rafmacalaba/data-use-mentions-extended huggingface dataset:
9 Three unportant caveats should be noted at the outset. First, some key variables may be difficult to analyze in a houses o!d survey. For instance, price elasticities are difficult to measure because, in many cases, there is very small variation at the time of the survey. Second, household data may be inappropriate for analyzing macro-issues. Household surveys are for understanding patterrs at the micro-level, especially the relationships between different types of household characteristics. Just because one finds, for example, a relationship between expenditure and energy use at the micro-level does not mean that the same relationship will hold at the village or national level. Macro-structural changes in energy use may be better explained by migration patterns, government policy or changes in the industrial structure of the country, rather than the characteristics of a household. Third, the level of detail which a household energy survey should achieve depends on the informational requirements and implementational capabilities of policymakers and energy planners. Thus, in designing a survey, one should not lose sight of the impact which a marginal increase in valuable information may have on feasible policy decisions. Given these overal! onsiderations, one can proceed to the mechanics of actual household energy surveying.

# Sample Design

Sample design is an important procedure which usually makes use of formal probability theory. While it may be necessary to deviate from this process for practical reasons, this can result in scicction of an unrepresentative sample which may render subsequent results unreliable when extrapolated to the population as a whole. Some of the factors which may limit the application of statistical techniques include: (a) lack of a master sample or data on the overall population from which the sample is to be drawn; (b) physical inaccessibility of population sub-greups; (c) language barriers; and (d) insufficient financial or human resources. Whether or not statistically valid samples are drawn, they should be designed so that they reflect the survey's objectives, especially household characteris¶ics which are thought to be relevant to energy consumption.
# **Figure 4 Subsidies as a share of infrastructure SOEs’ operating income, by sector**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>Power Airlines and airports Roads Railways Average<br>Subsidies as percent of operating income<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOEs Database. _Note_ : Data are for 2009–18.

The roads sector is the most heavily subsidized, but there are large differences in magnitude within the sector, with subsidies averaging less than 35 percent of income for partially privatized road SOEs and more than 50 percent of income for fully owned road SOEs. The subsidy differential is even larger in the airlines and airports sector (1 percent versus 13 percent) and in the power sector (1.6 versus 4.8 percent). Fully owned and partially privatized SOEs in the power sector are similar in terms of both profitability and the magnitude of the subsidies received.

To reveal how sustainable the operations of infrastructure SOEs are, figure 5 shows operating subsidies as a share of operating expenses. It focuses on operating expenses because unlike investments, they cannot be postponed or cancelled in bad times. It is particularly hard to cut employment costs. Operating subsidies pay for a large part of total operating expenses in the roads (44 percent) and railways (25 percent) sectors; they cover smaller shares in the power (closer to 5 percent) and airports and airlines (10 percent) sectors. The average across sectors is almost 11 percent. The bulk of the operating subsidies disclosed in SOE financials are in uppermiddle-income and high-income countries.

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other assessments. As such, the DVA can be seen as a screening and filtering system to identify countries that are potentially at risk.

# **6 Conclusion**

This paper describes a methodology for analyzing public debt vulnerability. Four distinct elements make up this framework: (1) A Fundamental Index identifies vulnerable countries by mapping default risk to changes in fundamentals; (2) a Default Probability Index measures the degree of weakened perceptions regarding risk; (3) a Count Index maps expert opinion to debt vulnerability; and (4) a Theory Index estimates debt thresholds that vary by the probability of a panic for each market access country in the study.

The Fundamental Index consists of a large set of variables related to debt vulnerabilities and summarizes risks by looking at available low-frequency data. The Fundamental Index shows rising/abating vulnerabilities by country.

Two default probabilities are calculated to create the Default Probability Index. One uses required primary balances and actual primary balances to produce a debt sustainability default probability related to a government’s ability to finance its debts. The other one uses interest rate spreads to produce measures of defaulting expectations. The two default probabilities track each other for most of the sample, but diverge just before default. The difference between the two probability estimates is a proxy for a probability of a panic - if markets perceive a higher risk than what the fundamentals suggest, then it can lead to an increase in roll-over risk.

Thresholds from the IMF-WBG DSAs are used in the construction of the Count Index. The index measures the proportion of variables of a country that exceed a threshold. Countries that have many variables exceeding their thresholds are more likely to default.

