rafmacalaba/datause-encoder-data huggingface dataset:
- 23 -

3.19 In Asia, urban projects in Indonesia and India triggered additional institutional improvements. As a result of the Third Urban Project in Indonesia, the city of Surabaya established a separate Cleansing Department within the municipality, thus increasing the status and subsequent resources assigned to solid waste management. In Calcutta, implementation of the second urban project resulted in the reorganiz ion of the solid waste structure within the municipality, designation of a qualified senior engineer to direct solid waste operations, and establishment of a hierarchy of positions and responsibilities. The project also supported the improvement of employee working conditions and the design of a management system for operations and maintenance, Despite these accomplishments, however, one project officer noted that the failure to sustain local commitment to solid waste management was an important factor inhibiting long-term improvement. Solid waste institutions have remained weak and local governments have continued to assign higher priority to other urban expenditures.

# B. Proiect Issues

3.20 To complement the evaluation of component design and implementation, the review of World Bank MSWM investments focused on the size and scope of the solid waste investments, level of borrowing, financial considerations, and private sector participation in municipal solid waste services. The following sections consider key findings related to these issues and highlight pertinent questions raised in the course of reviewing MSWM projects and components.

# Size and ScoRe of Investments

3.21 Notwithstanding sume of the large, focused MSWM projects or components, the majority of components had very limited scope to provide vehicles and equipment to municipal departments. Little attention was given to specific improvements in the MSWM subsector. Furthermore, this approach raises serious questions about the usefulness of vehicle procurement as the main project subsector objective when rudimentary maintenance capacity is generally lacking. For Bank lending to have real impact on subsector performance, the project design must incorporate a broader, more comprehensive approach as illustrated by projects in Nigeria, Indonesia, and Mexico (See Annexes 3, 5 and 9).

3.22 Based on an analysis of MSWM component size, many of the projects - appear to have included solid wastes as an afterthought even though MSWM is a major budget item for cities. One fifth of the components had a component cost of less than US$1.0 million, and represented less than two percent of the respective total project cost. Moreover, this group of components was included largely for procuring collection equipment. The median value of the MSWM components was US$3.4 million; the median share was 4.5 percent of total project costs. By contrast, about one third of the projects had sizeable solid waste components costing in excess of US$5.0 million and accounting for more than 10 percent of project costs. The cost and size distribution of 66 projects for which MSWM component cost data were available is shown in Figures 5 and 6.
included high-resolution work on new road construction and deforestation in Brazil, where satellite monitoring has been available for a longer period (Laurance et al., 2009) and Bolivia, Panama, Paraguay and Peru (Reymondin et al., 2013).

More recently, Li et al. (2015) have investigated the potential impact of decreased travel time from improvement of road links in the Democratic Republic of Congo, but their cross-sectional analysis employs land use information (JRC 2003) that predates the new high-resolution satellite data. Damania et al. (2018) and Dasgupta and Wheeler (2016) have advanced the state of the art by estimating the potential impact of road upgrading on forest clearing and biodiversity in Bolivia, Cameroon, Myanmar and eight Congo Basin countries. Their deforestation models incorporate the latest high-resolution satellite data on forest cover changes, as well as quality estimates for specific links in the regional road system. They also create a biodiversity threat index that combines and synthesizes several measures of potential biodiversity loss. This paper builds on the work of Damania et al., (2018) and Dasgupta and Wheeler (2016), while extending it with a significantly-broader index of potential biodiversity loss and explicit adjustment of biodiversity risk assessment to incorporate the latest information about remaining forested areas.

# **3.  Modeling the Economics of Road Improvement and Deforestation**

From a formal analytical perspective, this research treats road improvement as a problem of competitive selection among corridors that traverse the same region. The corridors differ in length, construction cost conditions, biodiversity value and potential agricultural income. The optimum corridor choice reflects maximization of a social utility function that values both income and biodiversity, subject to a fixed budget constraint, feasible road quality improvement in each corridor (reflecting the budget constraint), and the corridor-specific impacts of road quality 4
# **Figure 1: Stock market returns, July 2007 – April 2009**

Figure shows the percentage change in the stock market index for each country between July 2007 and April 2009. Stock market index data comes from Bloomberg.

