rafmacalaba/data-use-mentions-extended huggingface dataset:
Learning from Developing Country Power Market Experiences: The Case of Colombia <!-- Start of picture text --> 6,000.0<br> 5,000.0<br> 4,000.0<br> 3,000.0<br> 2,000.0<br> 1,000.0<br> -<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Unrelieved constraints (BCOP)<br>AGC commercial responsibility (BCOP)<br>Volume of energy transactions on pool market (BCOP)<br>BCOP<br><!-- End of picture text --> Figure 6-7 Evolution of total volume of transactions in the Colombian pool, considering energy, reserves (Automatic Generation Control) and transmission constraints.

Source: Own elaboration based on data collected by the World Bank.

As described previously (see section 3.2), the day-ahead pool in Colombia is a single-node market which disregards transmission congestions and any other system constraints. Although these costs are not reflected directly in spot prices ( _“Bolsa”_ prices), these constraint costs do pass-through to final retail tariffs (as described in section 3.2). Constraint costs are highly dependent on hydrology and particular operating conditions. Yearly unrelieved constraint costs in the day-ahead pool increased by 71% in real terms over the past decade, from 285 BCOP in 2005 (expressed in 2015 COP) to 487 BCOP in 2015. As a share of total pool trades, constraint costs have varied from 8% to its highest level of 54% in 2011. Considering the importance of these constraint costs, it has been argued that significant operating cost savings could be achieved by implementing a more efficient congestion management mechanism in Colombia, based on a market clearing more reflective of the physical realities of the transmission grid, and based on Locational Marginal Prices instead of a single-node price (Mcrae & Wolak, 2016). It
from the HIES and satellite-imagery-based model, compared to the existing “top-down” products?

The “top-down” products considered include, WorldPop for the years 2010 and 2015, the Global Human Settlement Layer (GHSL) for 2014, High Resolution Settlement Layer (HRSL) created by Facebook for 2015, the Center for International Earth Science Information Network’s (CIESIN) Gridded Population of the World (GPW) for 2010 and 2015, and LandScan for 2010. We use simple statistical measures of association to confirm that top-down estimates are poorly correlated with each other and with the census at the village level. WorldPop 2015 and Facebook are the exceptions, because they use high-resolution satellite imagery and are calibrated to the latest census data at geographically fine levels.<sup>2</sup> However, since even the most accurate population products use the census to redistribute population, they may quickly become outdated as the census ages, necessitating “bottom-up” methods to track changes more frequently.

> 2 At Divisional Secretariat level (one level above the village), however, all estimates are highly correlated with each other and with the census, implying that accuracy at the coarser levels is easier to achieve.

2
Miracles in Calvo et al. (2006a). We then consider each of the 9 episodes in detail and re-visit the GDP and macro credit numbers in each country to examine if output began to recovery without a recovery in credit within a two-year period after the deepest point of the crisis (the trough). We categorize countries as _Phoenix Miracles at the macro level_ if GDP began to recover without a simultaneous recovery in credit and we classify countries as Non-Miracles at the macro level if both GDP and credit began to recover simultaneously.

We categorize countries as _Phoenix Miracles at the micro level_ if there was recovery in sales without a recovery in short-term borrowing (both sales and short-term borrowing aggregated across all firms in our sample in the country) within a two-year period after the deepest point of the crisis. We classify countries as _Non-miracles at the micro level_ if either aggregate sales never show a recovery or both sales and short-term borrowing recover simultaneously within a two year period after the deepest point of the crisis. For our firm-level analysis, we apply the same definition to classify each individual firm as a _Phoenix Miracle at the firm level_ if it experiences a recovery in sales without a recovery in credit. At the firm level we define credit both strictly as just short-term borrowing as well as a broader definition that includes short-term borrowing, long-term borrowing, and equity issuances.

