rafmacalaba/data-use-mentions-extended huggingface dataset:
the LGUs and the relevant national government agencies to coordinate with each other. In some sectors (e.g., agriculture), there may also be a need to revisit the whole policy framework that governs the government’s approach to service delivery before tweaking the distribution of roles and resources across levels of government and in some cases this may require a sectoral legal reform. The prospect of expenditure-side reforms through administrative measures would also depend on whether or not higher-tier entities, be they NGAs or provinces, have the ability to induce voluntary cooperation of the LGUs to achieve more rational distribution of expenditure responsibilities vertically across levels of government and horizontally among LGUs at the same level.

This paper mainly focuses on assessing the political feasibility of a legislative reform on the revenue side of the decentralization framework, with particular focus on the reform of the inter-governmental fiscal transfers, Internal Revenue Allotments (IRA). LGUs’ incentives to provide public services are analyzed in a separate paper based on an original survey of LGUs and households in a single province (Khemani 2011).

# **Political Economy of Fiscal Decentralization Reforms: Three Analytical Perspectives**

The fiscal reforms needed to address the imbalances in the current system of decentralization involve redistributing financial resources from the central government to LGUs and from some LGUs to others. We would never expect the first set of reforms to be approved by central government officials unless 4
districts adhered to the randomization varied considerably.<sup>8</sup> Jombang, Bondowoso, Blitar, and (to a lesser extent) Madiun performed well in this respect. Banyuwangi and Probolinggo performed less well, with triggering detected in only 50 percent of treatment villages. Situbondo and Ngawi adhered the least to the randomization plan, with treatment detected in only four of the 10 treatment villages (40 percent). The contamination of control communities does not vary markedly across districts.

Next, we look at the extent of exposure to TSSM by households in treatment communities. Table 6 presents evidence of triggering activities as reported by households in the endline survey.<sup>9</sup>

> 7This means that one or more of the four community leaders surveyed (village head, community head, health cadre, head of women’s organization, or other community leader) reported that the village had received a triggering. 8An independent study by Mathematica (2011) found similar results, in which program implementation varied heavily across the six districts they studied (three of the six districts are in our sample), depending upon capacity levels and cross-sectoral commitment to the program.

> 9This is in response to the question, “In the past two years, has there been a community event (such as a triggering/motivational talk) where sanitation was mentioned in this village?” Respondents were subsequently asked

11
survey data is the likelihood of underreporting, which often happens because women fear stigma or retaliation or because they may normalize incidents of domestic violence in cultures where IPV is “acceptable.” Surveys that only include a module on violence result in a lower incidence of reported violence relative to surveys dedicated to violence, such as Demographic and Health Surveys, which allow more opportunities for women to reveal instances of victimization (UNSD 2014). These discrepancies suggest that underreporting leads to measurement error in many of the outcome sources that aim to capture the prevalence of IPV. Self-response technology through questionnaires can improve on this because they allow literate individuals to report violent incidents without fear of stigma from the enumerator. This is the case of the British Crime Survey (Anderberg et al. 2016). Nonetheless, Cullen (2020) finds that, in Nigeria and Rwanda, self-response technology continues to underreport by up to 50 percent compared with list experiments, which provide greater anonymity. Agüero and Frisancho (2017) find that, in Peru, underreporting is more likely to occur among specific groups of women, especially women with more 3
# _2.3.1 GHG footprints_

Life cycle assessment (LCA) is a widely used technique for environmental impacts evaluation of products and processes over a full life cycle. However, life cycle inventory data are often decentralized and very difficult to harmonize (Green et al. 2020). Hence, we used data that represent averaged production methods in a given country or in the world instead of using LCA data that are specific for a production method. In the absence of LCA data, the less ideal GHG footprints data should be both item specific and country specific. However, such information was not available for all items in our analysis. Thus, we categorized our items into country specific (CS) items and non-country-specific (NCS) items. Within each of the two categories, the items were further grouped into animal food items, and plant-based food items plus other non-animal food items. CS footprint data come from GLEAM-i<sup>4</sup> tool as made available in Kim et al. (2020) which only reports animal food items. Therefore, CS items are all animal food items whereas NCS items are mostly plant-based food plus several insects and aquatic products. For NCS items, as CS footprints were not available and unapplicable, we used items’ footprints distribution (Kim et al. 2020) and substituted item specific footprints with world weighted average footprints. These imported NCS items are deemed to be imported from the “world” in terms of GHG footprints, even though they might have trade records indicating their country of origins (COOs) in FBS or TM data sets.

