rafmacalaba/datause-encoder-data huggingface dataset:
Bank 2020b), financial benchmarks in the World Bank’s Global Financial Development Database (Čihák et al. 2012), IMF’s Index of Financial Development (Katsiaryna 2016), or the recently developed Fintech Activity Index (World Bank 2021).

Despite their intuitive interpretation, equal weighting schemes are also criticized for their normative assumptions and subjective judgements, oversimplification, and missing the point of differentiating between essential and non-essential dimensions (Greco et al. 2018; OECD 2008; Paruolo et al. 2013). However, as we discuss below, other aggregation methods also have drawbacks and we consider the assumption of equal weighting to be least problematic given the lack of information in the literature and priors that could guide alternate weighting schemes.

If propositions of many multilateral institutions such as the UN on how to estimate the housing deficit are taken in absolute terms (i.e., a housing unit can only be adequate if all adequacy dimensions are present at the same time), we obtain what we call the _strict Adequate Housing Index (sAHI_ ). The _sAHI_ aggregates the adequacy dimensions based on the most lagging dimension and can be represented as a non-linear function: sAHIi,c = min{a1i, a2i ... a7i} (3) This strict _AHI_ considers an HH to live in inadequate housing if there is a deficiency on _any_ of the seven dimensions. With this method, the most lagging dimension in terms of adequacy drives how many housing units are currently in need for adequate housing. Structural deficiencies which need to be addressed at regulatory level (e.g., improving tenure security through digitization of tenure records, etc.) drive the _sAHI_ . While it has important implications for the realization of the Agenda 2030, the _sAHI_ does not reflect the intensity of the inadequacy of housing (i.e., how many dimensions are deficient) and therefore might be too simplistic for practical applicability. Results reported in the remainder of this paper are, therefore, based on the _AHI_ – if not otherwise explicitly stated as _<u>strict</u> Adequate Housing Index (sAHI)_ . Comparisons of _sAHI_ and _AHI_ at country level can be found in Annex 4.

While the _AHI_ offers an easy to interpret, unidimensional measure of housing adequacy that is comparable within and across countries, there are also several limitations. First, the _AHI,_ by its nature, reduces complexity and hence also reduces information on the seven adequacy dimensions. Composite indices like the _AHI_ are, therefore, often critiqued for only being able to convey “big picture” messages without being able to identify concreate policy actions (e.g., OECD 2008). Yet, while the _AHI_ reduces the visible size of the seven adequacy dimensions, we clearly present the underlying information for further analytical work that can rely on such disaggregation. As housing adequacy is a multidimensional concept which cannot solely be captured by one of the adequacy dimensions such as access to water, the _AHI_ provides a holistic and comprehensive
Second, unions act as a transmission mechanism between the CPV and working people and are supposed to represent these people in the highest levels of government decision making. Third, unions are tasked with protecting workers’ rights and representing workers’ interests vis-à-vis firm management and providing welfare services to workers. The explicit roles for employers’ associations are similarly varied.

**Figure 6 – Union membership (in percent of wage employment)**

<!-- Start of picture text --> 90.00%<br>80.00%<br>70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br>China Vietnam Vietnam New Australia Japan Singapore Philippines Indonesia Korea Malaysia<br>(public (private Zealand<br>sector) sector)<br><!-- End of picture text -->

**Sources: World Bank staff calculations based on 2013 labor force survey, VGCL (2014) and World Bank (2012a).**

**Notes: Data for Indonesia and the Philippines are for 2005, those for Vietnam for 2013, all others for 2010. Public sector includes government, public services and state-owned enterprises, private sector includes household enterprises, domestic private firms and foreign-owned private firms.**