# **_2.3 Sample Construction_**

For our sample of emerging markets, we collect firm-level accounting information from Bloomberg for over 2500 publicly traded firms in the above 3S episodes over a five-year window, two years preceding the trough of the output collapse to two years after the trough. In identifying if the countries are Phoenix miracles at the micro level, we restrict the sample to firms with non-missing sales and short term borrowing over the five years. In detailed firm-level analysis, where we identify individual firms as Phoenix Miracles, we further restrict the sample to 1326 firms across 5 countries after dropping firms with key non-missing balance sheet and cash flow statement information for each of
0.19|
|2018|0.999|0.09|45%|0.04|
|2019|1.001|-0.07|43%|-0.03|
|2020|1.001|-0.11|40%|-0.05|
|2021|1.000|-0.03|45%|-0.02|
|**Average per-annum**|**0.986**|**1.95**|**46%**|**0.61**|
|**Total (1995-2021)**||**52.66**||**16.47**|

_Note:_ Authors’ calculation based on six-digit disaggregated data. See text for detailed explanation.

Table 6: _Import Price Bias and the Gains from Variety in Rwanda_ 22
0.05|
|Paraguay|0.11|0.18|0.15|0.18|0.23|0.26|0.09|0.21|0.19|
|El Salvador|0.14|0.20|0.15|0.23|0.28|0.23|0.15|0.28|0.12|
|Uruguay|0.26|0.26|0.21|0.32|0.42|0.29|0.32|0.41|0.32|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : Each data point is determined considering circa concepts (see Table A.1). The log of real hourly wages in the main occupation (labor income in 2005 purchasing power parity) was regressed on dummies of level of education (five levels), five-year intervals of experience, and all possible interactions. The sample was restricted to individuals between ages 18 and 65 years who were employees or self-employed and between the 1st and 99th percentiles of the wage distribution.

55
business profits over the control group or a 0.63 standard deviation improvement. In contrast, the highly negative and statistically significant coefficient on the interaction variable shows that the effect on businesses with high exposure is significantly lower, and overall statistically insignificant (p-value of sum of interaction and main variable is 0.29). Note that firms with narrow exposure tend to benefit significantly more from the finance training as well, but the magnitudes are statistically much smaller when compared to the marketing training (p-value of difference is 0.09).

Hence, consistent with a growth focus explanation, we find that small business owners who have narrow exposure tend to do significantly better when they receive the marketing and sales training program. Participating in a training program to build marketing skills helps these firms overcome a lack of exposure by encouraging them to look beyond their existing business context and develop new perspectives on products, customers, distributors and suppliers which, in turn, assists with expanding sales and improving top line performance.

# **5.5. External Validity, Spillovers, and General Equilibrium Effects**

The results presented and discussed above are consistent with the hypotheses developed in this paper. In this subsection, we address three potential concerns related to the field research.

First is the issue of external validity or generalizability of the results to other settings. Given the sample selection methodology explained in Section 3.1, businesses in our starting sample were selected based on their interest in business training and willingness to complete a screening survey; other characteristics such as whether they operated out of a physical structure, education level of the owner, and business registration status also factored in the screening process. Hence, it is instructive to understand how our sample differed from a typical business in South Africa. In Appendix Table 7, we compare sample characteristics between our study and the 2010 FinScope Small Business Survey, which utilizes a nationally representative sample of 5,676 small business owners in South Africa.

The comparisons in Panel A show that while race
capita consumption, (4) Gini coefficients, (5) two inequality measures of the generalized entropy family, and (6) the variance of log consumption per person.

In general, the simulation framework offers the user the opportunity to control the simulation process by setting the following parameters: (1) the forecast horizon, (2) the poverty line, (3) the base and survey years, (4) the survey year population, (5) the country’s purchasing power parity exchange rate, (6) the base and survey year consumer price indexes (CPIs), (7) the sectoral output growth rates for each projection year, (8) the population growth rates and employment growth rates for each projection year, (9) the survey year sectoral GDP and employment shares, (10) the GDP deflator and the CPI for 14
among teenagers aged 15-19 (23.3 percent). One explanation for why minimum wage disproportionally affects youth is because the increase in minimum wage for those younger than 35 is significantly larger than for older workers. About 20.7 percent of workers without secondary levels of education are potentially affected by the policy, compared to 10.0 percent of those with secondary education and 2.6 percent with post-secondary education. Women are more likely to be affected than men (9.4 percent vs. 7.8 percent). We also analyze whether breadwinners are more likely to be affected, where breadwinner is defined as the sole worker in a household. Our results suggest that unique breadwinners are less likely to be affected by the policy than non-unique breadwinners (6.5 percent vs. 9.5 percent). In 2018, only 0.82 percent of workers reported earnings lower than the minimum wage (almost perfect compliance).

