rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. Resources Available to Utility Regulatory Agencies in DTEs**

This is an issue of which there has been general awareness for some years. Attention has been focused on two different aspects:

- (i) Financial resources; and

- (ii) Human capital resources – particularly the availability of scarce, highly skilled professional staff resources.

The former is a relatively straightforward issue where the willingness to provide and ensure adequate funding is essentially a sign of the commitment (or the lack of it) by governments and/or legislatures to accept independent regulation in practice. It is very rarely if ever a genuine question of resource insufficiency. The human capital resource issue is the more serious problem and the one on which we will focus in this paper.

Warrick Smith’s 1997 World Bank Public Policy for the Private Sector Notes included some discussion of the issue as, in passing, did Levy and Spiller (1994) in their discussion of the role of resource endowments. In particular, Smith’s papers considered the case for having multi-industry or multi-sector regulators as a way of economizing on scarce, specialist staff resources. Noll (2000) also raised the issue in the context of whether countries should combine to provide multi-national regulators.

These papers all discussed the issue from the perspective that an inadequate supply of staff with good regulatory knowledge and experience would lead to poor and longdrawn out decision-making. Inadequate regulatory resources would particularly jeopardize _discretionary_ regulation. Hence, this became an important issue for those who advocated regulation by contract rather than by agency.

Data on the number of regulatory staff in a large sample of DTE agencies was first systematically reported in Stern (2000). Drawing on World Bank and other sources, he reported staff numbers in electricity or energy regulatory agencies for 24 countries. For African countries, staff numbers were clearly low (around 20) and in some cases under 10. However, Argentina, Brazil, Mexico, Malaysia and the Philippines all had over 140 staff, although there was still the question
,107|0.2%|1.1%|
|**5**|22.9%|6,946|30,367|143,328|143,115|0.4%|1.9%|
|**6**|22.8%|4,557|19,967|170,249|169,540|0.4%|1.6%|
|**7**|27.2%|5,034|18,504|203,287|202,935|0.4%|1.4%|
|**8**|25.3%|7,525|29,757|245,532|247,397|0.4%|1.7%|
|**9**|29.2%|17,411|59,569|318,732|324,248|0.7%|2.4%|
|**10**|34.6%|21,704|62,762|672,770|699,677|0.6%|1.8%|
|**Overall**|21.8%|6,822|31,299|208,224|266,367|0.4%|1.6%|

Source: Calculations based on EDAM 4. Note:<sup>+</sup> Deciles based on the distribution of consumption per capita. The coefficient of variation of telecom usage was 20 percent or higher in deciles 1 and 2 (greyed out) and thus may be interpreted with caution.

To conduct the simulation, we apply the World Bank-developed tool on Welfare and Competition (WELCOM). The tool allows simulation of the distributional effects of changes in market competition through prices. In short, the tool: i) estimates the expected change in
suppliers, conditions in the property (collateral) markets, and changing country context. Further, we assume that: where B3 is the Basel III implementation shock, and ε1�...ε6� are shocks to bank conditions, firm risk characteristics, property market conditions, conditions of buyers and suppliers, non-bank credit institutions, and country context, respectively.

Substituting equations (2) to (7) into equation (1), the reduced-form solution takes the following form: It is used as a basis for the formulation of our reduced-form linear regression in section 6. Although the simple model in equations (1) to (8) is not derived from behavioral micro-foundations, it illustrates how the thinking framework in figure 1 could be translated into a formal model. In this way, it also helps motivate the regression model specifications that we present in section 6.

# **4. Data**

We use firm-level data from the World Bank’s Enterprise Surveys as our primary source and match them with several other databases (World Bank’s Global Financial Development Database, BankScope/Fitch, and BIS Financial Stability Institute’s report on the implementation of Basel reforms). The World Bank Enterprise Surveys, which are conducted periodically, are a survey of a representative sample of firms with global coverage. We focus on the EMDEs for which an Enterprise Survey has been conducted before and after countries in the survey implemented Basel III—that is, we have in our sample countries that implemented and did not implement Basel III.

