rafmacalaba/datause-encoder-data huggingface dataset:
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# **Table 3.6: Cost Recovery in Selected Urban Transport Systems**

|**Country/City**|**% Cost Recovery**|
|---|---|
|Mumbai bus transport|70%|
|Mumbai Rail|98%|
|10 European Transport Systems|25-29%|
|US Commuter Rail|48%|
|US Buses|29%|
|Canada Light Rail|50%|
|Canada Buses (1960s and 1970s)|70-95%|

Sources: Cropper and Bhattacharya (2007) for Mumbai and Estupiñán et alia (2007) It is interesting to note that Mumbai’s rail company seems to achieve the best cost recovery of this entire sample, and the bus system has cost recovery that is better than many of the high-income countries. One reason for the good cost recovery performance is the very high utilization of both networks and resulting crowding in both buses and trains. One could expect these levels may not be sustained as this crowding is alleviated, but this level of subsidization is nothing like the issue of cost recovery for water. Expansion of the metro system is unlikely to improve the cost recovery in urban transport in India. The estimates suggest that subsidies are already quite deep in Delhi’s metro, and high-income country metro systems do not fully recover costs even after over a century of operations. (See for example Mohan: 2006). The ubiquitous and long lasting subsidies in urban transport around the world suggest that, barring a revolution in congestion charging for roads or a radical shift from subsidies in kind to cash transfer to poor households, urban transport will represent a net drain on municipal budgets that must be covered from either local benefit tax revenues or transfers from higher levels of government.

# **Section 4: Key Features of the Environment for Urban Infrastructure Finance and Potential for Development**

Earlier sections have discussed different models for structuring urban finances and cost recovery internationally in comparison with India. In this section, we examine in more detail a number of the operating features of Table 1.1b in the Indian context, as a means for considering the changes that are on the critical path for improving the flow of finances for infrastructure in cities.

# **_India’s repressed real estate markets narrow the horizon for local urban finance._**

Local finances in India’s cities draw upon a relatively narrow base among the variety of potential sources of revenues shown above, with heavy emphasis on the property tax. It is quite typical for broad based local benefit taxation to rely upon real estate market values as its tax base, for a number of good reasons.

24
# **I. Introduction**

UN General Assembly Sustainable Development Goal 1— _End poverty in all its forms_ by 2030— explicitly pledges that “no one will be left behind.” To achieve this goal, the availability of highquality household consumption surveys is essential, and it is equally important for these surveys to be inclusive and cover marginal populations, such as refugees and internally displaced persons (IDPs). Unfortunately, household consumption surveys rarely include forcibly displaced populations, despite that these populations are among the most vulnerable and deprived. They lack fundamental rights such as freedom of movement and the right to work, have eroded human and physical capital, and face more frequent shocks than surrounding host communities do.

This is a significant and growing challenge, particularly in Sub-Saharan Africa. The global number of forcibly displaced persons grew from 43.3 million in 2009 to 70.8 million in 2018. Among them, there are 25.9 million refugees, 3.5 million asylum seekers, and 40.3 million IDPs. Almost four out of five refugees live in a country neighboring their home country, and some 84% of them live in developing countries. Sub-Saharan Africa hosts around one-third of the world’s refugee population and witnessed an increase of 1.1 million refugees in 2017, which represents a 22% increase from 2016. In 2018, Sub-Saharan African countries represented half of the 10 countries with the highest refugee population relative to the national population and six of the 10 countries with the highest numbers of IDPs. The region also has the highest poverty rates in the world, and data for the region are typically scarce or of low quality. Therefore, measuring poverty among displaced populations in Sub-Saharan Africa is a particularly important task that is severely hampered by missing household consumption data.<sup>2</sup> <mark>There is an established literature in statistics that has developed methods to impute missing data (see Little and Rubin (2002) for a review of these methods). Survey-to-survey imputation methods have been widely employed in economics to estimate household welfare trends across time periods in the context of repeated cross-section or panel data (e.g., Dang and Lanjouw 2018), across geographical areas in the context of poverty mapping exercises (e.g., Elbers et al. 2003), or across</mark>

