rafmacalaba/data-use-mentions-extended huggingface dataset:
girls aged 10-17 were married at the time of the survey. These rates are much higher than boys who have married in the similar age group (0.10 percent). Compared with this national figure, the early marriage rates for girls are higher in the selected provinces, at 2.4 percent in South Kalimantan and 2.2 percent in East Java. Within the four selected districts, the prevalence of girls with early marriage ranges between 1.31 and 4.0 percent. Banjar Baru and Sampang have the highest prevalence of female early marriage, at 2.08 and 4.0 percent, respectively.

**Table 5. Early marriage rate in selected regions**

|**Region**|**Male**|**Female**|
|---|---|---|
|Indonesia|0.10|1.41|
|East Java|0.11|2.22|
|South|||
|Kalimantan|0.20|2.49|
|Banjarmasin|0|0|
|Banjar Baru|0|2.08|
|Bangkalan|0|1.31|
|Sampang|0.48|4.0|

Source: Susenas 2018, processed.

The 2003 Law on the National Education System states that all citizens have a right to obtain a quality education. This commitment was echoed by MoEC officials in discussions with the survey teams. Nonetheless, numerous school principals did not seem to be aware that students, in particular girls, are legally allowed to remain in school even if married. An education officer in Banjarmasin District, South Kalimantan, noted that while girls can continue their education in non-formal schools ( _ujian persamaan_ ) if they get married, they are not allowed in formal schools. A MoRA officer in Surabaya noted that there are no regulations that students cannot continue in school if married and that there is no regulation that children in school cannot get married. The officer
Financial Regulation and Development Table 4: Largest Disasters in Ethiopia

|Type|(1)<br>Date|(2)<br>Total Deaths|
|---|---|---|
|Flood|13 August 2006|364|
|Flood|5 August 2006|498|
|Epidemic|September 1988|7,385|
|Drought|June 1987|367|
|Epidemic|January 1985|1,101|
|Drought|May 1983|300,000|
|Epidemic|January–December 1981|990|
|Drought|December 1973|100,000|
|Epidemic|January 1970|500|
|Drought|July 1965|2,000|

_Note:_ This table reports the most important disasters in Ethiopia between 1960 and 2015, from the most recent to the oldest. In recent years, Ethiopia has not experienced any disaster that could be related to the policy introduction. The data source is EM-DAT (http://emdat.be).

by one of the most severe droughts Eastern Africa has experienced in the past 60 years,<sup>12</sup> and this may be a reason for concern. As clarified by Figure 12, this disaster affected mostly Somalia, Kenya, and Ethiopia. However, this might be a limited concern for this study because while Somalia was hit in the most densely populated region of the country (around the capital city Modagishu), Ethiopia was hit in a low-density and predominantly rural area, as clarified by the lower panel of Figure 12. In particular, according to some controversial relief statistics, the number of Ethiopians affected by this disaster was between a few hundred and 700,000,<sup>13</sup> which is a sizable number, but limited relative to the 2011 population of 89.39 million. In Table 4, we also report a list of the major disasters that have occurred in Ethiopia since 1960, and verify that this drought does
# **3.2 Data Sources, Instruments, and Outcomes**

All the outcomes measured in this were selected based on the frame- paper conceptual 2.2. work presented in Section A summary of outcomes, data source, and type of outcome is shown in Appendix Table A3.

## **A. Main Outcomes**

We use administrative data provided by schools to measure the three primary outcomes: behavior at school, school dropout, and academic performance (math grades). As described in Section 3.1, we collected data on behavior at school and on math grades at baseline and endline. School-dropout data were collected at endline only.

- _a. Behavior at school._ To proxy for bad behavior at school, we use teachers’ reports. Each country has a different way of assessing behavior:

- El Salvador evaluates behavior using a Likert scale that ranges from Good, Very Good, and Excellent.

- Honduras provides an evaluation of behavior using a Likert scale that includes the following four categories: Good Behavior, Very Good Behavior, Excellent Behavior, and Outstanding Behavior.

- Guatemala does not conduct a behavior evaluation, but schools participating in the treatment were to evaluate behavior the same Likert scale as in El requested using

- Salvador.