10
Schools were randomly assigned to either (i) a control group that received the “standard” AGE program, consisting of the information intervention with a USD 500-700 grant, or (ii) a treatment group that received the same information intervention but with double (USD 1000-1400) the standard grant amount.<sup>5</sup> The design of the first experiment allows us to estimate the average treatment effect of doubling the grant for parent associations with the parental involvement program. Baseline data for the first experiment were conducted in 2007, with follow-up data collection in 2008, 2009 and 2010.

In practice, the additional grant money in treatment schools is modest. Parents in the 250 public schools in experiment 1 reported spending an average of 9 USD per year per child on school materials, prior to the intervention. With an average of 80 students in these schools, the “double grant” amounts to an additional 7.5 USD per year per student, which covers approximately 83% of the out-of-pocket cost that is typically spent by parents.

The second experiment consists of 180 public schools that had never participated in AGE. Schools were randomly assigned to either (i) no intervention or (ii) the information intervention. The design of the second experiment enables us to estimate the average treatment effect of the information intervention of the parental empowerment program. The duration of the second experiment was only one year, as baseline data were collected in 2009, with follow-up data collection in 2010.

# **3 Data and Descriptive Statistics**

Data for this study come from three sources: the school census data (Estadistica 911), the national standardized exam (ENLACE), and detailed self-reported surveys by students, parents, and teachers in grades 3-5 of the schools participating in the two experiments. The school census data and standardized exam scores were obtained through the National Council for Education Development (CONAFE). The survey data are publicly available on the World Bank Microdata Library.<sup>6</sup
|**Table 3. Su**<br>**Author**<br>**(Date)**|**mmary of studies tha**<br>**CCT program**|**t examine the impact of Co**<br>**Program aims**|**nditional Cash Transfer (**<br>**Data**|**CCT) programs on fem**<br>**Type of study**|**ale child outcomes**<br>**Observed outcomes**|
|---|---|---|---|---|---|
|Holla et al.<br>(2007)|Apni Beti Apna<br>Dhan, Haryana,<br>India|Balance child sex ratio,<br>delay age of marriage for<br>girls, improve the<br>valuation of and<br>investments in girls|Pooled birth-history<br>data from first two<br>waves of the NFHS<br>(1992-93; 1998-99)|Quasi-experimental<br>(difference-in-<br>difference)|Reduced SRB, but positive<br>effects for girl children erode<br>over time|
|Sinha and<br>Yoong<br>(2009)|Apni Beti Apna<br>Dhan, Haryana,<br>India|(See above)|Pooled birth-history<br>data from all three<br>waves of the NFHS<br>(1992-93; 1998-99;<br>2005-06)|Quasi-experimental<br>(difference-in-<br>difference)|No change in preference for girl<br>children; some positive effects<br>observed in terms of female<br>child survival and educational<br>attainment|
|Sekher<br>(2010)|Different CCT
# 3.1. Indicators of infrastructure quality

This section analyzes the quality of infrastructure services perceived by firms and the role of flood exposure in explaining service disruptions. It aims to answer the following question: what is the impact for firms of being directly exposed to floods compared to the being indirectly impacted? In other words, do firms which are directly exposed to floods have significantly worse outcomes than firms situated outside of flood zones?

While infrastructure disruptions indeed physically occur with increased frequency and intensity in certain hotspots, infrastructure systems are networks that transmit the adverse effects of disruptions across wide areas. The level of localization however varies across different types of infrastructure. For instance, in the case of electricity, a disruption in one location (e.g. a tree falling on transmission lines during a storm) will affect whole segments of the distribution network.

**Electricity outages:** The spatial distribution of electricity outages is assessed using ES data on firm-level power outages. For each firm, the number of hours without electricity in an average month is calculated. In addition, firms are asked to assess what percentage of their sales they are losing due to power outages. It is found that firms in cities with longer outages forego higher sales, on average. Still, some variation exists.

