rafmacalaba/data-use-mentions-extended huggingface dataset:
their broader role in local, national and international governance with an expanded array of responsibilities associated with serving as nodes of national and international connectivity and special needs of a knowledge based local economy. Grant design also must incorporate incentives and accountability mechanisms to ensure responsible and accountable local governance. This paper provides a synthesis on conceptual underpinnings of this literature as well as providing a brief overview of practices across the world based upon a review of 41 metropolitan areas. It must be noted at the outset that the assignment of responsibilities must underpin any design of grant program (see McMillan, 2008). With appropriate assignments or reassignments, it is possible to minimize need for higher level assistance for metropolitan areas. However, this paper takes these assignments (or mis-assignments) in practice as given and examines options in grant design to facilitate better functioning of metropolitan governance. An overall theme of this paper is that grants can be (and should be) properly designed in most any institutional/organizational setting --- even if the organizational setting may not seem to be ideal.

The rest of the paper is organized as follows. Section 2 provides a typology of grant instruments and discusses their rationale and relevance for metro areas. Section 3 provides conceptual guidance on grant financing metropolitan services. Section 4 outlines stylized models of metropolitan governance and draws implications for the design of higher level transfers. It also discusses impliations of exsting institutional arrangements for developing a grant strategy for 3
capita**|0.72|0.35|0.32|0.40|0.38|-0.32|-0.25|-0.02|
||(0.00)|(0.02)|(0.03)|(0.01)|(0.01)|(0.04)|(0.10)|(0.90)|
|**Ln GDP**|0.90|0.52|0.15|0.41|0.54|-0.48|-0.35|0.16|
||(0.00)|(0.00)|(0.31)|(0.01)|(0.00)|(0.00)|(0.02)|(0.34)|
|**Ratio of exports to GDP**|0.27|0.53|0.08|0.45|0.43|-0.32|-0.32|-0.03|
||(0.08)|(0.00)|(0.59)|(0.00)|(0.00)|(0.04)|(0.04)|(0.84)|

Notes: P-value shown in parentheses. The measures used in the correlations are based on measures in the countryyear level dataset (file CY.dta) averaged across the 2006-2008 period for each country for each country except Kuwait for which averages are across the period 2009-2010 and Portugal for which averages are across the 20032005 period.

Table 4 and the figures in Appendix 3 show that the results are quite similar when the correlations are established with either the economic size of countries or the importance of their export sector instead of their level of development. In terms of magnitudes, the
We show that the receipt of in-kind benefits, primarily education, is what drives which groups receive the largest net benefits from the fiscal system. This echoes the finding in Rodriguez and Wai-Poi (2021) that much of the progressivity of the fiscal system in Jordan comes from these transfers. Our results also show that the fiscal system in Jordan is reducing within-group inequalities, which represent over 80 percent of total inequality for both fiscal and care groups. Most of the reduction happens between market and disposable income, that is, when direct taxes and transfers are added to the fiscal system. The fiscal system has very little impact on inequalities across groups, although they are very small to begin with.

The next section briefly reviews recent relevant literature. Section III presents the methodology. Section IV shows the results for fiscal and care household types and Section V concludes.

# II. Literature review

Fiscal incidence studies have been conducted in many countries around the world, including various countries in the Middle East and North Africa (MENA) region,<sup>4</sup> following a recognised methodology largely used in developing countries known as the Commitment to Equity (CEQ).<sup>5</sup> This approach uses standard incidence analysis for each tax and transfer, where these fiscal instruments are allocated to households based on the information in national representative household surveys. This study is based on and extends the most recent fiscal incidence study for Jordan (Rodriguez and Wai-Poi 2021).<sup>6</sup> It assesses the incidence of Jordan’s main taxes and transfers as of 2018 using data from that year’s main household survey, the Household Income and Expenditure survey (HEIS). The main finding is that Jordan’s system of taxes and transfers is only modestly progressive, barely narrowing the pre-fiscal disparities. Comparing inequality based on households’ market incomes with that based on their post-fiscal incomes (after paying income and consumption taxes as well as receiving government transfers and subsidized services), the
GG GG GG G GG GG GG GG GG GG GG GG GG GG GG GG GG GG<br>G G G G G G G G G G G GG G G<br>G<br>G G G G G G G G G G G G G G<br>1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15<br>7 4e+05 25<br>6 3e+05 20<br>5<br>15<br>2e+05<br>4<br>10<br>1e+05<br>3<br>5<br>0e+00<br><!-- End of picture text -->

**Fig. 6.** FLS data: log posterior medians of _g_ , number of models visited and posterior mean model size The different priors on _g_ are ordered as in Tables 2 and 3. The top two rows correspond to fixed _θ_ (with different _m_ ), while the bottom two rows are for random _θ_ priors on model space

24
temporary structure. Only 58 percent had a toilet, 51 percent had drinking water and 40 percent had electricity (ibid).