> 2 <mark>Missing data issues are not a problem limited to displaced populations, but can emerge because of lack of survey data on a particular topic of interest, population group, or time period. These issues can also be caused by sampling errors, incomplete data due to unit or item nonresponse, data input errors, or post-survey data manipulations such as top-coding or censoring.</mark>

2
**Table 2. Population in Low Elevation Coastal Zones, 2000 and 2010**

||**Total**<br>**Population**|**LECZ**<br>**(millions)**|**Rural**<br>**Populatio**|**LECZ**<br>**n (millions)**|**Rural**<br>**(**|**Share**<br>**%)**|
|---|---|---|---|---|---|---|
||**2000**|**2010**|**2000**|**2010**|**2000**|**2010**|
|**Developing Country**|**499.2**|**577.9**|**236.3**|**267.2**|**47.3%**|**46.2%**|
|_By Income:_|||||||
|Low Income|80.8|93.6|58.0|66.2|71.8%|70.8%|
|Lower Middle Income|210.2|252.5|112.6|132.1|53.5%|52.3%|
|Upper Middle Income|208.2|231.8|65.8|68.9|31.6%|29.7%|
|_By Region:_|||||||
|East Asia & Pacific|279.9|313.8|123.2|133.0|44.0%|42.4%|
|Europe & Central Asia|4.7|4.7|1.5|1.5|31.6%|30.9%|
|Latin America & Caribbean|28.9|33.1|7.5|8.3|25.8%|25.1%|
|Middle East & North Africa|32.4|39.6|16.8|20.5|51.7%|51.7%|
|South Asia|129.5|155.2|79.2|93.3|61.1%|60.1%|
|Sub-Saharan Africa|23.7|31.4|8.2|10.6|34.5%|33.8%|
|**Developed Country**|**116.1**|**125.6**|**16.2**|**16.9**|**13.9%**|**13.4%**|
|**World**|**615.2**|**703.5**|**252.5**|**284.1**|**41.0%**|**40.4%**|

The low elevation coastal zone (LECZ) is the contiguous area along the coast that is less than 10 m above sea level.

Low‐income economies are those in which 2013 per capita income was $1,045 or less, lower‐middle‐income economies are those in which 2013 per capita income was between $1,046 and $4,125, upper‐middle‐income economies are those in which 2013 per capita income was between $4,126 and $12,745, and developed countries are those in which 2013 per capita income was more than $12,745, based on the Atlas conversion factor instead of simple exchange rates, as defined by the World Development Indicators, available at <u>http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world‐development‐ indicators.</u> The estimates of LECZ populations are from Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban‐Rural Population and Land Area Estimates Version 2. Palisades, NY: ‐ NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz <u>urban‐rural‐population‐land‐area‐estimates‐v2. Population and land area estimates are subdivided by elevation</u> zone as derived from Shuttle Radar Topographic Mission (SRTM) elevation data at ~90m (3 arc seconds) resolution.

9
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- Popovich, N., & Plumer, B. (2021, November 12). “Who Has the Most Historical Responsibility for Climate Change?.” _The New York Times_ . Retrieved from <u>https://www.nytimes.com/interactive/2021/11/12/climate/cop26-emissionscompensation.html</u>

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- <mark>World Bank. (2021). World Bank estimates based on data from the Global Subnational Atlas of Poverty, Global Monitoring Database. World Bank: Washington.</mark>

26
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31
# Appendix IV: How women find their jobs (STEP)

_Figure 16: Likelihood of women reporting each method, relative to men (marginal effects from Probit regression)._ <!-- Start of picture text --> How did you find your current job?<br>through social<br>0.06 network<br>(friends/relative/othe<br>0.05 r)<br>0.04<br>0.03<br>through<br>media/internet<br>0.02<br>through<br>0.01 university/school<br>career office<br>0<br>‐0.01 through private<br>employment agency employer contacted other<br>‐0.02 through public you job obtained<br>employment agency after<br>training/appre...<br>‐0.03<br>contacted employer<br>‐0.04 directly<br>Wageworkers with min. uppersecondary education (850)<br><!-- End of picture text --> _Table 7: Likelihood of being overqualified for current job (STEP)_

||Pro<br>o<br>|bability of b<br>verqualifie<br>|eing<br>d<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|female|‐0.018|‐0.016|‐0.027|
||(0.03)|(0.03)|(0.04)|
|Age||‐0.011|‐0.011|
|||(0.01)|(0.01)|
|Age squared||0.000|0.000|
|||(0.00)|(0.00)|
|Field of Education|||_Yes_|
|Observations|859|859|859|

