rafmacalaba/datause-encoder-data huggingface dataset:
V� �∑���� V�,� (2)

# **2.2. Flood Hazards and Impact on WWTPs**

Flood hazards and their impact on China’s WWTP are done in the context of climate change. Firstly, we apply and downscale the CaMa‐Flood model for 21<sup>st</sup> century China for RCP 4.5 and RCP8.5 at return periods greater than 30, 50 and 100 respectively. Based on these results, we then compute how the probability of flooding may change for specified geographical locations in China and superimpose the wastewater assets we collected onto the changing flood hazard maps. This way we are able to pinpoint the exact infrastructures that face increasing, decreasing or non‐changing flooding probabilities due to climate change.

# **2.2.1. Changing Flood Hazards**

We drive a global river routing model – Catchment‐Based Macro‐scale Floodplain (CaMa‐ Flood) – using the daily runoff of the Atmospheric and Oceanic General Circulation Models (AOGCMs) at a spatial resolution of 1° x 1° 1 (Hu _et al_ ., 2017). The CaMa‐Flood model routes the runoff input simulated by a land surface model into the oceans or lakes along a prescribed river network (Yamazaki et al., 2011). It calculates river channel storage, floodplain storage, river discharge, river water depth, and inundated area for each grid cell at a spatial resolution of 0.25° x 0.25°. A recently developed Global Width Database for Large Rivers (GWD‐LR) is also incorporated into it (Yamazaki _et al_ ., 2011).

For the AOGCMs, we adopt a historic (1970–2005) and future period (2006–2100<sup>2</sup> ) at representative concentration pathways (RCP) 4.5 and 8.5 (Stocker _et al._ , 2013). RCPs 4.5 and 8.5 are part of a new set of scenarios used in the new climate model simulations carried out under the framework of the Coupled Model Inter‐comparison Project Phase of the World Climate Research Programme. According to IPCC AR5, the RCPs are consistent with a wide range of possible changes in future anthropogenic (i.e. human) greenhouse gas (GHG) emissions. RCP 4.5 assumes that global annual GHG emissions (measured in CO2‐equivalents) peak around mid‐century and then stabilise around 2100 whereas RCP 8.5 emissions continue to rise throughout the 21st century (Moss et al.,2010 in IPCC 2014). From the daily river‐routing outputs, we extract the annual maxima value of the river water depth (spatial resolutions: 0.25° x 0.25°) for each AOGCM (1970‐2100 (131 years) or 1970‐2099 (130 years).

For each grid‐cell within each AOGCM, we extract the annual maxima river water depth of the period 1970–1999 to quantify the Gumbel distribution parameters, which represent extreme value statistics for the late 20<sup>th</sup> century. With these parameters, we estimate the return period of the annual maxima for 1970–2100 and 1970–2099. Based on the Gumbel distribution parameters of the baseline period (Hu et al., 2016, Section 3.4.1), we calculate the 'equivalent' river water depth of the baseline for each AOGCM as a way of reducing bias.

> 1 The full list of AOGCMs is available in the supplementary materials section of Hirabayashi et al. (2013), in Table S1.

> 2 For most of the AOGCMs, the period is 2006‐2100.  For BCC‐CSM1.1, we have 2006‐2099.

6
Figure A.2. Prejudice Prevalence and Migration Shares in Latin American Countries <!-- Start of picture text --> At Least One Variable Equals 1 Index<br>1<br>ECU<br>PER<br>BOL GTM COL<br>MEX DOM<br>CHL PAN<br>.9 CRI<br>HND<br>SLV<br>NIC<br>.8 PRY<br>BRA<br>ARG<br>.7 URY<br>0 .5 1 1.5 2 2.5 3 3.5 4 4.5<br>Share of Migrants (% of Total Population)<br>(mean) xen<br>Fitted values<br>Note: Pearson corr. p-value=0.525***<br>Average Index of Variables<br>.8<br>ECU<br>.7 BOL CHL PER COL<br>GTM<br>MEX<br>.6 DOM CRI PAN<br>NIC<br>.5 SLVHND PRY ARG<br>BRA<br>.4 URY<br>0 .5 1 1.5 2 2.5 3 3.5 4 4.5<br>Share of Migrants (% of Total Population)<br>Xenophobia (Index)<br>Fitted values<br>Note: Pearson corr. p-value=0.648***<br>Prejudice Prevalence (Index)<br>Prejudice Prevalence (Index)<br><!-- End of picture text --> _Notes:_ The figure illustrates the relationship between the level of prejudice and the share of migrants across 18 Latin American countries in 2020. The Prejudice Prevalence variable is constructed using the following indicator variables: (i) Acceptance of Venezuelan migrants having a negative impact [=1], (ii) Belief that migrants negatively affect the host economy [=1], Belief that migrants compete with hosts for jobs [=1], (iii)Belief that migrants contribute to increased crime [=1], and (iv) Belief that migrants impose a burden on the state [=1]. The At Least One Variable Equals 1 Index equals 1 if any of the aforementioned variables is 1, and the ”Average Index of Variables” represents the mean of these variables. The data for prejudice prevalence is sourced from the Latinobarometro survey for the year 2020, an annual public opinion survey representing the populations of 18 Latin American countries. The data for the share of migrants is calculated by dividing the total number of migrants in each country by the total population in the same year. The migrant variable represents forced migrants and is sourced from the United Nations Refugee Agency data portal, which includes refugees, peoples of concern, and asylum seekers. Total population data is obtained from the World Bank data portal.