These failures of service delivery reflect how schools function as a mechanism of patronage for Sindh’s feudal elite. In many cases, schools serve more as an extension to a landlord’s estate in a village than as a functioning public service (Gazdar, 2000). School buildings are frequently used for non-educational purposes, and politically-appointed teachers are protected from discipline and rarely attend schools (Babur, 2016); given the high rate of one-teacher schools, these high rates of absenteeism exacerbate school closures.<sup>3</sup> As a result of these governance failures, enrollment remains low in Sindh. According to the PSLM survey, 2010-2011, the Net Enrollment Rate (NER) at primary level (ages 6 to 10) in rural Sindh was 54 percent, compared to 67 percent in rural Punjab. Data from our baseline survey, conducted in three representative districts of Sindh, reveal a similar picture: fewer than half (49.8 percent) of school-age children in surveyed households were enrolled in school, and the majority of those unenrolled had never attended school **(Table 1)** .<sup>4</sup>

> 3 During the period of this study, the inadequacy of schooling facilities was exacerbated by extensive flooding in both 2010 and 2011. The 2010 floods destroyed an estimated nine percent of Sindh’s educational facilities and damaged an additional 19 percent and severely disrupted the livelihoods of about 865,000 Sindh households (Asian Development Bank and World Bank, 2011). The 2011 heavy rains resulted in the extent of damage similar to that of the 2010 floods (Asian Development Bank and World Bank, 2012). Schools which were not damaged were converted into temporary shelters for displaced people, disrupting teaching for several weeks. Overall, 66 percent of schools in the villages sampled for this study reported being affected by the floods at the time
Second, we analyze the response of selected labor market variables to CBI. If policymakers loosen regulations concerning contract and part-time work, we expect an increase in the incidence of part-time workers. For example, in the case of Korea, rising inequality has been attributed to an upshot in part-time and contractual work arrangements in the aftermath of CBI (Pirie, 2007). To capture this e↵ect, we take the incidence of part-time workers from the OECD Database (Teorell et al., 2018). In addition to an increase in more flexible work arrangements, we expect that a ‘flexibilization’ of labor markets and wage restraint agreements in social pacts lead to wage moderation (i.e., lower real wage growth). To capture wage moderation and to mimic wage dynamics, we use an aggregate inflation measure. The data come from (Teorell et al., 2018). The reason for using a rather wide measure for wage dynamics is that wages constitute an important component of inflation dynamics (Rudd and Whelan, 2007; Peneva and Rudd, 2017; Bobeica, Ciccarelli, and Vansteenkiste, 2019). For instance, Bobeica, Ciccarelli, and Vansteenkiste (2019, 1) analyzing four countries in the Eurozone find “ _there is a clear link between labor cost and price inflation_ .” Thus, we expect lower inflation to be strongly associated with wage moderation.

Finally, we verify that the constraining e↵ect of CBI on fiscal policy leads to a moderation of welfare expenditures. Take for instance the case of New Zealand. Shortly after implementing CBI in 1990, “ _most social benefit rates were reduced by approximately 9 percent_ ” (Evans et al., 1996, 1878). To capture changes in social policy outcomes, we rely on two measures. Besides analyzing changes in net household transfers, we study the response of social security benefit payments to CBI. For data, we rely on Berg et al. (2018).

domestic banking sector or directly invest into firms (Menaldo,
the constraints given in table 2. The aim of the merging process is to obtain regions as close to the target as possible and with population below threshold (e.g. 750) and area within a specified range (e.g. 0.1 to 9km<sup>2</sup> ) while ensuring that merged areas do not cut across obstacles or administrative borders. With a lower priority, the process also tends to produce shapes that are as compact as possible. By construction, the boundaries of the EAs resulting from the merging process will also follow geo-referenced features (or square sides, if quadtree squares are needed).

The merging process takes as inputs: 1/ the PU features defined in the split process, 2/ ranges of target population and area values for EAs (table 2), 3/ the gridded population density dataset to compute the population for re-merged region at each step, and 4/ a set of specified boundaries across which regions should not be merged (e.g. large rivers, delineation of urban and rural strata, administrative boundaries). Since this is a computationally intensive process, the method was applied to every 18 pre-war regions separately.

