rafmacalaba/data-use-mentions-extended huggingface dataset:
electricity affordable. First, the households most eager to be connected have the highest incomes and may well live in the same area making it easier on a per household basis to supply this group who tend to purchase larger amounts of electricity. Second, the costs of connection are higher to more remote areas and these tend to have lower incomes and consume less electricity. Thus, the last households to be connected will tend to be those of least interest to the utility, so that some government support may be needed to ensure access moves towards the 100 percent mark.

Access is defined by the existence of a connection to the grid by a household—all members of the connected household are counted as having access. Affordability is usually defined by the household having to spend less than a given fraction of income to purchase a given amount of electricity. When the household spends more for this quantity, or purchases less for the given expenditure, then at the given prices electricity is said to be “unaffordable”. In practice such a calculation tends to be carried out only when a large-scale expenditure survey is available.

# _3.2.1 Performance_

**Access** data are usually derived from household expenditure surveys, but these are not available annually and are typically based on modest sized samples. Only in census years can a solid estimate of the total population and average household size be obtained. However, a population census does not usually enquire about items such as access to mains electricity. For this, the

30
property between men and women. Recent work shows that this led to an increase in sex-selection (Bhalotra et al. (2018a)). We find that our results hold even if we restrict the sample to states that did not experience changes in inheritance rights, i.e., when we exclude Kerala, Andhra Pradesh, Tamil Nadu, Maharashtra, and Karnataka. These results are available on request.

**Gendered recall bias.** As we combine fertility histories of mothers interviewed in three DHS waves, recall of birth year varies within the sample but is, for some women, as long as 20 years. For the analysis in this paper, it is relevant to consider whether accuracy of recall of the year of birth or death of a child varies with the gender of the child. The firm gathering the NFHS data was alert to recall bias, and they report that the data were subject to several probes designed to address it. We, nevertheless, check for the presence of recall bias—if women are more likely to have forgotten girl deaths and if this error is increasing in distance of birth year from survey year, then this could, in principle, bias our findings. On the other hand, if families with firstborn girls had stronger gender bias in recall in the pre-ultrasound period then our EFM estimates will be conservative.

We examine gendered recall bias by exploiting the fact that a range of birth years is available in more than one NFHS round and that distance between a given year and the survey date varies by

> 36Almond and Edlund (2008) show that Indian, Chinese, and Korean families with no previous sons exhibit male-biased sex ratios at third parity but not before in the 2000 US Census and Bhalotra and Cochrane (2010)) show that the male-bias in the sex ratio at birth in India is increasing in birth order.

16
using PHBS 2014.</u> <u>Figure 3. Decline in Inequality from Market to Disposable Income</u> <!-- Start of picture text --> 0.00<br>-0.05<br>-0.10<br>-0.15<br>-0.20<br>-0.25<br>-0.30<br>Pensions as deferred income Pensions as transfers<br>Belgium (2014)* Germany (2014)* Hungary (2014)* Austria (2014)* Czech Republic (2014)* Croatia (2014)* Romania (2014)* Poland (2014) Estonia (2014)* Bulgaria (2014)* Russia (2010) Armenia (2011) Uruguay (2009) Brazil (2009) Chile (2009) Mexico (2010)<br><!-- End of picture text --> Source: Armenia: Younger et al (2016); Brazil: Higgins & Pereira (2014); Chile: Ruiz-Tagle & Contreras (2010) Georgia: Cancho & Bondarenko (2016); Mexico: Scott (2014); Russia: LopezCalvo et al (2016); Uruguay: Bucheli et al (2014); EU countries (*): Euromod (2014); Poland: own <u>estimates using PHBS 2014.</u> 16
Georgia averaged real GDP growth in the years 2012-2020 of 4.1 percent (source: IMF World Economic Outlook, October 2021), and GDP per capita reached USD 4,272 in 2019.<sup>7</sup> Both economies were somewhat integrated with the world economy prior to the pandemic, but Georgia mores so. In 2019, merchandise trade as a percent of GDP was over 61 percent for Jordan and 76 percent for Georgia and services trade comprised 28 percent and 40 percent of GDP, respectively (WDI, 2019). Georgia had also posted a higher level of exports as a share of GDP (54.8 percent) than Jordan at 36.3 (WDI, 2019) and had also seen faster growth in unit export values than _Table 3-1: Labor Market Statistics, 2019_ Jordan (WDI). Within services, Jordan was much more Unemployment Labor Force reliant on the travel and tourism sector, which accounted Rate Participation for around 18 percent of GDP and of total employment in <u>(percent age 15+)</u> Georgia 11.6 62.9 2019 (World Bank, 2021) relative to 7.6 percent of GDP Jordan 16.8 39.2 and 5 percent of total employment for Georgia in 2018 Source: WDI national estimates. (World Bank, 2019).