The Theory Index of the framework consists of identifying debt thresholds that are indicative of risky, safe, and default regions. These debt-to-GDP thresholds are time varying as opposed to static thresholds typical of DSAs. They measure an angle of default related to what it ought to be. The theoretical model controls for debt maturity, spreads, and a panic probability
structed using two sets of data: monthly changes in sugar prices reported in IPCA and the 1996 average sugar prices from household budget surveys (Pesquisa de Or camentos Familiares), also reported by IBGE. Since sugar prices could only be constructed for these 11 states, PNAD observations from other states were dropped, resulting in approximately a 25 percent reduction in the number of observations.

There are two types of sugar most commonly consumed in Brazil: refined sugar and crystal (direct mill white sugar). Crystal is usually cheaper and inferior in quality to refined sugar. For 5 states for which only the prices of crystal sugar are available from the budget surveys (Distrito Federal, Goi ́as, Pernambuco, Bahia and Minas Gerais) we converted crystal prices into refined prices by using the average ratio of refined to crystal prices in Brazil. Note that the ratio varies over years.

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earnings. Overall, living further from a road in good condition is negatively associated with seasonal employment, 1 log increase in distance from a good road decreases the probability of working seasonally overall by 5.5 percentage points. These findings are in line with the results of the analysis done on the ILCS data, presented in Table 2. When marginal effects are calculated separately for women and men, the coefficient of the average marginal effect of women having seasonal jobs becomes positive but statistically not significant. However, the marginal effect of the probability of seasonal employment on men is statistically highly significant - one unit increase in log distance decreases the probability of having a seasonal job by 10.7 percentage points. Unfortunately, limitations of the employment data in DHS surveys do not allow me to study deeply the seasonality outcomes. The descriptive statistics show 23
account for the fact that households also need to pay for food and other essentials besides electricity throughout the year.

After excluding observations based on the income criteria and after matching household survey data to the administrative data, the final sample contained 7,615 billing-cycle level observations comprising of 805 unique customers with an average of 13.4 billing cycles per customer in the data set. Ninety-six percent of these observations are from consecutive cycles, implying that a majority of households do not sort in and out of the panel. Tables 3 summarizes socioeconomic characteristics of the sampled households and shows the share of households by various employment categories.

The survey also collected information on ownership and intensity of use of electrical appliances, to estimate the demand for residential energy services. Detailed usage information in the survey combined with official wattage statistics of common household appliances, are used to calculate the demand for household energy services.<sup>15</sup> The demand for energy at the appliance level is computed by summing the total hours for which an appliance is used over all days in the billing cycle multiplied by its standard wattage information and divided by the number days in the billing cycle. Each of the 24

> 14 We selected the consumption distribution from January and February 2017 to produce this graph as these are the last two months in the administrative data that is closest to the period that we began survey data collection (April 2017). This allows us to best compare the consumption distribution across the two data sets.

> 15 We obtain wattage information of common household appliances from Bureau of Energy Efficiency standards for 2012-13 and online load calculators provided by Tamil Nadu Generation and Distribution Corporation <u>(https://www.tangedco.gov.in/load_calculato.html )</u> and Paschim Gujarat Vij Company Limited

> <u>(http://www.pgvcl.com/consumer/CONSUMER/calculate_n.php</u> )

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with secondary and those with post-secondary education. We classify occupations using the International Labor Organization codes from the household surveys (ILO 2012). While there is variation, across our study countries we see that among wage workers with secondary education, only one occupation – managers – earns more than teachers (Table 5A). Among wage workers with post-secondary education, there are just four occupations that earn more than teachers: managers, professionals, technicians, and skilled agricultural workers (Table 5B).<sup>30</sup>

> 28 While one might be tempted to conclude from this analysis that teachers in lower-income countries are relatively better off than those in higher-income countries, the poor living and working conditions in many low- and lower middle-income countries in our sample (not captured in our earnings data) may mean that even being paid several times the GDP per capita does not mean teachers enjoy the same quality of life as teachers in upper-middle- or highincome countries.

> 29 In their second jobs, teachers are mostly likely to work as small farmers, tutors, or vendors (Appendix Table A7, A8). See Appendix Table A9 for earnings of different teacher groups: teacher in public schools; primary school teachers; and secondary school teachers.

> 30 We also do this comparison by sector (Tables A13 and A14), using UN sector classification codes (UN 2007). With secondary education, teachers earn more than workers in any sector except mining. With post-secondary

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# _Data Challenges_

The last, but far from least, thorny issue with monitoring the SDGs is missing data. Missing data can simply indicate that data are not collected for some indicators. For example, the infant mortality rate is so low for some Nordic countries that these countries do not typically collect data on it. But oftentimes missing data points to either low performance, particularly for low-income countries, or inadequate statistical capacity.