**As depicted in Figure 9, the share of the affected group is higher among private-sector workers, workers holding informal-sector jobs, and workers holding permanent contracts** . Given the low share of potentially affected workers in the public sector, the impact on the wage bill in the public sector is likely small.<sup>22</sup> Figures 10 shows that the minimum wage is more likely to affect agriculture and commerce sectors, and in particular agricultural and service and market sales occupations, which are industries with a higher proportion of low-wage workers.

> 20 See a review of simple theoretical models in Neumark (2014).

> 21 For estimation we use wages imputed from LFS as described in Section 2. Because wages were reported in brackets, this estimate might vary from 3.5 percent to 11.7 percent, depending on the imputation technique.

> 22 Additional impacts on consumer prices may also occur as a result of rising minimum wages, but these effects go beyond the scope of this paper.

16
MONGOLIA'S INFORMAL SECTOR Appendix B: Methodologies and **Descriptive** Statistics

**41**

# Kiosk Sample and Population

117. A survey of 245 kiosks<sup>9 3</sup> was conducted in March, 1997. As with the survey of taxicabs, the purposes of conducting the survey of kiosks included the desire to assess the impact of the growing number of kiosks on the economy of Ulaanbaatar in terms of aggregate contribution to value added, employment and income. The analysis of kiosks is easier than that of taxicabs in one important respect. The population of kiosks is easily obtained, and the sample selection problem encountered with taxicabs is not a problem with kiosks.

118. The sample of kiosks was selected to conform roughly in proportion to the number of kiosks located in the six districts of Ulaanbaatar.9 Roughly 11% of the kiosks in Ulaanbaatar were surveyed. The number of kiosks in each district in both the population and the sample are depicted in Table B2, below.

Table B2. Kiosk Sampling

|**District**|**Kiosks in District**|**Kiosks Sampled**|
|---|---|---|
|Bayangol|283<br>12.7%|32<br>13.1%|
|Bayanzurh|551|70|
||24.7%|28.6%|
|Chingeltei|185<br>8.3%|20<br>8.2%|
|Han-Uul|158|21|
||7.1%|8.6%|
|Songino-Hairhan|802|102|
||36.0%|41.6%|
|Suhbaatar|250|0 (see notes)|
||11.2%|0%|

_Source:_ Ulaanbaatar City Administration, and the survey of kiosks conducted by the author.

## **Value Added from Kiosks in Ulaanbaatar**

119.
settlement layer illustrating the spatial distribution of 8 pixel classifications: water, very low density (rural), low density, rural cluster, suburban or per-urban, semi-dense urban cluster, dense urban cluster, and urban cluster.

# Road network model

We extract road networks from the OSM data set using convex hulls created from settlement cluster boundaries as spatial masks. OSM provides a free, openly licensed, volunteer-contributed repository of geographic information with a focus on streets and roads. As of August 2021, approximately 7.9 million contributors had created this database with more than 796 million roads, coastlines, administrative boundaries, and other linear features known as “ways”<sup>70</sup> . Barrington-Leigh et al.<sup>71</sup> assessed the completeness of OSM road network data in countries around the world as of 2017. They found that globally, OSM is 83% complete, and more than 40% of countries–including several in the developing world–have a fully mapped street network<sup>4</sup> . However, there still exists a significant discrepancy in the completeness of road networks—countries with good internet access tend to be more complete. The completeness of the OSM road network has a U-shaped relationship with population density—smaller towns and villages with medium population density are likely to have missing roads<sup>71</sup> .