The labor market context pre-COVID varied somewhat between Georgia and Jordan as well. They each approached the regulation of labor markets differently. Georgia’s labor market regulation is considerably more flexible in terms of permitted hours of work and contractual arrangements. It has no minimum wage, whereas Jordan’s minimum wage is approximately 50 percent of value added per worker (Employing Workers Database (EWD, 2020). Georgia’s regulatory requirements are also more flexible with respect to dismissing workers. No third-party notification or approval is required; nor are there priority retraining or rehiring requirements for dismissed workers. Jordan, in contrast, is among the 32 countries with the most stringent regulation
relationships. Section 4.c describes how we use data on pandemic aid transfers made during 2020 to construct a proxy measure of informal employment.

First, we obtained data on account ownership, from the Registry of Clients of the Financial System (CCS). The CCS includes information about every account held in financial institutions since 2001, including checking, savings, payments, and investment accounts. According to the Financial Citizenship Report 2018, information contained in CCS implied that 86.5 percent of the residents in Brazil aged 15 or older had a bank account.<sup>7</sup> The original data contains opening and closing dates for all accounts, which we turn into a monthly panel. Unfortunately, the CCS does not provide account balances, fees, and transactions.

Second, we have detailed information on credit. Set up in 2003 to monitor risk, the Credit Registry System (SCR) collects from lenders monthly information about every financial transaction conducted by clients who can cause the lender a loss greater than a given amount. This threshold was of BRL 5,000 from 2003 until 2012, when it fell to BRL 1,000. It was reduced again in June 2016 to BRL 200 (corresponding to around USD 46<sup>8</sup> ). We use data from 2016 onwards, since the SCR includes relatively few transactions for our sample before the threshold reduction to BRL 200. For each transaction, the SCR records the amount, credit category, interest rate, due date, and amount in delay or classified as a loss. In December 2019, the SCR contained information on over 127 million individuals (around 60 percent of the Brazilian population).

Third, we build a data set of individual microentrepreneurs (MEI) using the registry of firms from the Brazilian tax authority (SRF) for 2012 through 2020. MEI is a type of firm with a simplified registration process, created in 2008 by Complementary Law 128. The only tax MEIs need to pay is a flat
incentives also reverberate into the structure of the corporate sector. Allen and Gale (2001) maintain that bank-based financial systems may be more efficient at financing long-term investment projects that require long-term financing.<sup>4</sup> This is due to banks’ ability to renegotiate contracts following the realization of unforeseen contingencies. On the other hand, Dewatripont and Maskin (1995) contend that decentralized financial systems characterized by strong capital markets may be less inclined to roll over financing to inefficient businesses.

Banks’ tendency to evergreen loans is perceived to be particularly acute for some types of banks, such as domestic banks in developing countries. Several studies underscore that foreign banks may partially offset this bias of domestic banks. Similar to dispersed capital market investors, foreign banks establish looser personal relationships with domestic entrepreneurs, so they have less incentives to roll over loans (Mian, 2006). In fact, they typically have a more hierarchical structure than domestic banks, with the top management sitting in a foreign country. By contrast, domestic banks, especially government-controlled ones, have poor cash-flows incentives and are more susceptible to the pressure of domestic governments to keep businesses afloat. Moreover, foreign banks can increase the quality of banking services and promote competition (Levine, 2004; Dobson, 2008) and can facilitate trade by helping firms overcome information asymmetries (Classens et al., 2017).

# **3 Data and Measurement**

In this section, we provide details on the empirical methodology, data sources and measurement of the variables used in the empirical analysis.