41
in both rounds being nationally representative and matching rounds 1 and 3 of the four-round VHLSS. In 2010, prices had been surveyed in all communes that were part of the second (September) round of the three-round VHLSS and in a randomly drawn subset of the communes in the third (December) round. Analyses of the prices from both years reveal that spatial patterns in prices do not vary within-year, and for almost all items, the variation in prices over space is much greater than the between-month variation.

The nominal welfare variables and the data for the Engel curve analysis—food budget shares and covariates other than prices—come from the 2010 and 2012 VHLSS. For both surveys the consumption modules use a thirty-day recall of food purchases and consumption from ownproduction and gifts, another recall of spending during festive periods on twenty-four food and drink groups, a thirty-day recall for twenty-eight frequently purchased nonfood items and an annual recall for thirty-six other items. The only change in 2012 was that three of the fifty-four food groups from 2010 (rice, cooking oil and lard, and outdoor meals) were split (high and low quality rice, oil separate from lard, and meals by whether household members were at home or away). This slightly finer disaggregation may prompt recall of some forgotten food spending, so food shares in 2012 may be slightly higher than otherwise and people may appear poorer (and so a higher price index will be derived) than if there had been no change in design.

The 2010 and 2012 VHLSS marked a break from prior surveys. A “usual month” format and less comprehensive consumption aggregate than in 2010 were features of the prior surveys, which maintained definitions from 1993.<sup>12</sup> This link to the past caused growing understatement of consumption and overstatement of the food share as Vietnam got richer and people diversified

> 12. Usual month recall is based on reporting the number of months in which the item is usually consumed by the household, the usual expenditure in those months, and the quantity usually consumed.

11
Figure 4.1: _While firm formation is key for job creation, starting a business is not easy in many ECA countries_ <!-- Start of picture text --> Start-up cost index<br>12<br>10.0<br>10<br>8<br>6 5.0 5.25.55.55.65.86.16.26.36.46.56.66.66.66.86.97.07.57.77.77.98.18.28.28.38.48.9<br>4 3.4 3.54.1<br>2<br>0<br>Source: World Bank, Cost of Doing Business Database (2003); and Bank staff<br>calculations<br>Figure 4.2: Good access to finance is one of the main contributors to a good<br>employment performance<br>Access to finance index<br>8.5<br>9<br>8 7.4 7.7<br>7 6.0 6.3 6.4 6.7 6.7 6.9 7.0<br>6 5.0 5.0 5.6 5.6 5.7 6.0<br>5<br>3.8 3.9 4.0 4.1 4.3 4.5 4.6 4.8 4.9<br>4<br>3 2.4<br>1.6<br>2<br>1<br>0<br>Belarus Bosnia i Herz. Ukraine Hungary Albania SEE Azerbaijan Poland Serbia and Mont. Slovak Rep. Czech Rep. Turkey Macedonia, FYR ECA Croatia CIS Moldova Slovenia Georgia CEE Bulgaria Uzbekistan Russia Kyrgyz Rep. Kazakhstan Europe Lithuania Armenia Latvia Romania US Estonia<br>Romania Georgia Turkey Kyrgyz Rep. Kazakhstan Armenia CIS Belarus Russia Moldova Ukraine Lithuania Croatia Slovak Rep. ECA Azerbaijan CEE SEE Czech Rep. Bulgaria Poland Hungary Estonia Macedonia, FYR Slovenia Latvia Europe<br><!-- End of picture text --> Source: World Bank, Cost of Doing Business Database (2003); and Bank staff calculations Figure 4.2: _Good access to finance is one of the main contributors to a good employment performance_ Source: World Bank, World Development Indicators (2002); IMF WP/99/54; and Bank staff calculations.

