rafmacalaba/data-use-mentions-extended huggingface dataset:
which a crisis is ongoing.<sup>18</sup> Lopez employs the Arellano-Bond GMM econometric methodology which involves double differencing of this data, and this arguably places a premium on data accuracy.<sup>19</sup> Interestingly, he finds that the crisis variable is highly significant, with a t-statistic of over 4.<sup>20</sup> Five-year periods in which countries, however, poverty headcount is, however, on a downward medium-term trend which somewhat complicates the separate identification of the effects of shocks. The problem is illustrated in the data of Table 4, where a sharp fall in the poverty headcount in Ghana is the most dominant feature. Adjusting for the different trends in different countries uses up most of the available degrees of freedom.

17 Panizza (2004) also provides some evidence, but limits himself to the Latin American cases also studied by Lustig (2000).

18 Note the implicit assumption here that, if a country is not reported in the crisis database as having an ongoing crisis, then it is crisis-free for that period. However the authors of the crisis database have warned that it is an incomplete catalog of crises, and does not declare as crisis-free the countries and dates that are not mentioned.

19 And also reduces the degrees of freedom because of the need to exclude countries with fewer than three observations. From 953 observations on 137 countries, the double differencing reduces the effective sample size to 194 observations on 61 countries. Further merging with the policy and growth data reduces Lopez sample further to 134 observations on 41 countries.

20 His other policy variables include: initial GDP per capita, initial output gap, education (secondary enrolment), financial depth, trade openness, government consumption as share GDP, public infrastructure (telephone lines) price stability, governance (first principal component of the ICRG indicators), cyclical volatility, external imbalances, and the growth in the terms of trade. All but the last four are found to be significant in explaining
formal sector counterparts (modern supermarkets). Gonzalez and Lamanna (2007) analyze firmlevel survey data on formal manufacturing firms collected by the World Bank’s Enterprise Surveys for 14 countries in Latin America. Their findings suggest that about 40 percent of the firms in the region face significant informal competition, with sizeable variation across industries and firm sizes. Using Enterprise Surveys data for Nicaragua, Pisani (2015) explores the firm characteristics that determine the likelihood of formal firms to face informal competition. Mendi and Costamagna (2017) use data from Enterprise Surveys to estimate the impact of informal competition on formal sector firms’ innovation. Amin and Okou (2020) also use Enterprise Surveys data and find that informal competition lowers labor productivity of the formal sector firms by 20-24 percent. To reiterate, none of these studies assess the impact of informal competition on formal firms’ employment growth.

La Porta and Shleifer (2008) use Enterprise Surveys data to examine the expected effects of informality on the formal sector firms. However, the authors do not investigate the impact on employment growth. Instead, they analyze surveyed formal firms’ responses to whether “anticompetitive and informal” practices are an obstacle for their business. The authors acknowledge that the question is noisy because anticompetitive practices can also result from the behavior of the formal sector firms. Firms’ view of informality as an obstacle may also be driven by the perceived impact of informal activity on total revenues, profits, or productivity rather than employment growth. Moreover, La Porta and Shleifer (2008) use old data collected between 2002 and 2007.

Perry et al. (2007) argue that informality can have negative effects on formal firms’ investment and innovation decisions because it reduces their market share and profitability. However, they do not provide any empirical evidence to support this claim. Rozo and Winkler 4
the non-attriters sample shows the same balance as the whole sample, our main estimates do not adjust for attrition. Reassuringly, however, in section 4.7 we show that these estimates are robust to adjusting for differential attrition. We do so by implementing bounds by Behaghel et al. (2015).<sup>17</sup>

> 14See Table 1 (pages 132 and 133) in McKenzie (2017) for the attrition rates in 15 other vocational training studies in low and middle income countries.

> 15The attrition rate for the U.S. Current Population Survey, for example, is about 20%.

> 16Baseline characteristics include age, marital status, educational attainment, a dummy that indicates whether the participant has taken a previous coding course, a dummy that indicates whether the participant specialized in a STEM career during her education, employment status, whether the individual was employed in the formal sector, weekly hours employed, and labor earnings.

> 17Since we foresaw this differential attrition, we developed a thorough field protocol that collected information on how many call and email attempts were made for each subject in the sample.

10
Chile’s _empresa modelo_ (model firm) approach to cost-reflective tariff estimation and uses utility-level data from the World Bank’s IBNET database.

The paper is structured as follows: section 2 describes various approaches from the literature to estimating the cost of WSS services, section 3 details our methodology; section 4 presents the results of our analysis; section 5 describes the limitations to our approach; and the final section concludes with a summary of our findings and their policy implications.