There are two ways to address this data challenge: collect better and more frequent data, or employ recent developed statistical methods that can help impute the missing data. The first approach is also the more popular long-term data collection approach, and it should be implemented under the ideal circumstances. Yet, in practice, collecting data on all the 232 indicators requires both coordination of different government agencies and careful budget planning. This is not to mention the technical capacity required to ensure that the collected data have good quality. These practical issues have resulted in many indicators that are either missing or are available at infrequent intervals.

As an example, estimating the poverty rate—which is the first indicator under the first goal (Indicator 1.1.1)—may not be that simple to achieve, particularly among poorer countries. A recent study by Serajuddin et al. (2015) find that, over the period 2002-11, more than one-third (57) of the 155 countries for which the World Bank monitors poverty data have only one poverty data point or no data at all in the WDI database. Even where countries collect data on poverty, these may not be comparable over time due to bad quality. Indeed, Beegle et al. (2016) point out that just over half (27) of the 48 countries in Sub-Saharan Africa had two or more comparable household surveys for the period 1990-2012. These examples further illustrate the missing data challenge we discussed earlier with Table 4.

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this cross-sectional study does not aim to estimate the causal effect of slum declaration on tenure security or housing outcomes. Estimating the causal effect of slum declaration as the value reflected in housing rent would result in an underestimation if I statistically control the current housing conditions and the accessibility to basic services. This is because these variables have already been affected by slum declaration. Nevertheless, this study offers enlightening insight as to how slum dwellers evaluate the current benefit of slum declaration for their tenure security.

# **4 Methodology**

## **Data**

This paper relies on household surveys collected in the city of Pune in 2013 by the author. Based on a two-stage random sampling scheme, 56 slum settlements are randomly chosen out of the 477 slums listed in MASHAL (2011). Black dots in Figure 1 indicate the surveyed slums. From each of the selected slums, 10 households are randomly selected as respondents for the survey, amounting to 562 total respondents.<sup>6</sup> The survey includes questions about a variety of household and housing characteristics. Surveyors visit respondents, read aloud the questionnaire in either Hindi or Marathi, a local language spoken in Maharashtra, and write down answers on behalf of them. The locations of surveyed households are recoded in longitude and latitude by referencing the geographic information system (GIS) maps in MASHAL (2011) and Google Earth satellite images. In addition, I retrieve slum-level information from MASHAL (2011) and combine this with the survey data.

Table 1 reports summary statistics for housing, household, and slum and locational characteristics.<sup>7</sup> While 72 respondents currently pay monthly rent for their housing, the other households pay no rent. The survey asks the latter group of households to figure out the amount of money other people would pay for the monthly rent. Recovering imputed rent in such a manner is a common practice in hedonic literature (Malpezzi, 2003). Among the total of 562 respondents, 84 households do not
. This suggests that these firms are less able to hold on to their workers which could be costly if the negative shock is (expected to) be temporary. In the next section, we introduce a simple theoretical framework to show that the actual ability of a firm to smooth (or hoard) inputs is adversely affected by the presence of liquidity or credit constraints, and that this creates a welfare loss due to incomplete risk coping. Using this theoretical framework, we return to the survey data and analyze empirically whether credit constraints have indeed affected the ability of firms to smooth their labor (and other) inputs after the onset of the global financial crisis in Cambodia.

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because of differences in how male- versus female-owned assets are drawn down to cope with these events; women’s landholdings are smaller or less productive, for example, and so more likely to be sold off in the event of a negative shock.

To better understand the effects of survey approach on specific sub-groups, Table 5 presents the results from the estimation of a modified equation 1, augmented with the interactions between the business-as-usual/IHS4 identifier and a subset of controls that are closely related to household structure and land ownership, including individual headship, age (whether the individual is younger than 25, compared to all other adults), marital status, education, rural residence and variables on household composition – typically linked to norms/customs that can affect reporting around land ownership (see Desai and Barik, 2017, for a discussion using data from India, as well as Doss, 2013).