36
|**Table 3 Sex ratio at birth of the third child by sex composition of p**<br>**Taiwan (China), 1988-99**|**revious births,**|
|---|---|
|**Gender of previous two births**<br>**HBV+ mothers**|**HBV- mothers**|
|Bothwere girls<br>122.4|121.4|
|Atleast one boy<br>110.5|110.9|

Source: Lin and Luoh 2007: Table 6 and personal communication specifying the actual sex ratios underlying the regressions in Table 6 9
Table S11. Relationship Between Inequality and Human Capital (Alternative Measures of Human Capital)

|Inequality Variable is:|(1)<br>Gini<br>Brueckner et al. (2015)|(2)<br>Net Gini<br>Solt (2015)|(3)<br>Market Gini<br>Solt (2015)|
|---|---|---|---|
||Panel A: Depend|ent Variable is Average Yea|rs of Schooling|
|Inequalityit|18.92**<br>(9.72)|38.81**<br>(17.84)|24.50**<br>(10.01)|
|Inequalityit|-3.22**|-6.48**|-4.16***|
|*ln(yi1970)|(1.49)|(2.75)|(1.49)|
||Panel B: Dependent Varia|ble is Share of Population w|ith Secondary Education|
|Inequalityit|2.80**<br>(1.22)|6.19***<br>(2.18)|3.30**<br>(1.29)|
|Inequalityit|-0.45**|-0.99***|-0.51***|
|*ln(yi1970)|(0.19)|(0.34)|(0.20)|
|Country FE|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|Observations|497|677|677|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Inequality_ is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

55
vehicles that are distinguishable from the ICE models. In addition to displaying the conspicuous adoption of energy-efficient vehicles, the green plate also helps make EVs easily recognizable for relevant road traffic and parking infrastructure privileges. The policy was promoted through three waves starting from December 2016. The enrollment was restricted to five pilot cities in 2016. The coverage expanded further in 2017 to 20 cities and was implemented at national scale by the end of 2018 (effectively 147 cities in our sample) as shown in Figure 6.

In addition to the incentives above that explicitly target the demand side, the central government also issued policy plans to address energy efficiency in production activities. In September 2017, MIIT issued the mandate on new energy vehicles using dual credit point system for both corporate average fuel consumption (CAFC) for ICE models and new energy vehicles (NEVs), which could be considered as a modified version of California’s Zero Emission Vehicle (ZEV) mandate.<sup>22</sup> As an addition to the existing regulation on fuel consumption of ICE models, NEV production and importation will result in credits accumulation for the corresponding manufacturers. Manufacturers with production and import volume more than 30,000 are subject to the dual credit point system, and the credit proportion for NEVs are set at 10% and 12% for 2019 and 2020 respectively. The mandate is considered as a continuation of the subsidy policies with less financial investment from the government, and the incentives are injected through the supply channel instead.

# **2.3 Data Description**

**Vehicle data** We obtain EVs sales at the city-quarter-trim level from 2015 to 2018. Our sample of analysis has 150 cities. This list of cities includes (1) the top 40 cities with the largest aggregate EV sales during our data period, and (2) their neighboring cities in order to conduct city-border analysis. In 2018, the total sale of passenger EVs in these 150 sample cities were 0.82 million, accounting for 78% of the national EV sales.<sup>23</sup> Our analysis focused on sales to individual buyers (rather than institutions) because institutional purchases (e.g., by government agencies and rental companies) are subject to different subsidy policies and face different incentives from individual purchases. In the 150 sample cities, individual sales accounted for about 65% of total EV sales in 2018.

> 22Official document of full description on the mandate issued by the five central ministries: `http://www.gov. cn/xinwen/2017-09/28/content_5228217.htm`

> 23Among the EV sales, BEVs took up 76% with PHEV being the rest. The EV sales in the top 40 cities accounted for 69% of national EV sales while the 110 other cities in the sample accounted only 9%.

12
different pedagogical approaches to financial education. It may be that willingness to learn and change financial behavior is lower when financial education is _mandatory_ (cf. Collins 2013) or motivation to participate in financial education is not intrinsic but driven by _incentives_ provided by the offering institution. Lastly, these characteristics may be correlated with specific _settings_ (i.e., at school or at the workplace).

Next, and going further in this direction, it is coded whether participants are educated at a _teachable moment_ (i.e., that they have the possibility to apply their knowledge in a concrete case of interest to them, e.g., Doi et al. <u>2014; Miller et al. 2015). Thus, we capture whether the education addressed</u> immediate financial issues (such as borrowers already in default, or migrants confronted with deciding through which channel remittances are sent). Alternatively, financial education was generic and offered at an unspecific moment, as is often the case in large scale financial education programs (e.g., Bruhn et al. 2014).