# _2.3.2 Trade patterns_

To account for varying GHG footprints in different production systems, we traced food items back to their COOs. We used the two-step method (Kim et al. 2020) described below to decompose consumptions into quantities imported from all COOs. The imported quantities are proportional to COOs’ shares of total domestic supply, which can be obtained from FBS and TM data sets.<sup>
protection programs and other welfare indicators in the EU-SILC. Traditionally, it is possible to combine information via record linking by relying on common household identifiers between two surveys. This is not possible in the case of HBS and EU-SILC, as the sample of households included in each survey does not overlap.

**In this paper, we explore statistical matching techniques to combine both datasets and impute energy spending shares into the EU-SILC.** While datasets are traditionally merged via record linking, an increasing number of researchers has explored the potential of statistical matching for combining datasets without a common identifier, especially in the European Union (see, for example, Donatiello et al. (2016b) for an application to Italy; Serafino and Tolkin (2017) for an example using data from six European countries; Lamarche et al. (2020) for the European context more broadly speaking; Schaller (2021) for an application in Germany, France, and the Netherlands; Emmenegger et al. (2022) for an application in Germany). We build upon the existing body of literature and apply statistical matching techniques to impute the energy spending share in Bulgaria reported in the HBS 2019 into EU-SILC data collected in 2020. We use EU-SILC data from 2020, as income data in the EU-SILC always refers to the previous year (so 2019 in this case).

**Imputing energy expenditure shares and energy poverty into the EU-SILC facilitates future research questions, such as exploring the impact of rising energy prices on the poorest households.** Our imputation exercise is helpful given that we can identify if those households belonging to the lowest income quintiles are those most affected by energy poverty. In addition, having information on energy expenditure shares in surveys that provide information on monetary poverty indicators allows us to evaluate the impact of rising energy prices on the poorest households. Moreover, given that the EUSILC contains detailed information on social protection programs, we can explore the impact of potential mitigation strategies in response to rising energy prices by combining information from both surveys
was in textile and garments (see table 1).

> 6 In longitudinal establishment-level data studies, a pervasive finding is that idiosyncratic factors dominate the distribution of growth rates of output, employment, investment, and productivity across establishments. Seemingly similar plants within the same industry exhibit behave quite differently in terms of real activity at cyclical and longer-run frequencies. Even in the fastest-growing industries, a significant fraction of establishments decline substantially. Similarly, a large fraction of establishments in the slowest-growing industries grow dramatically. Hence, the underlying gross microeconomic changes in activity dwarf the net changes that we observe in published aggregates (Haltiwanger, 1997). Weights which rely on the stratification of random samples and that aim at reproducing aggregate dynamics may distort performance analysis at the micro level.

7
# **Digital Payments and the COVID-19 Shock:**

# **The Role of Preexisting Conditions in Banking, Infrastructure, Human Capabilities,**

**and Digital Regulation**

Robert Cull, Vivien Foster, Dean Jolliffe, Daniel Lederman, Davide Salvatore Mare, and Malarvizhi Veerappan

**JEL Codes:** G5 Household Finance; G53 Financial Literacy; H12 Crisis Management; C26; Instrumental Variables (IV) Estimation

**Keywords:** digital payments, financial inclusion, COVID-19, Global Findex Database
country under the assumption that every household in a developing country would be given access to simple or more advanced water and sanitation technologies:<sup>14</sup> where _DP_ is the absolute number of deaths prevented and _birth_ is the absolute number of children born per year in a given country. Following the methodology proposed by Mathers et al. (2001), we can convert the number of deaths prevented into a (discounted) sum of lifeyears saved (LYS): where _n_ is the standard (global) life expectancy at age 5 – our proxy for years lost due to death at age 5 - and _δ_ is the discounting rate for future years lived. As suggested by Mathers et al. (2001), we assume a 3 percent discount rate, uniform age weights and a standard life expectancy of 75 at the age of 5.

Last, we combine investment and maintenance costs and durability estimates of various categories of water and sanitation infrastructure (WHO/UNICEF, 2000; Hutton and Haller, 2004; Clasen et al., 2007; see Table 9 for details) with the national coverage levels displayed in Table 3, to compare the expected costs under the two scenarios to the expected benefits in terms of life-years saved. The estimated costs per life-year saved under a given scenario can be computed as where _costS_ , _costW_ are the investment and (discounted) maintenance costs for sanitation and water infrastructure over the estimated life time. _Covs_ , _CovW_ are the fractions of household with appropriate sanitation and water access prior to the interventions, and _HH_ is the total number of households in the country. Similar to equation (5), δ is the discounting rate for future years lived, which we fix at 3%. _T_ is the number of years the water and sanitation infrastructure of interest is expected to last.