Teachers submitted a printed report for each student enrolled in the school, which we subsequently digitized. We transformed the reports into numerical variables on a scale from 1 to 3 for El Salvador and Guatemala, and a scale from 1 to 4 for Honduras. In both cases, a higher value indicates better behavior. To standardize the measure of behavior reports among countries and schools, we define the outcome as a dummy indicator that equals 1 if the student has a behavior score above the median of the school they attend.<sup>33</sup>

> 33In El Salvador and Honduras, teachers were already collecting these data for school reports. In

18
A second warning is the size of foreign-currency (FX) denominated debt (Tomz and Wright (2013)), which exposes countries to currency risk. If countries only issued in domestic currency then a country can implicitly default by inflating debt away; an option not available for FX-denominated debt.

Political institutions matter for repaying debts. As an example, Biglaiser and Staats (2012) show that countries with strong courts are less likely to default. Another factor that could lead to default is the mere issuance of new debt to devalue existing debt - since new issuance increases the likelihood of default. This debt dilution can account for up to 78 percent of default risk and eliminating dilution decreases optimal duration by roughly two years (Hatchondo et al. (2015)).

”Twin-crisis” events occur when the financial and public sector default. Balteanu and Erce (2017) show that balance sheet interconnections (e.g. materializing contingent claims and guarantees), credit dynamics (non-performing loans increase due to higher interest rates that reflect both public and private sector risks) and economic growth (e.g. the link between asset prices, investments, and economic growth) are drivers of banking and public sector crises. This is due to falling capital ratios requiring public sector intervention in the case of a financial crisis. Balteanu and Erce (2017) also argue procyclical fiscal policy along with a lack in competition might make banks vulnerable to shocks. Obstfeld (2011) argues that foreign debt (both public and private), especially short-term liabilities, worsen the credit worthiness of the public sector.

The selected variables for the public debt fundamental index, which are associated with liquidity or solvency risk include: (i) public debt stock; (ii) financing needs (both public and external), (ii) exchange rates, (iii) interest rates, (iv) currency composition of debt, (v) debt service, and (vi) current account balances. Standard variables for liquidity risk are: (i)
since key economic and labor market indicators in Egypt are unaffected by oil prices’ In Figure 1 in Panel B, we present a scatter plot featuring the evolution of the inflation adjusted oil prices and migration patterns from the 1960s to 2010. The share of migrants is derived from the ELMPS (2012), using information on both current and return migrants, and on the year of migration. The fitted regression line shows a positive correlation between oil prices and the share of migrants. While Panel A provides support to the exclusion restriction of oil prices, Panel B shows that oil prices constitute indeed a strong predictor of migration decisions.

A potential threat to identification would be the non-orthogonality of oil prices (1) to legal status of migrants, or (2) to return decisions. We discuss these two issues and present evidence supporting the orthogonality of oil prices to these two decisions. In Figure 2 in Panel A, we present the yearly evolution of oil prices and the percent of illegal migrants derived from the ELMPS.<sup>21</sup> If oil price shocks directly affect the probability of illegal migration, this would suggest that when oil prices are high, Egyptian migrants might be more frequently willing to emigrate irregularly since it would be more profitable. Panel A in Figure 2 shows that the two series—oil prices and the share of illegal migrants—do not follow the same trend. Indeed, this figure shows several episodes of spikes in oil prices, during which irregular migration remained low. Conversely, irregular migration was particularly high in the late 1960s and early 1970s—a period with relatively low oil prices. We further corroborate this evidence in Panel B, where we present a scatter plot on the relationship between oil prices and the share of illegal migrants from the 1960s to 2010. Panel B in Figure 2 additionally confirms the orthogonality of oil prices with respect to irregular migration flows. Indeed, there does not seem to be any correlation between oil prices and illegality. We find a correlation coefficient of
Timilsina et al. 2019). In the existing CGE models, the AEEI are selected arbitrarily to adjust the baseline to satisfy the modeler’s judgement or policy makers’ expectation.

# **3. Data and Parameters**

In this paper, the benchmark year for the CGE model is 2012, and 31 SAMs of 31 provinces that are suitable to the structure of the multi-regional CGE model have been constructed as the database. Firstly, we constructed a 2012 multi-regional input-output (MRIO) table covering 31 provinces of China based on the published 2012 input-output tables of the 31 provinces (NBS,2016). Secondly, 31 macro SAMs of the 31 provinces were compiled to provide a consistent macroeconomic data framework. Thirdly, based on the 31 macro SAMs, 31 disaggregated SAMs are developed for the multi-regional CGE model. The provincial SAMs are available at World Bank’s Development data hive<sup>4</sup> .