To complete the merging step, we used the Automated Zone-design Tool (AZTool) since it is publicly available, most compatible compare to the other tools for the purpose of this study and user-friendly. AZTool was developed by Martin et. (2002) and is based on Openshaw’s (1977) Automated Zoning Procedure (AZP). The AZP methodology was developed by the Office for National Statistics (ONS) for the 2001 census in England and Wales (Cocking et al 2011) and to revise census geography in New Zealand (Martin and Lyndon 2009). AZTool iteratively combines and recombines sets of geographic building blocks to generate larger zones optimized to meet a set of pre-defined user-specified constraints. Such specified constraints include population threshold (Min, Max and target) and compactness of the shape. Criteria and constraints used in
The nature and prevalence of these issues of representativeness is often highly dependent on the type of sampling frame used. This choice affects the extent to which a representative sample can be achieved, and post-survey adjustments can improve representativeness ex-post. While in-person surveys in lowand middle-income countries often follow the common two-stage sampling approach, phone surveys adopt a more varied set of approaches. There are three predominant approaches to establishing a sampling frame and drawing a sample for a phone survey, each with distinct advantages and disadvantages when it comes to both implementation as well as representativeness (Lepkowski et al. 2007; Glazerman et al., 2020; Himelein et al., 2020; Gourlay et al., 2021):

**1. Recontact:** Respondents are drawn from a list of phone numbers from previous surveys or programs or from a recent listing of households/holdings conducted within selected EAs (or other groups).

**2. Random Digit Dialing (RDD):** A sample of randomly drawn numbers corresponding to the construction of phone numbers in the country is drawn and called.

**3. Telecom list:** Respondents are drawn from a list of active phone numbers obtained in most cases from network service providers.

When it comes to agricultural surveys, these three approaches will sometimes require specific recommendations and considerations, which will be noted in the sections to follow.

# _Mobile phone ownership and coverage_

One of the challenges in order for mobile phone surveys to yield data representative of the population of interest is at the first step tied to the level of mobile phone penetration in the population (Leo et al., 2015). Mobile phone ownership is not universal in low- and middle-income contexts: mobile penetration rates were at 67 percent of the general population globally in 2019, and at 45 percent in Sub-Saharan Africa (GSMA, 2020), though penetration continues to increase at a significant rate. Moreover, agricultural households are less likely to own phones than the general population (Ambel et al.,
Policy Research Working Paper 7692

# **Abstract**

In the past decades, natural disasters have caused substantial human and economic losses in Central America, with strong adverse impacts on gross domestic product per capita, income, and poverty reduction. This study provides a regional perspective on the impact of hurricane windstorms on socioeconomic measures in the short term. Apart from modeling the socioeconomic impact at the macro and micro levels, the study incorporates and juxtaposes data from a hurricane windstorm model categorizing three hurricane damage indexes, which lends a higher level of detail, nuance, and therefore accuracy and comprehensiveness to the study. One standard deviation in the intensity of a hurricane windstorm leads to a decrease in growth of total per capita gross domestic product of between 0.9 and 1.6 percent, and a decrease in total income and labor income by 3 percent, which in turn increases moderate and extreme poverty by 1.5 percentage points. These results demonstrate the causal relationship between hurricane windstorm impacts and poverty in Central America, producing regional evidence that could improve targeting of disaster risk management policies toward those most impacted and thus whose needs are greatest.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at oishizawa@worldbank.org, jjmiranda@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 this paper are entirely those of the authors. They do not
PNG. Lao PDR and Myanmar added new respondents in each round to account for attrition and maintain representativeness, and thus restricting the sample to just panel respondents across rounds would have limited the sample size substantially. Instead, new respondents were included in the sample, which was possible due to pre-pandemic employment information that was asked to new respondents in each round. For PNG, an entirely new sample was surveyed in round 2, which had greater representation of households in the bottom 40 percent of the welfare distribution. We thus utilize retrospective information from the round 2 survey, which included questions about employment at the time of round 1 as well as pre-pandemic.

> 7 The respondents of mobile phone surveys in low- and middle-income countries typically underrepresented women, older people, the less educated, and people in rural areas (Lau et al 2019).

> 8 In an effort to maximize comparability across countries, the pooled sample of individuals were not used for Myanmar and the Philippines, which also did not have representative weights for the pooled sample. In PNG, individual-level weights were only available for the pooled sample of heads and respondents, and hence this sample was used to estimate employment impacts.

> 9 Indonesia – which targeted the household head or primary breadwinner – is at one extreme, while Philippines and Mongolia are at the other and are more similar to the actual percentage (47 percent in the original Mongolia sample survey, 34 percent in 2015 Philippines Census).

6
# **Appendix to “Estimating the Impact of Weather on Agriculture”**

# **A Details on Remote Sensing Weather Data**

## **A.1 On Using Remote Sensing Products for Economics**

Uncertainty is present in all model outputs, and weather data sets are no exception. Spatial data sets of weather variables, like precipitation and temperature, that are produced using remotely sensed data, are not direct measurements of the variable of interest. Satellite sensors provide spatially continuous observation of reflectance from the earth’s surface in different parts of the magnetic spectrum. These values are used to estimate related phenomena, such as cloud presence, cloud top temperature or earth surface temperature. The continuous data sets are then used in combination with directly observed, but often sparsely distributed, gauge data to produce weather variables. Some inputs are common across products, but there are differences in other inputs as well as modeling techniques.