<!-- Start of picture text --> 20<br>0<br>-20<br>-40<br>-60<br>-80<br>-100<br>-120<br>Stock market returns (%)<br>UkraineIcelandSerbiaBulgariaCyprusLithuaniaBosnia and HerzegovinaEstoniaRomaniaLatviaIrelandCroatiaSloveniaKazakhstanUnited Arab EmiratesBermudaAustriaNigeriaLuxembourgGreecePeruHungaryBelgiumNetherlandsVietnamPolandSingaporeItalyCzech RepublicFinlandJapanNorwayPakistanKenyaRussian FederationPortugalKuwaitFranceDenmarkChinaCosta RicaThailandMaltaArgentinaSwitzerlandNew ZealandTurkeyUnited StatesPhilippinesMongoliaSpainNamibiaSwedenAustraliaBahrainBotswanaGermanyEgypt, Arab Rep.TaiwanUnited KingdomHong Kong, ChinaCanadaIsraelMexicoKorea, Rep.MalaysiaIndiaIndonesiaQatarSouth AfricaColombiaSaudi ArabiaSri LankaMauritiusOmanChileSlovak RepublicJamaicaBrazilJordanLebanonMoroccoEcuadorVenezuela, RBBangladeshTunisia<br><!-- End of picture text --> 34
25 set of functionings, but continues to average over the population as a whole, and ignores the source of inequality<sup>18</sup> . The equal-opportunity approach – as I have advocated applying it to a set countries – retains income as the objective, but disaggregates the population into types based upon circumstances that are beyond the control of individuals. The HDI approach says that human accomplishment along dimensions other than income is important, and the equal-opportunity approach says that inequality is bad only if it is of a certain kind.

Of course, it is possible to unite the two approaches. Instead of using income as the measure in my proposal, one could measure human development _disaggregated by types_ , where type continues to be defined according to a set of circumstances, and then the two-dimensional index _d_ would allow us to assess levels and degrees of development with regard to the various Sen-inspired functionings. It would be ideal to have data sets that permitted us to do this. The reason I have here proposed using only income is that I think, at this point, we do not have the data to compute the distribution of levels of human development by type for a large set of countries. However, the recent publication of the results of the Global Burden of Disease project ( see the entire issue of _The Lancet,_ December 13, 2012) indicates that this lacuna may be filled, as we may soon have available distributions of longevity by country and by type<sup>19</sup> .

Note that the issue of the ethical status of inequality is quite different from another way that inequality can be good or bad, and that is, with regard to its effect on 18 In the 2011 Human Development Report, the human development index is calculated by taking a geometric mean of national income, literacy, and longevity, rather than a convex combination of them.

> 19 In this issue of _Lancet_ , longevity and morbidity figures are given for almost all countries in the world.   Information on these measures of welfare by type is not, however, reported.
Chinese exporting industries facing newly initiated investigations during this period – including chemicals, iron and steel, machinery, and textiles. We return to a more detailed discussion of the same Chinese export products being targeted in different foreign markets in our more detailed discussion of some of the underlying cases in section 3 below.

**Figure 2.  Newly Initiated Import-Restricting Trade Remedy Investigations Targeting China’s Exports, 1Q 2007 - 1Q 2009 by Sector**

<!-- Start of picture text --> Other<br>Textiles and Apparel<br>Machinery<br>Iron and Steel<br>Chemicals<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>10<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>0<br><!-- End of picture text --> _Source_ : Compiled by the author from the _Global Antidumping Database_ . These are non-redundant AD, CVD, CSG at the product level.