# **2. Approaches to estimate the economic cost of water and sanitation**

Since subsidies are the difference between (i) the costs of service provision and (ii) the amount paid by users, defining and estimating the first element of the equation is fundamental to any analysis. When computing the costs of service provision, total economic costs (and, eventually, inefficiencies, or slack<sup>4</sup> ) are taken into account (see Figure 1).<sup>5</sup> In accounting terms, these costs include operating costs, debt service, return on equity, depreciation, taxes, and environmental costs. Beyond these, all service providers present some inefficiencies that result in additional, hidden, costs that should be taken into account.<sup>6</sup> An efficient level of regulation is assumed, which means that tariffs cannot be higher than the economic costs of service.

## **Figure 1 Costs of Water and Sanitation Service Provision**

<!-- Start of picture text --> E C Common Costs<br>F A E<br>Dams, aqueducts, networks, plants, etc.<br>F P F<br>I E Specific Costs F<br>C X Meters, connections, etc. E<br>I Common Costs C<br>E T<br>O Management, IT, etc.<br>N I<br>T P Inputs V<br>
** Across major US trade partners, OECD data suggests the average imported intermediate share is 0.32 for manufacturing exports (Figure 4), though there is substantial variation across countries. In general, larger or more closed countries/regions have a lower intermediate share, with the US, EU, Brazil and Japan all having manufacturing intermediate shares around half the cross-country average. Table 5

> 29As a robustness test, I also constructed an index which uses the average of index values for each good Ij,c,tsector = ��c<sup>′</sup> =c<sup>I</sup> i,j,c<sup>good′</sup> ,t<sup>/ �</sup> c<sup>′</sup> =c<sup>I</sup> i,j,c<sup>good′</sup> ,t−1� Ij,c,t<sup>sector</sup> −1<sup>whichisclosertotheBLS’spre-1997methodologyandproducesalmostidentical</sup> results. Another issue is that around 40% of product-month prices are missing in the import price program (Nakamura and Steinsson 2012). I follow the BLS methodology (and that of Nakamura and Steinsson 2012) by pulling forward missing prices (replacing missing price pi,t with pi,t−1 for each good i). Nakamura and Steinsson (2012) argue that neither pulling prices, nor reweighing indices (as the BLS has done since 1997), substantially affects long-run pass-through.
|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Developing Country|0.150|0.252**|0.176***|0.264***|1.578***|1.686***|
||(0.097)|(0.118)|(0.060)|(0.073)|(0.212)|(0.201)|
|Log Population Density|0.453***|0.481***|0.158***|0.177***|0.006|-0.566**|
||(0.052)|(0.056)|(0.039)|(0.043)|(0.166)|(0.259)|
|Log Population Density *<br>||-0.145||-0.140||0.807**|
|Developing Country||(0.136)||(0.094)||(0.331)|
|Constant|2.525***|2.534***|4.627***|4.631***|0.428***|0.220*|
||(0.040)|(0.042)|(0.023)|(0.024)|(0.122)|(0.125)|
|Observations|298|298|337|337|188|188|

Note: City-level pollution reflects PM2.5 data for 2014 for 298 cities (78 in developing countries). Congestion reflects the annual additional hours spent driving in rush hours, measured in 2018, and
key basic stylized facts of urbanization, development and structural transformation, this paper also makes two additional contributions to the literature. First, by implementing both the AI and cluster methods to defining urban areas, the paper provides the first systematic comparison of results between these two methods using a global data set. Secondly, because both of these methods rely on globally gridded population data sets for their implementation and there exist several alternative such data sets, the paper also provides the first systematic investigation as to how the choice of input data affects the results obtained from these methods. In particular, the paper analyzes how the use of three main gridded population data sets influence the results obtained from these methods. These data sets are the _LandScan 2012_ global gridded population data set that is produced by Oak Ridge National Laboratory, the _WorldPop_ family of gridded population data sets that are produced by the University of Southampton, UK, and the _GHSL Global Population Grid_ (GHS-Pop) that has recently been generated by the European Commission’s Joint Research Center (JRC).<sup>12</sup> 9 The requirement that the methods be implementable at a global scale automatically precludes methods that rely on commuting flow data. Applications of such methods have been restricted to either individual countries (e.g. Duranton, 2015; Nelson and Rae, 2016; Sanchez-Serra, 2016) or small samples of countries (OECD, 2012).

10 Uchida and Nelson built on earlier work by Chomitz, Buys and Thomas (2005) which identified population density and remoteness from large metropolitan areas as important dimensions along which to classify an area’s degree of rurality (and, therefore, implicitly, its degree of urbanization). Chomitz _et al._ found that many small settlements in LAC that are officially classified as urban are actually embedded in an agriculturally based countryside. See also the World Bank’s 2005 LAC Flagship report “Beyond the City: The Rural Contribution to Development” (de Ferranti _et al_ ., 2005), for which
apprentices by quotas set by firm size and lowered the minimum wage of apprentices. They found that firms in sectors requiring high skills inclined to avoid training apprentices due to high cost of training in these sectors, while firms in low-skill sectors sought apprentices. They also showed that in general equilibrium the reform increased the overall output of the economy and the welfare of apprentices in all sectors, but it reduced the welfare of firms in high skilled sectors and workers in low skilled sectors, indicating a replacement of a higher wage employee with a lower wage apprentice.