**Insufficient water supply:** While electricity outages are usually felt with the highest immediacy, disrupted water infrastructure similarly affects firms in many cities in our samples. Based on the Enterprise surveys we calculate the share of firms in each city that experienced insufficient water supply in the year before the survey was carried out. These disruptions of firms relying on water as an input for production or the provision of services are likely to impose significant economic costs.

**Transportation disruptions:** Turning to transportation infrastructure, our analysis shows that the firms suffer from significant disruptions in all 13 cities in our sample. The ES relies on a subjective measure to assess transportation infrastructure disruptions experienced by firms. Firms assess transport as an obstacle
Despite the recognition of housing being a driver for social and economic well-being, existing data on adequate housing and on the respective housing deficit within and especially across emerging economies are scarce. Within countries, there is a wide disparity across income segments when it comes to the quality of housing. Housing quality also varies significantly across other HH characteristics such as location, education, occupation, and gender. These within-country dynamics of housing deficits are often conceptually noted, yet not quantified in a way that is actionable for policy makers and private sector companies along the housing value chain. In addition, existing country-specific figures are often outdated. Nigeria, for instance, the most populous country in Africa, with an urbanization rate of 4.8 percent, since 2000, is estimated to have a housing deficit of at least 17 million. This figure is steadily cited for the last decade, not factoring in the rapid population growth that might have increased the deficit (Bah et al 2018: 7). Globally, the UN estimates that about one billion people live in makeshift structures lacking at least one of the basic services which jointly constitute adequate housing, projecting that the number will rise to 3 billion people by 2030 (UN-Habitat 2012; UNDESA 2013; UNSD 2019). Yet, this is a broad projection mostly estimating the number of people living in slums and is therefore likely underestimating the number of urban dwellers in formal or informal settlements who are lacking access to these basic services.

While providing valuable insights, existing housing deficit estimations, which often rely on macrolevel data, hitherto lack a global standard and a consistent underlying framework that allow for cross-country comparison of deficit numbers. Micro-founded analyses across countries, applying the same rigorous methodological approach to estimate housing deficit for different segments of the population, are, largely, absent. A key constraint to establishing a uniform methodology is often the limitation to consistently identify what qualifies as adequate housing across and within countries. Addressing this void, in this paper, we introduce a micro-founded methodological framework to estimate the
order indicators to explicitly account for choice error and bias. In section 3.6 we use our approach to show with data from South Africa that (i) there is significant order bias in MPL data that affects estimated WTP, and (ii) many existing estimation approaches may exacerbate measurement issues.

# **3 Accounting for choice error and bias in MPL design and WTP estimation**

## **3.1 Accounting for bias: MPL design**

As discussed above, while the MPL format has many advantages, such as ease of understanding and rich data, it is plausible that it also induces some framing or anchoring effects. A challenge in examining order bias is that typical MPL implementations do not 15
**Figures 3 and 4: Source of EPZ investment projects (2009)**<sup>**13**</sup>

<!-- Start of picture text --> 100% 100%<br>90% 26% 20% 90%<br>80% 32% 43% 42% 35% 80%<br>70% 52% 51% 57% 70%<br>69% 60%<br>60%<br>50%<br>50%<br>40%<br>40%30%20% 68% 74% 57% 48% 58% 65% 80% 49% 43% 31% 30%20%10%<br>10% 0%<br>0%<br>Foreign‐controlled Local‐controlled East Asia South Asia Europe Africa USA Other<br><!-- End of picture text --> Source: SEZ Investor Survey<sup>14</sup> Specifically, the non‐African zones have shown some evidence of the shift from foreign to local investment, as seen in the Malaysia and Mauritius cases. For example, in Vietnam local share of investment projects rose steadily from around 23% in 1995 to over 50% by 2008. Local investors have also played critical roles in the zones programs in Honduras and the Dominican Republic. In the case of the African zones, however, the same pattern does not appear to hold. For example, new programs like Tanzania have had high shares of local investment from the outset. This, in combination with the low levels of FDI attracted into the African zone programs to date, suggests that the relatively high share of local investment may reflect as much the failure to attract FDI as it does success in attracting local investors. A second explanation, particularly in programs like Senegal, is the existence of “single unit” licenses and the propensity of locally‐based firms to
_al_ . (1997), Sutton (2003), and Small _et al_ . (2005). More recent applications at either a regional or a global scale include Zhang and Seto (2011), Zhou _et al_ . (2015), Ellis and Roberts (2016), and CAF (2017).

