rafmacalaba/datause-encoder-data huggingface dataset:
11 2014. We compute this as the (estimated) under-five stunting rate in the year when the median aged worker was aged 2. We estimate the median age of today’s workers using the distribution of the population across five-year age bands from 15 through 55 using the population age structure data in the World Bank’s World Development Indicators (WDI). Childhood stunting rates are available only for relatively recent years in the Joint Child Malnutrition Estimates (JME) data set jointly prepared by UNICEF, WHO and the World Bank,<sup>3</sup> so we used the modeled estimates in the data set of Paciorek et al. (2013). Their data go back only to 1985, so when the median-aged worker was two in an earlier year, we use the 1985 childhood stunting rate.

As an example, take a country like Bangladesh. The median age worker for Bangladesh as from the WDI age structure in 2014 was 30. Even though the stunting prevalence in Bangladesh has almost halved in the past three decades, the relevant stunting prevalence for this exercise is the year when the median age worker was 2, i.e. 1986 (2014-30+2). The childhood stunting in 1986 (∆S�~over 70%) among today’s workers is used to compute the country-specific income penalty from equation (4) using the estimated effects of stunting on education, height, and cognition as summarized below.

# _2.3 Results_

The results are shown in Table 2 and Figure 1. The rates of childhood stunting among today’s workforce vary considerably across countries depending on the historical stunting rate and the age distribution of the population. Only 6% of the workforce in Hong Kong SAR, China, was stunted in childhood. In Chile, the figure was 8%. By contrast, two-thirds of India’s current workforce was stunted in childhood. Over 70% of Bangladesh’s workforce was stunted in childhood.

> 3 See <u>http://data.unicef.org/jme_master_2015_127fcff.xlsx?file=jme_master_2015_127.xlsx&type=topics.</u>
**Table A.6:** Robustness Checks on Bureaucratic Organization (Table 2)

||**Villag**|**e heads**|**Village gove**|**rnment**|
|---|---|---|---|---|
||New leader|Tenure (yrs)|% New appts|% Reshuffing|
||(1)|(2)|(3)|(4)|
|1. Baseline|0.835<sup>_∗∗∗_</sup><br>(0.101)|-4.908<sup>_∗∗∗_</sup><br>(1.527)|0.182<sup>_∗∗_</sup><br>(0.100)|0.089<sup>_∗_</sup><br>(0.052)|
|2. No controls|0.831<sup>_∗∗∗_</sup><br>(0.098)|-4.006<sup>_∗∗_</sup><br>(1.930)|0.139<br>(0.102)|0.068<br>(0.053)|
|3. Region fxed effects|0.805<sup>_∗∗∗_</sup><br>(0.090)|-4.268<sup>_∗∗∗_</sup><br>(1.500)|0.160<br>(0.100)|0.087<sup>_∗_</sup><br>(0.051)|
|4. 3rd-Degree polynomial|0.809<sup>_∗∗∗_</sup><br>(0.126)|-4.698<sup>_∗_</sup><br>(2.452)|0.127<br>(0.152)|0.106<br>(0.065)|
|5. 1/2 MSE-opt. bandwidth|0.825<sup>_∗∗∗_</sup><br>(0.228)|-5.502<sup>_∗_</sup><br>(2.776)|0.094<sup>_∗_</sup><br>(0.150)|0.053<br>(0.042)|
|6. 3/4 MSE-opt. bandwidth|0.813<sup>_∗∗∗_</sup><br>(0.153)|-5.145<sup>_∗∗_</sup><br>(2.317)|0.141<br>(0.131)|0.082<br>(0.055)|
|7. 2x MSE-opt. bandwidth|0.888<sup>_∗∗∗_</sup><br>(0.087)|-4.369<sup>_∗∗∗_</sup><br>(1.347)|0.141<sup>_∗∗_</sup><br>(0.092)|0.092<sup>_∗_</sup><br>(0.048)|
|8. Excl. Suharto villages|0.832<sup>_∗∗∗_</sup><br>(0.103)|-5.079<sup>_∗∗∗_</sup><br>(1.752)|0.199<sup>_∗∗_</sup><br>(0.100)|0.097<sup>_∗_</sup><br>(0.054)|
|9. Fuzzy RD|—|-6.507<sup>_∗∗∗_</sup><br>(2.513)|0.115<br>(0.139)|0.120<sup>_∗_</sup><br>(0.073)|
|10. Excl. lame-duck|0.839<sup>_∗∗∗_</sup><br>(0.104)|-4.701<sup>_∗∗∗_</sup><br>(1.127)|0.179<sup>_∗∗_</sup><br>(0.102)|0.092<sup>_∗_</sup><br>(0.054)|
|Observations|442|443|510|510|
|Control mean|0.035|7.96|0.33|0.036|
|MSE-opt. bandwidth|15.8|31.2|22.0|18.9|