Country level studies of ALMP in developed countries reveal mixed results. For instance, PES participation in France led to increased employment rates (Fougère et al. 2005), while a reemployment program in the US state of Nevada raised participant earnings (Manoli et al. 2018). However, in the US city of Detroit, job placement programs increased earnings solely through direct-hire placements, whereas temporary help placements had zero or negative effects on earnings (Autor et al. 2017). Moreover, in France and Denmark, although job search assistance increased the employment of participants, it also decreased the employment of non-participants (Crepon et al. 2012; Gautier et al. 2018). As for training programs in developed countries, Kluve (2010) discovered that integrated training programs in Europe increased employment rates by 40–50 percent compared to traditional methods. Yet, youth-focused programs had a 40–60 percent lower success rate in enhancing employment compared to adult programs.

# **3. Description of UB and ALMP Data and Stylized Facts**

This section details the novel UB and ALMP data utilized in the paper. It includes a statistical analysis to reveal patterns and correlations with other economic indicators. In addition, drawing on World Bank's Social Protection and Jobs Policy Inventory data, it offers a descriptive analysis of the UB and ALMP reforms implemented during the Covid-19 pandemic.

## **3.1. Description of the data**

UB and ALMP data were
levels to investors, it seems somewhat easier for developed countries to attract PPPs without IRAs. There are also a number of countries in which the sector has not relied on any PPP but has adopted the IRA model, less than 6% for developed economies and less than 8% for developing ones. The differences are somewhat stronger for the groups of countries with PPP but no agencies. Finally, in our sample of 54 developed economies, there is no instance of countries without an IRA and without a PPP jointly.

This very basic data analysis already points to the uncertainty of the size and sign of the correlation between the existence of an IRA and any experience with PPP. But there are clearly other factors that will drive the outcome and the extent to which an IRA may help or not access to PPP. These differences may actually be quite important in the context of developing countries since the initial conditions on core water market characteristics are likely to differ. Coverage is indeed not yet universal for the water sector and even less so for sanitation. The ability to pay also varies across countries, reflecting wide differences in income per capita. Institutional differences are also likely to influence the supply of PPP since it drives the levels of risks for investors.

The initial conditions of the sector are approximated by the access rates available from the World Development Indicators database and the quality of the governance of the sector. Institutional quality is approximated by three types of indictors: a regulatory quality indicator, a rule of law indicator and the control of corruption approximated by the ratings provided by the World Bank Governance Indicators. These indicators run from ‐2.5 to 2.5, with higher values corresponding to better governance for each country. We also consider the possible relevance of the extent to which water regulation is decentralized based on a survey produced by the OECD (2016). Because we are interested in the supply of private capital, it seems reasonable to include a price variable. We approximate this driver of supply by the inflation rate.
domestic credit to the private sector, urban population share, population growth, and the share of working age in total population.

- iii. **Access to basic infrastructure:** This includes the share of the population with access to electricity, the share of people using at least basic drinking water and sanitation services, and the share of people using the internet. We also include an indicator for rural connectivity (the Rural Access Index<sup>24</sup> ).

- iv. **Governance:** This includes type of regime (authoritarian, democratic or hybrid) and a set of World Bank Governance indicators such as voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law and control of corruption. This category also includes indicators of violent conflict from the Uppsala Conflict Data Program (UCDP)<sup>25</sup> ( average number of total fatalities per 1,000 people, dummy for years when total fatalities exceeded 0.08 per 1,000 people and dummy for years when total fatalities exceeded 1,000 people<sup>26</sup> ).

To assess whether the effect of economic growth on household welfare as measured by surveys is mediated by any of the specific variables mentioned above, we estimate the following specifications: ∆L i i i ) +∈i Where L i is the change in household consumption or income measured from surveys during time ii0 ii0 LLYY = αα+ ββ∆L LLG PP_C CC + γγXX + δδ(∆L LLG PP_C CC ∗XX period t (a spell between two successive surveys), ∆L i is the change in GDP per capita (from LLYY national accounts) during the time period, and is the variable that potentially mediates the effect of LLG PP_C CC GDP growth on household welfare growth. All regressions also include regional dummies (to control for ii0 XX time-invariant omitted variables at the regional level), the baseline survey mean (survey
strong communication strategy to reduce the risk of stigmatization and implementing the CBT workshops and math tutoring during normal schooling hours will be crucial.<sup>11</sup> Policy makers in Mexico and throughout Latin America need to recognize that secondary school dropouts are the outcome of elementary education systems that are not providing all students with sufficient foundational skills. Basic numeracy, literacy and socioemotional skills are essential to face the academic requirements of upper secondary and, later in life, to secure a formal, wellremunerated and stable job. PODER begins to show that, even among youth with a large deficit in foundational skills at age 15, progress can still be possible.