## **3.1 Data sources and empirical strategy**

Our data on exporters’ size, distribution and dynamics aggregated at the industry level are drawn from the World Bank Exporter Dynamics Database (EDD). The data cover 62 countries across all geographic regions for the 1997-2014 period. While countries of all income levels are included, the bulk of the sample consists of middle- and low-income countries.<sup>5</sup> The database
harvesters, and 10% for other services (Harris and Seid 2021). The lack of an exemption threshold for the _turnover tax_ and the high tax rate for most services make it particularly burdensome for microbusinesses, resulting in taxpayer dissatisfaction (Dixon, Freedman, and Yesegat 2019). In other countries, it is common to introduce a single small business tax or a presumptive tax regime, which replaces the VAT and other income taxes (Engelschalk and Loeprick 2016, IMF 2015).<sup>7</sup> In contrast, in Ethiopia, small and microbusinesses are subject to two taxes – the presumptive tax and the _turnover tax_ - with differing thresholds.<sup>8</sup>

# **3. Data and methodology**

# **_3.1 Data_**

The analysis uses two rounds of the Ethiopia Socioeconomic Survey (ESS) 2018/2019 and 2021/2022, a nationally representative panel survey, consisting of 6,770 households in 2018/2019 and 4,959 households in 2021/2022, although the Tigray region was excluded from the ESS 2021/2022.<sup>9</sup> The nonfarm enterprises in the sample are representative of those operated by the households. Period 1 is defined to be the ESS 2018/2019 and period 2 is the ESS 2021/2022. The panel survey includes a nonfarm enterprise module with questions on whether anyone and who in the household owns a nonfarm enterprise, the enterprise activity, the average monthly revenue, and whether the business is registered. It also includes a question on whether the enterprise paid taxes on income as well as the amount paid. The focus on Category C enterprises (with an annual turnover of less than 500,000 birr) resulted in 109 firms being excluded because their revenue exceeded the cut-off. The annual turnover is estimated by multiplying the self-reported average monthly revenue by the number of months of operation in a year.

The resulting sample consists of 1,523 nonfarm household enterprises in
have IO tables (e.g., in the US, the BAE provides data at the state level), and table reconstruction using simple rules is sometimes necessary (see, e.g., Flegg et al., 1995; Hallegatte, 2008; Flegg et Tohmo, 2010).

The indirect impacts of a disaster largely depend on direct losses, and data on direct losses are often broadly aggregated and rarely disaggregated by sector. Also, data are often based on insurance industry data, and focus therefore on insurable goods. Non‐insurable goods, and especially infrastructures, are often absent from these estimates. Again, simple methods may be used to recreate this data, but these data reconstruction methods add an additional large source of uncertainty in the assessment. Examples of methods are: Sector disaggregation can be carried out using the sector values added, assuming that each sector is af‐ fected proportionally: if a sector is twice as large (in terms of VA or productive capital) than another, then it would have suffered from losses that are twice as large (Hallegatte, 2008).

If sector‐scale data on real‐event losses are not available, models can be used to assess direct losses. Examples of models are catastrophe models from risk modelling companies (e.g., RMS, EQECat, Air‐ Worldwide). This method can be applied only for events and regions in which risk modelling is available, i.e. mainly rich countries (e.g., hurricanes in the U.S.).

Infrastructure losses can be estimated assuming that the ratio between insurable and non‐insurable losses is the same for all events. Using cases in which this information is know (e.g., Katrina in New Or‐ leans), infrastructure losses can be reconstructed for other events (see Hallegatte et al., 2010, on coastal floods in Copenhagen).

29
**Table 7.  Estimates of model (8) with the Economic Freedom Index as the dependent variable**

|**Variable**|**Equa**|**tion(8)**|
|---|---|---|
|Dependent var.: Economic Freedom Index|**Coeff.**|**Robust s.e.**|
|∑���<br>�<br>A�<br>�.�= # policy actions raised to γ = 0.0, cumulated|0.994|0.248***|
|Cumulated length of engagement|‐0.0487|0.0132***|
|GDPper capita at PPP|0.000102|0.000134|
|Aid/GDP|‐19.0|5.77***|
|Political Rights|‐0.205|0.304|
|Country&year fixed effects|Yes||
|N|2518||
|R‐squared,within|0.217||
|Number of countries|154||

Note: * = significance level of 10% or less; ** = 5% or less; *** = 1% or less. Standard errors are adjusted for 145 clusters.

