rafmacalaba/datause-encoder-data huggingface dataset:
first of its kind— systematically inventorying public land and estimating its value using GIS and land records. The methodology developed in the study could be used in conjunction with planning for land use, transport and financing of infrastructure. The information here on market values, location and ownership types and the significance of public land on total availability of buildable land could add considerably to the usefulness of current records of land holdings in the hands of various government agencies such as the AMC, the Revenue Department and Central Agencies owning land in the city.

The task required gathering of information, consulting the agencies in charge to obtain existing maps and documents related to Town Planning Schemes and Cadastral Records. Attributes from these documents constituted the database needed for the GIS application. This substantial task was conducted over a period of eight months and involved professionals in the fields of urban planning, GIS technicians, cartographers and land assessors. It is important to note that the GIS database was not the end in itself but it helped to analyze and build scenarios to aid the decision makers in framing their policies for public land asset management and future development.

In the present exercise, land use zoning and FSI as prescribed in the Development Plan 2002 were taken as given. It could be promising to revisit these aspects with alternative land use scenarios. AUDA has published the Draft Development Plan 2012 for public consultation. Since this happened after most of the technical work was completed, the proposals of the Draft Development Plan could not be taken into account. Once the plan is approved, new valuations could be developed. Furthermore, revising land use and FSI regulations in the context of raising resources for infrastructure investments through monetizing public land after due public consultation and consideration of environmental and social issues could be a sound policy. Oftentimes public land is identified as surplus precisely because the current use is out of date. The first and best use of the land freed up can be quite different. Especially when a large well located urban parcel is freed up, a multiplicity of objectives, fiscal, environmental and social can be served. Other countries do follow such an approach and McIvor (forthcoming) offers examples of how this has been done in Canadian cities<sup>21</sup> .

Although Central Government, State Government and the Ahmedabad Municipal Corporation are identified as the three land-owning public agencies, within each of these there are many authorities that are in possession of land and decide use of land. Within Central Government, the Ministry of Defense is involved in case of Cantonment; the Ministry of Civil Aviation is involved in Airport etc. In case of State Government too there are various departments such as Food and Civil Supplies Department in case of warehouses for food grains, the Home

> 21 Gordon McIvor Urban Land Development And The Monetization Of Land Assets In Cities Canada Lands Company: A Canadian Success Story forthcoming World Bank

30
most of these variables were worse for firms inside SEZs compared to outside (and worse than for Bangladeshi SEZ firms). Indian outcomes have been poor, with far fewer jobs created in SEZs than anticipated. The Arab Republic of Egypt provides another example.<sup>22</sup> For unclear reasons, it sought to restrict manufacturing activity to SEZs, many of them located in remote peripheral regions. This created upwards pressure on land prices in SEZs, to which government reacted by deciding to allocate land without a market, giving it for free to a lucky few. This is arguably not the best way to foster industrial development in a country.

In some situations, SEZs may be able to remedy one key bottleneck for development. For instance, the main advantage of Bangladesh’s SEZs seems to be their ability to offer well-serviced land to manufacturing investors.<sup>23</sup> To take another example, the Government of Afghanistan is considering retrofitting former U.S. military bases as SEZs. Offering a more secure environment is arguably a crucial issue in this country. If the objective is to attract internationally mobile firms, then the reference level is meeting international standards, not just surpassing local standards.

Effective implementation of policy matters. This requires action that is coordinated across functions (tax, land, infrastructure), so it requires that the organization running the SEZ is empowered to deliver these functions. There must also be credible commitment to policy for many years ahead. Taken together, these considerations mean that commitment is needed from the highest level of government. At the same time the SEZ authority needs to be responsive to the concerns of firms in the zone.

