rafmacalaba/datause-encoder-data huggingface dataset:
0.987 for _r_ =0.25), there are some large changes. For _r_ =0.5, Zimbabwe’s index rises by over 300%, from the lowest value (by far) of 0.140 based on ������ to 0.454; it also rises relatively, to be the 12<sup>th</sup> lowest—reflecting the fact that the additivity property of the Chakravarty index puts a higher premium on Zimbabwe’s schooling attainment. Using _r_ =0.25, the upward revision to Zimbabwe’s index is even more dramatic, with ����.��� =0.583. The largest decrease is that for New Zealand, for which the index falls by 0.094 in switching to ����.�� , and the ranking falls from third place to 18<sup>th</sup> . The differences are small at high HDIs using _r_ =0.25 (Figure 12).

# **6. Conclusions**

The Human Development Index was introduced in 1990 as an alternative to using national income per capita as the metric of development success. Until 2010 the index was an equally-weighted mean of scaled attainments in three dimensions: life expectancy, education and income. The simplicity of the HDI gave it a transparency that was clearly appealing to many users, although the HDI was never quite as simple as one might think at first glance, given the transformations embedded in its components. Over 20 years, the Human Development Reports (and numerous offshoot reports at national level) have applauded those countries that do well in the HDI, and offered advice to others on how they might do better in the HDI stakes.

A new version of the index was introduced in the 2010 edition of the HDR. The main change was to switch from the original additive aggregation function (the arithmetic mean of the three components) to a multiplicative function (their geometric mean). The main reason given for this change was to allow for imperfect substitutability between the HDI’s three components.

However, good intentions alone do not make for good measurement. The 2010 HDI is both more complicated and more problematic in its tradeoffs across core dimensions. Longevity in poor countries has been substantially devalued, though it seems unlikely that this was intended. The HDI’s valuation of longevity in the poorest country is now a mere 0.006% of its value in the richest country—a far greater difference than in their average incomes (for which the poorest country has 0.2% of the national income per capita of the richest). A poor country experiencing falling life expectancy due to (say) a collapse in its already weak health-care system could still see its HDI improve with even a small rate of economic growth. By contrast, 17
The data collection had two components: a household survey and a student tracking survey. In the household survey, we gathered information on household rosters in 2010 and 2000, schooling and work histories of biological siblings (of our sample students), household income (2010) and asset holding (2010 and 2000), parents’ participation in school governance for each sibling, and public service and infrastructure access (2010 and 2000). On assets owned in 2000, only quantities were captured in each category (we asked both values and quantities of assets in 2010). To impute the value of asset holdings in 2000, we used the current prices calculated from the values and quantities of assets in 2010. The survey was supplemented by barangay (community) leader, the parent–teacher community association, and school surveys.

In the student tracking survey, we tracked our sample students to collect detailed information on their schooling and work histories as well as marriage, anthropometry, and illness. Either face-to-face or phone interviews were conducted. There were two stages in tracking activities. First, the teams tracked students residing within their original divisions. This took place immediately after the household survey. Second, in the case of out-of-division tracking, the teams attempted to schedule face-to-face interviews with students residing in National Capital Region, Baguio, and Cebu. For students residing in other provinces, we used primarily phone interviews. However, the teams tried to visit students residing near or within the province of Laguna and in between northern Luzon and Manila to conduct face-to-face interviews. We did not track overseas migrants, and we omitted several students who could not be interviewed even by phone.<sup>22</sup> Although our sample has different age groups (cohorts) due to its sampling design, the majority of our students completed four years of high school. This is as expected, since even TEEP students (grade 6 in school years 2004/05 and 2005/06) are expected to reach age 17 in the survey period (note that children graduate from high school at age 15 or 16 if they have no delays or repetitions in schooling).

The tracking survey captured information on schooling and work histories in detail. Since our sample students were grade-6 enrollers in particular school years, we omit questions on their schooling histories up to that stage. Detailed information starts from high school entry and onward. Similar to the sibling section of the household survey, we collected information on school (name and school ID), school type (public or private), age started, graduated or not, age graduated, age

> 22There were 36 cases untracked out of the total of 3,487 students being tracked. Therefore, the tracking rate is about 98.97 percent.

