rafmacalaba/data-use-mentions-extended huggingface dataset:
remittances promote financial development, measured by the ratio bank deposits to GDP, and the share of bank credit to GDP. Gupta et al. (2009) use a similar methodology as Aggarwal et al. and find analogous results for a sample of Sub-Saharan African countries. Focusing exclusively on Mexico and using municipality-level data, Demirgüç-Kunt, Lopez-Cordova, Martínez Pería, and Woodruff (2011) find that municipalities where a higher proportion of households receive remittances have a higher number of bank branches and accounts per capita, and larger shares of deposits to GDP.

Our paper contributes to the study of the impact of remittances on the financial sector in several ways. First, to our knowledge, this is the first paper to analyze the impact of remittances on financial inclusion directly. While previous studies have looked at the link between remittances and aggregate deposits and credit amounts, this is the first study to examine whether remittances foster the use of deposit accounts and credit by households. This is important given the literature that has identified beneficial effects from financial inclusion. Second, by looking at whether households apply for loans, this study is able to examine the impact of remittances on the demand for credit and not purely on credit outcomes. This is useful because it can allow us to begin to assess to what extent remittances might relax credit constraints. Third, by using household-level panel data, this study can circumvent some of the limitations of the previous studies. In particular, the survey data used in this study has the potential to capture remittances flows received through formal and informal channels, minimizing concerns about measurement error in remittances.<sup>4</sup> Also, because we use household-level panel data we are able to control for unobserved household characteristics that can affect both remittances and financial inclusion, something that was not possible in the earlier studies. Finally, our study offers evidence on the impact of remittances on financial inclusion for a new country - El Salvador – for which remittances represent a very significant share of GDP.

We conduct probit estimations
Equation (3), in turn, presents the reduced form equation specification for individual transitioning that specifically defines the host occupation of the transitioning. That is, _P_ ( _T_ i= _k_ ) accounts for the probability of transitioning into the _k_ -th occupation category, i.e., transitioning into inactivity, unemployment, informality or formality, respectively: Results confirm several findings from transition matrices (Table 8, Column 1). Age increases the probability of transitioning but at a decreasing pace, the turning point being 42 years old. Education also has an inverted U pattern but it is not statistically significant. Household heads are less likely to transition than others household members. The occupational category in 2008 also plays a role into determining the 2009 labor status, consistent with the transition matrices results. Informal workers are more likely to transition than formal workers, but the formal self-employed are the most likely to transition and, as seen below, more so into inactivity rather than informal jobs. Being covered already by health insurance reduces the probability of transitioning, but pension coverage does not have an impact on that decision. Our constructed indicator for preference biases—capturing the degree of unbalance between a high valuation of a given program and a low level of knowledge about it—is not found to be statistically significant either. In other words, even when preferences may play a role in labor decisions, personal and labor circumstances remain dominating factors.

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Doss, Cheryl. 2013. “Intrahousehold Bargaining and Resource Allocation in Developing Countries.” _World Bank Research Observer_ 28 (Oxford University Press): 52–78.

Doss, Cheryl R., Carmen Diana Deere, Abena D. Oduro, and Hema Swaminathan. 2011. “The Gender Asset and Wealth Gaps: Evidence from Ecuador, Ghana, and Karhnataka, India.” Indian Institute of Management, Bangalore.

_Economic Times._ 2021. “Maharashtra Offers 1% Stamp Duty Concession to Women Homebuyers.” March 8, 2021. https://economictimes.indiatimes.com/news/politics-and-nation/maharashtra-budgetconcession-in-stamp-duty-for-women-increased-tax-on-liquor/articleshow/81393614.cms.

Goldstein, Markus, and Christopher Udry. 2008. “The Profits of Power: Land Rights and Agricultural Investment in Ghana.” _Journal of Political Economy_ 116 (6): 981–1022. doi:https://doi.org/10.1086/595561.

Grosch, K., and H. A. Rau. 2017. “Gender Differences in Honesty: The Role of Social Value Orientation.” _Journal of Economic Psychology_ 62: 258–67.

Harding, Michelle. 2020. “In Tax, Gender Blind Is Not Gender Neutral: Why Tax Policy Responses to COVID-19 Must Consider Women.” _Ecoscope_ , June 1, 2020. https://oecdecoscope.blog/2020/06/01/intax-gender-blind-is-not-gender-neutral-why-tax-policy-responses-to-covid-19-must-consider-women/.