# **ii) Indirect quantity effects: Links and growth**

A successful SEZ will have an internal dynamic of spillovers between firms, agglomeration, and productivity growth. This will have a horizontal element, with a large number of firms in the same sector building up thick labor markets and other agglomeration economies. And a vertical element, with co-location of input suppliers and the growth of forward and backward linkages. This process encounters the first-mover or “all-at-once” coordination problem discussed above – it is hard to start a cluster. Involvement of one or several large firms is one route to kick-start this, as with the multinational electronics companies (including AMD, Fairchild semiconductor, Intel) initially attracted to Penang Malaysia, or Philips-van-Heusen’s project in Hawassa, Ethiopia (see Box 3.3). Attracting such companies requires intense ‘soft policy’ from government, working closely with the companies and committing to deliver international standards.

Links from the SEZ to the local economy include development of skills in the local labor market; expanding the technological capabilities of local firms; increasing use of local firms as suppliers

> 22 World Bank staff, personal communication.

> 23 World Bank staff, personal communication.

28
# **China**

Table 9a: Returns to levels of education - Employed for wages

||1999|2005|Change (%)<br>2005-1999|
|---|---|---|---|
|_Basic controls only_||||
|- Primary|0.095|0.131|_37.9_|
|- Junior Sec./primary|0.054|0.159|_194.4_|
|- Senior Sec./ primary|0.192|0.466|_142.7_|
|- Tertiary/primary|0.607|0.958|_57.8_|
|_Basic controls + Industry dummies_||||
|- Primary|0.108|0.089|_-17.6_|
|- Junior Sec./primary|0.052|0.163|_213.5_|
|- Senior Sec./ primary|0.178|0.468|_162.9_|
|- Tertiary/primary|0.538|0.930|_72.9_|

Table 9b: Returns to skill over time – Employed for wages

||1999|2005|Change (%)<br>2005-1999|
|---|---|---|---|
|_Basic controls only_||||
|- Upper Sec. and above/ Low sec. and below|0.271|0.475|_75.3_|
|- At least some tertiary/Secondary and below|0.454|0.609|_34.1_|
|_Basic controls + Industry dummies_||||
|- Upper Sec. and above/ Low sec. and below|0.239|0.456|_90.8_|
|- At least some tertiary/Secondary and below|0.397|0.578|_45.6_|

18
of green occupations; 47 are what O*NET GEP refers to as green increased demand occupations (occupations without green tasks); and most importantly, all green occupations according to O*NET GEP (62 green enhanced skills occupations and 78 green new and emerging occupations) are also green according to the text analysis. However, our methodology also classifies 112 additional occupations as green (meaning they have at least one green task) which are not green according to O*NET GEP (shaded cells in Table 3.5); 17 of these are green increased demand occupations (see Appendix F for the list of all occupations that are green to us but not to O*NET GEP).

**Table 3.5 O*NET GEP occupations vs Text analysis methodology**

||**Text analysis based on nar**<br>|**rowgreen dictionary**<br>||
|---|---|---|---|
|**O*NET occupational classification**|**Zero GTI**|**GTI task**|**Total occupations**|
|**Non-green occupations**|675|95|770|
|**Green increased demand (GID)**|47|17|64|
|**Green enhanced skills**||62|62|
|**Green new & emerging**||78|78|
|**Total occupations**|722|252|974|

# _Source_ : authors

When comparing the results of the GTI under both methodologies, although highly correlated (0.9), the text analysis methodology proves to be better for understanding the greenness (or intensive margin) of occupations. Figure 3.4 plots the occupational GTI when calculated through the text analysis methodology versus when calculated based on O*NET GEP. The figure shows that there are many occupations that are either all green or all non-green according to O*NET GEP, while when applying text analysis, occupations have different levels of greenness.