_Notes_ : This table reports robustness checks on Table 2. The first row reports our baseline estimates. In the second row, we remove election year dummies and our control for the survey experiment treatment, which are included in our baseline estimation. In the third row, we include region fixed effects and dummies for pairs of election years (2015-2016, 2017-2018, etc.). The main regions in our sample are Java, Sulawesi, Sumatra, Kalimantan, and NTB-Bali. In the fourth row, we use a 3rd-degree polynomial to construct the point estimator. In the fifth, sixth, and seventh rows, we use a RD bandwidth equal to 1/2, 3/4, and twice the MSE-optimal bandwidth from Calonico et al. (2014), respectively. In the eighth row, we exclude villages with bureaucrats appointed during the Suharto era. In the ninth row, we report fuzzy RD estimates of _γ_ in equation (1). We use 1 (margin _jt >_ 0) from equation (1) to instrument for a dummy equal to 1 if the current village head in our survey sample is a different individual from the incumbent who competed in the last election. In the ninth row, we exclude villages where the current village head is serving in their third term. The dependent variables are identical to those in Table 2. The bottom panel reports the bandwidth size for the baseline estimation. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

46
17

## **Box 3: International experience on SPV regulation**

Bonds and loans should be payable from a special fund where revenues generated by the SPV’s investments or a special tax collection are deposited. The special fund avoids that the funds for debt repayment be diverted to the subnational government’s general budget or commingled in such manner as to lose their separate identity.

Restrictions on the sale of property assets related to the operation of the SPV must be established to ensure that the remaining property assets are sufficient to meet the SPV’s liabilities.

SPV must follow sound accounting standards (e.g. GAAP) and disclose annual financial statements which are independently audited.

A legal opinion needs to be issued for each bond issuance, certifying that the bonds comply with existing regulations and the issuer has legally-binding obligations to service the debt incurred. If applicable, the legal opinion should also indicate the existence of any rate covenant and special fund to repay debt.

Regulations can be imposed also on the lenders financing SPVs to contain the risk of nonperforming loans and excessive lending. For instance, lenders must set aside risk-adjusted capital reserves, with higher (lower) reserves required for less (more) creditworthy SPVs, so that the relationship between the markets’ lending and credit risk assessment are reinforced through regulatory action.

_<u>Source: Liu (2010)</u>_

# **IV. Analytical tools for monitoring subnational debt indicators and determining debt thresholds**

Subnational debt thresholds seek to ensure subnational indebtedness is both sufficient to finance infrastructure investment expenditure and consistent with debt sustainability. It is then natural to help determine these thresholds by using analytical tools that are standard in the assessment of debt sustainability, namely a macro-fiscal framework and a debt dynamics model. The basic elements of these analytical tools are described in the following paragraphs.

For any level of government in any country, the stock of its debt (measured as a share of GDP, GSDP, revenue, or any other variables associated with repayment capacity) depends on four key variables: (i) the public debt stock inherited from the past, which results from past borrowing choices; (ii) the primary balance, associated with the current fiscal policies and institutions concerning taxation, spending patterns, and schemes for revenue-sharing between different government entities; (iii) the cost of borrowing, represented by the average interest rate charged on the inherited public debt stock; and (iv) the growth rate of the repayment capacity-related variable, e.g. the growth rate of GDP, GSDP, or revenue.

Thus, in practice, a standard debt dynamics model applied to a subnational government would postulate a basic equation to determine the subnational debt-to-GSDP ratio:
# **3.0 - VULNERABILITIES AND FAILURES**

The following results consider all reviewed data including outage information, facility-specific curtailments and case studies. Table 2 presents the major outages in the United States (2000-2016) summarized by cause. Half of all events are attributed to natural hazard events. Notably, where outages were due only to damage to operability disruptions, more than half of all classified events were due to the transmission and distribution infrastructure.

<u>Mukherjee, Nateghi, and Hastak (2018)</u> examined the cause, location, duration and impact (MW lost, customers affected) of every major electrical outage in the United States between 2000-2016. Over 1,500 events were included in the dataset, and each outage event was categorized by the major cause. Fifty percent of all major outage events in the examined period were due to natural hazard events, Table 2. System Operability Disruptions account for approximately 8% of outages, of which more than 50% were transmission/ distribution interruptions issues (where classified). Fuel Supply Emergency caused outages 3% of the time, with data on the fuel type for each outage: coal (35%), hydroelectric facilities (12%), natural gas (14%) and petroleum (2%) and unknown accounting for the rest.

McCall et al. (2016) examined the impact of water-related issues on generation facility operation. It focused on all water-related curtailments in the United States between 2000-2015. A total of 37 events were recorded for nuclear, coal and hydroelectric facility operations.