In addition, past studies<sup>71–73</sup> revealed relatively lower levels of completeness in road networks that are lower in the road hierarchy such as tertiary and residential roads. Such discrepancy will introduce selection bias which could potentially invalidate the subsequent travel simulations in this study. To address this issue, we first extract all drivable roads<sup>5</sup> from the OSM data set for all clusters at the global scale and then evaluate the relative completeness of tertiary and residential roads based on the extracted road network. For each settlement cluster, we compare road network density (total
Price percentile<br>‐100%<br>‐100%<br>Fertilizer Fertilizer<br><!-- End of picture text --> Source: 1: see Figure 3.5, 2‐4: staff calculations using yield responsiveness and cost of inputs estimated in the OFRA Response Function Database (2017) from the University of Nebraska. Wholesale prices from UBOS, 2000 ‐ 2012.

15
The general conclusion from the interest spread regressions is that reductions in overheads led to lower spreads for all banks during this period, but the results were especially pronounced for the foreign-owned banks. Other factors such as the reduction in the T-bill rate and lower inflation also contributed to lower spreads during this period, but those reductions were only significant for the foreign banks. The regressions therefore indicate that the lion’s share of the sectoral reduction in interest spreads that we began this analysis with (Figure 1) is attributable to the foreign banks. Moreover, the lack of significant results for the other ownership types suggests either that they are competing in a separate market niche (as seems likely for many of the private domestic banks) or that they are pursuing some of the same clients as foreign banks but pricing loans in a less rationale way (as seems likely for the government-influenced banks).

# **5. Access to Financial Services**

FinAccess 2006 and FinAccess 2009, two household surveys, conducted by the Financial Sector Development Trust Kenya jointly with the Central Bank of Kenya, confirm three previouslyassumed conclusions about access to financial services: (a) a large proportion of the Kenyan population has no access to financial services, whether formal or informal; (b) there is a general tendency for access to services from formal and semi-formal providers (banks, SACCOs, and MFIs) to decline as one goes from urban to rural, from high-income to low-income, and from better-educated to not educated; and, (c) although the percentage of the population that is served is similar in urban and rural districts, the mix of those services is different.<sup>16</sup> In urban areas, respondents rely more heavily on services from banks and semi-formal sources (SACCOs and MFIs) while in rural districts, there is greater reliance on services provided via informal groups.

Table 11 shows that the use of formal financial services in Kenya is at similar levels as in other East African countries, but below that
# – Putting a Price on Safety a Hedonic Price Approach to Flood Risk in African Cities<sup>*</sup>

Alvina Erman and Ingrid Dallmann<sup>†</sup> JEL Classification: O18, O55, O57, Q54, R20, R31 Keywords: natural disasters, disaster risk management, hedonic regression, poverty, resilience, urban housing, sustainable cities, urbanization, urban floods, access to jobs, Africa, Ethiopia, Ghana, Madagascar, Tanzania

> *We thank Samira Barzin and Paolo Avner for data and codes on job density, and Nathalie Picarelli for data on job accessibility in Ghana. We are very grateful to Kirsten Hommann, Catherine Lynch, Shohei Nakamura, and Steven Rubinyi, for detailed and invaluable feedback and suggestions. We also thank to participants in the GFDRR seminar analytic team.

> †GFDRR, World Bank, � aerman@worldbank.org
|
|Share exposed to noise below 50kmh|4.84|2.00|5.00|1.00|13.00|
|Share with obesity|14.46|2.17|14.00|9.00|22.00|
|Share of non-heavy drinkers|39.94|4.87|40.00|30.00|58.00|
|Share of smokers|19.64|2.72|19.00|14.00|31.00|
|Mean PM2_._5|10.22|1.33|10.69|6.95|12.04|
|Mean PM10|17.28|1.61|17.74|13.60|21.09|
|Mean Van Donkelaar PM2_._5|14.60|1.77|15.01|10.15|18.05|

# **6.2 Additional analysis results**

This section presents a number of additional estimations. In particular, section 6.2.1 presents Poisson-type regressions that use case counts as dependent variables in line with earlier studies on the relationship between air pollution and viral spread, the influence of possible outlier observations or the discussed distributional issues is investigated in section 6.2.2, in section 6.2.3 the main analysis is re-estimated using confirmed cases from March 30 to show that the conclusions are not dependent on the date of the case snapshot, the main analysis is also repeated using confirmed hospital admissions from March 31 in section 6.2.4 to provide further evidence that the conclusions are not dependent on measurement error in the confirmed cases. Section 6.2.5 investigates correlations between covariates and the case hospitalization rates used to proxy for symptom severity. Finally, additional analysis in section 6.2.6 re-estimates the main analysis using alternative satellite-derived PM22 _._ 5.

