public-private partnerships and implementing reforms to stimulate private investment.

- Strengthening the efficiency of public investment includes streamlining the process for the development, approval, and selection of projects (IADB 2016). Transparency in the project selection process and its monitoring and coordination between multiple stakeholders can help remove inefficiencies.

- Several countries have begun to develop public-private partnership frameworks (Chile, Colombia, Peru). If designed well, these can improve the efficiency of public investment spending (Engel, Fischer, and Galetovic 2014).

- LAC economies rank low on ease of business startup and tax compliance (South America and Central America), as well as trading across borders and registering property (Caribbean and South America) (World Bank 2017b). Reforms to ease these constraints can also encourage investment.

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# **_i. Introduction_**

Poorly implemented energy subsidies are economically costly to taxpayers and damage the environment through increased emissions of greenhouse gas and other air pollutants. Energy subsidies also create distortive price signals and result in higher energy consumption or production, or barriers to entry for cleaner energy services. Subsidies to consumption, by lowering end-use prices, can encourage increased energy use and reduce incentives to conserve energy efficiently. Universal energy-price subsidies tend to be regressive as benefits are conditional upon the purchase of subsidized goods, and increase with expenditure. The proportional adverse impact of energy subsidy removal can be greatest for the poor, even though the rich receive most of the value of the subsidy (IEA, OPEC, OECD, World Bank, 2010).

This synthesis paper is based on a broader study (Vagliasindi, 2012) aimed at providing the emerging lessons from a representative sample of case studies that could help policy makers to address implementation challenges, including overcoming political economy and affordability constraints, by looking at complementary instruments to compensate vulnerable groups for energy-price increases.

# **_ii. Methodology_**

To achieve this objective, the study selected a representative sample of case studies in 20 developing countries, based on a number of criteria, including the country level of development (and consumption) and energy dependency (distinguishing between net energy exporters and importers), as reported in Table 1 below.

**Table 1 Sample Selection for Case Studies Analysis**

||**Net Energy Importer**|**Net Energy Exporter**|
|---|---|---|
||**Group A**|**Group C**|
|**L d**<br>**AFR**|Ghana*|Nigeria|
|**ow an**<br>**Lower**<br> <br>**EAP**||Indonesia*|
|**Middle**<br>**Income**<br>**ECA**|Armenia*, Moldova*|Azerbaijan|
|**MNA**|Morocco*, Jordan*|Egypt*, Iran, Yemen*|
|**SAR**|India*, Pakistan*||
||**Group B**|**Group D**|
|**Upper-**<br>**Middle**<br>**EAP**||Malaysia*|
|**and High**<br> <br>**ECA**|Turkey*||
|**Income**<br>**LAC**|Chile, Dominican Rep., Peru|Argentina*, Mexico*|

_Note:_ Selected sample based on income, region, energy net import-export

* indicates if the country is characterized by macro unbalances

(either budget deficit higher than respectively 4% of GDP or public debt higher than 40% of GDP).

The case studies were supported by data collection related to direct budgetary subsidies, fuel and electricity tariffs, and household survey data, from official documents, complemented by 2
high tech activities (Berry and Glaeser 2005). A second reason is the comparative advantage universities can acquire to conduct basic and applied research. However, it should be noted that even in the United States, universities account for only about 5 percent of all patents, and although they have a larger share of the invention patents, in the areas of applied research and innovation, their role is small (see Figure 16).

**Figure 16: Share of US Utility Patents Granted to US Universities and Colleges**

<!-- Start of picture text --> 6.00%<br>5.00%<br>4.00%<br>3.00%<br>2.00%<br>1.00%<br>0.00%<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>U.S. University-Owned Patents as a % of Total Utility Patents<br>-U.S. University-Owned Patents as a % of all U.S.-Owned Utility Patents<br>-U.S. University-Owned Patents as a % of U.S-"Non-Federal-Organization-Owned" Utility Patents<br><!-- End of picture text --> Source: US Patent and Trademark Office Whether a strong push to raise the quality and relevance of instruction in tertiary institutions in Penang and to build research capacity can accelerate the industrial transition in Penang is an open question. How quickly success in this regard would affect the local industry is another question. However, what we do see from international experience is that successful industrial clusters benefit from proximity to high quality universities. Firms interviewed in Penang indicated that their R&D, or product / process development efforts were being impeded by shortages of specialized skills. MNCs hire some technical personnel to work on design, testing and product development<sup>18</sup> but as

> 18 AMD employs 30 workers mainly on process innovation rather than contributing to the design of AMD chips, although 80% of AMD’s chips are produced in Penang.  Motorola assigns 1,000 engineers for

27