Applications of NTL data to delineate metro areas have invariably relied on data products that have been derived by the National Oceanic and Atmospheric Association (NOAA) from sensors (Optical Line Scanner, or OLS, sensors) on-board the Defense Meteorological Satellite Program (DMSP) constellation of satellites. The derived DMSP-OLS data products cover the entire globe and are available at a resolution of 30 arc-seconds, which is, again, equivalent to approximately 1 km<sup>2</sup> at the equator. One deficiency of DMSP-OLS NTL data, however, is that they suffer from a well-documented “overglow” or “blooming” problem, whereby the light emitted from a given point on the earth is recorded as covering an area that extends beyond that point.<sup>10</sup> This creates a tendency for the lit area of a metro to overstate its “true” extent – for example, the Pacific Ocean can be lit up as far as 50 km from the coastline near Los Angeles (Pinkovskiy, 2013). The most common approach to dealing with this overglow problem has been to threshold the NTL data, considering only pixels in the data that exceed a certain luminosity, or digital number (DN), value as part of the area of a city or metro (see, for example, Imhoff _et al_ ., 1997; Small _et al_ ., 2005; Zhou _et al_ ., 2015; Ellis and Roberts, 2016). A contiguous cluster of grid cells that falls above the applied threshold is then classified as constituting a “metro” area.

More recently, however, DMSP-OLS NTL data have been superseded by NTL data collected from a new satellite sensor, the Visible
at the end of 2011. Section 3 reviews the existing literature on the broader mobile money phenomenon. Section 4 describes how mobile money works in Kenya and shows the growth of mobile money usage over time. Section 5 describes the data on mobile savings analyzed in this

> 1 These 1999 telephone ownership figures are based on census data, the 1999 mobile phone figures are drawn from the International Telecommunications Union statistical database, and the 2011 mobile ownership figure is based on the 2011 Afrobarometer survey.

2
reduces trade growth by reducing foreign firms’ incentives to invest in serving the market, by inducing consumers’ precautionary saving and by affecting firms that are part of global value chains.

The implication of this result is that economic policy uncertainty has contributed to the sluggishness in world trade growth in recent years. However, its consequences for global value chains are less clear. We find that trade linked to GVCs responds to economic policy uncertainty in a way that it is broadly similar to overall trade. As discussed above, this could be the result of opposing forces linking uncertainty to GVC trade. In the long term, we would expect the negative forces to dominate as economic policy uncertainty would induce firms to withhold investments today that would promote GVC trade tomorrow.

We also look at trade policy uncertainty, which has been an important component of economic policy uncertainty in recent times. We rely on the work of Ahir et al. (2019) that builds on the EPU index methodology to include trade-related keywords and construct a world trade uncertainty (WTU) index. The index documents the large increase in trade policy uncertainty since 2017. But when we use the WTU index in our econometric analysis, we do not find a consistent negative effect of trade policy uncertainty on overall and GVC trade. Rather, this effect varies by country and is at times negative and at times positive.