The majority of curtailments occur due to water intake and/or discharge issues related to water temperature (>90%) with many of these (79%) occurring during heatwave/drought conditions. Specific design and operation parameters associated with environmental regulations and permitting were the major cause of curtailment (water temperatures

**Table 2 - Major Outages in the Continental United States** (2000-2016), summarized from Mukherjee et al. 2018.

|**Cause**|**Percent of all**<br>**events**|
|---|---|
|**Natural Hazards**|**50%**|
|Hurricane|10%|
|Heat Wave/Drought|1%|
|Earthquake|<1%|
|Winter Weather|18%|
|Flooding|<1%|
|Wind|11%|
|Wildfire|1%|
|Storm|29%|
|**Fuel Supply Emergency**|**3%**|
|Coal|35%|
|Hydroelectric|12%|
|Natural Gas|14%|
|Petroleum|2%|
|**System Operability**<br>**Disruptions**|**8%**|
|Classified events:<br>Transmission<br>Distribution|54%|
|**Intentional Attack**|**27%**|
|**Other**|**11%**|

exceeding permitted levels and/or water availability). The remaining curtailments are attributed to winter weather (McCall et al. 2016).

When combined with the case studies assessed in this analysis, similarities were found across geographies, hazard events and other parameters that might affect design standards, policy practices and cost considerations. Three general categories with common corresponding causes were identified in the vulnerability of the power grid system: 10
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Finally, despite these undeniable achievements in absolute terms, the widening urban-rural gaps in access to improved water and sanitation remain, at 33 and 30 percent respectively (figure 12), remains an enormous challenge. These gaps underscore the ongoing challenge of extending services to rural areas— where 70 percent of the population lives—while still keeping up with urbanization. Furthermore, investments in sanitation, particularly in rural sanitation, have been insignificant, as highlighted by the March 2008 Public Expenditures Review in the rural water and sanitation sector. The majority of rural and semi-urban water projects under implementation (or recently implemented) are accompanied by hygiene and family latrines promotion campaigns, but the share of resources allocated to sanitation remains negligible.

**Figure 12. Access to improved water and sanitation has increased in Mali**

a. Rural and urban access to improved water and improved sanitation

|||1987|1996|2001|2006|
|---|---|---|---|---|---|
||Rural|20.6|27.1|32.5|45.8|
|Improved water|Urban|52.2|65.7|70.4|78.9|
||Rural|33.5|32.2|39.4|40.9|
|Improved sanitation|Urban|58.0|56.3|65.3|70.4|

- b. Change in rural-urban gap between early 2000s and late 2000s

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>Burkina Faso Cote d'Ivoire Liberia Mali Senegal<br>improved water improved sanitation<br>% change<br><!-- End of picture text --> Source: WHO–Joint Monitoring Programme 2010, from Demographic and Health Surveys.

# Power

# **Achievements**

Mali’s installed generation capacity is almost evenly split between hydropower and oil. It was dominated by oil generation (60 percent) until the start of operation of the dam in Manantali in 2002, after which hydropower accounted for the majority of generation. As demand has increased since 2004, 24
data used in this section is inevitably incomplete, and as such the conclusions drawn are subject to limitations.

Key point 6: ODA for refugee education is dominated by development finance, in marked contrast to all other ODA for refugee situations ODA for refugee education in LICs and MICs is overwhelmingly delivered as ‘development’ aid, as shown in Figure I. This is in marked contrast to all other ODA for refugee situations, which is dominated by humanitarian financing (Hesemann et al., 2023).

promoting long-term economic and social development in recipient countries; ‘humanitarian aid’, designed to provide emergency relief and address immediate and urgent needs; and ‘peace aid’, which is directed at peace building and preventing, resolving, and recovering from conflicts (OECD, n.d.).

65 percent of all ODA for refugee situations (excluding education) is delivered as ‘humanitarian’ and ‘peace’ aid, with just 29 percent delivered as ‘development’ aid. By comparison, ‘humanitarian’ and ‘peace’ aid constitute just 13 percent of ODA for refugee education, with ‘development’ aid comprising 87 percent of all ODA for refugee education. Education financing to refugee situations is thus dominated by ‘development’ aid in stark contrast to all other forms of ODA for refugee situations, as shown in Figure I .