15
**Table A.8:** Public Goods and Nepotism: Correlations (OLS)

||**Public Goods Index**|**Citizen**|**Perceptions**|
|---|---|---|---|
||_Podes_2021|Access|Quality|
||(1)|(2)|(3)|
|Any nepotistic appointee|-0.226<sup>_∗∗∗_</sup>|-0.051<sup>_∗_</sup>|-0.017|
||(0.086)|(0.029)|(0.011)|
|Sample mean|0.36|0.010|0.80|
|Observations|376|8819|8821|

_Notes_ : This table reports OLS estimates of each outcome on a dummy variable for whether a village has at least one bureaucrat who reports having a parent who served in the village government. The dependent variable is: in column 1, a standardized index of local public service provision constructed using the 2021 Podes data; in column 2, a standardized index of access to local services constructed using our citizen survey data; and in column 3, a standardized index of service quality constructed using our citizen survey data. See Section 4 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust standard errors in parentheses for column 1 and robust standard errors clustered by village in parentheses for columns 2-3.

48
in the hostcountries that make up our sample can also be observed in Table 2. U.S. banks have 26 subsidiaries and 24 branches, followed by German banks with 42 subsidiaries and 6 branches.

> 11 Peru's low share data is more subtle and it is due to the importance of Banco de Crédito del Peru and Interbank's shareholders which are companies from Bermudas and Bahamas respectively.

7
fixed effects. The northeast panel displays results from model specifications (2b) and (3b), which include household fixed effects, but not covariates. The southwest panel displays results from model specifications (2c) and (3c), which include fixed effects and covariates. At the top of each “column” in the specification chart is the mean adjusted R<sup>2</sup> and the 95% confidence interval on the mean for the set of 1,296 regressions run. Below, the chart indicates the extraction method and model specification associated with the statistics.

Considering first the northwest panel: mean adjusted R<sup>2</sup> values are not substantially different across extraction method within model specifications (2a). The mean adjusted R<sup>2</sup> value for any one extraction method passes neither our weak nor strong difference test when compared to any of the other extraction methods. Similarly, when comparing across extraction method within model specification (3a), no mean adjusted R<sup>2</sup> is weakly or strongly different from any other. Though not a formal statistical test, our heuristic fails to reject the null for these two model specifications.

We conduct the same exercise for results from the extraction methods and model specifications presented in the northeast and southwest panels. As with the first panel, the mean adjusted R<sup>2</sup> value for any one extraction method is neither weakly nor strongly different than any other method. Our heuristic fails to reject the null for any model specification. Based on this, we conclude that remote sensing weather data from any one extraction method does not explain a substantially larger amount of the variance in our outcome variables relative to any other extraction method.

Despite the failure to reject the null based on either the strong or weak criteria, the pattern of which extraction methods result in the largest adjusted R<sup>2</sup> values is remarkably consistent. Bilinear extraction
to dream big. On the other hand, if students’ expectations of their future are more salient, expected constraints may curb motivation, and interventions could focus on removing actual constraints and on fostering the perception that they are surmountable.

We examine this issue for a large sample of students in junior secondary school in Indonesia. We use a cross-sectional survey of approximately 56,000 eighth-grade students (age 14 on average) from 2,095 public schools on the Indonesian islands of Java and Sumatera. The data allow to distinguish aspirations, measured from self-reported preferences of students’ future completed educational level in a hypothetical scenario in which they had no constraints, and expectations, which are their selfassessed most likely outcomes given their current situation. Having lower expectations than aspirations would mean that the student perceives binding constraints to achieve his or her educational aspirations.

2