38
different, which means that (i) the set of exogenous covariates removed in X in Equation 1 is much more limited and (ii) we can noisily observe country effects and so must apply a shrinkage approach there as well. Abstracting from country effects, observables, and some other teacherspecific terms, the shrinkage factor in Equation 6 is the same as Equation 9 in Chetty et al. (2014) and Equation 5 in Kane and Staiger (2008).<sup>21</sup> Another important difference is that we estimate the precision of leader estimates and the set of “statistically significant” leaders (discussed next).

# **Precision and the set of statistically significant leaders.** In addition to knowing the “best”

point estimate of the leader effect, it is important to know how accurate that estimate is and whether we are confident it is positive or negative. Since we assume leader quality and growth averages are normally distributed, so are the LS leader estimates: (7) −i L 2 )) where L −i ) ii ii ii μμ ii ii ii ii ii Equation 7 says that the distribution of leader quality, conditional on observing μμ |gg̅ , gg̅ ~NN(μμ̂ , σσ (1 −ψψ μμ̂ = ψψ (gg̅ −γγ gg̅ and −i , is L ii normally distributed with a mean of the LS leader estimate (from Equation 4-6) and a standard gg̅ gg̅ ii deviation ~~�~~ , where is the shrinkage factor from Equation 5. This can be derived from μμ̂ μμ ii ii the properties of multivariable normal distributions (see Online Appendix 1).σσ 1 −ψψ ψψ<sup>22</sup> Alternatively, one can take a Bayesian perspective where L is both the maximum and mean of the posterior ii μμ

(2010) finds that some assumptions of VA models are violated, which can lead to future teachers affecting past test scores, and that teacher VA estimates fade out quickly.

scores, and that teacher VA estimates fade out quickly. 21 Specifically, to get the equivalence, one needs to abstract from country effects ( 2 = 0) and classroom effects ( 2 = 0 ) and assume there is one student per class ( ). Teacher’s tenure ( ii and the variance of student σσ 2 error θθ in Chetty et al. (2014) correspond to leader tenure and the variance of annual GDP growth errors here. σσ nn= 1 tt−1) 22 We thank Aart Kraay for pointing out this way to derive the least squares mean and variance. εε� ii σσ TT 16
3 Figure 1: United States: Size and trade linkages (A) Size of major economies, 2010-15 (B) Share of global trade, 2010-15 <!-- Start of picture text --> Percent of total Percent of total United States<br>United States China Germany Japan 40 China<br>50 Germany<br>Japan<br>40<br>30<br>30<br>20 20<br>10<br>10<br>0<br>GDP (market GDP (PPP) Population<br>exchange 0<br>rates) Trade Exports Imports<br><!-- End of picture text -->

- (C) Share of global GDP and trade over time

- (D) U.S. trade openness over time

<!-- Start of picture text --> Percent of total Percent of GDP<br>60 United States China Germany Japan 35 1980s 2010-15<br>40 30<br>25<br>20<br>20<br>0<br>15<br>10<br>5<br>GDP (market GDP (PPP) Trade<br>0<br>exchange<br>Exports Imports Trade<br>rates)<br>(E) U.S. share of global imports, (F) Exports to the United States,<br>2010-15 2010-15<br>Percent of world imports Percent of regional GDP<br>20 7<br>16<br>6<br>12<br>5<br>8<br>4 4<br>0 3<br>2<br>1<br>0<br>EAP ECA LAC MNA SAR SSA<br>1980s 2010-15 1980s 2010-15 1980s 2010-15<br> Food Textiles<br>Agricultural products Chemicals Transport equipment Electronic equipment<br>Fuels and<br>mining products Iron and steel<br><!-- End of picture text --> Sources: World Bank, International Monetary Fund, UN Population Statistics. A.C. "PPP" stands for purchasing power parity exchange rates.