The key element in this estimation is that cumulated policy actions (raised to the power 0) have a positive and statistically significant impact on the quality of government policy as measured by the Economic Freedom Index. The coefficient on the cumulated length of the engagement is negative and statistically significant. These findings buttress the earlier findings using the CPIA as the dependent variable. GDP has the expected sign (positive) but is insignificant. Aid/GDP is negative this time, and significant.

# **6. Robustness tests**

In this section we present the results from three robustness tests: quantile regression, a Tobit model and split samples
|[0.0434]|
|Share of male employment in construction|-0.103|-0.0837|-0.0931|-0.0894|-0.0772|
||[0.0781]|[0.0914]|[0.0782]|[0.0830]|[0.0800]|
|Share of male employment in white services|0.0121|0.046|0.0162|0.0534|0.055|
||[0.0815]|[0.0905]|[0.0814]|[0.0747]|[0.0815]|
|Share of male employment in other services|0.0143|0.0225|0.0091|0.0257|0.0304|
||[0.0626]|[0.0658]|[0.0623]|[0.0643]|[0.0628]|
|Constant|-0.833|-0.677|-0.849|-0.822|-0.767|
||[0.234]***|[0.126]***|[0.235]***|[0.135]***|[0.125]***|
|Observations|1,696,260|1,696,260|1,696,260|1,696,260|1,696,260|
|R-squared|0.256|0.255|0.256|0.255|0.256|
|Department fixed effects|Yes|No|Yes|No|No|

Sources: 2018 National Census of Guatemala, 2017 and 2018 municipality statistics (FUNDESA and INE), 20172018 Census of Human Resources in Central Government (INE), 2017 Latinobarometro survey.

Notes: Women ages 25 to 49 who either
<!-- Start of picture text --> Ahmedabad -Change in built-up density between 1991 and 2001<br><!-- End of picture text --> <!-- Start of picture text --> 400<br>350<br>© Density in built-up areas in 1991<br>ga ® Densities in built up area in 2001<br> _<br>|, 250<br>&<br>a<br>i=]<br>E 200 5<br>E<br>&<br>: | . | | | | | | l l l [|<br>||||||<br>12 a 4 5 6 7 a 9 10 al 12 13 14 15<br>Distance from the city center (km)<br>Sources: census of 1991 and 2001 per ward. Vectorization ofbuilt-uparea of 1991 (landsatjand2001 (Google Earth)<br><!-- End of picture text -->
country and year are 147 million US$, which is 17%<sup>21</sup> of the average development aid (880 million US$) received by the countries in our sample in 2009.<sup>22</sup> For scenario 2 (providing all households with high-end water and sanitation technologies) the estimated average costs amount up to 347 million US$ or about 40% of current development aid received per country and year.

It is worth highlighting that all of the cost-benefit ratios presented here are likely overstating the true cost, and understating the true benefit of water and sanitation investment for two main reasons: First, as we have made clear in the preceding section, we have selected the upperbound cost estimates from Hutton and Haller (2004) to avoid underestimating the true cost of providing infrastructure.<sup>23</sup> Second, and more importantly, our mortality estimates only capture the direct child mortality effects of water and sanitation infrastructure, that is, the private mortality benefits that accrue at the child or household level, without taking into account any positive externalities, and without taking into account morbidity effects. Water, and in particular sanitation, can be presumed to have substantial positive spillovers within clusters and neighborhoods (see also Günther and Fink, 2010). Improved water and sanitation can also be assumed to be associated with considerable improvements in morbidity and overall wellbeing. The cost-benefit ratios presented in this paper should thus be viewed as a lower bound, since the benefit estimates shown are likely to underestimate the true social benefits of water and sanitation, while the cost estimates are likely to overstate the true financial commitment required for their provision.

# **5. Discussion and Conclusion**

In this paper we have used micro-data from 38 developing countries to estimate the mortality effects of water and sanitation, and to quantify the degree to which improvements in water and sanitation infrastructure could contribute to achieving the mortality objectives established in the Millennium Development Goals. We have shown that the
16 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity complementary since they allow differentiating between an irregularly compact city and a smoothly compact city, the latter being closer to a perfect circle.