# **2.6 The size of the imports under attack from the use of such trade remedies**

Here we provide a first attempt to assess the size of the potential economic problem these potential trade remedies pose. I.e., how much trade is coming under attack from the increasing resort to trade remedies?

Table 4 provides a first pass at this question by documenting a number of pieces of data on the size and relative importance of the 2007 (i.e., pre-crisis) level of trade at the level of 6-digit Harmonized System (HS) imports associated with the products and exporting countries that are the targets of most of the G-20’s newly initiated investigations since the beginning of 2008 and continuing through 1Q 2009.<sup>10</sup> The table reports four columns of data: the 2007 value of imports subsequently

> 10 The 2007 imports are the last year for which the data is systematically available across all of these countries.

12
Table C.1: Alternative earnings specifications

||(1)<br>Earnings (Min)|(2)<br>Earnings (Min)|(3)<br>Earnings (Max)|(4)<br>Earnings (Max)|(5)<br>Log earnings|(6)<br>Log earnings|(7)<br>Log earnings|(8)<br>IHS earnings|(9)<br>IHS earnings|(10)<br>IHS earnings|(11)<br>IHS earnings|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Treated|19027.1<sup>_∗∗_</sup>|11296.5<sup>_∗_</sup>|19734.9<sup>_∗∗_</sup>|12508.4<sup>_∗_</sup>|0.188<sup>_∗∗_</sup>|0.202<sup>_∗∗_</sup>|0.162<sup>_∗∗_</sup>|0.939|0.942|0.921|0.162<sup>_∗∗_</sup>|
||(7568.3)|(6398.1)|(7774.3)|(6584.0)|(0.087)|(0.082)|(0.069)|(0.578)|(0.579)|(0.580)|(0.069)|
|Constant|22503.5|12324.9|23707.8|15079.3|11.411<sup>_∗∗∗_</sup>|10.725<sup>_∗∗∗_</sup>|3.082<sup>_∗∗∗_</sup>|8.247<sup>_∗∗∗_</sup>|9.245<sup>_∗∗∗_</sup>|-2.678<sup>_∗∗∗_</sup>|3.285<sup>_∗∗∗_</sup>|
||(21606.7)|(16681.1)|(22226.2)|(16930.8)|(0.068)|(0.219)|(0.897)|(0.421)|(1.637)|(7.262)|(0.953)|
|Mean outcome control|76335.5|112040.9|78244.9|114843.3|11.41|11.41|11.41|8.25|8.25|8.25|12.10|
|Baseline controls|✓|✓|✓|✓||✓|✓||✓|✓|✓|
|Baseline outcome|✓|✓|✓|✓|||✓|||✓|✓|
|N|368|263|368|263|263|263|263|368|368|368|263|
|R-sq|0.151|0.458|0.149|0.448|0.018|0.135|0.372|0.007|0.033|0.041|0.372|

_Notes_ : Standard errors in parentheses, *, **, *** denote significance at 10%, 5% and 1% levels. _Baseline controls_ : sex, age, completed middle school, number of beds in household, number of children in household, number of adults in household, total (formal and informal) household debt, total household savings, household income. Dependent variable in columns (1) and (2) is earnings reported by workers at end-line using the minimum value of the earnings range while the dependent variable in columns (3) and (4) computes earnings using the maximum value of the earnings range. Dependent variable in columns (5) to (7) is the log of earnings reported by workers at end-line. Dependent variable in columns (8) to (11) is the inverse hyperbolic sine transformation of earnings reported by workers at end-line. Unemployed individuals and individuals with zero earnings are excluded from the sample in columns (2), (4), (5) to (7) and column (11).
# 5. **Evaluation results**

This section focuses on the presentation and the discussion of the key evaluation results, with a focus on the outcomes of interest related to social behavior and crime. Before discussing the main results, the first subsection describes the socioeconomic context of Port Moresby, and presents descriptive evidence on the extent of crime and anti‐social behavior from the baseline sample.