While household heads overall tend to report higher exclusive ownership and rights (Appendix Tables A4a-b), Table 5 shows that under the business-as-usual approach, economic ownership is lower among women heads as compared to reported ownership, as seen earlier across Table 2 and Figure 2 as well. For other effects, Table 5 shows relatively similar patterns across reported and economic ownership. On land ownership, across age (being younger than 25), marital status (relative to married couples, being separated/divorced or never married), and education, we see that the business-as-usual approach tends to raise exclusive ownership among men and women. On the other hand, being widowed or living in a larger household with more dependents (greater share of children and women aged 65+) reflects lower exclusive/higher joint ownership under the business-as-usual approach. Interaction effects of the business-as-usual approach also tend to be stronger for men overall, including a positive effect on both men’s exclusive reported and economic ownership in rural areas.

Regarding _SDG Owner_ , on the other hand, Table 5 shows that the interaction effects take on statistically significant coefficients more so among
is inflated compared to survey data, including survey data supplemented with estimated tax income. Tax evasion and off-the-books wages could account for the overstated prevalence of minimum wage earners in tax records.

**The prevalence of minimum wage recipients exhibits noteworthy variation across several employee categories, economic sectors, geographical regions, and types of firms.** Among genders, women outpace men in being minimum wage earners. This trend is also observable among part-time workers compared to their full-time counterparts. The youngest employees stand out, with a higher proportion being minimum wage earners. Across various sectors of the economy, the proportion of minimum wage earners displays substantial diversity. Specific sectors (such as construction, food and accommodation, transport, and storage) witness over 40% of their workforce fall within this category. Conversely, in other sectors (like mining, education, public administration, and electricity, gas, and steam), the proportion of minimum wage earners is below 10%. Microenterprises exhibit a notably elevated share of minimum wage earners, potentially attributed to unreported payments and tax compliance challenges.

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**There is also scope for calibrating coverage ratios and fees** .  Some schemes should consider reducing slightly their coverage ratios to levels closer to international standards. Most schemes should consider linking both coverage ratios and fees more closely to risk. Hybrid payment rules could be tested in some countries, depending on the effectiveness of loan collection procedures, but most MENA PCGs should probably avoid excessively demanding payment rules, as this could reduce significantly the attractiveness of the scheme.

**In some MENA countries guarantee schemes could play a more proactive role in capacity building** .  The low share of SME lending in some MENA countries may be due not only to weak financial infrastructure but also to weak lending technologies.  In these cases, the PCG can play a fundamental role in jumpstarting SME lending while also improving risk management practices of domestic banks.

**Finally, guarantees schemes should institutionalize a comprehensive review process.** In order to ensure cost-effectiveness, MENA guarantee scheme should conduct systematic assessments of outreach, additionality, and customer satisfaction (bankers and borrowers). This comprehensive review should be conducted in a regular basis using appropriate analytical tools, including an SME survey and a banking survey. One of the best practices to consider is  he _Comprehensive Review_ conducted in Canada every five years by the Small Business Financing Program (Table 19).

**Table 19: Building** **<u>blocks for a comprehensive review</u>**

|**Measurement of**<br>**Additionality**|Measure of the financial and economic impact of the guarantee fund, using<br>survey and econometric techniques.|
|---|---|
|**Cost-benefit analysis**|Comparison of all costs and benefits of the guarantee scheme, in order to<br>measure its net social impact.|
|**Identification of SME**<br>**finance gap**|Identification of the characteristics of firms that are credit constrained
that predictions from imported atmospheric transport models are spatially correlated with gridded observations on local pollution.

Urban air quality modelers in developing countries have generally adopted more modest approaches tailored to limitations in data availability, technical resources and institutional constraints. Some of the more sophisticated statistical exercises in this domain have been applied in Nepal (Giri et al. 2008); Egypt (Elminir 2005); China (Zhang et al. 2015); Mexico (Csavina et al. 2014); Turkey (Ocak and Turalioglu 2008); Vietnam (Hien et al. 2002); the Islamic Republic of Iran (Hosseinibalam and Azadeh 2012); and India (Punithavathy et al. 2015). These studies relate air pollution near ground monitoring stations to actual and estimated emissions from nearby sources, with meteorological controls drawn from available measures of temperature, pressure, precipitation, wind speed and wind direction. However, air pollution data are typically drawn from a few ground monitoring stations that are located where pollution is expected to be highest. In addition, technical problems often create random, lengthy gaps in observations from individual monitoring stations. Appropriate spatial econometric panel estimators are difficult or impossible to apply under these conditions. In addition, the concentration of ground monitors in areas where pollution is expected to be high creates a severely-truncated sample of citywide air pollution.

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