In addition to the SAM, there are two types of parameters that should be estimated before the CGE model can be used for policy analysis: one is a share parameter, such as consumer expenditure share, and the other is an elasticity parameter, such as the elasticity of substitution in the production function. The share parameters can be estimated from the SAM using a calibration method that assumes the benchmark year represented by the SAM is an equilibrium solution for the CGE model, but the elasticity parameters need to be obtained based on the relevant literatures (Bao, Q., et al, 2013; Zhai Fan and Hertel, T.W, 2006; Zhang, Z., 1998). The CO2 emission factors from fossil fuels are calculated based on IEA statistical data (2014).

The BAU scenario is constructed using assumptions on labor supply growth and projected real GDP growth, as shown in Table 2.

**Table 2** The Labor supply growth rate and GDP growth rate from 2012 to 2030

|Year|Labor supply growth rate|Average annual GDP growth rate|
|Electricity-wood-<br>kerosene|13.0|Electricity-wood|12.4|LPG-kerosene|7.9|
|4|||LPG-electricity|11.5|LPG-electricity|12.1|Electricity-<br>kerosene|7.3|
|5|||Electricity-wood|8.2|||Kerosene|4.6|
|6|||LPG-electricity-<br>kerosene|4.0|||Kerosene-wood-<br>coal|4.5|
|Rank|Sudan||Urban Kenya||Urban Nigeria||||
|1|LPG-electricity-<br>charcoal|17.3|Electricity-<br>charcoal-kerosene|17.2|LPG-electricity|25.2|||
|2|LPG-electricity|16.6|Electricity-<br>kerosene|12.2|Kerosene-electricity|22.0|||
|3|Wood-charcoal|10.9|LPG-electricity|12.2|Wood-electricity|9.6|||
|4|Wood|9.5|LPG-electricity-<br>kerosene|7.9|Kerosene-<br>electricity-wood|8.2|||
|5|LPG-electricity-<br>wood-charcoal|7.6|LPG-electricity-<br>charcoal|7.8|LPG-electricity-<br>kerosene|7.7|||
|6|Electricity-wood-<br>charcoal|5.9|Charcoal-kerosene|6.6|||||

_Source_ : World Bank staff calculations using household survey data. _Note_ : Ranking is in order of decreasing number of households falling under any given combination. Only the top choices covering about two-thirds of all households, subject to inclusion of at least two combination choices containing LPG, are shown. The households in Nigeria add up to 73 percent to show two categories of LPG users. HHs = households.

A more comprehensive list of combinations of energy sources used by households, taking
changes that are correlated with project characteristics. With rich enough **X** st, and assuming that systematic differences across the two states remain fixed from 2007 to 2009, ordinary least squares estimation of [1] will deliver causal estimates of the reform’s impact on works procurement outcomes.

# _4.2 Data description_

As mentioned throughout the paper, we utilize works procurement data from two water and sewage utilities operating in Brazil: SABESP, a company that serves most of the São Paulo state market, and COPASA, the largest water utility in the state of Minas Gerais.

SABESP and COPASA are similar water and sewage operators in many respects. Both companies are publicly traded and majority state-owned (and therefore subject to public procurement laws). Each provides water to at least 10 million people and had 2009 revenues in excess of $1 billion.<sup>10</sup> Importantly from a works procurement perspective, both companies have announced sizable capital expenditures for the next several years. For 2009-2010 alone, the combined capital expenditures of both companies will stand at about $3 billion ($2 billion by SABESP, $1 billion by COPASA). They also operate in similar business and socioeconomic environments within their states, as São Paulo and Minas Gerais (a) have consistently ranked among the 10 Brazilian states with the highest per capita income over the past several years, and (b) register a Human Development Index score, as measured by the UN, that is markedly higher than the national average.

