rafmacalaba/datause-encoder-data huggingface dataset:
land plot, which is the traditional placement. Although, under the group isolation principle, a separate playground would be provided for each group of children with all of these little playgrounds looking identical, the designers of the new kindergartens suggested making each group’s playground in a different style and based on a specific activity. Moving the groups of children around these different playgrounds according to the daily curriculum schedule will expand the children’s learning experiences and involve them in various play activities.

When the kindergarten in Beloyarski City featuring this new learning environment is constructed, it will be necessary to provide additional training to the teaching staff to enhance their professional skills and to ensure the learning environment is used to its full capacity.

It will also be necessary to carry out the post-occupancy evaluation including all active users of the facility: children, pedagogues, administrative staff, and parents. During these assessments, the above-mentioned hypothesizes may be tested providing additional qualitative and qualitative information for further research.

# **_Potential Energy Savings from the Hypothetical Application of the New Design to a Kindergarten in Beloyarski City_**

To analyze the energy savings that might be achievable when new kindergarten is constructed in the Khanty-Mansyisk region in accordance with this new design, we looked at the existing Semitsvetik kindergarten in Beloyarski City, which has a total capacity of 220 children and whose facilities include a swimming pool. We chose this particular kindergarten, because it represents a typical modular preschool facility in the same municipality and has the same capacity (number of children) as the new one.

We analyzed the layout, dimensions, and energy use of the existing Semitsvetik kindergarten building as of 2013, and these characteristics are presented in Table 2. The data came from Beloyarski City administration, as well as City Arch, the engineering 16
# **Appendix B** : **Returns to Household Characteristics**

Table B1: Association between Household Welfare and Characteristics (OLS), 2001

|Variable|Coefficient|Std. Error|t-Statistic|Prob.|
|---|---|---|---|---|
|Constant|13.51477|0.054986|245.7851|0.0000|
|Male|-0.252236|0.012708|-19.84878|0.0000|
|Age Head|-0.013132|0.002086|-6.294348|0.0000|
|Age Head Squared|0.000115|2.13E-05|5.364778|0.0000|
|Age <5 (% Household)|-0.010059|0.000376|-26.77648|0.0000|
|Age 5 to < 10 (% HH)|-0.011668|0.000384|-30.39831|0.0000|
|Age 10 to < 15 (% HH)|-0.012098|0.000386|-31.38033|0.0000|
|Age 15 to <20 (% HH)|-0.007770|0.000312|-24.90647|0.0000|
|Age 20 to <25 (%HH)|-0.002712|0.000284|-9.559997|0.0000|
|Schooling (years)|0.042913|0.001502|28.57954|0.0000|
|Land|0.000697|0.000255|2.730257|0.0063|
|Access to Credit|0.173503|0.024411|7.107560|0.0000|
|Has Migrant (s)|0.010560|0.012250|0.862027|0.3887|
|Distance Nearest Hospital|-0.002191|0.000940|-2.329860|0.0198|
|Distance Nearest Tarred Road|-0.000401|0.000178|-2.254167|0.0242|
|Public Sector|0.251825|0.026740|9.417647|0.0000|
|Private Sector Formal|0.229712|0.025264|9.092582|0.0000|
|Agriculture|-0.112104|0.022771|-4.923096|0.0000|
|Non-Agriculture Informal|0.043645|0.023147|1.885578|0.0594|
|Unemployed|-0.009947|0.030035|-0.331179|0.7405|
|Urban|0.147945|0.013763|10.74932|0.0000|
|Adamaoua|-0.037007|0.025103|-1.474206|0.1405|
|East|0.083856|0.024896|3.368302|0.0008|
|Far-North|0.011048|0.021435|0.515419|0.6063|
|Coast|-0.003824|0.017847|-0.214292|0.8303|
|North|0.103944|0.023954|4.339278|0.0000|
|North-West|-0.195854|0.022640|-8.650777|0.0000|
|West|-0.011768|0.021172|-0.555841|0.5783|
|South|-0.066760|0.023641|-2.823927|0.0048|
|South-West|-0.135006|0.024006|-5.623858|0.0000|