**Figure 3.4 Occupational GTI text analysis vs O*NET GEP**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>0 20 40 60 80 100<br>GTI text analysis<br>O*NET Non-green O*NET Green increased demand<br>O*NET Green enhanced skills O*NET New & Emerging<br>GTI O*NET GEP<br><!-- End of picture text --> _Source_ : authors 24
||**Table 3 B**<br>(at con|**asic Industri**<br>stant price,|**al Informati**<br>1996=100)|**on**||
|---|---|---|---|---|---|
|||1994|1995|1996|1997|
|State|Value added|9.34E+1 1|8.55E+1 1|8.74E+1 1|9.09E+1 1|
||Fixed capital asset|2.08E+12|2.64E+12|2.83E+12|3.04E+12|
||Labor|43710000|43970000|42774000|39459000|
||Share of capital|66.6%|66.1%|65.4%|63.4%|
||Share of labor|66.4%|66.5%|66.3%|64.7%|
||Share of value-added|53.8%|53.8%|48.5%|46.2%|
||K/L ratio|47546.37|60109.52|66227.38|76929.88|
||Output/K|0.42||0.28|0.27|
||OutputVL|20663||19834|22455|
|Non-state|Value added|8.04E+111|7.35E+11|9.28E+11|1.06E+12|
||Fixed capital asset|1.04E+12|1.36E+12|1.5E+12|1.75E+12|
||Labor|22110000|22130000|21726000|21534000|
||Share of capital|33.4%|33.9%|34.6%|36.6%|
||Share of labor|33.6%|33.5%|33.7%|35.3%|
||Share of value-added|46.2%|46.2%|51.5%|53.8%|
||K/L ratio|47137.51|61237.77|68919.77|81318.07|
||OutputVK|0.79||0.61|0.58|
||Output/L|27017||33354|39017|

Source: China Statistical Yearbook 26
foreign owned firms but not significantly different from domestic privatized or stateowned firms. The two studies hence suggest that _de novo_ firms are more productive than or at least as productive as SOEs privatized to domestic owners.

As may be seen from Panel B of Figure 1, effects of privatization on TFP growth have been estimated by country only in the CEE region. The results suggest that in CEE privatization had a positive effect on the rate of change of TFP in the early transition period and that the effect disappears in the later stage. The studies do not distinguish between domestic and foreign categories of private ownership. Commander and Svejnar (2007) have estimated the effect of privatization to domestic and foreign owners on TFP growth on a sample of 27 transition economies, thus combining CEE and CIS countries. Using data from 2002-2005, they find the two effects to be both statistically insignificant. It is hence possible that foreign owners brought about a sizable increase in efficiency in the period immediately after acquiring the local firms in the 1990s, but that later on the rate of change in efficiency has been on average similar in all the principal types of ownership of firms.

# _4.2 Profitability_

Profitability is an important indicator of company performance, although in the transition economies, as in many other developing countries, profits may be underreported by firms to evade taxes, and may reflect market power as well as technical efficiency.

In Figure 2 we summarize the effects of ownership on profitability from 10 studies. Most studies pertain to CEE and show a small positive or insignificant effect of privatization to domestic or foreign owners on profitability levels in the early as well late transformation periods (Figure 2A). This is accompanied by insignificant effects of privatization to domestic and foreign owners on the rate of growth of profitability (Figure 2B).

A further analysis of this overall pattern indicates that the effect varies across types of ownership (bank, investment fund, individual, etc.), with the positive effects in the case of foreign owners being brought about by industrial (non-financial) companies as owners, while in the case of domestic owners it is usually some form of financial ownership that generates positive effects on profit. In this finer categorization, however, the effects vary 22
The remainder of this document is organized as follows. Section 2 summarizes the environmental policy debate in Russia which explains the interest of Russian policy makers on potential trade-offs between trade liberalization and environmental quality. Section 3 provides a brief literature overview on trade and the environment. Section 4 presents the CGE model and the data sources underlying our impact assessment of Russia’s WTO accession. Section 5 motivates the policy scenarios to be investigated and discusses simulation results. In section 6 we assess the sensitivity of our results to key modeling, data and parameter assumptions. Section 7 concludes. Detailed documentation of the environmental data is in the appendix.

# **2. Environmental Policy Debate in Russia**

Russia is the largest country in the world, spanning nine time zones with a population of 143.7 million people.<sup>1</sup> It is also one of the richest countries in the world for oil, gas and a wide range of minerals. Russia’s natural endowment has been a key driver of economic growth in the current millennium, based on production and export of raw materials. The impacts of extraction and manufacturing industries from the Soviet-period to today have not been neutral to environmental damage and public health. Many industries are characterized by high resource use, extremely high energy inefficiency and obsolete production processes. The quality of environmental conditions in Russia is inadequate on about 15% of the country’s territory which is home to 58% of the population.<sup>2</sup> Due to high levels of air pollution, the average life expectancy of the population is reduced by about one year, while in more polluted cities by about four years. It is assumed that this factor is the reason for about 8% of deaths annually. The main sources of air pollution are Particulate Matter (PM), Sulphur Dioxide (SO2) and Nitrogen Oxide (NOx) linked to fossil fuel combustion (IFC 2008).

