rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Annex A: List of countries covered by the OECD STRI data**

Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Costa Rica, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, the Republic of Korea, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, the Russian Federation, Slovak Republic, Slovenia, South Africa, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States.

# **Annex B: List of sectors covered by the OECD STRI data**

Accounting and auditing services; Accounting services; Air transport; Architecture services; Audiovisual – Broadcasting; Audiovisual - Motion pictures; Audiovisual - Sound recording; Auditing services; Commercial banking; Computer services; Construction – Engineering; Construction services; Courier services; Distribution services; Engineering services; Insurance; Insurance – Actuaries; Insurance - Broking and agency services; Legal services; Legal services - Domestic law; Legal services - International law; Logistics cargo-handling; Logistics customs brokerage; Logistics freight forwarding; Logistics storage and warehouse; Maritime transport; Rail freight transport; Road freight transport; and Telecommunication.

# **Annex C: List of restrictive regulatory measures on MNP covered by the OECD STRI data**

A temporary licensing system is in place; Appointed actuaries must be nationals or residents; At least one engineer must be licensed for the issuance of construction permits; Domicile required for License to practice; Foreign construction engineers are required to practice locally for at least 1 year; Foreign construction engineers are required to take a local examination; Foreign professionals are required to practice locally for at least 1 year; Foreign professionals are required to take a local examination; Foreign providers have to completely re-do the university degree, practice and exam in the domestic country; Labour market tests: contractual services suppliers; Labour market tests:
<br>(2.11)|
|Rangpur|1,937<br>(63)|9,434<br>(343)|42.32<br>(3.22)|
|Sylhet|2,184<br>(93)|13,936<br>(727)|28.10<br>(3.02)|

Note: Data for the estimation of these statistics comes from the HIES 2010. All estimates reported in this table have been produced using population level weights to match official statistics. For the proposed sampling design, however, we use household level weights. Standard errors adjusted for survey design are reported in parenthesis.

A second important decision when designing a sample is selecting accuracy statistics. These are used to evaluate the performance of the proposed sample design to achieve a certain level of precision in the estimation of these statistics for the target variables of interest. The most common accuracy statistics are “standard errors” (SE) of the mean and “relative standard errors” (RSE) of the mean; most household surveys measuring living conditions try to achieve SEs of the mean of the target variable(s) that are no more than 5 percent, and RSEs of the mean that are no more than 10 percent.<sup>12</sup> For HIES 2016/17, we use three accuracy statistics to assess

> 12 <mark>The RSE of a sample mean is the SE of the mean divided by the mean and then expressed as a percentage.</mark> 12
analogous to the database on bound tariffs in the context of goods trade. With the rapid increase of regional and bilateral agreements by some WTO Members in which commitments were closer to applied policies, new data sets were built which provided a better sense of the policies at the time these agreements were signed. However, unlike the applied tariffs database that accompanied the bound tariffs database, no database was initially available on services applied policies.

Prior to 2008, information on applied services trade policies could be found in national (e.g. US International Trade Commission), regional (APEC STAR database), supranational (European Commission) or international databases (e.g. UNCTAD or OECD databases on investment policies).<sup>13</sup> Some information could also be gathered from the WTO's trade policy reviews. However, these sources only provided a partial picture, in terms of the type of information, and the economies, sectors and modes covered.

It was only in 2008 that the World Bank went a step further by collecting and compiling information on applied services policies (Borchert et al., 2012, 2014). The World Bank released its Services Trade Restrictions Database in 2012, presenting data on applied measures for 103 economies and 5 main sectors (financial services, telecommunications, retail trade services, transportation and professional services), which were further broken down into 19 subsectors.<sup>14</sup> Data mainly referred to the year 2008, with data for a number of economies updated in 2011. The database presented 4 tabs reflecting the STRI, the key restrictions, the main policy measures and policy measures at the most detailed level collected in the questionnaire. The last tab included information that was broken down into 9 categories of policy measures, namely Sector openness, Forms of entry, Licensing, Operations, Regulatory Environment, Conditions of provision of service, Restrictions on service providers, Type of entry and Entry condition.