Source: Authors’ Calculations (R-Squared: 0.42) 47
# 1. Introduction

Correctly pricing private vehicles and their use is paramount to building sustainable, safe, and equitable transportation systems. Although cars and motorcycles provide great mobility, their use is disproportionately responsible for road injuries and deaths, congestion, transport-related greenhouse gas (GHG) emissions, and local air pollution. With the accelerated motorization in many low- and middle-income countries, managing vehicle demand is an increasingly pressing challenge. The urgency is underlined by the World Bank’s Country Climate and Development Reports (CCDRs), which have consistently found transport sector emissions to be among the most rapidly growing (World Bank, 2023). Sound management of transportation demand is crucial for achieving the decarbonization objectives of the Paris Climate Accords and contributes to several Sustainable Development Goals (SDGs), including building inclusive, safe, and sustainable cities and communities, reducing inequality, and taking climate action. Price instruments on private vehicles and their use – including taxes, subsidies, fees, and rebates – are important tools in the transport policy toolbox. Using these instruments to change the price of vehicles that households face enables policy makers to incentivize more sustainable behavior. However, in many developing and developed countries alike they are underutilized or inappropriately designed for this purpose. Determining the “right” price, i.e., combination of instruments on vehicle purchase, ownership, and use, is a complex problem. There are many, and often competing, objectives involved in determining what to tax and by how much. In addition to being useful levers for building sustainable transport systems, vehicle and fuel taxes serve as important sources of government revenue. In many cases, portions of these revenues are earmarked for transportation infrastructure investment and maintenance. Hence, any changes will have wider implications on fiscal budgets. On the consumer side, there is often a broad aversion towards increasing taxes. Making matters worse, taxation of vehicles and fuels will generally burden some people more than others, and choosing winners and losers inevitably creates contention. Consequently, the determination of vehicle and fuel taxes is intensely political, and concerns about emissions and economic efficiency often end up falling by the proverbial wayside. Furthermore, the optimal tax scheme for a given country today is different from that of next year or that of a neighboring country. The optimal tax depends on consumer preferences, which continue to evolve with spatial development and technological innovation. Crucially, the proliferation of electric vehicles (EVs) can fundamentally change the calculus of fuel and vehicle 2
choose to enter the formal economy or not (e.g., de Soto 1989, Bruhn 2011). These studies have been very influential in the design of policies to aid regional economic growth and development. Addressing these issues at the local level is one of the most pressing challenges for regional planners in many developing economies.

This study makes a very simple contribution. While the unorganized sector has been remarkably persistent over the past, we describe how much of this persistence is due to increased ownership by women of firms in this sector. Increased women’s participation in economic activities is celebrated in most circles and seen as an important stepping stone for further advancement at the individual and national levels (e.g., Duflo 2005, 2011, World Bank 2008, 2011, 2012). Our work documents that this increased participation comes at an establishment scale and magnitude that is mostly responsible for the persistence of India’s informal sector.

This is a stark trend, and we suspect that it is true for other developing countries as well. Our work thus highlights the importance for policymakers to understand that the informal sector is dynamic and actively changing among gender lines. Thus, new policies aimed at affecting the informal sector need to take into account the increasingly female constituent base among both workers and entrepreneurs. Likewise, policy efforts that encourage female entrepreneurship— either directly or indirectly through avenues like infrastructure investment—will also be influencing the relative size and persistence of the informal sector.

17
Policy Research Working Paper 8758

# **Abstract**

There is a growing body of literature exploring the skill content of jobs. This paper contributes to this research by using data on the task content of occupations in developing countries, instead of U.S. data, as most existing studies do. The paper finds that indexes based on U.S. data do not provide a fair approximation of the levels, changes, and drivers of the routine cognitive and nonroutine manual skill content of jobs in developing countries. The paper also uncovers three new stylized facts. First, while developed countries tend to have jobs more intensive in nonroutine cognitive skills than developing countries, income (in growth and levels) is not associated with the skill content of jobs once the analysis accounts for other factors. Second, although adoption of information and communications technology is linked to job de-routinization, international trade is an offsetting force. Last, adoption of information and communications technology is correlated with lower employment growth in countries with a high share of occupations that are intensive in routine tasks.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at hwinkler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 11 -

# Table 2.1 (continued)

Operating Department: Vice-President Assistant to Vice-President Manager, Budget Controls and Statistics General Superintendent, Motive Power and Equipment Chief Engineer, Motive Power and Equipment Manager, Research General Superintendent, Transportation Chief of Yard and Terminal Operations Superintendent, Car Service Chief [Civil] Engineer Signal Engineer Superintendent, Communications Fuel Agent Manager, Station Operations Manager, Highway Transportation and Warehouses Superintendent, Safety Department Superintendent, Floating Equipment New York Terminal Region: General Manager Central Region: General Manager Westem Region: General Manager Chicago Terminal Region: General Manager Police: General Superintendent Dining Car and Commissary: Manager Personnel Department: Vice President Research and Development Department: Vice President Purchasing Department: General Purchasing Agent Source: Adapted from the reprentation of The Baltimore & Ohio Railroad Company in the _Official Guide of the Railways,_ February 1959, pp. _352-353._
4 Such a response would tend to reduce the ratio of motor vehicles to roads in urban areas with high income levels.