A major challenge for Russia to address air pollution is the potential increase of the share of coal in the fuel mix, as export markets take priority for oil and natural gas use. Russia is currently planning to triple the share of coal in the energy mix so that the amount of coal burned will grow to between 322 million and 393 million tons of coal per year by 2030.<sup>3</sup> New coal generation is potentially a good option for Russia to increase its generation capacity, but care will need to be taken to identify the best “clean-coal” technologies to use in these new plants.

> 1 As of 1st of April, 2014, Rosstat (2014).

> 2 According to Rosgidromet (2013): “In 138 cities in Russia (57% of the urban population), the level of air pollution is characterized as high or very high.”

> 3 See “Energy strategy of the Russian Federation up to 2030,” appendix 4 entitled “Estimated energy balance of Russia until 2030,” by the Ministry of Energy of the Russian Federation. Available at: http://minenergo.gov.ru/aboutminen/energostrategy/pr_4.php

3
# Table 2. Subsidy by Type of Fuel

||2009|2010Q1|2010Q2|
|---|---|---|---|
|Diesel||||
|Share in total subsidy (percent)|69|63|65|
|Domestic price (PEC & large users)|17|32|74|
|Domestic subsidized price (small users)|35|38|41|
|Price at the Yemen border (incl. tax, freight, etc.)|158|123|134|
|Total annual diesel subsidy (in billion YER)|264|90|95|
|Gasoline||||
|Share in total subsidy|14|22|23|
|Domestic price|60|63|68|
|Price at the Yemen border (incl. tax, freight, etc.)|87|121|130|
|Total annual gasoline subsidy (in billion YER)|55|31|33|
|Total subsidy reduction (savings) YER/liter|0|3|5|
|LPG||||
|Share in total subsidy|11|12|8|
|Domestic price in YER/liter|23|30|42|
|Price at the Yemen border (incl. tax, freight, etc.)|52|70|64|
|Total annual LPG subsidy (in billion YER)|41|17|11|
|Total subsidy reduction (savings) YER/liter|0|7|11|
|Kerosene||||
|Share in total subsidy|4|1|2|
|Domestic price|36|38|41|
|Price at the Yemen border (incl. tax, freight, etc.)|112|121|134|
|Total annual kerosene subsidy (in billion YER)|15|1|3|
|Total subsidy reduction (savings) YER/liter<br>Jet Fuel|0|2|3|
|Share in total subsidy|2|2|2|
|Domestic price|36|39|43|
|Price at the Yemen border (incl. tax, freight, etc.)|97|123|136|
|Subsidy (in billion YER)|9|3|4|
|Subsidy as a percentage of import prices|63|69|68|
|Total fuel subsidy (in billion YER)|385|142|146|

Sources: Ministry of Finance (2010), IMF (2010), and World Bank (2010).

Subsidies impact economic sectors and households differently, depending on the respective share of fuel in production and in consumption. Households consume about 10 percent of all fuel products (Table 3); most of the fuel is consumed as intermediate inputs in agriculture, industry, and services. About 40 percent of all fuel is used for transportation, followed by the mining sector (mainly oil production) and industries. Agriculture consumes about 12 percent of all fuel, mostly for irrigation. Interestingly, fuel is the single largest expenditure item for agricultural production despite the petroleum subsidy.<sup>7</sup> The transport sector as the biggest consumer of fuel constitutes also an important input for the production of other sectors; industry and services are the most transportation-intensive sectors, with transportation making up 14 and 8 percent of their output, respectively.