The type of analysis matters in assessing weather data sets for use in economic research. Is the goal to understand climate trends, capture characteristics of a particular agricultural season, or identify extreme weather events occurring in near real-time? This can help determine the relative importance of different data set characteristics, such as spatial detail, temporal frequency and length of record, with respect to the intended analysis. The data sets used in this analysis were constrained by certain minimum criteria, leading to elimination of some commonly used data sets, such as the product from the Center for Climatic Research at the University of Delaware. We also did not consider proprietary data sets, preferring to use sources currently in the public domain. Despite the exclusions imposed by our minimum criteria, the data sets summarized in Table 1 represent a range of spatial resolutions and model types commonly used by economists. Further details on the specifics of each remote sensing product are provided below with the goal of providing economists with direction to a data set that meets the requirements of their analysis.

### **A.1.
# FMIS Budgetary Coverage

Information on the total number of expenditure transactions processed through it in a given period (e.g. an FY) and the amount of budgetary resources transacted by these transactions give the extent of its use and scope of its coverage. When these figures are calculated for the center and the provinces or other subnational levels of government separately, they provide information on the percentage of budgetary resources spent at the center and the percentage at the provinces.

The total amounts processed through the FMIS represent a high percentage of the total domestically financed budget for 2017 and this shows that the coverage of the FMIS is very good on an overall basis. Donor funds are not processed through the FMIS at present.

Table 8 shows that 62.9 % of the budgetary amounts are transacted at the National level and 37.1% at the provinces. However, the total number of transactions processed at the national level is only 8.7% compared to 91% at the provinces. This suggests that this area needs to be investigated further. This has been done and the reason for this disparity are explained below by looking at the granularity of the transaction data.

_Table 8 Total Amount Processed through the FMIS and Values they Represent_

||**Total no of**<br>**transactions**|**Share of total**<br>**no**|**Total amount**<br>**(Riels)**|**Share of the total**<br>**amount**|
|---|---|---|---|---|
|National level|19,004|8.70%<br>|12,794,095M|62.90%|
|Provincial|||||
|treasuries|198,470|91.30%|7,530,580M|37.10%|
|Total|217
for the firms during the recovery period. First, we re-define credit to not only include net financing from changes in short-term debt, long-term debt, equity issuances, and other financing activities but also cash from other sources such as that which could have been saved by cutting dividends, capital expenditures, and acquisitions. Specifically, when we include cash that could have been generated by cutting Dividends (data item #21), Capital Expenditures (data item #128), Net Investments ( _Sale of Investments_ data item #109- _Increase in Investments_ data item #113), Changes in Short-term Investments (data item #309) and Other Investing Activities (data item #310) and cash generated from Sale of Property, Plant and Equipment (data item #107) and Acquisitions (data item #129), we find that the percentage of candidate phoenix miracles in our sample decreases to 5.54%. This further reduces to 2.76% (92 firms out of 3338) once we include the Cash Flow from Operating Activities (data item #308).

> 22 We prefer to use the balance sheet data for constructing changes in short-term and long-term borrowing over 2009-2010 because the corresponding items in the Statement of cash flows are missing for a large portion of the sample.

25
# **Figure 2: Employment's Link to Education and Household Composition in Uzbekistan**

<!-- Start of picture text --> Employment rate by educational Employment rate by number of children<br>attainment in household<br>100% 100%<br>90% 90%<br>80% 74% 80%<br>70%<br>70%<br>60%<br>60%<br>50% 43%<br>50%<br>40% 33%<br>40%<br>30%<br>30%<br>20% 11%<br>10% 0% 20%<br>0% 10%<br>primary general high vocational tertiary 0%<br>or less secondary schoo; 0 1 2 3 4 5 6 7+<br>Male Female Male Female<br><!-- End of picture text --> _Source: Household Budget Survey of Uzbekistan, 2022_ In all three societies, intrahousehold power tends to be imbalanced, and gender norms tend to be more traditional, with women expected to prioritize caregiving responsibilities and men expected to be the primary breadwinners (figure 3). These expectations can limit women's participation in the labor market and their opportunities for career advancement. Additionally, women in all three countries are underrepresented in leadership positions and are more likely to work in lower-paying occupations (Muradova and Seitz (2021) show this is due in part to discriminatory hiring practices).

**Figure 3: Survey-based perceptions of women's role in work and family life**

<!-- Start of picture text --> Agree: Women should earn less than men to Agree: Women should spend less time<br>sustain peace in the family working than men to dedicate time at home<br>Urban Urban<br>Rural Rural<br>National 34% National 77%