The relative prices of motor vehicle services and roads are also likely to change with development because motor vehicles are traded goods (whose prices are reasonably constant around the world), and roads are nontraded goods (whose prices vary with wage levels across countries). The ratio of the prices of nontraded to traded goods rises with wage levels and income. Hence roads become more costly relative to vehicle services as incomes rise (assuming minimal congestion at the national level), and the ratio of motor vehicles to national roads should rise with income as traffic volumes increase on national road networks.

Finally, road quality is also an issue. Motor vehicle speeds rise and operating costs fall as road quality improves, so arguments based on economic efficiency suggest that road quality-particularly the share of roads that are paved-should increase as the value of time (related to income) and the intensity of road use rise.

Of course, these hypotheses stem from economic principles. Because public road providers face few market incentives, however, a substantial question is whether predictions based on economic reasoning have any predictive power.

# 3. **Summary Statistics**

Summary statistics on vehicle ownership and road provision at the national and urban levels for developing and high income countries, shown in table 1, provide a crude test of the hypotheses based on economic reasoning. The cross-country data set and crossurban area data set summarized in table 1 cover a wide range of country income levels, ranging in 1990 from $260 to $28,000 per capita in 1987 US dollars at market exchange rates.6 The national and urban samples summarized in table 1 do not overlap precisely in geographical terms or in time, but they provide a useful comparison of urban and national measures for developing countries and high-income countries.

Relative to countries as a whole, urban areas in both developing and high-income countries obviously have much higher population densities, higher road network densities, somewhat more vehicles per thousand persons, much less road length per person, and more motor vehicles per kilometer of road. Although it is used here as a rough proxy for the volume-to-capacity ratio frequently used in traffic analysis, the number of motor vehicles per kilometer of road is an imperfect measure because it does not contain information on average vehicle utilization and traffic peaking. However, the large difference between National data on population, vehicles, and road length include urban and rural areas. Data on urban road length are from a variety of sources and are not broken down into paved and unpaved categories, as are national data. Typically a greater proportion of urban roads than of national roads are paved, so the analysis of urban roads may be more comparable to the analysis of national paved roads than total roads.
5 Finally, instead of focusing on the partial correlation between individual policy indicators and growth, we construct three indexes designed to characterize **the** macroeconomic, international, and dometic financial sector onvironments. These three indexes are constructed by combining individual policy indicators. In this way, we attempt to broaden the scope of our analysis by moving away from the narrow focus on indiviual Indicators and moving toward, for example, an overall index of macroeconomic stability. Furthermore, using these overall indexes, we study policy regimes. By classifying countries as having "good" or "bad" financial, macroeconomic, and international policies, we can categorize countries into eight different "regimes." The investigation of policy regimes suggests that the state of the financial sector is importantly associated with long-run growth and the overall index of macroeconomic stability is more strongly linked vith growth than any individual fiscal or monetary indicator.

# II. EXTREME BOUNDS ANALYSIS

# 1. Motivation

Previous cross-country growth analyses identify over fifty different policy and political indicators as significantly correlated with long-run per capita growth rates.' Are these results believable? Should they change our views and policies? To answer these questions, we need an empirical definition of "believable." Levine and Renelt (1992) (LR) use a narrow definition to show that most existing "empirit al facts" are not believable. Their definition of believable is derived from Edward Leamer's work on extreme bounds analysis (EBA).' Basically, LR show that small alterations in the conditioning information set, i *e, small alterations in the right-hand-side variables, change the statistical significance of most existing results. LR term results that
**Figure 6: Comparison of Kernel densities of Consumption Expenditure of HBS and NPS Figure 6.a: Comparison of HBS 2007 and NPS 2008/09 Figure 6.b: Comparison of HBS 2011/12, NPS 2010/11 and NPS 2012/13**

<!-- Start of picture text --> 6 8 10 12 14 16<br>6 8 10 12 14 16 ln(monthly expenditure per adult equivalent, real)<br>ln(monthly expenditure per adult equivalent, real)<br>HBS2 (2011/12) NPS2 (2010/11)<br>HBS1 (2007) NPS1 (2008/09) NPS3 (2012/13)<br>.6<br>.6<br>.4 .4<br>D en sity<br>D en sity<br>.2<br>.2<br>0<br>0<br><!-- End of picture text --> _Source_ : HBS 2007 and 2011/12 and NPS 2008/09, 2010/11 and 2012/13.