The data gathered comprise the full universe of procurement processes for works carried out by each company in 2007 and 2009 under both the _tomada de preços_ and _concorrência_ invitation mechanisms (i.e., the largest projects by size). For each bidding process, we observe data on the type of work being bid on (which makes possible segregating projects by their nature or segment), the duration (in days) of the bidding process (from the opening of the first envelope to the
: The effects of mineral deposits’ and mining sites’ presence on lights Gini index Dependent variable: district lights Gini index

||No|prices|Constant|mineral prices|Average|mineral prices|
|---|---|---|---|---|---|---|
||<br>Model 1|<br>Model 2|<br>Model 3|<br>Model 4|<br>Model 5|<br>Model 6|
|Equation [2]:|||||||
|<br>Deposits [1=present]|0.087<sup>***</sup>|0.081<sup>***</sup>|0.190<sup>***</sup>|0.180<sup>***</sup>|0.176<sup>***</sup>|0.166<sup>***</sup>|
||[0.000]|[0.004]|[0.048]|[0.048]|[0.043]|[0.043]|
|Equation [3]:<br>Mining sites|0.011<sup>***</sup>|0.010<sup>***</sup>|0.095<sup>***</sup>|0.090<sup>***</sup>|0.088<sup>***</sup>|0.083<sup>***</sup>|
||[0.000]|[0.001]|[0.024]|[0.024]|[0.021]|[0.022
<!-- Start of picture text --> Figure 5. Labor Force and Employment<br>Employment to Population Ratio Occupied Adults<br>(% of total population ages 15-64) (as a share of number of adults)<br>100 90<br>78.3 77.8<br>90 80 73.2 75.7<br>80<br>70<br>70<br>60 60<br>50 48.4 50.1<br>50<br>40<br>30 40<br>20 30<br>10<br>20<br>0<br>10<br>2000 2010 2004 2010 2000 2009<br>0<br>Bangladesh Peru Thailand Bangladesh, Peru, 2004- Thailand, 2000-<br>2000-2010 2010 2009<br>Men Women Initial period Final period<br>Source: Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br>Figure 6. Non-Labor Income Growth<br>Subsidies and Other Social Transfers International Remittances<br>(percent of GDP) (percent of GDP)<br>9 14<br>8<br>12<br>7<br>10<br>6<br>5 8<br>4<br>6<br>3<br>4<br>2<br>2<br>1<br>0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Bangladesh Peru Thailand Bangladesh Peru Thailand<br>Source: WDI, 2011.<br><!-- End of picture text --> 24
objectives suggest that MENA schemes interpret the additionality objective more liberally than schemes in other countries.

# _4.2 Eligibility Criteria_

_General guiding principles and international experience_

**Eligibility criteria should target financially constrained SMEs while providing for some**

**flexibility.** Targeting is important to ensure additionality, although overly restrictive eligibility criteria should be avoided because there is uncertainty in practice about the firms that are credit constrained and the type of financing that is lacking.  Very low ceilings, excessive restrictions on the types of loans or eligible sectors may exclude firms that are credit constrained and generate threshold effects (excluding many firms just above the threshold, even if credit constrained).  The relevance of eligibility criteria can be strengthened through market surveys that identify SME financing gaps.

**Most guarantee schemes in the benchmark group target SMEs in a broad sense and generally do not restrict sectors or types of loans (Table 3)** .  All the countries in the benchmark group allow start-ups to apply for guarantees (though there is no uniform definition of start-ups across countries other than in the EU). It is also noticeable that these schemes do not impose restrictions on sectors (except for a general restriction on agriculture in the case of Canada), or type of loan (again, except for Canada, which does not guarantee working capital loans).

**The main differences seem to lie in the limits imposed on firm and loan size** .  Korea does not impose any limits on firm size, while France and the Netherlands target SMEs following the EU’s definition (maximum turnover of 50 million euros and 250 employees).  The other countries impose much lower limits on firm size, especially regarding turnover.  However, the limits imposed on loan size are probably the binding ones, and here the ranking changes significantly, especially when the limit is defined in relation to per capita income.  As shown in Table 3, the Asian schemes look more generous in this case, while the Canadian, Dutch and US schemes look
(27.3%). Romania also stands out when undeclared work is measured in terms of total labor input, with one of the highest shares of the EU (European Commission, 2017). The standard dual economy models of labor market segmentation suggest increasing the minimum wage can drive the least productive workers from the formal sector into the informal one. However, other effects may be observed. Gindling (2018) contends that certain evidence suggests wage hikes in the informal sector due to "lighthouse effects," where employers compete for workers with the formal sector, resulting in higher wages and reduced employment in the informal sector. In contrast, other studies, such as Papps (2012) and Carneiro and Corseuil (2001), have found no discernible impact on wages in the informal sector.