Figure 7 uses data from the VGCL to show the time series of informal wildcat strikes in Vietnam. In the 1990s, Vietnam generally saw less than 100 wildcat strikes per year. The time series of wildcat strikes has since broadly tracked Vietnam’s inflation rate. At the same time, the number of strikes has increased significantly, reaching a peak of almost 900 in 2011. Figure 7 also shows that almost no strikes occur in SOEs. In the SOE sector, wages and benefits are relatively high, and relations between workers’ representatives and management are close. In contrast, the foreign-owned sector appears particularly exposed to strikes. In addition to strikes, the sector has also been plagued by high labor turnover and absenteeism (cf. Chi and van den Broek, 2013). This particular weaknesses of the industrial relations institutions in the foreign-owned sector cannot be explained by a lack of union

- 17 -
neighboring Côte d’Ivoire. But the program led to virtually no exit of illicit activity, no change in peer networks, nor any reduction in aggression or attitudes towards violence.

# 3. **The UYEP intervention’s design and implementation**

The UYEP aims to contribute to PNG’s National Youth Policy priorities to support livelihood promotion, poverty reduction, and socially‐healthy behavior for youth, by assisting the growing segment of disadvantaged youth who have dropped out of school or university and lack the basic competencies required to compete in the labor market. The program provides unemployed, out of school youth in Part Moresby with human capital/training and work experience, as well as income, from temporary employment opportunities, and to ultimately increase their employability. This is done through three main components: The Youth Job Corps (YJC), the Skills Development and Employment Scheme (SDES) and Project Management.

The YJC involves a week of Basic Life Skills Training (BLST) followed by a two‐month (40 days) public works program that serves roughly three‐quarters of the applicants. The SDES is made up of Basic Life Skills Training (BLST), followed by one month of the classroom Pre‐Employment training (PET), and five months of the on‐the‐job (OJT) training with an employer in Port Moresby. People for PET are selected on the basis of having the highest scores in the tests, and doing well in the BLST. This ensures selectivity into a limited number of PET positions. OJT is offered to roughly one‐quarter of the BLST/PET graduates who are rated highly by their trainers and pass tests with the highest marks. The final component is Project Management, which insures that all Project implementation activities are on track and are properly monitored and evaluated. Given this intervention for youth is something new in PNG, great effort was put into establishing mechanisms, systems and capacity to deliver program services efficiently to targeted youth.

Youth are accepted into the program through continuous intakes, which occur once every 3 months. The enrollment of each intake into the Project consists of several steps:

- (i) First, the Project Management Unit (PMU) of the NCDC initiated a communication and awareness building campaign in selected districts inviting youth that meet the described eligibility criteria to apply. This process usually took 2‐4 days in a given neighborhood/district of the city. The PMU also worked with community leaders to reach out to potentially interested youth.

- (ii) Second, interested applicants showed up at a screening site on a given date to undertake the Eligibility Screening Survey (ESS), which was administered by an independent survey firm. The ESS collected basic information, including the socio‐economic characteristics of youth.

- (iii) Third, the survey firm collected and processed the ESS data to establish eligibility. To be eligible for selection into the Project, youth were required to be of eligible age (16‐35),<sup>8</sup> have

> 8 It is worth noting that while initially the upper age limit was set at 25 years, it was subsequently (starting from intake 4) increased to 35 years.

6
- 17 -

Table 3. Censored Bivariate Probit Model for Bidders’ Entry and Auctioneer’s Rejection