<!-- Start of picture text --> ODA for refugee education by<br>ODA by purpose code<br>recipient type<br>100% 1%<br>5%<br>12%<br>90% 7% Research/ Private sector<br>1%<br>80%<br>70%<br>Not<br>60% 59% classified<br>18%<br>50%<br>87% NGOs and<br>40% CSOs<br>8%<br>30%<br>Host<br>Multilateral org<br>20% country 62%<br>29% gov.<br>10%<br>11%<br>0%<br>Education All other ODA<br>Development Humanitarian Peace Other<br><!-- End of picture text -->

# _Source: REFD (2024)_

of education aid for refugee situations in LICs and MICs went directly to host country governments in 2021, as shown in Figure I , compared to 17 percent of all donor financing for 15
# **II. Conceptual Framework**

The conceptual framework presented in this section builds upon the previous literature on human capital under competitive markets (Becker 1964, Mincer, 1974). One individual achieves the optimal choice of human capital accumulation when the marginal benefits of investments in human capital equal the marginal costs of such investments. Marginal benefits of investments in human capital are expected to decrease as human capital increases, while the marginal costs are expected to increase. The market’s demand for human capital determines the returns of human capital investments, which are not uniform across all domains and skills (i.e., some activities are more skills-intensive than others) (Acemoglu and Autor, 2012). As markets are competitive, an increasing supply of human capital in the economy will tend to increase the relative output of skillintensive activities and hence reduce the skill premium that educated workers enjoy (Goldin and Katz, 2007).

Human capital augments effective units of labor and makes workers more productive. At the macro level, the aggregate production of the economy ( _Y_ ) is dependent on physical capital ( _K_ ); human capital ( _HK_ ), measured as efficiency units of labor; and technology ( _A_ ) (Goldin and Katz, 2007). The production function is assumed to have constant returns to scale on _K_ and _HK_ : At the micro level, ��<sup>�</sup> �<sup>denotes the human capital accumulated by an individual at age</sup> _a._ The model assumes that human capital accumulation has a life cycle skill formation (Behram et al. 2014; Cunha and Heckman, 2007). The stock of human capital accumulated at age _a,_ denoted by ��<sup>�</sup> �<sup>is dependent on the human capital accumulated until age ��1 and on the marginal</sup> human capital acquired at age _a_ . In other words, investments made in previous years influence the decisions individuals make concerning human capital investments today. Additionally, the framework allows the possibility that human capital depletes at an exogenous rate ��, which can vary with time: 4
Policy Research Working Paper 6685

# **Abstract**

In 2003 Kenya abolished user fees in all government primary schools. Analysis of household survey data shows this policy contributed to a shift in demand away from free schools, where net enrollment stagnated after 2003, toward fee-charging private schools, where both enrollment and fee levels grew rapidly after 2003. These shifts had mixed distributional consequences. Enrollment by poorer households increased, but segregation between socio-economic groups also increased. The shift in demand toward private schooling was driven by more affluent households who (i) paid higher ex ante fees and thus experienced a larger reduction in school funding, and (ii) appear to have exited public schools partially in reaction to increased enrollment by poorer children.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at bold@econ.uni-frankfurt.de.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
# **2.  Description of the 2005 Data Update**

In this section we briefly describe the update of our governance indicators for 2005, as well as some minor backwards revisions to the biannual indicators for 19962004. This year we report data for 2003, in order to create a panel of annual observations covering 2002-2005. We also for the first time will be distributing on the web the bulk of data from the individual indicators of governance that underlie our aggregate indicators. We therefore also describe how this data from the individual indicators can be used to aid in the interpretation of the aggregate indicators.

Our methodology has not changed from past years, and a detailed discussion can be found in Kaufmann, Kraay, and Mastruzzi (2004). As before we construct indicators of six dimensions governance:

1. _Voice and accountability (VA)_ , the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and free media

2. _Political stability and absence of violence (PV)_ , perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including political violence and terrorism 3. _Government effectiveness (GE)_ , the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies 4. _Regulatory quality (RQ)_ , the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development 5. _Rule of law (RL)_ , the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violence 6. _Control of corruption (CC)_ , the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.

4
# Protecting Who?

# Optimal Social Protection Responses to Shocks with Limited Information

Carlos Ospino Hernandez, Jamele Rigolini, Sarah Coll-Black and Ana Maria Oviedo<sup>1</sup> JEL Classification: D6, H5, I3, Q5.

Keywords: Social Protection, Adaptive Social Protection, Disaster Risk Management, COVID-19, Targeting.

> 1 World Bank. Contact author: Jamele Rigolini, The World Bank, 1818 H St. NW, Washington, DC 20433. Email: <u>jrigolini@worldbank.org. The authors would like to thank Leonardo Lucchetti and Tom Bundervoet for their</u> guidance in modeling government responses in North Macedonia and Moldova, respectively, Edith Zink for the support with the simulations for Moldova, and Javier Baez, Phillippe Leite, Cem Mete and Roman Zhukovskyi for their valuable comments. This analysis was supported, in part, through a trust fund from the Global Facility for Disaster Reduction and Recovery.