# _5.1 Context_

PNG is disproportionately young, as 67% of the national population is below 35 years of age and 25% are aged 16 to 35.<sup>12</sup> Urban youth face several challenges. For example, in the 2009/10 Household Income and Expenditure Survey, 58% of urban youth had dropped out of school or tertiary education. 60% of urban youth were unemployed, and less than 7% of those employed are in formal wage jobs5. Furthermore, PNG’s rapid GDP growth has had a limited effect on the well‐being of PNG’s youth. Despite real GDP annual growth of almost 8% in the last decade, urban wage employment decreased by 11 percentage points between 1996 and 2009/10. Rapid increase in rural‐urban migration, high rates of poverty and inequality, and high levels of unemployment create community stress that contributes to crime and violence, which is highly prevalent in the main urban centers. For example, Port Moresby and Lae City reported 33 and 66 homicides per 100,000 persons, respectively, between 2010 and 2013. According to surveys in Port Moresby, Youth aged 19 to 29 are the most active in crime; in fact, criminal activity is so pervasive that 54% of youth report to have been a victim of crime, and 35% report to have been engaged in crime at some point.<sup>13</sup> The national government is well aware of these challenges, and established “Human Capital Development, Gender, Youth, and People Empowerment” as one of the 7 pillars in its long‐term ‘Vision 2050’ plan. This plan states that “programs and systems must be improved and expanded to encourage greater participation from youths in all organizations and institutions, and in development programs”. In addition, the Development Strategic Plan (DSP) 2010‐ 2030 sets ambitious targets to provide universal education for all, ensure that all school graduates enjoy employment opportunities, and also, to halve the rate of youth crime.

Given the emphasis on youth well‐being and crime, the evaluation was collected on a variety of behavioral and criminal characteristics, which are divided into three groups. The first set is anti‐social behavior associated with either being a victim of or engaging in criminal activity. These include hanging out with friends in the streets late at night, having friends who have engaged in a criminal activity, or frequently getting drunk. The second set is self‐reported engagement in criminal activity, such as stealing, assault (physical or verbal), alcohol or drug‐related activity (e.g., fights), trespassing and domestic violence. The final group of indicators is the reported incidence of being a victim of a criminal activity.

> 12 Derived from 2011 Census Report.

> 13 National Youth Policy 2007‐2017, National Youth Commission, 2007.

17
Majumder, S., De, K., Kumar, P., Sengupta, B., & Biswas, P. K. (2021). Techno-commercial analysis of sustainable E-bus-based public transit systems: An Indian case study. _Renewable and Sustainable Energy Reviews_ , 144, 111033. <u>https://doi.org/10.1016/j.rser.2021.111033</u>

- McAndrew, R., Mulcahy, R., Gordon, R., & Russell-Bennett, R. (2021). Household energy efficiency interventions: A systematic literature review. _Energy Policy_ , 150, 112136. <u>https://doi.org/https://doi.org/10.1016/j.enpol.2021.112136</u>

Morrow III, W. R., Hasanbeigi, A., Sathaye, J., & Xu, T. (2013). _Assessment of Energy Efficiency Improvement and CO2 Emission Reduction Potentials in India’s Cement Industry_ . Berkeley: Lawrence Berkeley National Laboratory.