||Coef.|Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|
|Equation_d_1|||||
|ln_LENG_|–0.039|(0.092)|0.823|(1.085)|
|ln_LANE_|–0.886|(0.427) <sup>**</sup>|6.679|(2.950) <sup>**</sup>|
|ln_COST_|–0.145|(0.128)|–5.741|(1.815) <sup>***</sup>|
|ln_MONTH_|0.026|(0.312)|27.871|(10.268) <sup>***</sup>|
|ln_BKLG_|0.176|(0.005) <sup>***</sup>|0.010|(0.012)|
|ln_CAID_|–0.084|(0.084)|–26.505|(10.843) <sup>**</sup>|
|Constant|4.805|(0.876) <sup>***</sup>|40.519|(19.833) <sup>**</sup>|
|Equation_d_2|||||
|ln_LENG_|–0.125|(0.106)|–0.782|(0.256) <sup>***</sup>|
|ln_LANE_|0.296|(0.464)|–1.169|(0.669) <sup>*</sup>|
|ln_COST_|–0.188|(0.126)|–0.041|(0.133)|
|ln_MONTH_|–0.250|(0.339)|0.001|(0.601)|
|ln_BKLG_|0.015|(0.014)|0.018|(0.008) <sup>**</sup>|
|ln_CAID_|0.074|(0.088)|1.275|(0.333) <sup>***</sup>|
|Constant|1.140|(1.054)|0.060|(1.773)|
|_ρ_|–0.310|(0.476)|–0.109|(0.374)|
|Obs.|285||285||
|Wald-chi2|1482.66 <sup>*</sup>|<sup>**</sup>|1390.33|<sup>***</sup>|
|No. of country dummies|(0)||(4)||
|No. of bidder nationality dummies|(0)||(5)||
|Chi2 test statistics:|||||
|H0: Coef. of country and bidder|||315.36|<sup>***</sup>|
|nationalitydummies_=_0|||||

Note: The dependent variables are the bidder's entry decisions at individual stages. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

Source: Author’s calculation.

Finally, the consistent equilibrium bid function is presented in Table 4. The first column model is the simple OLS estimation, which may not be consistent because of uncontrolled selectivity bias. As usually expected, nonetheless, the competition effect is negative, meaning that the equilibrium bid, whence the government procurement cost would decrease with the number of participating bidders.<sup>19</sup> It is also reasonable that the submitted bids would be higher for more valuable contracts, longer highways, and wider roads.

When correcting the possible double selection bias, it is found that the competition effect is still statistically significant but slightly larger (in absolute terms) at –0.707.<sup>20</sup> It means that unrealized bids for some reason would be pro-competitive if they were observable. This can be interpreted as the potential benefits of unrealized competition. The other explanatory variables have the similar coefficients to the OLS regression.

> 19 For more discussion on this static competition effect, see Estache and Iimi (2008a).

> 20 Note that the standard errors were corrected by accommodating the additional disturbance caused by the two bias correction terms; but the coefficient still remains significant.
15. Consistent with the stronger recent growth of private schooling in rural areas indicated by school census data, Figure 4 shows an equalizing trend in the growth of private schooling in a recent period of approximately five years in each country, based on household survey data. The lower income quintiles account for increasing shares of private schooling while the richest quintile sees a decreasing share, although the lowest quintile still seems at the margin of the phenomenon. (Data is available for Bhutan but the private schooling rate is too low for decompositional analysis). Relatively poorer households have been able and willing to afford more private schooling perhaps because their absolute levels of income and aspirations for their children have increased. Improvements in overall economic and social conditions could also improve the supply of teachers and infrastructures, as well as the ability and drive to organize these supply-side factors into functioning schools. Recent education reform initiatives in the region that have targeted educationally disadvantaged populations may have also contributed to this trend. The FSSAP in Bangladesh and the FAS and PPRS initiatives in Pakistan mentioned above are some examples of such initiatives. Interestingly, Nepal and Pakistan, the two countries with the largest shares of unaided private schools, saw the starkest equalization in the demonstrated period, suggesting that increased affordability and desperation for quality in the face of poor government services may be important underlying factors.

- **c. Prevalence and socio-economic patterns of outside-classroom tutoring in Bangladesh, India, Nepal, Pakistan and Sri Lanka, as reported in household and school surveys, and over-time evolution of outside-classroom tutoring in Bangladesh, as reported in household surveys**

**Figure 5.** Growth in outside-classroom tutoring in Bangladesh

<!-- Start of picture text --> 2000 2005 2010<br>80<br>60<br>40<br>20<br>0<br>All students Male students Female Urban students Rural students Richest Poorest<br>students students students<br>(percent)<br>Private tutoring takeup rate 6-10 11-15 16-18 6-10 11-15 16-18 6-10 11-15 16-18 6-10 11-15 16-18 6-10 11-15 16-18 6-10 11-15 16-18 6-10 11-15 16-18<br><!-- End of picture text --> _Source:_ Authors, based on data from the Household and Income Expenditure Survey (HIES).