B. Trade is the sum of exports and imports of goods.

D. Trade is the sum of exports and imports of goods and services. E. Goods imports.

F. "EAP" stands for East Asia and Pacific; "ECA" stands for Europe and Central Asia; "LAC" stands for Latin America and the Caribbean; "MNA" stands for Middle East and North Africa; "SAR" stands for South Asia; and "SSA" stands for Sub-Saharan Africa.
20 Figure 7: Poverty impact of policies implemented during 2010-11 food price spike (A) Global poverty impact of a 10 percent rice, wheat, and maize price increase (B) Global poverty impact of 10 percent rice, wheat, and maize price increase <!-- Start of picture text --> Percentage point Rice, wheat and maize Millions Percent<br>0.25 Rice 2.5 0.25<br>Wheat<br>0.20 Maize 2.0 0.20<br>0.15 1.5 0.15<br>0.10 1.0 0.10<br>0.05 0.5 0.05<br>0.00 0.0 0.00<br>SSA LAC SAR EAP MNA Number Percent (RHS)<br><!-- End of picture text --> (C) Regional poverty impact of the 2010-11 food price shock (D) Global poverty impact of policy responses to the 2010-11 food price shock <!-- Start of picture text --> Millions Millions Combined action Percent<br>7 10.0 Individual action 1.05<br>6<br>9.6<br>1.00<br>5<br>9.2<br>4<br>0.95<br>3 8.8<br>0.90<br>2<br>8.4<br>1<br>0.85<br>8.0<br>0<br>-1 7.6 0.80<br>SSA SAR EAP MNA LAC Number Percent (RHS)<br><!-- End of picture text --> Source: Authors’ estimates.

Note: Based on estimates using the MIRAGRODEP computable general equilibrium model described in Annex 6.3.

A. Change in the poverty headcount measured at $1.90 per day.

A.C. EAP = East Asia and Pacific; LAC = Latin America and the Caribbean; MNA = Middle East and North Africa; SAR = South Asia; SSA = Sub-Saharan Africa.

C.D. Assuming increases in the price of maize, rice and wheat as represented in Figure 4.D and based on a poverty line of $1.90 per day.
human capital heterogeneity. The Lithuanian labor market does not fully meet these conditions. The risk of loosing a job is relatively high, however the chances of finding a new one are rather low. Unemployment spells thus tend to be of long duration. Poorly educated, low-skilled and inexperienced workers find it particularly difficult to find employment. One possible reason is compression of wages at the lower end of the distribution, which is likely to inhibit employment chances of less productive workers.

# **Labor Flows**

The risk of losing a job and the chance to find a new one are best depicted by the so called transition matrixes, which show estimated probabilities that workers move across different labor force states, such as employment and unemployment. Large labor flows between employment and unemployment indicate a dynamic labor market, where there is a lot of both firing and hiring. Many workers loose their jobs but they find it relatively easy to find a new one, thus their unemployment spells tend to be short. In contrast, limited labor flows point to a stagnant market, where few workers loose their jobs but those who become unemployed have little chances to find work. Unemployment is a stagnant pool, workers tend to stay jobless for a long time, with their chances to find a job gradually declining, often up to the point they become detached from the labor market and become virtually unemployable.

We will focus on the analysis of yearly labor flows (transitions) as this allows us to do some international comparison. However, it should be noted, that yearly transition data can be inaccurate due to the problem of "round-tripping", that is uncaptured movements between different labor force states that take place within one year. Thus, yearly transition data underestimate the true labor flows by disregarding the labor movements that took place during the year.

> Lithuanian workers face a high risk of loosing a job. As much as 5.7 percent of workers who were employed in May 2000 were unemployed one year later (Table 13, Panel A). This is a high job separation rate by standards of both transition and OECD economies (Boeri 1998). For example, the job separation rate in Poland in the mid to late

> 1990s varied between 2.2 and 3.4 percent (Bell 2001), that is roughly half the magnitude observed in Lithuania. Similarly, in Slovakia-another high unemployment country-

42