17
Moreover, there is an added level of complexity of estimation error arising from imputation estimates as well as from the regression. Running regressions at this level of aggregation can only provide a general sense if host community poverty rates are correlated to the size of the foreign-born population.

The most likely scenario is that refugees are attracted to locations with healthy economies and attractive wages, or locations that show promising growth and good job opportunities. These unobservable circumstances would be negatively associated with poverty rates of the host community. The variable of interest _Rry_ would be negatively correlated to these types of unobservable conditions. Therefore �� would be positively biased. In other words, estimates are reasonably lower bounds.

# **_Results_**

Table 5 illustrates results from the empirical estimation of Equation (1). There is no evidence that poverty rates of the host community are linked to the proportion of the foreign-born population. OLS regressions without any control variables do show a positive correlation between the proportions of foreign-born and host community poverty. However, this is primarily due to regions in the East and near the Syrian border historically being the poorest regions in the country. Once region and time controls are added to the regressions, the correlation between the host community poverty and the size of the foreign-born no longer exist. Similarly when controls on average characteristics of the regional population are included into the specification, there is no relationship between the explanatory variable of interest and the dependent variable.

Table 5. OLS Regression Results, the Host Community

|(1)|(2)|(3)|
|---|---|---|
|Proportion of Foreign-Born in the Population<br>0.957**<br>(0.423)|-0.0910<br>(0.126)|-0.00385<br>(0.0753)|
|Constant<br>0.203***<br>(0.0386)|0.108***<br>(0.00177)|0.505<br>(0.289)|
|Year Dummy|X|X|
|Region Dummies|X|X|
|Labor and Demographic Controls||X|
|Observations<br>52|52|52|
|R-squared<br>0.091|0.944|0.992|
|Notes: * p<0.10, ** p<0.05, *** p<0.01.<br>_Source_: LFS 2011 and 2013. World Bank author’s ca|lculations.||

Del Carpio and Wagner (2015) find some negative impacts, displacement effects, from the increase in foreign-born from 2011 to 2014. The displacement effect is largest among low-skill, agricultural, and loweducated workers. There are various reasons why results in this paper are neutral compared with those of DCW (2015). Their analysis is based on the 2014 LFS, which exhibits a much larger foreign-born population than the 2013 survey that is used in this paper. Unfortunately, due to changes in survey collection 15
tion strategies against the socioeconomic crisis induced by COVID-19 may increase child marriage and child labor (UNICEF, 2021a).

The facts mentioned above make the context of Nigeria all the more relevant to our study. It is a Sub-Saharan African country with a Poverty Headcount Index of 40.10%.<sup>3</sup> Furthermore, _Demographic and Health Survey_ (DHS) evidence shows a 43% prevalence of child marriage in Nigeria among married women aged 20 ́ 24. Additionally, data from the Multiple Indicator Cluster Survey (MICS) indicate a high prevalence of child labor among children aged 5 ́ 17 in Nigeria. For example, in 2007, the prevalence of child ́ labor among children aged 5 14 years was 28.9%, while in 2011, this share was 57.1% for children aged 5-11. In 2016-2017, around one in two children aged 5 ́ 17 was involved in child labor (50.8%) of which 54.3% were boys, and 47.2% were girls.<sup>4</sup> Both these figures are well above the Sub-Saharan Africa region’s average of 37% for child marriage and 29% for child labor. They underscore the importance of education in Nigeria.

To identify the effect of nationwide COVID-19 lockdown measures on children’s probability of school attendance after schools’ reopening, we use a panel of school-age individuals observed just before schools’ closure and just after schools’ reopening and rely on the quasi-randomness of the occurrence of the pandemic. We draw from Mahmud and Riley (2021) by estimating a fixed-effect linear probability model. However, despite the plausible exogeneity of the COVID-19 shock, the potential simultaneous occurrence of other covariate shocks (e.g., climate shocks) can confound the identification of its effect on children’s school attendance. Indeed, in the context of Nigeria, whose territory is partially located in the drought-prone western Sahel region, the concomitant occurrence of climate shocks such as drought or floods is highly probable. We control for exposure to climate shocks and shocks to household size both before the COVID-19 shock and post-shock to account for this potential threat to identification.