16. Outside-classroom tutoring is a common phenomenon in most countries in the region, as reported in available household survey data and the existing literature. Information on tutoring is reported in household surveys for Bangladesh and Nepal. (It is also reported for Bhutan but not discussed here as tutoring in Bhutan is prohibited by law and the rate is less than 1%.) Figure 5 shows that, in Bangladesh, over a period of 10 years, the tutoring take-up rate among students 13
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

# Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure

- Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

# Information and communication technologies

Adamou, M. Iro. 2009. ―Réforme du secteur des télécommunications en Afrique: Cas d’échec de la privatisation de l’opérateur historique au Niger.‖ http://www.apc.org/fr/system/files/CICEWA_Niger_20090911.pdf.

- Aker, Jenny C. 2008. ―Can You Hear Me Now? How Cell Phones are Transforming Markets in SubSaharan Africa.‖ Center for Global Development Notes.

http://www.cgdev.org/content/publications/detail/894409.

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

- Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

# Irrigation

World Bank. 2008. _Développement de l’Irrigation au Niger: Diagnostic et Options Stratégiques-Revue Sectorielle de l’Irrigation_ . Washington, DC.

- FAO (Food and Agriculture Organization). 2004. Aquastata: Niger Country profile. http://www.fao.org/nr/water/aquastat/main/index.stm.

- Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

45
The rest of this paper proceeds as follows: Section 2 presents a limited literature review that assesses the quality of the 2011 PPP data and discusses the urban-bias adjustment used for poverty estimation based on the 2005 PPP data. Section 3 first describes our approach to estimating a revised, methodologically consistent and comparable poverty line for 2011, and then presents an alternative poverty line that addresses sensitivity concerns of the existing methodology. Section 4 provides our analysis of the implications of using the 2011 PPP data on re-estimated profiles of global poverty. This section also essentially replicates the poverty projections presented in World Bank (2014b) but based on the 2011 PPP data and shows what the projected path of poverty to 2030 looks like. Section 5 presents some concluding discussion.

# **II. Assessments of the 2011 PPP Data and Implications for Global Poverty**

Reactions to the release of the 2011 PPP data have been varied, ranging from the view that these data reflect an unambiguous improvement over the 2005 data (World Bank, 2015) to a view that seems to suggest that they are sufficiently problematic that they should not be used for global poverty measurement.

# **_A. Comparisons of the 2005 and 2011 PPP Data_**

Deaton and Aten (2014) believe that the 2011 PPP data are superior to the 2005 PPP data, and in part reverse an error in the 2005 PPP estimates. The 2005 PPP data are essentially the product of two price indices – one index established purchasing power parity _within_ regions and the other established a price index _across_ regions. The cross-region index is based on data from a set of 18 so-called “ring” countries in which a distinct commodity list was used to price out goods that were not unique to any particular region. Deaton and Aten argue that the cross-region ring index 6
**Table 5: Optimal all-demand-side instruments, optimal all-supply expansions and optimal all-demand-side instruments and all-supply expansions.**