Table 2. Summary Statistics

|Variable|Obs.|Mean|Std.Dev.|Min.|Max.|
|---|---|---|---|---|---|
|_A Descriptive statistics at the_<br>|_study-level_<br>|||||
|RCT|126|0.405|0.493|0.000|1.000|
|TOT|115|0.452|0.500|0.000|1.000|
|Delay|93|82.231|273.613|0.000|1566|
|1/SE|126|57.535|210.450|2.480|1636.712|
|Intensity|87|11.211|14.929|0.100|87.000|
|Duration|76|7.341|14.150|1.000|103.000|
|Age|109|30.717|14.120|9.000|63.870|
|Percent female|123|54.011|18.493|0.000|100.000|
|Low income clients|102|0.529|0.502|0.000|1.000|
|Years of schooling|126|11.270|2.843|3.200|13.600|
|FL in population|124|50.419|11.658|24.000|66.000|
|Mandatory|96|0.292|0.457|0.000|1.000|
|Incentivized|86|0.314|0.467|0.000|1.000|
|Teachable moment|126|0.397|0.491|0.000|1.000|
|_B Descriptive statistics at the_|_estimate-level_|||||
|RCT|539|0.672|0.470|0.000|1.000|
|TOT|510|0.282|0.451|0.000|1.000|
|Delay|463|93.742|292.025|0.000|1566.000|
|1/SE|539|41.260|124.389|2.740|957.167|
|Intensity|451|15.384|23.444|0.100|144.000|
|Duration|434|7.908|14.236|1.000|103.000|
|Age|494|31.814|11.720|9.000|63.870<br>_-continued-_|

15
Why Are Connection Charges So High? An Analysis of the Electricity Sector in Sub-Saharan Africa<sup>_⇤_</sup> Moussa Blimpo Shaun McRae Jevgenijs Steinbuks The World Bank ITAM The World Bank JEL: L51, L94, O12, O55 Keywords: connection charges, electricity access, Sub-Saharan Africa, Uganda

> _⇤_ The authors thank Mike Toman and Albert Zeufack for guiding and motivating us to write this paper, and Malcolm Cosgrove-Davies, Kabir Malik, Subodh Mathur, Justice Mensah, seminar participants at ITAM, the University of Maryland (AREC), the World Bank, the Sustainable Energy Transitions Initiative (SETI) Annual Meeting, the annual workshop on Empirical Methods in Energy Economics, the EAERE annual conference, the Bolivian Conference on Development Economics, and the CSAE conference on economic development in Africa for helpful comments and suggestions. They also thank Yanbin (Tracy) Xu and Ruifan Shi for outstanding research assistance. This paper is a part of a series of background papers to a “Regional Study on Electricity Access in Sub-Saharan Africa” produced by the Office of the Chief Economist of the Africa Region (AFRCE), World Bank, under the project code P156903. 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.
# **Table 18a.  Impact on Output by Sector and Regional Market of WTO Accession and Simultaneous “Cap and Trade” to Reduce Carbon Emissions by 20 Percent. (Results are Percentage Change from Base Year)**