Additionally, we control for age-specific school attendance trends to account for the effect of age on school participation. Moreover, security shocks such as _Boko Haram_ ’s deadly incursions in the North-East part of Nigeria are also potential confounders of the

> 3See the World Bank’s Global Poverty Working group at `https://data.worldbank.org/indicator/ SI.POV.NAHC?locations=CO&view=map&year_high_desc=true`

> 4UNICEF global databases, 2019, DHS and _Nigeria – Multiple Indicator Cluster Surveys_ (MICS).

3
from CGIAR-CSI.<sup>15</sup>

# **2.2 Descriptive statistics**

We compare access to infrastructure for paved roads, electricity, and internet broadband for Nigeria, Cameroon and Chad after 2000. Figures 1, 2, and 3 report summary statistics of the infrastructure variables used in the next sections at the country level, and Figure 4 focuses on the sub-region around Lake Chad.

Nigeria has the highest level of access to paved roads and electricity with more than 90% of the districts and population having access to a paved road in 2018. While access to paved roads has barely changed since 2000, access to electricity has increased significantly from 35% to 56% of districts having access to electricity between 2003 and 2018.<sup>16</sup> In 2018, 23% of districts are connected to the fiber network as defined by the presence of a node from the fiber backbone.

In Cameroon, 60% of the communes and 80% of the population are connected to a paved road. The coverage falls when restricting access to fair or good paved roads. Access to electricity and internet covers a small number of communes but the most populated ones as the percentage of population is almost twice the percentage of communes. Between 2003 and 2018, the number of communes covered has more than double but the percentage of population covered has increased by only 7% percentage points from 41%. The additional communes that have received electricity coverage over the last two decades are much less populated.

Access to infrastructure in Chad is very limited compared to other countries. In 2014, only 2.6% of communes had access to a paved road, 3% to the internet broadband, and 6% to the electricity network. The covered communes are the most populated ones, as 20% of the population has access to electricity and 15% of the population has access to the internet broadband. Recent improvements of the paved road network since 2014 as shown in Figure A18 in the Appendix show that the percentage of communes and population having access to a paved road has largely increased.

The Lake Chad area, which includes The Extreme North region in Cameroon, the regions of Kanem, Lac, Hadjer-Lamis, and Chari-Baguirmi in Chad, and the regions of Borno, Yobe and Adamawa in Nigeria as depicted on Figure A21 in the Appendix, is characterized by a limited access to infrastructure. Only 30% of the 80 locations - districts in Nigeria, communes in Cameroon and Chad - have a paved road, 16% access to electricity, and 10% access to Internet. Only half of the population has access to a paved road, 20% to electricity, and 30% to the Internet broadband.

> 15Global elevation (in meters) are from Shuttle Radar Topography Mission (SRTM) dataset (v4.1) at 500-meter resolution. See Jarvis et al. (2008).

> 16A district is classified as electrified when at least 50% of its population has access to electricity as monitored with night-time lights.

9
bonds with only step-up coupon as the financial incentive and 0 otherwise; Controlsi remain the same as specified in equation [1]. The regression is run at the bond-level as the decision for callability is made at the bond level, not penalty level. There are 5 SLBs in our sample that have step-up penalties along with other types of financial incentives (across multiple targets).<sup>7</sup> These are dropped from the analysis for simplification.

Table 5 presents the results from the logistic regression. Marginal effects at means are reported. Upon insertion of issuer-level controls in Model (3) and (4), we find that the SLBs with stepup penalties are significantly and positively related to the likelihood of penalty-minimizing call options, in line with our expectations. We also find that such options are strongly related to speculative grade SLBs. Such SLBs account for 75 percent of the penalty-minimizing call options. Almost half (45.9 percent) of such SLBs have such call options embedded in them. This varies substantially from investment grade bonds, of which only (11 percent) include such call options. Moreover, most (75.3 percent) speculative grade SLBs also have step-up penalties as their financial incentive.