The differences between the HBS and NPS data sets in poverty and inequality levels and trends as well as in the distributional pattern of growth are puzzling and deserve an in‐depth investigation of the sources of such differences. The following sections will explore whether these discrepancies are due to differences in survey methods or to differences in the approaches used for the construction of the consumption aggregates, measurement of the poverty lines or estimation of the price deflators. We start by reviewing the survey methods, focusing on the method of data capture, length of reference period for reporting consumption and the level of commodity details. We then examine the approaches used to estimate poverty and consumption in both surveys and investigate how the estimates vary if the same approach is used in both data sets.

# **III. Comparison of HBS and NPS Survey Methods**

The section reviews the differences between the HBS and NPS data sets in survey characteristics and methods of collecting consumption data.

9
- Rosendahl, K. E., Nov. 2004. Cost-effective environmental policy: implications of induced technological change. Journal of Environmental Economics and Management 48 (3), 1099–1121.

- Smulders, S., Van Der Werf, E., 2008. Climate policy and the optimal extraction of high- and low-carbon fossil fuels. Canadian Journal of Economics/Revue canadienne d’ ́economique 41 (4), 1421–1444.

- Tinbergen, J., 1956. Economic policy: principles and design. North Holland Pub. Co., Amsterdam.

- van der Ploeg, F., Withagen, C., 2012. Too much coal, too little oil. Journal of Public Economics 96 (1–2), 62–77.

- Vogt-Schilb, A., Hallegatte, S., 2011. When starting with the most expensive option makes sense: Use and misuse of marginal abatement cost curves. Policy Research Working Paper 5803, World Bank, Washington DC, USA.

- Vogt-Schilb, A., Meunier, G., Hallegatte, S., 2012. How inertia and limited potentials affect the timing of sectoral abatements in optimal climate policy. Policy Research Working Paper 6154, World Bank, Washington DC, USA.

# **AppendixA. Proof of lemma 1**

Proof. As _ci_<sup>_′_</sup> is strictly growing, it is invertible. Let _χi_ be the inverse of _ci_<sup>_′_</sup> ; applying _χi_ to (9) gives: The relation between the sectoral potential ( ̄ _ai_ ), the MICs (through _χi_ ), the SCC ( _μ_ ) and the time it takes to achieve the sectoral potential _Ti_ reads: Let us define _fχi_ such that: Let us show that _fχi_ is invertible: _χi_<sup>_′_</sup> _>_ 0 as the inverse of _c_<sup>_′_</sup> _>_ 0, thus _dfdtχi >_ 0 and therefore _fχi_ is strictly growing. Finally: When the marginal cost function _ci_<sup>_′_</sup> is given, _χi_ and therefore _fχi_ are also given. Therefore, _Ti_ can always be found from _a_ ̄ _i_ . The larger the potential, the longer it takes for the optimal strategy to achieve it. □ 15
Table **16:** Loans (as **a** percentag- of assets)

||1970|1975|1980|1985|1990*|
|---|---|---|---|---|---|
|UK|0|0|0|1|0|
|us|-|-|-|-||
|Germany|31|33|37|36|36|
|Japan|52|30|22|15|13|
|Canada|0|0|1|0|0|
|Netherlands|46|52|63|52|39|
|Sweden|22|24|26|22|10|
|Switzerland|33|31|27|21|14|
|Denmark|1|1|4|1|1|

* 1989 for Canada 1987 for Denmark

Table 17: Property (as **a** percentage of assets)

||1970|1975|1980|1985|1990*|
|---|---|---|---|---|---|
|UK|10|15|18|10|9|
|Us|-|-|-|-|-|
|Germany|12|12|9|7|6|
|Japan|27|21|6|3|2|
|Canada|1|1|2|3|3|
|Netherland3|16|15|14|11|11|
|Sweden|0|0|0|0|1|
|Switzerland|16|20|18|18|17|
|Denmark|-|-|_|_||

* 1989 for Canada 1987 for Denmark

29a
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