Mustapa, S. I., Ayodele, B. V., Mohamad Ishak, W. W., & Ayodele, F. O. (2020). Evaluation of cost competitiveness of electric vehicles in Malaysia using life cycle cost analysis approach. _Sustainability_ , 12(13). https://doi.org/10.3390/su12135303

- Nassif, G. G., & Almeida, S. C. A. D. (2020). Impact of powertrain hybridization on the performance and costs of a fuel cell electric vehicle. _International Journal of Hydrogen Energy_ , 45(41), 21722-21737. https://doi.org/10.1016/j.ijhydene.2020.05.138

Ng, A., & Zheng, D. (2018). Let's agree to disagree! On payoffs and green tastes in green energy investments. _Energy Economics_ , 69, 155-169. <u>https://doi.org/10.1016/j.eneco.2017.10.023</u> Nikolidakis, S. A., Kandris, D., Vergados, D. D., & Douligeris, C. (2015). Energy efficient automated control of irrigation in agriculture by using wireless sensor networks. _Computers and Electronics in Agriculture_ , 113, 154-163. <u>https://doi.org/https://doi.org/10.1016/j.compag.2015.02.004</u> Nisar, S., Benbi, D. K., & Toor, A. S. (2021). Energy budgeting and carbon footprints of three tillage systems in maize-wheat sequence of north-western Indo-Gangetic Plains. _Energy_ , 229, 120661. https://doi.org/https://doi.org/10.1016/j.energy.2021.120661 OECD (2001). _Measuring productivity - OECD manual_ . Paris: OECD.

OECD (2005). _Oslo Manual_ . Statistical Office of the European Communities. Luxembourg: OECD.

50
38 Table S. Model of Contraceptive Use and Fertility in the Last Five Years in Tunisia

||||Structural Equation|s|l|
|---|---|---|---|---|---|
|Variables|Addition<br>Children<br>Wanted 5 Years<br>Ago|Proportion of<br>Last 5 Years<br>Protected by<br>Temporary<br>Modem<br>Methods|Proponlion of<br>Last S Years<br>Protected by<br>Traditional<br>Methods|Probability +<br>Sterilized in<br>Last 5 Years|Births in Last<br>5 Years|
|Age 15-25|1.92 (17.64)"'e|0.83 (0.73)|-0.82 (1.11)|-0.91 (3.29)"'-|1.48 (7.97)..|
|Age 26-30|1.42 (22.70)..|0.56 (0.75)|-0.42 (0.75)|40.66 (5.23)*'|1.16 (9.19)" .|
|Age 31-35|0.72 (12.23) ..|0.29 (0.94)|-0.29 (1.16)|-0.15 (1.74)|0.88 (9.65).'|
|Education spouse|-0.01 (0.74)|-0.01 (1.38)|0.06 (4.80)"*|-0.03 (1.33)|.0.00 (0.17)|
|Education 4-6|0.20 (2.38)"'-|0.15 (1.03)|0.01 (0.08)|40.03 (0.03)|0.21 (2.15)"|
|Education 7+|0.22 (1.70)'|0.20 (1.20)|-0.16 (0.97)|-0.06 (0.26)|0.11 (0.65)|
|Rural 12|-0.02 (0.36)|-0.06 (1.36)|-0.19 (2.25)"|0.05 (0.56)|0.05 (0.52)|
|Small city|0.06 (0.74)|-0.03 (0.42)|-0.06 (0.55)|0.19 (1.41)|0.16 (1.60)|
|Urban|-0.15 (0.72)|0.28 (2.23)"|0.44 (1.47)|-0.60 (1.63)|-0.22 (1.68)'|
|Remote|0.13 (1.98)"|-0.05 (0.60)|-0.12 (0.84)|-0.14 (0.89)|0.15 (1.95)'|
|Sum facilities < 5, 5 years ago|-0.05(1.79)'|||||
|Sum facilities e 10, 5 years ago|0.03 (0.95)|__|_|||
|Community monality|3.32 (3.45)".|_||4.56 (1.91)'||
|Educ opportunities<5|0.02 (0.40)|||0.17 (2.04)"_||
|Educopportuntiies < 10|-0.02 (0.25)|||-0.01(0.07)||
|Doctor < 5, 5 yrs ago||0.08 (1.37)|0.01 (0.01)|-0.16(1.62)||
|Clinic 2 < 5, 5 yrs ago||0.08 (1.00)|40.13 (1.26)|0.05(0.27)||
|Clinic 3 < 5, 5 yrs ago||0.11 (1.97)"|-0.02(0.17)|-0.08(0.70)||
|Hospital<_5,_5 yrsago||-0.10(1.27)|0.20 (1.!9)|0.00(0.01)||
|Pharmacy e 5, 5 yrs ago||-0.03(0.41)|0.10 (0.90)|-0.17(1.39)||
|Doctor<10, 5 yrs ago#||0.18(0.82)|-1.10(5.35)"'-|0.11 (0.30)||
|Clinic 2 < 10, 5 yrs ago #||0.28 (2.09)"0|0.51 (1.86)'|0.11 (0.51)||
|Clinic 3 < 10, 5 yrs ago #||0.16 (1.32)|0.20 (1.22)|-0.15 (0.82)||
|Hospital < 10, 5 yrs ago #||-0.31 (2.10)"|-0.14 (0.44)|-0.32 (0.74)||
|Pharmacy e 10, 5 yrs ago #||0.19 (1.63)|0.23 (0.95)|0.41 (1.50)||
|Still want, 5 yrs ago||-0.35 (0.63)|0.16 (0.43)|||
|Proportion 5 yrs modem temp.|___||||-2.67 (6.71)"'|
|Proportion 5 yrs, traditional||_-|1.18||(0.68)|
**Table 4: Percentage of Responses with Complete or Some Distrust in Courts**