**<u>Table 5: Bond-Level Regression Analysis for Call provisions</u>**

||(1)<br>Model 1|(2)<br>Model 2|(3)<br>Model 3|(4)<br>Model 4|
|---|---|---|---|---|
|Step-up SLB dummy|-0.004|-0.118|0.158**|0.109*|
||(0.072)|(0.096)|(0.073)|(0.065)|
|Investment grade dummy||-0.349***||-0.158**|
|||(0.071)||(0.076)|
|Amount issued (US$ billions)||-0.010||0.044|
|||(0.079)||(0.058)|
|Tenor (years)||0.013||-0.002|
|||(0.012)||(0.008)|
|Total assets (US$ billions)|||-0.006***|-0.002|
||||(0.002)|(0.002)|
|Leverage (%)|||0.190|0.016|
||||(0.128)|(0.117)|
|Public organization dummy|||-0.024|-0.075|
||||(0.064)|(0.075)|
|Observations|202|168|106|86|
|Pseudo R<sup>2</sup>|0|0.152|0.191|0.378|

_Robust standard errors are in parentheses *** p<.01, ** p<.05, * p<.1_ Within SLBs with step-up penalties, the paper tests whether the amount of the potential penalty an issuer faces influences the choice to embed a penalty-minimizing call option. We hypothesize that issuers will be more incentivized to act to minimize penalties if they face a higher potential penalty payoff. To test the relationship, the following logistic regression model at the bond level is estimated: Callablei = α+ β. TotalStepUpPenaltyi + Controlsi + εi [4]

> 7 These represent only 3 percent of the total sample.

17
**Figure 1. Trend of WB FMIS engagement**

<!-- Start of picture text --> 10 400<br> 360<br>8 320<br> 280<br>6 240<br> 200<br>4 160<br> 120<br>2 80<br> 40<br>0 -<br>1985 1990 1995 2000 2005 2010 2015 2020<br>Fiscal Year<br>Number of operations Commitment amount<br>Commitment (US$ millions)<br>Number of FMIS operations<br><!-- End of picture text --> _Source:_ World Bank FMIS Community of Practice Database and Business Intelligence.

1.5 The Africa region has dominated the FMIS engagement both in terms of number of operations as well as  total loan amounts. Close to two billion US$ were invested in the region, making up about 45 percent of total commitment amounts. In terms of numbers, the Africa region made up 38 percent of total projects, an indication that the average loan size was at about US$ 36.0 million, also the highest across all regions. Both in terms of lending and commitment the Africa region is followed by Latin America and the Caribbean and subsequently the Europe and Central Asia region. On the other hand, the Middle East and North Africa region stands out with relatively limited engagement. A regional overview of the engagement is provided in table 1. While there is a potentially important role for development policy finance (DPF) operations, the predominant vehicle for engagement has been through investment loans reflecting information technology (IT) infrastructure needs and technical assistance.

**Table 1 Regional breakdown of FMIS engagement (FY1985-FY2017)**

|**_Region_**|**_Total commitment (US$ m)_**|**_Number of projects_**|**_Mean project size (US$ m)_**|
|---|---|---|---|
|AFR|1,833.5; (45%)<br>|51; (38%)|36.0|
|LCR|685.5; (17%)<br>|33; (25%)|33.4|
|ECA|635.2; (16%)<br>|19; (14%)|32.4|
|SAR|486.1; (12%)<br>|15; (11%)|30.2|
|EAP|332.6; (8%)|11; (8%)|21.2|
|MNA|105.9; (3%)|5; (4%)|20.8|
|**Total**<br>|**4,078.8; (100%)**|**134; (100%)**|30.4|

_Source:_ World Bank FMIS Community of Practice Database and Business Intelligence.