7 This is largely due to the pumped ground water needed to irrigate farms.

6
**Figure 11: Targeting performance in selected African countries during the COVID-19 pandemic**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>Morocco Tunisia Egypt Ethiopia (rural) Ethiopia Nigeria (rural) Nigeria (urban) South Africa<br>(urban)<br>Income Quantile by Country<br>Percentage of hhs receiving aid<br><!-- End of picture text --> _Sources_ : Duchoslav and Hiroven (2022), Kohler and Bhorat (2020), and Krafft et al. (2021).

# **5.3 Timeliness**

Concerns about proper targeting are in tension with another major challenge in social protection programming: timeliness. Across Africa, there was generally a trade-off between fast registration of new beneficiaries on existing systems, with the associated challenges reviewed above, versus rolling out new programs that targeted the right households but arrived too late (Devereux, 2021). Compared to other regions, Sub-Saharan Africa had the longest lag time between the stay-home orders and first payments at 132 days, about twice the global average (Beazley, 2021). This is likely to be driven by lack of institutional emergency responses mechanisms, absence of digital infrastructure and social registry to identify potential beneficiaries. In Kenya, just 50% of target beneficiaries in a new program to support urban informal workers had received support by August 2020, long after the most severe economic impacts of the lockdown (Kimani, 2021). The difference for household welfare between receiving aid during the most severe impacts of lockdowns compared to months later could be substantial. Suggestive evidence on the importance of timing is given by a study in Bangladesh, which showed that anticipatory transfers that provide support to 28
**Table 7: Changes in agricultural income: robustness checks (including** **<u>year fixed effects)</u>**

||All<br>households|Bottom 40<br>percent|
|---|---|---|
|Distance to output market (km)|-0.0251<br>(0.02688)|-0.00153<br>(0.0381)|
|Any extension in village in past 12 months<br>|0.188***<br> (0.0572)|0.215**<br>(0.0873)|
|Log of rainfall (percent of needs measured by WRSI)|2.210***|3.170***|
||(0.558)|(0.824)|
|Log of maize price|-0.0918|0.313|
||(0.355)|(0.467)|
|Log of beans price|1.260***|1.447***|
||(0.454)|(0.542)|
|Log of number of fatalities|-0.147**|-0.144*|
||(0.0619)|(0.0769)|
|Constant|-5.717|-13.80**|
||(4.209)|(5.748)|
|Observations|6,852|3,334|
|Number of HHID|2,044|966|
|Year fixed effects|Yes|Yes|

Source: Staff calculations using UNPS 2006-2012. Note: Dependent variables is log of real per capita crop income. Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 As Figure 2 shows, the external environment was changing in different ways across the four regions during the period 2006-2012. The Northern region in Uganda is the most drought prone and although rainfall was in general good during 2006 to 2012, the rainfall shortfall in 2010 was much larger in the north than elsewhere in the country. The Eastern region also experienced substantial variability in rainfall. The north is also the part of the country that experienced conflict until the cessation of hostilities in the late 2000s, and thus it is this north that saw the largest decline in the number of fatalities as the result of peace. Maize prices are expected to be particularly important in the north and east, both on account of its predominance in production in the east, but also because a lot of maize trade into Kenya and South Sudan comes from these regions. There are also large and increasing regional variations in welfare across Uganda. The Western and Central regions are more economically developed, and households in these regions exhibit more diversified incomes as a result. They have had many more years of stability than the Northern region and these regions have seen substantial development during this time. More stable climatic conditions and rapid urban growth in and around Kampala has also helped.

Given these divergent starting points and experiences, regressions are run separately for each of the four regions and presented in Table 8. Weather is a strong driver of crop income growth in the north and east, but not in other regions. And particularly in the north: a 10 percent rainfall shortfall results in a reduction in crop income of 38.3 percent in the north. Prices have been important in all regions, but maize prices are only important in the north and east. A 10 percent reduction in the maize price results in a 6.6 percent and 11.1 percent reduction in the east and north respectively, while it had no impact in the center and west. Bean prices are important in all regions, with a 10 percent increase in the bean prices increasing income by 6.3 to 13.5 percent across regions. The results also indicate that the cessation of violence in the late 2000s only impacted crop income growth in the north.

20