|**Good**|**Central**|**Siberia**|**South**|**North**|**Urals**|**Far East**|**Moscow**|**St. Pete.**|**Tumen**|**Northwest**|
|---|---|---|---|---|---|---|---|---|---|---|
|`NFM`|37.2|15.6|50.8|22.3|47.9|13.5|69.1|48.1||192.6|
|`TMS`|14.5|9.0|13.5|20.6|0.9|21.5|-0.6|21.7|10.7|23.3|
|`TRK`|5.4|3.3|5.5|8.5|0.6|11.2|1.3|10.0|4.6|11.0|
|`OIL`|12.9|0.7|6.9|5.8|3.1|5.7|2.6|4.4|1.4||
|PST|5.2|3.7|5.0|5.2|3.3|5.4|3.4|5.2|4.6|6.6|
|TRD|2.6|2.8|3.7|4.4|2.5|5.5|3.1|5.0|4.2|5.5|
|`TPP`|-4.9|-2.7|-10.9|0.3|-2.5|41.8|-13.3|-2.2|8.9|4.2|
|`CRU`||0.9|2.1|2.2|1.0|2.1|||1.3|2.0|
|HEA|1.4|1.2|1.3|1.2|1.1|1.1|0.8|1.3|1.2|1.9|
|CLI|4.3|-0.2|1.6|1.0|1.8|0.1|0.5|-0.3|1.4|1.2|
|`SCI`|0.4|0.9|-0.9|0.7|-1.6|1.8|0.8|7.7|-6.8|6.4|
|`CON`|2.1|-1.3|0.1|-1.4|-0.5|-2.1|1.1|-1.2|-1.8|-0.1|
|**Chemicals**|**2.1**|**-7.2**|**-2.4**|**-10.2**|**-2.8**|**-10.1**|**-6.8**|**-2.4**|**-1.1**|**34.2**|
|HOU|-0.4|-1.3|-0.9|-0.6|-1.5|-1.1|-1.9|-0.6|0.1|0.8|
|`MWO`|-3.9|-6.0|-0.7|-4.8|-7.5|-5.6|12.1|5.6|-6.4|2.8|
|`AGR`|3.1|-2.0|0.3|-3.5|1.7|-4.5|-0.2|-4.8|-1.8|-3.1|
|ELE|-1.7|-1.6|-1.7|-1.2|-2.4|-1.4|-2.5|-0.6|-3.1|0.6|
|`OTH`|5.7|-3.0|0.0|-3.6|2.5|-5.4|0.8|-6.2|-1.8|-5.7|
|`RLW`|-2.0|-5.2|-1.3|-2.0|-3.9|-0.3|-1.3|0.0|-1.8|-2.2|
|`OTI`|-0.1|-6.2|-1.2|-3.8|-2.3|-5.4|2.3|-0.5|-6.0|-1.6|
|`AIR`|-1.3|-6.4|-4.4|-2.8|-4.7|-4.0|-5.1|-3.8|-5.3|-0.5|
|`FOO`|-3.4|-5.3|-2.7|-3.1|-4.2|-5.5|-7.3|-5.1|0.9|-4.2|
|`MAR`|-1.2|-9.1|-4.6|-2.5|-3.8|-0.1|-5.0|-3.2|-8.8|-4.5|
|`FIN`|-6.1|-6.4|-5.8|-6.2|-6.7|-5.7|-6.0|-5.5|-8.2|-5.0|
|`TRO`|-6.1|-11.9|-8.1|-3.3|-16.5|-1.2|-19.0|-1.5|-14.9|-0.2|
|`CNM`|-4.7|-12.4|-5.6|-8.2|-7.5|-9.9|-11.7|-10.8|-4.0|-9.1|
|**Pipelines**|**-10.9**|**-12.7**|**-9.6**|**-11.5**|**-8.8**|**-10.2**|**-23.0**|**-11.1**|**-4.2**|**-25.4**|
|**Ferrous Metals**|**-8.5**|**-21.0**|**-12.4**|**-14.8**|**-19.2**|**-15.0**|**-15.7**|**-6.0**||**-13.8**|
|**Coal**|**-15.2**|**-18.3**|**-16.1**|**-16.2**|**-18.6**|**-16.7**|||||
|**GAS**||**-21.4**|**-16.4**|**-19.7**|**-17.4**|**-21.3**|||**-21.4**|**-18.9**|

See table 1 for sector codes. **Source: Authors’ estimates.** 60
4

# **IH. THE** PROBLEM

9. This report tries to answer the following questions on the use of transport tax and the problems of evasion and avoidance:

- How urgent is the need to improve tax effort in developing countries?

- What is the magnitude of tax evasion in the transport sector, and are there significant gains to be had from better enforcement?

- What policy instruments are currently used for dealing with compliance and what role can they play as part of an agenda for improving enforcement of transport tax?

- How can policy makers reconcile the revenue raising and resource allocation objectives of transport tax?

# iWA Problems in LDCs

10. The 1988 _World Development Report_ (World Bank, p. 182), describes the poor conduct of fiscal policy in developing countries as leading to "... heavy foreign borrowing, high inflation, and stagnant private investment". The economic patterns and fiscal problems facing low-income countries vary and in each produce a different response, making generalizations difficult. However, some fiscal situations occur repeatedly in many countries.

11. One is that govemment expenditure drives the problems of budgetary imbalance in low income countries. In most cases, the ratio of tax to GNP is remarkably stable, but the expenditure share may increase dramatically over short periods of time. This creates short run (in some cases long run), situations of substantial deficit and inflationary financing. The following common situations explaining the inability of governments to control spending are observed in LDCs:

- Public services are deficient, and pressure to upgrade and fill the most severe gaps is intense. In response, governments often spend for capital improvements even when their forecasted tax revenue will not support the expenditures. Governments borrow without the capacity to repay, do not adequately maintain existing capital stock, and do not operate some facilities to proper standards.

- Central govemment budgets are squeezed by public employee compensation demands, demands to increase transfers to local governments, and the increasing price of non-labor inputs (utilities, asphalt, gasoline, and so on). Inflation exacerbates these pressures.
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