**13**

**13**
where _P_ ( _Y_ = _j X_ ) is the probability of observing _j_ ∈{ 0,.., _J_ } outcome of the dependent variable _Y_ conditional on the vector _X_ of individual country characteristics, Λ is the standard logistic distribution function, and β is the vector of regression coefficients to be estimated by the Maximum Likelihood method. Denoting _Y_ * the latent variable driving the observed outcomes of _Y_ and μ _j_ the _J_ − 1 cutoff points such that * * μ _j_ − 1 < μ _j_ , the model assumes that _Y_ = 0 if _Y_ < μ 0 , _Y_ = _j_ if μ _j_ − 1 < _Y_ < μ _j_ , and * _Y_ = _J_ if μ _J_ − 1 < _Y_ .

Our choice of explanatory variables is based mainly on the theoretical and empirical analyses by Oates (1972), Wallis and Oates (1988), Panizza (1999), and Arzaghi and Henderson (2005). We use _GDP per capita_ , _Population_ and _Land Area_ to capture the effects of the size of the economy and the size of the country. Oates (1972), Panizza (1999) and Arzaghi and Henderson (2005) all found that these are negatively correlated with centralization. If decentralization is a normal good, we would expect a negative relationship between income per capita and centralization (Panizza, 1999). Wallis and Oates (1988) argue that smaller states may benefit more from centralization as economies of scale may not be exhausted due to small population size at decentralized levels. Wallis and Oates also argue that population concentration in urban areas increase the benefits from decentralization. Hence, we would expect a negative relationship between urbanization and centralization. We use _Share of Urban Population_ to find the effect of population concentrations. Both Panizza (1999) and Arzaghi and Henderson (2005) point to the importance of democracy and other institutional factors in decentralization. In Panizza’s model level of democracy is inversely related with the willingness of the central government to extract rent and ignore the preferences of the 15
to riskier, low-income borrowers for newer, more expensive cars. They thus concluded that collateral and repossession play a crucial role in the liberalization and democratization of credit in Brazil.

# 5.2.2 Payroll lending reform

In December 2003, the Brazilian Congress passed a law (Law 10.820) that allowed banks to oer loans with repayment through automatic payroll deduction (Lei do Crédito Consignado), which turned future income into collateral. The law regulated the procedures through which commercial banks underwrite payroll loans to private sector employees and to those receiving social security benets from the Instituto Nacional do Seguro Social (INSS), the federal payas-go pension system. Payroll lending has existed in Brazil since the establishment of Law 8.112, which was enacted in December 1990 to regulate the provision of such loans to public sector retirees and public servants. However, private sector retirees and employees were not included in the scope of the law. The new legislation introduced in 2003 extended the mechanism of payroll lending to private sector workers and social security beneciaries. Coelho et al. (2012) estimate the impact of the new law. They nd that payroll lending caused a reduction in interest rates and a large surge in personal loans in Brazil. Their results suggest that policies that strengthen collateral have a major impact on lenders' ability to underwrite, and they thus improve borrowers' access to nance.

# 5.2.3 Bankruptcy reform

In early 2005, the Brazilian Congress approved a new bankruptcy law (Law 11.101, Lei de Falências). One of the most signicant changes was to enhance the protection given to creditors, which was implemented through two channels. First, the new law increased their priority order to receive proceeds. Second, it allowed them to actively participate in the reorganization procedure. The law also aimed at increasing the eciency of the bankruptcy system by reducing the cost and time of both reorganization and liquidation, leading to an increase in the value of distressed rms. It improved on existing rules by providing in- and out-of-court options to reorganize, and by allowing the conversion of reorganization proceedings into liquidation. Using data from Brazilian and non-Brazilian rms, Araujo et al. (2012) estimated the eect of the bankruptcy reform on debt variables. In particular, they compared Brazilian rms to non-Brazilian rms from Argentina, Chile and Mexico with respect to the behavior of debt related variables. They found a reduction of approximately 8 percent in the cost of debt and increases of 10 percent and 23 percent in the amount of both total debt and long-term debt in Brazil, respectively. Since secured creditors have beneted more from 26