# References

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- Adelman, M.; Haimovich, F.; Ham, A.; and Vazquez, E. (2017). “Predicting School Dropout with Administrative Data: New Evidence from Guatemala and Honduras”, _World Bank Policy Research Working Paper_ #8142.

- Atkin, D. (2016). “Endogenous Skill Acquisition and Export Manufacturing in Mexico”, American Economic Review, American Economic Association, vol. 106(8), pages 2046-2085, August

- Avitabile, C.; and de Hoyos, R. (2018). “The Heterogeneous Effect of Information on Student Performance: Evidence from a Randomized Control Trial in Mexico”, _Journal of Development Economics_ 135: 318-348.

- Barrera-Osorio, F.; Bertrand, M.; Linden, L.L.; and Perez-Calle, F. (2011). “Improving the Design of Conditional Transfer Programs: Evidence from a Randomized Education Experiment in Colombia”, _American Economic Journal: Applied_ 3(2): 167-195.

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- 68 -

more diversified local economy. But there are equally vast regions where agricultural resources are uniformly poor. If agricultural investments do not have a sufficiently high rate of return to enable a poor region to compete effectively in national or international markets characterized by depressed commodity prices, undertaking such investments wiii not benetit such a poor region. If Investments are provided on a subsidized or a grant basis, they may simply be a disguised income transfer. In such instances, it is often more cost-effective to invest in nonagricultural development or to provide income transfer in some other form. But in this case, rapidly growing populations of resource poor regions must be given opportunities to migrate to lower density areas, as labor data indicate that not all people can be provided with employment in urban areas. To forego these benefits for political reasons is as erroneous as foregoing gains from trade. The task is to find ways to mitigate the political problems and to protect both immigrant and resident populations from adverse effectst not to stop migration or to fail to encourage it.

140. However, such movements must be planned and controlled to achieve stability. In some instances, years of good extensio I work result in local prosperity, only to be lost in one season by a wave of immigrants and their livestock. In addition, uncontrolled immigration and settlement on steep land and poor soils may offer certain disaster in a few years.

141. Agricultural systems can adapt successfully to very high population densities. Nevertheless, the long-term prospects for rural populations can be improved by reducing the rate of population growth.
2017.

Being unbanked in ECA is also associated with lack of labor force participation. About 60 percent of unbanked adults in developing ECA are out of the labor force. The share of account ownership among people out of the labor force was 12–16 percentage points lower than the average for ECA in 2011, 2014, and 2017. There are a few exceptions. In Azerbaijan and Uzbekistan, where overall account ownership is low (30–40 percent), the share of unbanked adults who are out of the labor force was lower than the ECA average.

# Lack of Trust in Banks Is a Major Barrier to Account Ownership

To help understand why people may remain unbanked, the 2017 Global Findex survey asked adults without a financial institution account why they did not have one. Globally, the most common reason offered was having too little money to use an account. Two‐thirds of unbanked adults across the world cited this as a reason for not having an account, and about one‐fifth cited it as the only reason. About a 11
be used for harmonized targeting of several different social welfare programs. Finally, an advantage of the PMT over other targeting methods is its transparency and verifiability. It provides a clear, unbiased determination of a household’s eligibility whereas other approaches, such as simple means tests and community‐level targeting, are more susceptible to reporting errors and imprecise definitions, and may be inconsistently applied in different areas. Despite these arguments for using PMT in Sri Lanka, we recognize that this method of identifying beneficiaries of government transfers may fall short in effectively reducing poverty (Brown et al. 2016).

We have estimated and tested three models that parsimoniously predict household welfare from observable and verifiable information. These models have similar targeting performance, but the model that includes characteristics of the household head slightly outperforms those that exclude these characteristics. Any of these proposed PMT formulas would significantly improve the targeting efficiency and coverage of Samurdhi relative to the existing targeting approach, which is based on self‐declared income. We expect that an examination of other programs, including elderly support and disability benefit programs, would show similar potential improvements in targeting performance from adopting a PMT.

An important issue when considering a PMT in the Sri Lankan context is that Samurdhi eligibility is currently determined separately for each family, whereas the PMT formula is based on a household‐level survey. It is not possible to accurately verify whether the definition of the household unit used in the HIES is comparable to that of the family unit used by the Samurdhi program. This is complicated by the fact that neither does a formal definition of a family exist, nor does there appear to be one applied uniformly by 19