|No|Commercial<br>court|Number of<br>respondents|Complete or<br>some distrust|
|---|---|---|---|
|1|Belgrade|260|62.93|
|2|Kragujevac|100|50.00|
|3|Kraljevo|80|43.04|
|4|Leskovac<sup>*</sup>|20|85.00|
|5|Niš|60|68.33|
|6|Novi Sad|80|65.00|
|7|Pančevo|60|46.67|
|8|Požarevac|40|60.00|
|9|Sombor|40|77.50|
|10|Subotica|40|57.50|
|11|Užice<sup>*</sup>|20|25.00|
|12|Valjevo|40|65.00|
|13|Zaječar|60|66.67|
|14|Zrenjanin|100|65.00|
||Total|1000|59.83|

* Note that commercial courts Leskovac and Užice are excluded in our analysis Source: 2006 Life in Transition Survey

**Table 5: Summary Statistics**

|2003-2011||||||||||
|---|---|---|---|---|---|---|---|---|---|
||N|mean|min|max|p10|p25|p50|p75|p90|
|**_# of new firms registered_**|108|559|70|4507|110|166|246|353|1020|
|**_# of new firms entering_**|108|559|72|4755|103|161|237|356|1053|
|**_Mean employment_**|108|4.85|0.81|91.7|1.49|1.8|2.45|4.09|9.61|
|**_1-year survival rate (%)_**|96|89.8|71.5|99.6|82.5|85.9|89.9|93.5|98.2|
|**_2-year survival rate (%)_**|84|81.4|58.6|98|73.8|77.8|80.3|83.9|93.1|
|**_1-year growth rate (%)_**|96|-9.65|-151|28.4|-29.1|-16.1|-7.56|0.45|8.59|
|**_Distrust_**|108|0.61|0.43|0.78|0.47|0.54|0.64|0.66|0.68|

Pre-reform period: 2003-2004

||N|mean|min|max|p10|p25|p50|p75|p90|
|---|---|---|---|---|---|---|---|---|---|
|**_# of new firms registered_**|24|374|70|2364|85|109|191|253|609|
|**_# of new firms entering_**|24|336|72|2243|72|93|185|224|571|
|**_Mean employment_**|24|11.6|1.33|91.7|1.49|2.62|4.27|12.7|23.7|
|**_Distrust_**|24|0.61|0.43|0.78|0.47|0.54|0.64|0.66|0.68|

Sources: Data from Serbia’s Financial Accounts Registry and 2006 Life in Transition Survey 10