|(**NOT**|**E:**All numbers in MAD<br>|are per trip. Percent<br>|changes from the base<br>|line in parentheses.)<br>||
|---|---|---|---|---|---|
||**All supply ex**|**pansions**|**All demand-side**<br>**instruments**|**All demand-side i**<br>**and supply ex**|**nstruments**<br>**pansions**|
||**Ground level roads**|**Elevated roads**||**Ground level roads**|**Elevated roads**|
|Fuel tax rate(��|0.54|0.54|**2.54**|**1.9**|**4.40**|
|Bus fare(���|3.45|3.45|**1**|**0**|**0**|
|Parkingtax(�)|5|5|**35**|**9**|**29**|
|Bus supply (B)|**2266**|**2416**|866|**2566**|**3100**|
|Road Area(sq. km)|**64.77**|**24.98**|22.7|**63.07**|**23.2**|
|Tramline(km)|**31**|**31**|31|**31**|**31**|
|**Mode Choices (Person-trips)**||||||
|Private Car|1,074,459 (9)|968,168 (-2)|760,739 (-23)|974,628 (-2)|643,063 (-35)|
|Motorcycle|426,064 (9)|381,993 (-2)|273,174 (-30)|398,027 (2)|257,333 (-34)|
|Taxi|1,057,828 (-12)|1,145,382 (-5)|1,452,102 (21)|1,114,507 (-7)|1,394,118 (16)|
|Bus|407,648 (13)|449,419 (25)|445,223 (24)|476,686 (32)|640,757 (17)|
|Tram|32,576 (-46)|53,613 (-11)|67,337 (12)|34,727 (-42)|63,304 (6)|
|**Traffic Load(car-equivalent)**|1,363,597 (5)|1,279,556(-2)|1,117,287(-14)|1,289,744(-0.9)|1,027,865(-21)|
|**Traffic load -to-capacity**|4.4 (-63)|10.6(-11)|10.2(-14)|4.25(-64)|9.2(-23)|
|**Fuel consumption(1,000 lit/year)**|<br>577,768 (-11)|627,791(-3)|517,832(-20)|542,941(-16)|474,147(-27)|
|**Bus waiting time**|<br>8.3 (-28)|8.2(-29)|11.5|8(-31)|7.5(-35)|
|**Bus occupancy**|<br>45 (-58)|46(-57)|126(19)|45(-58)|47(-56)|
||<br>**Travel time**|**(one-way minutes)**||||
|Private Car|10.9 (-53)|20.8 (-10)|20.1 (-13)|10.7 (-53)|18.4 (-20)|
|Motorcycle|10.9 (-53)|20.8 (-10)|20.1 (-13)|10.7 (-53)|18.4 (-20)|
|Taxi|14.9 (-43)|24 (-8)|23.3 (-10)|14.8 (-43)|21.8 (-16)|
|Bus|23.5 (-46)|37.1 (-15)|39.5 (-9)|22.9 (-47)|33.1 (-24)|
|Tram|26 (0)|26(0)|26 (0)|26 (0)|26 (0)|
|**Expected utility/MUI(MAD)**|902,120 (1.7)|889,794(0.3)|883,978(-0.33)|900,561(1.5)|885,567(-0.2)|
|**Social welfare(MAD)**|896,081 (0.9)|888,748(0.10)|888,567(0.08)|896,208(0.9)|889,774(0.22)|
|**Social welfaregain as % of income**|9.17|1|0.8|9.3|2.2|
|**Fuel tax revenue(MAD)**|635 (-11)|690(-3)|2,684(276)|2,106(195)|4,260(497)|
|**Park tax revenue(MAD)**|<br>493 (9)|443(-2)|2,360(421)|814(80)|1,717(279)|

26
comparison to traditional financial institutions such as banks to help clarify the role that post offices can play in advancing financial inclusion. Controlling for a host of individual characteristics and country fixed effects, we find that post offices are relatively more likely than traditional financial institutions to provide accounts to individuals who are most likely to be financially excluded such as the poor, less educated, and those out of the labor force.

We also use data from the Universal Postal Union (UPU) to explore the degree to which different postal business models and the size of the postal network help explain differences in account ownership patterns. The results suggest that post offices can boost account ownership— perhaps at both the post and other financial institutions— and that the size of the postal network matters. The larger the network–relative to the network of traditional financial institutions– the more likely it is that adults have an accounts at the post office.

Overall, our results suggest that post offices can play an important role in advancing financial inclusion. Leveraging their existing postal network infrastructure may be one of the ways that developing countries can use to address the financial inclusion challenges they face. However, more research is needed to better understand under which circumstances and under which business models posts can best expand financial inclusion.

15