_Hindustan Times._ 2021. “October Saw the Highest Ever Revenue Collection of Rs3,200cre from Stamp Duty: Balasaheb Thorat.” November 13, 2021. https://www.hindustantimes.com/cities/punenews/october-saw-highest-ever-revenue-collection-of-rs3-200cr-from-stamp-duty-balasaheb-thorat101636819430276.html.

Janaagraha Centre for Citizenship and Democracy (JCCD). 2017. Annual Survey of India’s City-Systems. https://janaagraha.org/asics/report/ASICS-report-2017-fin.pdf.

Joshi, Anuradha, Jalia Kangave, and Vanessa van den Boogaard. 2020. “Gender and Tax Policies in the Global South.” K4D Helpdesk Report
# Annex **1  Industry Groupings: WIFO Taxonomies**

Industry groupings are differentiated by various criteria, including a combination of factor inputs and market strategy criteria (taxonomy 1), and labor skills (taxonomy 2), borrowed from the Austrian Institute of Economic Research (WIFO)<sup>60</sup> taxonomy which provides a whole variety of different classifications (Peneder 1999, 2000).<sup>61</sup> The list of criteria is derived from trade and growth theories and includes labor intensity, capital intensity, skill intensity of workers, and technology intensity. All the distinct industry types are summarized in Table 12, below.

**Table 12 The WIFO taxonomies of manufacturing industry**

|_Taxonomy I_: Factor input combinations|
|---|
|•Marketing driven industries (MDI)<br>•Labour intensive industries (LI)<br>•Technology driven industries (TDI)<br>•Capital intensive industries (CI)<br>•Mainstream manufacturing (MM)<br>|
|_Taxonomy II_: Skill requirements|
|•Low-skill industries (LS)<br>•Medium-skill blue-collar industries (MBC)<br>•Medium-skill white-collar industries (MWC)<br>•High-skill industries (HS)|

The taxonomies were initially intended to offer a coherent set of empirical tools that facilitates inquiries into industrial performance with respect to the intangible sources of competitive advantage of European industries.

_Taxonomy I_ is based on US data for wages and salaries, investments in physical capital, advertising outlays and R&D expenditure. These are assumed to span four independent dimensions of productive inputs for revenue generation. Ratios to total value added have been calculated for wages and physical capital. Expenditures on advertising and R&D are represented by their ratio to the total sales. The latter are directly derived from balance sheet data.

_Taxonomy II_ is based on occupational data, distinguishing first the two types of whitecollar and blue-collar workers, and then for each,
to potentially none (Jones 2014; Caselli and Ciccone 2018).

In this paper, we provide a more direct and reliable measure of the quality of schooling on a global scale. We construct human capital stocks using our learning outcome data and produce development accounting results in a motivating application. We follow the literature (see Caselli (2005) for a review) and begin with a standard aggregate production function in its per-capita form following Klenow and Rodriguez-Clare (1997): 11
Determinants of export growth at the extensive and intensive margins: Evidence from product and firmlevel data for Pakistan José Guilherme Reis, Daria Taglioni<sup>1</sup> JEL Codes: D22, F10, F13, F14, F15 Keywords: Trade competitiveness, Exports growth; Extensive and Intensive margins Sector Board: Economic Policy 1 Both authors are from the International Trade Department of the World Bank. We want to thank Jose R Lopez Calix, Thomas Farole, J Daniel Reyes, Megha Mukin, Eric Manes, Nihal Pitigala, Florencia Pilacci, David Rosenblatt, David Gould and Paul Brenton for invaluable comments and inputs provided. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent 1
adjusted health benefits using information from the S-HDI (D’Souza, Gatti and Kraay, 2019). We adjust health benefits to account for the income quintile gradient in under-five stunting rates.

In a second sensitivity scenario, we follow an _insurance approach_ to allocate health in-kind benefits. While typically in-kind benefits are assigned based on service utilisation, it can be argued that being able to access those services, even if they are not needed, is what matters to both households and for fiscal incidence analysis. This coverage is provided by health insurance, as it is in principle a mechanism to smooth the risk associated with the possibility of illness and unexpected costs for treatment. Thus, public health insurance is a valuable service independent of the actual use of health care services. We thus conduct an alternative scenario where we allocate health in-kind benefits to individuals that are publicly insured in the CIP and thus would be able to access health care if they need it. In Jordan, the uninsured are also de-facto covered, as they can receive treatment in public facilities at a fraction of the cost.

The benefit level is allocated using a unit cost approach, dividing the total public cost of health care provision (inpatient and outpatient) over the total number of people (Jordanians). Health insurance withholdings are deducted from the benefit for those who report making monthly contributions from their salary. As uninsured individuals also receive care from public health care providers but must pay small co-payments, they are allocated a discounted benefit (80 percent of the benefit accrued by insured individuals and without any deductions for contributions).