> 14 Child mortality reflects the likelihood of a child born in period _t_ dying in the period _t_ to _t+5_ . Under the simplifying assumption that the previous cohorts are of approximately
operational question for governments is whether taxpayers believe that taxes are, in fact, used for national development – and, if not, how to build greater trust.

Surveys across the developing world suggest that taxpayers are, at least in principle, strong believers in the importance of taxation to fund government. Data from the Afrobarometer survey, for example, find that two-thirds of respondents on average believe that citizens should pay their taxes to support national development.<sup>132</sup> Faith in the general importance of taxes often exists even in areas of very poor service delivery. A recent survey in the Democratic Republic of Congo, for example, found that over 70 percent of respondents support taxation to fund national development.<sup>133</sup> It is occasionally suggested that developing country taxpayers may not understand, or value, the connection between taxation and national development. However, the empirical evidence shows unambiguously that broad support for effective taxation exists across a wide range of contexts.

Accordingly, there is mounting empirical evidence that if tax revenues are translated into public goods, this is associated with higher levels of tax morale and compliance. Recent survey and experimental evidence from Latin America offers clear support for a connection between perceived reciprocity and willingness to pay taxes.<sup>134</sup> This replicates more basic data from a variety of surveys from across the African countries.<sup>135</sup> An OECD survey conducted in 26 Asian countries showed that a majority of their citizens are in favor of tax increases if they are linked to more government spending and provision of public goods.<sup>136</sup> While taxpayers express an in-principle commitment to taxation, they frequently harbor significant doubts about whether tax revenues are, in fact, used to support the broader public good. Survey respondents in the DRC, as noted above, expressed strong in-principle support for taxation, but 40 percent of respondents said that in practice they had refused or would refuse to pay taxes.
Policy Research Working Paper 8595

# **Abstract**

A burgeoning area of social science research examines how state capabilities and bureaucratic effectiveness shape economic development. This paper studies how the management practices of civil service bureaucrats correlate to the delivery of public projects, using novel data from the Ghanaian Civil Service. This paper combines hand-coded progress reports on 3,600 projects with a management survey in the government ministries and departments responsible for these projects. The analysis finds that management matters: practices related to autonomy are positively associated with project completion, yet practices related to incentives/monitoring of bureaucrats are negatively associated with project completion. The negative impact of incentives/monitoring practices is partly explained by bureaucrats having to multitask, interactions with their intrinsic motivation, their engagement in influence activities, and project characteristics such as the clarity of targets and deliverable outputs. The paper discusses the interplay between management practices and corruption, alternative methods by which to measure management practices in organizations, and the external validity of the results. The findings suggest that the focus of many civil service reform programs on introducing stronger incentives and monitoring may backfire in some organizations, and that even countries with low levels of state capability may benefit by providing public servants with greater autonomy in some spheres.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at drogger@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in
# _Reluctant Private Sector_

The financial modalities of the large-scale infrastructure projects are still unclear. One issue of public-private co-funding is a key aspect. As in most East Asian countries, public sector provision was for a long time the only service provision model. The costs of low tariffs were absorbed by government budgets, quasi-fiscal loans from statecontrolled financial institutions, capital consumption or reducing operations. The bulk of infrastructure spending occurred through state enterprises, many of which established during the 1970s. Some of them are viewed to be efficiently run (Thai Airways, Petroleum Authority Thailand, Airports of Thailand, EGAT), while others are judged to be inefficient and providing poor services (WMA and State Railway Thailand). Only in the late 1980s, the private sector became an increasingly important force. The private sector has invested approximately $190 billion in East Asian infrastructure since 1990. However, this is a minor share of the region’s needs, and a minor share of total infrastructure investment in the past.

Private sector involvement in infrastructure provision in Thailand is limited both sectorally (expressways, electricity generation, water supply, telecommunications, and housing for workers) and spatially (extended Bangkok region, including the Eastern Seaboard, Ayutthaya, and Western Amenity regions) (Kaothien and Webster 1996). In fact, in 2003, private infrastructure spending amounted to no more than 7 percent of public and private spending (Figure 14.C). Increased private sector participation is constrained by political resistance to user charges even in urban areas, and lack of clear regulatory environments both at the national and local levels (Webster and Theeratham 2004).<sup>13</sup> Given the private sector’s caution to engage in large-scale projects, it is not evident that many private companies will be forthcoming with resources. A 2004 perception survey of 50 private companies active or interest in East Asia infrastructure investment shows that private sector sentiment towards infrastructure investment is contingent on policy improvements and reduced risks (ADB, WB and JBIC 2005).
consumed by the rich but also by the middle-classes and some poor in _rich_ countries;<sup>16</sup> second, income of rich people in each individual country that is missed out by surveys (for example, property incomes). All actual recipients of these incomes are globally-rich because the middle-class and even the poor from the rich world are in the top quintile of global income distribution, but the difference between the two aggregates is spread much more widely: some of it is imputed to the poor in the poor countries which, we know, receive none of it.