The OECD developed a regulatory database as a basis for
# **Box 1.  Nairobi: Land Cover vs Land Use –Is There a Difference?**

# **Land use vs Land cover**

In 2005 the Government of Nairobi released a city land use map based on the manual digitizing of 2003 aerial photos. Land was divided into many more categories than used in this study, making it difficult to compare results. For example, the land use map has land owned by universities and churches labeled as ‘institutional’. But if these institutions have large fields or parking lots a land cover map would label part of this land as vegetation or barren. Despite these inconsistencies, the classification of informal settlements is loosely comparable. The land use map identified slums as areas with “small mostly temporary buildings that re randomly distributing in high density clusters” and warned that the distinction between residential formal and residential slum “was highly subjective.”<sup>38</sup> Similarly the algorithm in this study attempted to identify neighborhoods with small tightly packed roof tops and label them as “Residential shanty”. The graphs below compare the results, showing how the coverage of slum/irregular land changes as one moves away from the city center. In the first 4 kilometers the land use map claims almost no slums, while the land cover map identifies a small portion. After the 5 kilometer mark, the maps agree almost perfectly, with a steady rise in slum/irregular land followed by a tapering off at around 10 kilometers. Similar comparisons between a semi‐automated approach and manual digitization have been explored in Accra Ghana.<sup>39</sup> This study found the land cover layer derived from VHR imagery was able to accurately detect 94% of the land designated as ‘slum’ by the UN Habitat/Accra Metropolitan Assembly <u>(UNAMA).</u> <!-- Start of picture text --> Nairobi Land Cover c. 2003<br>Nairobi Land Use 2003<br>(
share of significant pairwise comparisons has fluctuated between 61 and 65 percent. This indicates that while margins of error are both substantial and important to consider when comparing countries, it also is possible to use the WGI data to identify many significant pairwise differences in estimates of governance across countries.

# 4.2 Over Time Comparisons

Figure 2 illustrates changes over time in estimates of governance, for Voice and Accountability and Rule of Law over the period 1996-2023. We plot the estimate of governance for 2023 on the vertical axis, and for 1996 on the horizontal axis. Points above (below) the 45-degree line correspond to improvements (declines) in estimates of governance over this period. As with comparisons across countries, considering confidence intervals is important when making comparisons over time. As we discuss in detail in Kaufmann, Kraay and Mastruzzi (2005), the same rule of thumb of considering whether confidence intervals overlap when making comparisons across countries also applies when 17
For each country-year we build three instruments. The first is the exogenous components of all FDI flows, regardless of their source. This is the main instrument used in the analysis. The second instrument only includes flows from advanced economies to emerging market and developing countries (“North-South” flows). The third instrument only includes flows among emerging markets and developing economies (“South-South” flows).<sup>12</sup> As we build more than one instrument for FDI, we can use multiple instruments in the same regression and then test for their validity with over-identifying restrictions tests.

A good instrument for FDI should only capture exogenous push factors driving FDI inflows. We think that our instrument meets this requirement because it only uses variables that are exogenous with respect to the recipient country. A more challenging requirement for a good instrument is that the instrument should not have a direct effect on GDP growth in the host country. One possible criticism of our instrument is that the factors that drive FDI inflows also drive trade. This would be the case if the main source countries for FDI are also the main trading partners of the host countries. To allay this concern, all our regressions control for trade. We discuss this potential violation in the next section. The third requirement for a good instrument is relevance. An instrument is relevant when there is a strong partial correlation between the instrument and the endogenous variable. Unlike the exclusion restrictions, relevance can be tested. To this aim, we report first stage F statistics on the instrument and a standard weak instrument test.

# **4.2 Cross-Country IV Regressions**

We start by estimating a set of cross-sectional regressions similar to those described before using _zit_ as the instrument for FDI. As in the previous cross-section analysis, we estimate the sample over different 20-year growth spells.

Since the bilateral data on FDI flows that are necessary to build our instrument are only available from 1990, we test if the time variation of the FDI coefficient emerges also in the IV
We present the results of the effects of warmer temperature on contemporary poverty in Table 1. Our main focus is the headcount poverty rate at the subnational unit level, using the $3.65/day or $6.85/day poverty thresholds.<sup>14</sup> To further highlight the differences between estimates using country-level data versus using subnational unit level data, we aggregate the subnational unit level data to the country level.<sup>15</sup> We begin with an examination at the countrylevel (Panel A), followed by the localized analysis at the subnational level (Panel B). We employ two models: the fixed-effects model and the long differences model, as shown in Equations (2) and (4). In each panel, the results of the fixed-effects panel model are presented in Columns (1) and (3), with the results of the long differences model in Columns (2) and (4).<sup>16</sup> Across both panels, the findings reveal strong evidence of the negative effects of rising temperature on poverty, regardless of the poverty measures used.

Furthermore, the estimates derived from the subnational-level analysis (Panel B) exhibit stronger magnitudes compared to the country-level analysis (Panel A). Results of the equality tests confirm that these variations between the two levels of analysis are significant, indicating that an analysis based on country-level data may underestimate the effects of increased temperatures. This finding is consistent with that in another study using different poverty data (Dang _et al._ , 2023a) and aligns with earlier research that has identified stronger effects of temperature on economic growth at the subnational level (e.g., Damania _et al._ , 2020; Kalkuhl and Wenz, 2020).