Insurance information is obtained from HEIS, as is information on health insurance withholdings. To distinguish the subsidised component of CIP in HEIS we use primarily information on participation in social assistance programmes as NAF beneficiaries are referred to the Ministry of Health to obtain an insurance card. In addition, poor uninsured households can apply to the Ministry of Social Development for referral; we simulate the conditions of eligibility<sup>44</sup> and randomly allocate
outages: worker hours increase in response to blackouts, weekly wages paid by firms decrease in response to blackouts, and the data fail to reject that blackouts have no effect on (average firm-level) worker hourly wages. The context discussion in Section 2 suggests that the key to rationalizing this set of findings may be found in exploring the skill composition of these labor input adjustments.

Ideal data for identifying differential effects on the labor inputs of skilled and unskilled workers would individually classify workers by skill level and wages by individual worker. Worker-level data of this type would allow for direct tests of both the skill composition of labor input adjustments, and the effects of blackouts on the total and hourly earnings of workers by skill level. Unfortunately, the firm-level panel data enumerates workers 1 through n, but does not differentiate them by skill level. A related data limitation is that the wage data in the firm-level panel is lumped together as all wages paid by the firm in a given week and thus cannot be attributed to individual workers.

Given these data limitations, the study uses information on the composition of the workforce in the garment making industry in Ghana to draw speculative conclusions about the mechanisms behind the

> 17Note that inventory costs in Ghanaian garment-making firms tend to be relatively small. As with other skilled small businesses in Ghana, garment making customers typically supply the raw materials (fabric), leaving little in the way of materials inputs from the firm’s perspective.

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sea level.<sup>18</sup> This storm surge-prone area is composed of 432,068 cells (approximately 18.70 percent of all cells). For each of these cells, we also calculate the shortest path—that is, Euclidean distance—from the centroid of the cell to the coast. The average distance to the coast is 23.44 kilometers.

# **3.2.Mangroves for coastal protection**

As described in Section 2.3, our mangrove distribution data come from two sources: we used Spalding, Blasco and Field (1997) to identify areas that have historically supported mangrove habitats, and Giri et al. (2011) to precisely measure the presence of mangrove at the beginning of our sample period (2000– 12).

Mangrove protection benefits can be derived from their location in areas that are naturally more protected, such as on a continental shelf, on a rugged coastal topography with elevations rising up to 200 meters, or between the shoreline and the continental slope (Jackson 1997). Hence, we began by excluding from our analysis any areas that have not historically supported mangrove habitats. Specifically, for every cell in the storm surge-prone area, we excluded cells that Spalding, Blasco and Field (1997) define as having no mangrove on their shortest path to the coast. We found that 20,504 cells (4.74 percent of all cells in storm surge-prone areas) have mangrove on their path to the coast.

For each of the remaining cells, we then calculated the mangrove width in two steps. First, we identified the line segments along the shortest path to the coast that overlap mangrove forests as defined by Giri et al. (2011). Second, we summed the line segments to measure cumulative mangrove width on the shortest path to the coast. Figure 4 provides a visual representation of the mangrove width calculations on the coast. We found that in our sample, the average mangrove width is 0.25 kilometers,
incomes than urban residents), educational attainment (less-educated adults tend to be poorer), and age (younger adults aged 25 and under and older adults aged 65 and over are both more likely to live in lower-income households than prime working-age adults). Each of these subfactors is also associated with being unbanked, as discussed below.

Employment status is strongly associated with account status in several countries: 70 percent of unbanked adults are unemployed or out of the workforce across the Middle East and North Africa; in Egypt specifically, where 73 percent of adults are unbanked, 65 percent are unemployed or out of the workforce.

The unbanked are also more likely to live in rural areas.<sup>4</sup> In Sub-Saharan Africa, for instance, rural residents represent 62 percent of unbanked adults. In Tanzania, 71 percent of the country’s unbanked live in rural areas; in Uganda and Zambia, 70 percent and 67 percent of the unbanked do.

Low educational attainment, which the Global Findex defines as having a primary education or less, is also associated with unbanked status, in that globally, 64 percent of unbanked adults have low education. That share varies by region and economy. In Sub-Saharan Africa, for example, 74 percent of the unbanked have low education. That share is even higher in Mozambique, where 90 percent of unbanked adults have low education; in Côte d’Ivoire, where 83 percent do; and in Tanzania, where 88 percent of the unbanked have low education. In Kenya, where 79 percent of adults have an account, 62 percent of unbanked adults have a primary education or less.