3
# **I.   INTRODUCTION**

There is growing research interest on the topic of credit cycles, which shows the importance of credit fluctuations for the macroeconomy and their role in the propagation of shocks in the economy. Various studies have estimated cyclical components in credit in the advanced economies. Schularick and Taylor (2012) study the credit booms and leverage from 14 countries during 1870 to 2008 and show that there is a relation between credit growth and banking crisis. They show evidence of deeper recession following credit-intensive booms. Jorda et al. (2013, 2011 a, and 2011 b) provide empirical evidence for a deeper recession and sloppy recoveries after credit-intensive booms. Claessens et al. (2009, 2012) find that recessions that are linked to credit crunches are relatively deeper and more prolonged. Drehmann et al. (2012) using data for five advanced countries during 1960 to 2011 show that financial cycle peaks are related to the financial crisis and that after the changes in monetary policies and financial liberalization the length and amplitude of financial cycles incremented. Credit cycles have important implications for macro-prudential policies. The study of credit cycles has implications for policy coordination, as monetary policy affects banks' balance sheets and risk-taking on financial markets and thereby might interfere with macro-prudential policies (Adrian and Shin, 2011; Gertler and Karadi, 2011). While the literature on this topic is increasingly growing, most of the work has been done on the advanced countries.

In terms of methodology most studies that estimate the cyclical components in credit use univariate bandpass filters (e.g., Aikman, Haldane, & Nelson, 2015; Drehmann, Borio, and Tsatsaronis, 2012). There are other studies that have employed spectral methods (Schüler, Hiebert, and Peltonen, 2015) and autoregressive integrated moving average (ARIMA) models (Strohsal, Proaño, and Wolters, 2015) to assess cyclical properties of the series.

The general finding of these studies is related to the highly predictive linkage of credit cycles to the emergence of financial
||

Source: Own estimations using HBS and SILC data Education and health in-kind benefits have inequality-reducing effects. As noted earlier, we assume that the benefit received by the individual is equal to the amount spent per capita. Figure 6 shows that overall education spending is progressive, with primary education being the most redistributive (Table 4). The concentration coefficient for all levels of education except tertiary is negative, suggesting that a larger share of expenditures goes to the lower deciles. However, these results do not capture heterogeneity in the quality of services. For example, the PISA 2018 results<sup>18</sup> suggest that in reading, socio-economically advantaged students outperformed those from more disadvantaged background by 73 score points. Though this is still smaller than the average difference between the two groups across OECD countries (89 score points), the performance gap in Serbia had increased since 2009 when it was 66 points (87 points on average across OECD countries). Health spending is equally distributed across the income quintiles, given the way health benefits are assigned, but is progressive in relative terms since it makes up a larger share of income at the bottom of the distribution.

> 17 This result is based on quintiles of <mark>market income plus pensions per capita that excludes pregnancy and maternity allowance, which may differ from other studies looking at targeting based on quintiles of disposable income.</mark>

> 18 <u>https://www.oecd.org/pisa/publications/PISA2018_CN_SRB.pdf</u>

21
latter still being IDA eligible. The cap meant that Pakistan’s IDA18 allocation was capped at 7 percent of IDA’s total country-allocable envelope. For some of the countries subject to the cap, the IDA financing as a share of gross domestic product (GDP) can become very low,<sup>7</sup> and as a result, they may graduate from IDA sooner than they would have otherwise.

A second measure of vulnerability is the degree of dependence on resource revenues. As such, we include natural resource rents as a share of GDP. Because prices of oil, metals, and other commodities are

> 5 The PovcalNet methodology is described as “interpolating and extrapolating for economies in which survey data are not available in the reference year but are available either before, after, or both. The reference years range from 1981 to 2015. The more survey data are available (that is, the more data for different years), the more accurate the interpolation.” http://iresearch.worldbank.org/PovcalNet/methodology.aspx

> 6 For countries in which ICRG data were not available, we used the Worldwide Governance Indicators to estimate the ICRG value to maintain a larger sample size. This was done through a linear regression of ICRG rating on these indicators, with the predicted ICRG strongly correlated with actual ICRG.

> 7 For India, average IDA commitments were just 0.1 percent of GDP in the years of their last replenishment before graduation.