**The empirical literature examining the impact of the minimum wage on employment is extensive, yielding a range of mixed findings; research focused on the least-skilled workers reveals more**

> 6 <mark>Due to constraints binding workers to particular firms, hiring additional workers increases the cost of existing ones. Consequently, market-driven employment may drop below the economically efficient competitive level. Furthermore, in the monopsony model, a minimum wage can occasionally result in increased employment.</mark>

> 7 For example, the EU-LFS allow distinguishing between permanent and temporary contracts. However, it does not allow identification of workers without a contract.

> 8 The World Bank has constructed an informal database of informality covering up to 196 economies over the period 1990-2020, but Romania was not included. https://www.worldbank.org/en/research/brief/informal-economy-database 9 The ILO publishes a database with global statistics on informal employment, but Romania estimates are not available. <u>https://ilostat.ilo.org/topics/informality/#,.</u>

7
FDI projects from HICs to LMICs, by source country, 2004-2015 (natural logarithm scale)**

<!-- Start of picture text --> 14.00<br>United States<br>Germany<br>12.00 France<br>Italy<br>Spain<br>10.00 UK<br>South Korea<br>Switzerland<br>8.00 Sweden<br>Finland<br>Canada<br>6.00 Austria<br>Denmark<br>Belgium<br>4.00<br>Australia<br>Norway<br>Portugal<br>2.00<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Source: Calculations based on fDi Markets Database 6
# **Figure 16. LAC: Correlates of investment growth slowdown**

_The investment slowdown has coincided with severe terms-of-trade deteriorations, sharp output growth slowdowns, slowing FDI inflows, political tensions, and domestic policy tightening. Over the medium term, investment growth is expected to remain low._

# **A. Regional output growth**

# **B. Long-term investment growth forecasts**

<!-- Start of picture text --> Percent8 1990-2008 avg 2003-08 avg 11Percent LACBrazil ArgentinaChile<br>Colombia Mexico<br>6 10 Peru<br>9<br>4<br>8<br>2<br>7<br>0<br>6<br>-2<br>5<br>4<br>SA MCC LAC EMDE 2010 2011 2012 2013 2014 2015<br>C. Terms of trade changes D. Political stability<br>Percent ICRG Index (0-100, 100=best)<br>15 70<br>10<br>68<br>5<br>66<br>0<br>-5 64<br>Energy exporters<br>-10 Non-energy commodity exporters 62<br>-15 Commodity importers 60 Commodity exporters<br>-20 Commodity importers<br>2010 2011 2012 2013 2014 2015 58<br>2010 2011 2012 2013 2014 2015<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br><!-- End of picture text --> Sources: Haver Analytics, Consensus Economics, World Economic Forum (2016), World Bank. A. GDP-weighted averages. SA is South America. MCC is Mexico, Central America, and the Caribbean. B. Five-year ahead consensus forecasts for investment growth.

C. GDP-weighted average annual change in terms of trade. Negative value indicates deterioration. Energy exporters include Bolivia, Colombia and Ecuador. Non-energy commodity exporters include Argentina, Brazil, Chile, Costa Rica,
|
|Senegal|$0.06|
|Madagascar|$0.07|
|Gambia, The|$0.08|
|São Tomé and Príncipe|$0.09|
|Burundi|$0.09|
|Rwanda|$0.09|
|Sudan|$0.10|
|Ethiopia|$0.10<br>|
|Botswana|$0.13|
|Comoros|$0.16|
|Guinea|$0.16|
|-$0.05<br>$0|.00<br>$0.05<br>$0.10<br>$0.15<br>$0.20|

_Source_ : World Bank staff calculations based on utility annual reports and financial statements.

# 6.5 Breakdown of regional quasi-fiscal deficits into hidden costs

Figure 13 presents the total hidden costs across the 39 countries. Underpricing is the dominant hidden cost. For all countries except South Africa, underpricing represents a weighted average of 40 percent of the hidden costs. T&D losses represent 30 percent of hidden costs, while bill collection inefficiencies and overstaffing represent 20 and 10 percent respectively. South Africa is treated separately because of its 50