> 4 The account ownership averages based on rural residence are available only for 54 countries in which the Global Findex survey was conducted face-to-face in 2021. Therefore, rural residence is not included in the regression estimation. However, all results are robust for this smaller sample of countries.

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Section 6 presents policy implications/recommendations. Section 7 concludes the paper.

# **2. Overview of Nepal’s Brick Industry**

In 2018, there were about 1,349 operating brick kilns in Nepal,<sup>11</sup> and they produced an estimated 5.14 billion bricks, and the brick industry contributed 4% to the GDP, and employed nearly

> 10 Resource-efficient bricks here refer to bricks produced using sand, stone dust, cement, and soil mixed in different proportions and that do not go through the firing process. Since they are uncooked, their lifetime is shorter (20-30 years) as compared to the fired bricks which last hundreds of years.

> 11 The brick sector in Nepal is a poorly regulated and unorganized sector, and informal in nature. The brick sector statistics, such as the number of registered brick kilns, their production volume and kiln technology types, the number of people involved, and the quantity of energy consumption and types, are in general not well documented. The task of capturing these data is challenging. For instance, there is a wide variation in number of brick kilns (registered or in operation) and volume of brick production statistics in Nepal. For example, ILO et al. (2020) reported 966 kilns producing 3.04 billion bricks in 2018, ICIMOD (2019a) reported 1,349 kilns producing 5.14 billion bricks in 2018, FNCCI (2017) estimated 1,100 kilns in operation in 2012 with a production

4
effects are unlikely to occur). ThinkHazard (TH) levels are derived from hazard maps applying the spatial distribution of hazard intensity (e.g., flood depth) at a given frequency or 'return period' based on a probabilistic analysis. We consider the most common climate hazards in LAC, i.e., river floods, urban floods, coastal floods, landslides, droughts, cyclones/hurricanes, wildfires, and extreme temperatures. However, for some countries, there are hazards that do not show variability in the index (for example, small countries present a very high or very low damage event in the total area of the country from a specific hazard). These cases are left aside from the analysis. This index is available at level 2 of GAUL (ADM 2), which means it is calculated based on data for a specific sub-geographic unit within each country state. When the imputation of TH data is at the state/province level (i.e., level 1) due to the survey design, we calculate an average TH index using the indexes of level 2. This is done to ensure that the imputation accurately reflects the variability of the index at level 2 rather than using the maximum principle of TH. However, for all other situations where imputation is not at level 1 and is at level 2, we rely solely on the TH index at level 2.

> 9 In the model proposed by Skoufias et al. (2023), community-level variables include the average distance to an all-weather road, the proportion of households engaged in agriculture, the proportion of household heads employed, the proportion of household heads with at least secondary education, and the average dietary diversity. The community level variables are derived by taking the average of those households sampled from each unit. Gunther and Harttgen (2009) include as community-level variables an infrastructure index using fourteen characteristics estimated via principal component analysis (bus stop, community road, provincial road, national road, secondary education facility,
how these are addressed. Section 5 presents the results. Section 6 discusses implications for the theory of how clientelism translates into under-investment in pro-poor policy, and for institutions of accountability to remedy this. Section 7 concludes.

# **2. The institutional context, and sampling strategy in the Philippines**

The Philippines provides an institutional context particularly suited to empirically examining the policy consequences of clientelism. Municipal governments in the Philippines are responsible for delivering what can be characterized as a pro-poor public service: health programs for maternal and child well-being that are typically the only source for such services available to poor households.<sup>13</sup> At the same time, municipal governments are directly elected, with substantial variation in political characteristics across municipalities. This paper uses data from a unique survey that was purposefully designed to measure investments in public health services, the broad public service that local governments are responsible for delivering in the Philippines’ context, as well as a particularly salient practice of clientelism in the country: vote buying. The survey was fielded in February 2010.

The decentralization of service delivery and electoral competition in the Philippines provides opportunities to measure both variation in clientelist political strategies and service delivery at local levels. Such disaggregated measurement allows for establishing a correlation with health services of that part of the variation in vote buying that arises from idiosyncratic differences across municipalities. This enables the interpretation of the correlation as the policy consequence of vote buying politics, in the absence of any systematic variable that independently (and simultaneously) explains both vote buying and health service delivery.

The sampling strategy focused on gathering data from all municipalities in one province in the Philippines to enable cross-municipal comparisons while keeping province level characteristics fixed. The province of Isabela was selected for the study because it was sufficiently large to draw a sample of municipalities across which variation in service delivery could be examined.<sup>14</sup> Of the 35 municipalities in the province, 30 were selected for the study because cost considerations did not allow all