_Fullness_ measures the presence of built-up areas within the urban extent as a fraction of the urban extent area. We used the GHS built-up 1990 raster layer at 250 meters resolution (GHS_BUILT_LDS1990_GLOBE_R2016A_54009_250, Pesaresi et al., 2015; Pesaresi et al., 2016) to calculate this variable. The GHS built-up layer values are expressed as decimals from 0 to 1, and correspond to the fraction of the pixel that is covered by a building (Pesaresi et al., 2016). Fullness was measured as the mean value of all the pixels of the GHS built-up 1990 layer within the urban extent. This measure can be understood as the complement of the sprawl index proposed by Burchfield et al. (2006) defined as the measure of absence of built-up area. Based on the extensive literature on the relationship between compact cities and productivity, we would expect a “full-city” to be one that is also a more compact city, hence allowing for greater interaction and therefore higher agglomeration economies which ultimately increase productivity. However, a city that is “too-full” might also be suggestive of lack of public space, which can both be a dissamenity and reflect lack of planning. For these reasons we can expect a non-linear relationship between _fullness_ and productivity.

Following the above-mentioned rationale, our hypotheses regarding the effect of these indicators of city shape on productivity are as follows:

- **H1** : An increase of the _Exchange_index_ suggests a more circular urban extent, which is expected to be associated with higher productivity levels of the city.

- **H2:** An increase of the _Perimeter_index_ implies smooth perimeters that are associated with higher productivity levels of the city.

- **H3** : An increase of the _Fullness_index_ implies a
Relative poverty line**|||||||||||||
|Poverty incidence|26.9|33.2|26|32|27.9|34|26.9|32.9|29.6|35.4|22.5|28.8|
|Poverty gap|9.5|11.7|8.9|11.1|9.9|12.1|9.3|11.5|10.4|12.8|8|9.6|
|Poverty severity|4.6|5.7|4.3|5.2|4.9|5.9|4.5|5.5|5.1|6.2|8.9|6.8|
|**Absolute poverty line**|||||||||||||
|Poverty incidence|20.8|26.3|20|25.4|21.6|27.1|20.7|26.2|22.6|28.7|17.1|21.5|
|Poverty gap|6.9|8.5|6.5|7.9|7.2|8.9|6.8|8.3|7.6|9.4|5.9|6.9|
|Poverty severity|3.3|3.9|3|3.6|3.4|4.1|3.1|3.7|3.6|4.3|9.6|6.3|

Source: Authors’ calculations; ESS 2018/19data.

In general, looking at the final income, we see that the fiscal actions reduced poverty and inequality for both men and women. Moving along the income concepts, we see the
in the last decade up to 5.5% annual average (with respect to gross generation) in 2015, from 2% in 2005.

Market concentration has fallen steadily over the past decade in Peru, from 2,783 in 2005 to 1,509 in 2015 (see the Herfindahl-Hirschman Index evolution in Figure 5-3). Although competition has increased in the Peruvian power market, 68% of the market remains concentrated in four major actors as of 2017 (including government-owned power utilities) (OSINERGMIN, 2018).

18
municipal budget. In the case of a self liquidating investment - water supplies or markets, for example - capacity to service a loan depends upon the ii,ternal financial viability of the investment project, not on the general state of the municipal budget. In the - case of non-self liquidating projects roads, schools etc - capacity to repay a loan depends upon current revenues exceeding regular commitments (personnel, operation and maintenance); ability to service a loan should be judged by estimating the foreseeable trends in both elements. The French Caisse des Depots bases its lending to individual municipalities on financial forecasts, not on debt service ratios. The Jordan CVDB has had to change its eligibility criteria to individual financial forecasts, after the misleading quality of debt service ratios became clear.

40. Two of the Indian experiments represent serious attempts to reconcile lending targets to municipal capacity. In the case of the Calcutta Metropolitan District, debt service is part of the notioaal expenditure forecast used in calculating deficit grants, which assume certain target levels of frugality in spending and efficiency in revenue collection. (A very similar provision existed in Uganda in the 1960s). In the case of the Tamil Nadu Fund (still under negotiation) a varying mix of grant and loan is to be used to keep debt service within limits established for each municipality by a forecast of expenditure and revenue, again assuming target standards of performance.