We then turn our focus to the subnational analysis to interpret the estimation results. Specifically, Column (1) of Panel B illustrates that a 1<sup>◦</sup> C increase in temperature
**Table 6. Fostering innovation and building capacity: Policy objectives and performance indicators**

||**Policy objectives**|**Performance indicators**|
|---|---|---|
|**Bols**<br>**incl**|**tering productivity, production and innovation capacities,**<br>**uding human capital and other resources**|•<br>Computer, communications and other services, ICT goods and<br>services imports/exports (WDI)<br>•<br>Investment in telecoms with private participation (WDI)|
|•|Innovation policies and incentives (e.g., R&D, innovation<br>centers) and adaptation/diffusion of technologies in trade-<br>oriented sectors|•<br>Firms offering formal training (WDI)<br>•<br>Number of patent applications filed by residents and non-<br>residents, domestically and abroad (WDI, WIPO)|
|•|Education and training to match domestic skills with<br>international standards and demand in trade-oriented sectors /<br>|•<br>Education statistics – secondary and tertiary education,<br>specialties, male/female, etc. (UNESCO, ILO, WDI)|
||upgrading of available skills<br>|•<br>Global competitiveness index – business sophistication (WEF|
|•|Development of production hard (e.g., storage, conditioning,<br>cooling chains, etc.) and soft (e.g., value-chain management)<br>capacities in trade-oriented sectors<br>|GCI 11.01-11.09);<br>– innovation (WEF GCI 12.01-12.07)<br>•<br>Extent of staff training (WEF GCI 5.08)|
|•<br>•
We implement these calculations using the set of globally representative data sources in the WGI, reporting the resulting estimates of the global mean and standard deviation of governance in Figure 5. The six panels of the graph correspond to the six dimensions of governance measured in the WGI. Each panel reports the estimated global mean and standard deviation (on the vertical axis) plotted against time (on the horizontal axis). For ease of visual reference, the graphs also show horizontal lines at zero and at one. Figure 5 shows little evidence of trends over time in the global mean and standard deviation of governance. While the graphs show some small fluctuations from year to year, the overall pattern suggests that our assumption in the baseline UCM estimates of the WGI that the global mean and standard deviation of governance are constant over time is not unreasonable. This in turn means that the changes over time in countries’ positions in the WGI relative to the global mean can reasonably also be interpreted as absolute changes.

We conclude this section with one caveat. These estimates of trends in global averages of governance are based on the information in the cross-country averages of the globally representative data sources that feed into the WGI. If some of these underlying data sources themselves measure governance in relative rather than absolute terms, it is possible that that global averages of governance could be changing even though the data sources themselves do not register any changes in their global averages. However, we can conclude that, conditional on the information contained in the underlying data sources, the normalization of the global mean and standard deviation of governance to be the same in every period in the baseline WGI estimates is a reasonable approximation.

# 6. Conclusions

This paper has provided a summary of the WGI data sources and aggregation methodology as of the time of the 2024 WGI annual update. While the core methodology of the WGI has not changed over the lifetime of the project, the set of underlying data sources continues to expand and evolve as new data become available, and in some cases
) p. 536 (citing A. Shokhin “Vneshii Dolg Rosii” ZAO Rasters (1997); B. Kheifets “Kreditania istoriia Rossii ot Ekateriny II do Putina” Editorial URSS (2001); V. Tikhomirov “Russian Debt Problems in the 1990s” Post-Soviet Affairs, 17-3 (2001)) for the other estimates for 1992.

10 However, under the U.S. Foreign Sovereign Immunities Act of 1976 the inquiry as to whether a stateowned enterprise is an instrumentality of the government does not consider the political system. _See e.g. Edlow International Co. v. Nuklearna Elektrarna Krsko_ , 441 F.Supp. 827, 831-832 (D.D.C. 1977). In that contemporaneous case the plaintiff sued a Yugoslav “workers’ organization” for breach of contract. The court found _Nuklearna_ to be legally separate from the Yugoslavian state and thus found itself lacking jurisdiction despite existing within a de jure socialist regime. _See_ Joseph W. Dellapenna, _Suing Foreign Governments and Their Corporations: Sovereign Immunity (part 1)_ , 85 Com. L.J. 167, at 171 (1980). In _Nuklearna_ , however, the court’s decision left room to draw a distinction between a socialist government like that of former Yugoslavia and a command economy like that of the former USSR, since the government of Yugoslavia provided no subsidies and was not involved in the management of the company. _See id_ .