Consider the following example. Let the poverty line be $PPP1 per capita per day. Let average per capita disposable income from surveys of several groups of the poor in India may be $0.75, $0.8, $0.85. Now, suppose (very realistically) that India's GDI is some 35 percent higher than disposable income. We know that most of these 35 percent is received by the rich, either because they benefit from publicly-funded education or because they fail to report their property incomes. What the authors (in particular, Sala-iMartin, 2002 and Bhalla 2002) then do is to multiply incomes of the poor by the factor of 1.35. Then, suddenly, none of them is any longer poor: they have all crossed the poverty threshold!<sup>17</sup> But if we decide that “income” in global studies should be the same concept as in national studies of inequality—that is household per capita disposable income—the problem is not solved yet. This is because national definitions of survey income are very different, and the more countries we include, the more different they become. A huge effort, conducted by Luxembourg Income Study, has gone into standardization of national definitions. A similar project is underway at the World Bank using Living Standards Measurement Surveys (LSMS). Yet the standardization covers only a small portion of all countries and
evidence for intergenerational transmission of entrepreneurship is stronger for men than for women.<sup>17,18</sup> For purposes of comparability the results of univariate probit models are presented in Appendix D; these are very similar to the ones obtained using the multivariate probits presented here.

# **5. PRODUCTIVITY**

# **(a) Female Firms Are Smaller and Less Productive**

Female owned firms have significantly lower sales on average than male owned firms in all countries surveyed. This is in part because they are significantly smaller, as is demonstrated in figure 1 which plots the densities of firm-size by gender for each country. Note that the majority of female firms employ only one worker. Table 4 presents descriptive enterprise statistics by gender, and shows that female firms are more likely to be household enterprises than male run firms and that, with the exception of Indonesia, they tend to have lower labor productivity, measured in terms of sales per worker. The likelihood of having a female manager is inversely correlated with firm size. A similar inverse correlation between size and female management participation has also been documented for large, urban-based manufacturers in Latin America (Bruhm, 2009), Africa (Halward-Driemeier, forthcoming) and Eastern Europe (Sabbarwal and Terrel, 2008).

Gender differences in average output per worker are large, but vary dramatically across countries. For example, the largest gender difference is found in Bangladesh where the US dollar equivalent of mean log output per worker in female firms is $282 (5.64 in logs), while it is $2345 (corresponding to 7.76 in logs) for male run firms. In Indonesia, by contrast, there are no significant gender differences on average, with mean log output per worker in female firms being equivalent to $1720 for women and $1863 for men (7.45 vs 7.53 in logs respectively). While female firms are on 15
The need to protect vulnerable assets against natural disasters is apparent and one of the first steps towards achieving this is the understanding of how vulnerable assets are exposed to hazards. In this report, we make use of an accurate dataset to identify the number and locations of Chinese wastewater treatment plants exposed to changing probabilities flooding in the context of climate change, as well as those plants that are exposed to significant earthquakes. We limit wastewater network impacts to treatment plant facilities, ignoring any conveyance infrastructure, overflows, and pumping assets located upstream. Further to this analysis, we calculate the number of users each WWTP asset serves, according to population and usage data. Only direct outage, i.e. those caused directly by the hazard are considered. Outages caused by dependent infrastructures, such as to electricity networks supplying the WWTPs are not considered herein.

# **2. Methodology and Data**

To examine how China’s WWTPs are exposed to flooding and earthquakes, we intersect their spatial locations with hazard and susceptibility maps. We present detailed methodology below.

# **2.1. Wastewater Treatment Plants and Users**

We firstly assemble a data set for wastewater treatment works (2,743 assets) from the Chinese Ministry of the Environment (Ministry of the Environment, 2013). The dataset contains full name of the treatment plant, daily capacity, actual amount of wastewater treated and city information. As we do not know the exact location for each plant, we resort to a thorough process of searching plant addresses online and geocoding these in Google Earth and/or Baidu to our database. We then validate this dataset with Google Satellite Images to verify their exact locations. Unfortunately, the location information of several of these treatment plants is either missing or inaccurate, with sometimes the stated plants locations being kilometers away from their actual locations as seen by satellite imagery. As such, we were able to locate 1,346 WWTPs, out of the orginial number 2,743. It is noted that the