11 End of 2021 total external debt stock statistics from the Central Bank of Russia using a residency definition for “external.” Real terms adjustment performed by the author using the US GDP deflator retrieved from <u>FRED.</u>

> 12 Julianne Ams et al., _Sovereign Debt: A Guide for Economists and Practitioners, Chapter 7: Sovereign Default_ , at 3 (explaining the different definitions of sovereign default).
In addition, there is a methodological concern in estimating agglomeration e↵ects based on wages in developing countries, where a majority work in self-employment instead of wage employment (Gindling and Newhouse 2014).<sup>1</sup> Equally important but understudied is the link between urbanization and the way in which workers are allocated to tasks. As well described by Fafchamps (2012), agglomeration potentially has a profound e↵ect on the mechanism by which workers are allocated to tasks by extending gains from specialization and circulating information conducive to innovation in technology, organization, and business practices. Of particular relevance is the transition from specialization driven by the market-based task allocations to hierarchical task allocations within firms. The former is dominated by self-employment in small towns, while the latter prevails in large cities in the form of wage employment.<sup>2</sup> The shift from informal/self-employment jobs to formal/wage employment potentially helps low-skilled workers escape from poverty. However, this transition to wage employment does not automatically take place as self-employment remains prevalent in many large cities in the developing world.

Our study examines the nexus between town population size and job allocation mechanisms in one of the least-urbanized Sub-Saharan African countries: Ethiopia. We focus on urban Ethiopia, where approximately a quarter of the country’s population currently resides. This study reveals whether the transition of self-employment to wage employment, as well as other labor outcomes, is associated with the town population at the early stage of urbanization. This paper focuses on the following key questions. First, we examine whether and to what extent larger towns create more wage and/or formal jobs than smaller towns. Second, we assess who are likely to benefit from labor transitions due to the larger population size (or agglomeration). Finally, we investigate how other labor outcomes, such as labor force participation and unemployment/underemployment, change in tandem.

Our empirical approach to cross-sectional labor force surveys is an instrumental variable (IV) regression of the town population
we use these responses to calculate price averages for domestic and imported varieties separately, given large differences in price and quality. Finally, we convert nominal values to real values using a consumer food price index.<sup>5</sup> We use the FAO Food Composition Tables for the Near East to convert daily food quantities into kilocalories; we then divide by the effective household size to get per capita daily caloric intake.<sup>6</sup> The effective number of household members incorporates guests eating meals within the home and deducts when members are not regularly eating meals at home.<sup>7</sup> The effective number of household members is greater than household size for the relatively richer households, and less than household size for the poorest of households. The value of expenditure on food away from home is included in the calculation of the value of food consumption, but not included in the calculation of caloric intake since quantity data on such food was not collected. Food away from home constitutes about 2 percent of total food expenditure on average; it is less than half a percent for the poorest 20 percent of the population and about 4 percent for the richest 20 percent of the population.

To measure household dietary diversity, we use the food consumption score (FCS) developed by the World Food Programme (WFP). It is a weighted sum of the frequencies with which households consume foods within eight food groups over the previous week.<sup>8</sup> The food groups include staples, pulses, vegetables, fruit, meat/fish, milk/dairy, sugar, and oil/fat. Higher scores denote a more varied diet and are suggestive of a higher quality diet with a potential for higher micronutrient intake. The FCS has been used in food security assessments throughout the world. In Afghanistan, the national average is 61. The WFP uses a cutoff of 48 for an acceptable diet in countries where most households consume staples and oil every day.
Internationally there is a movement towards more sophisticated methods of road tolling to generate funds for road building and maintenance, including vehicle miles travelled and weight miles travelled user fees. Fuel taxes do not capture distance‐related externalities closely because they are insensitive to time and location, and fuel consumption is only weakly correlated with vehicle characteristics such as size and axle weight that contribute to congestion, accidents and road damage.

There is growing evidence that distance based user charging is the most efficient method, especially on a national basis. It can be implemented in a number of ways including: automatic number plate recognition using fixed cameras, short range sensors together with tags or global navigation satellite systems (GNSS). GNSS allow the full network to be included in the charging system. However, these systems are often complex to implement and administer – particularly in comparison to fuel taxes – and may be met with resistance from users.

## **Box 7: International examples of road user pricing reforms**

In 2006 Namibia introduced a mass‐distance charging (MDC) system to recover the excess variable cost for HGV’s that cannot be recovered using fuel levies only. MDCs are expressed in terms of a rate per distance and rates increase with the weight of a vehicle. Initially the MDCs were based on the mass of HGV’s and was payable annually on the basis of an assumed average travelling distance. However, this was strongly resisted by the organized trucking industry as the payment was based on an assumed distance travelled instead of actual distances. In 2007, a revised system was introduced based on self‐assessments for the actual distances travelled in Namibia per annum.

Several methods of road user charging have been used across EU countries, including tolling systems based on a period of time (time vignettes), network wide electronic tolling and concession tolls. Distance based charging systems are considered to be the ideal long‐term solution, and has the most support from industry who considers it the fairest way to charge road users.