rafmacalaba/data-use-mentions-extended huggingface dataset:
groups of host nationals, yet simultaneously be viewed by other host members within the same neighbourhood as contributors to local economies and development. Moreover, at the local community – or even individual – level, host nationals can hold seemingly contradicting beliefs. For example, while urban hosts are less tolerant of refugees than rural hosts, and often see refugees as an economic burden, they are more progressive in a rights perspective and feel less threatened by refugees’ presence.

Based on these findings, we make a series of recommendations for programmes seeking to improve refugee-host social cohesion: 1) facilitate opportunities for refugee-host interaction, including through economic exchange; 2) target interventions promoting intra-group attitude change at the neighbourhood and household levels; 3) focus differently on the perceived ‘winners’ and ‘losers’ of refugee-hosting; 4) design distinctive social cohesion programmes for urban and camp-like contexts; and 5) incorporate data collection relating to both perceptions and interactions into UNHCR and World Bank socio-economic household surveys focusing on refugee and host communities.

3
This is equivalent to saying that a household is considered vulnerable if the probability of falling below the poverty line in any given year is 29%.<sup>10</sup> Ultimately the choice of the threshold has an impact on who is considered vulnerable. As the threshold increases (decreases), and the required probability of falling under the poverty line increases (decreases), fewer (more) households are going to be identified as vulnerable.<sup>11</sup> There are three important main caveats associated with the method that are important to bear in mind. First, the cross-sectional variance of consumption is assumed to approximate the intertemporal variance in consumption. Given that information on how household consumption varies due to shocks experienced over time is not available, the method assumes that the variability of household consumption over time can be approximated by how consumption at a given point in time varies across households with the same characteristics. The rationale behind this assumption is that differences in the consumption of households with the same characteristics at a given point in time is likely to be due to the different shocks experienced by these households. The extent to which this assumption is tenable or not can only be settled empirically by comparing the cross-sectional estimates of the variability of consumption among households with the same characteristics with the estimates of the variability of household consumption over time (and ideally with significantly more than the 2 or 3 survey rounds that could be found in short panel surveys). This issue will remain unsettled until a panel data set with multiple rounds sufficient for reliable estimation of householdspecific consumption over time becomes available. Second, potentially large consumption measurement errors can overestimate consumption variance, especially from idiosyncratic shocks, which may in turn result in misleading vulnerability estimates. These estimates can be tested by carrying out simulations based on alternative measurement error assumptions (see Appendix Table A.3 in Gunther and Harttgen, 2009). Third, this model can shed light on generic “covariate” or “idiosyncratic” sources of vulnerability but cannot
Some of the difficulty appears to be inherent in the policy changes themselves: managing water for the simultaneous satisfaction of supply, quality, economic, and environmental values is significantly harder than managing for one of those values alone, and even the existence of a recognized river basin authority with considerable staff and revenues does not assure successful implementation and performance of this multifaceted mission. Some of the difficulty appears to be due to organizational inertia, as an agency that had one role for 60 years adjusts to new roles and in the meantime falls behind on some of its newly assigned tasks. Last but certainly not least, some of the difficulty appears to be due to patterns of stakeholder involvement and stakeholder relationships with basin authority personnel. The formal structure of the basin authority gives greater weight to the traditional irrigation constituency that is interested primarily in the authority’s water supply augmentation functions, and the basin authority’s decision making practices reinforce that constituency’s favored position. Irrigators are a well organized set of water users in the basin who are quite clear about what they want, and know how to use the basin authority’s organizational structures and decision making processes to get it. By contrast, public and private organizations serving or representing urban centers and environmental concerns, or promoting economic approaches to water management, appear to have less influence within the basin authority and have sought alternative means to pursue their goals. In these respects, the experience of the Guadalquivir basin appears to reflect trends and tendencies in Spain as a whole.

# **2. Analytical Framework**

To analyze the data gathered for this project from the case studies and from the survey of river basin organizations, the project team has developed a framework that identifies a number of political and institutional factors which may be associated with the emergence, sustainability, and success or failure of decentralized approaches to integrated water resource management at the basin scale. These factors, and their hypothesized relationships with basin management in a country that has decentralized or is attempting to decentralize water resource
their preferential access to credit during the banking crisis, although this was not necessarily evidence of a credit crunch.

New evidence on the credit crunch hypothesis comes from a recent study by Dell’Ariccia _et al_ . (2005). To identify the real effects of banking crises, this paper follows the ‘difference-in-difference’ approach adopted by Rajan and Zingales (1998) to study the effects of finance on growth. Using a panel of countries and industry-level data, the authors test whether more financially dependent sectors perform significantly worse during banking crises, after controlling for all possible time-specific, country-specific, and industry-specific shocks that may affect firm performance. The main result is that indeed more financially dependent sectors suffer more during crises, evidence in favor of the credit crunch hypothesis. The results are robust to controlling for other possible explanations, such as flight-to-quality during recessions, the effects of concomitant currency crises, and the exposure of bank portfolios to specific bank-dependent industries. Furthermore, the magnitude of the effect is non-trivial: more financially dependent sectors lose about 1 percentage point of growth in each crisis year compared to less financially dependent sectors. Finally, consistent with the theory, the differential effects are stronger in developing countries, in countries where the private sector has less access to foreign finance, and where the crises are more severe.

# _Intervention policies and the costs of crises_

A few studies have used cross-country empirical analysis to study which intervention policies can minimise the costs of a banking crisis. This question is as important to policymakers as it is difficult to answer through empirical analysis. One problem is that compiling accurate information on intervention policies for a large enough sample of crises is a laborious task. Another difficulty is that the sequence, timing, and specific modalities of a bank support 25
Policy Research Working Paper 9045

# **Abstract**

How do trade reforms impact households in different parts of the income distribution? This paper presents a new database, the Household Impacts of Tariffs data set, which contains harmonized household survey and tariff data for 54 low- and lower-middle income countries. The data cover highly disaggregated information on household budget and income shares for 53 agricultural products, wage labor income, nonfarm enterprise sales and transfers, as well as spending on manufacturing and services. Using a stylized model of the first-order impacts of import tariffs on household real income, this paper quantifies the welfare implications of agricultural trade protection. On average, unilateral elimination of agricultural tariffs would increase household incomes by 2.50 percentage points. Import tariffs have highly heterogeneous effects across countries and within countries across households, consumers, and income earners; the average standard deviation of the gains from trade within a country is 1.01 percentage points.

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/prwp. The authors may be contacted at eartuc@worldbank.org, guido.g.porto@gmail.com, and brijkers@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 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
of goods, services and information between urban and rural areas will also come down with the emergence of small towns. While important studies like Dorosh and Thurlow (2013, 2014) and Gibson et al. (2017) have studied how urban growth in large versus small towns affects rural poverty reduction, we still know very little about the exact mechanisms through which this happens.

Second, given their proximity, towns can be particularly important in facilitating the first move, which is often the hardest, thereby potentially instigating a virtuous cycle of physical and economic mobility. The results from the Kagera life histories highlight the difficultly and importance of the first move, an oft ignored phenomenon. It is difficult because it is often a step in the dark that prospective migrants need to undertake with little preparation 18
distant countries is the presence of per unit rather than iceberg transport costs.

The quality competition (or quality sorting) models depart from the price competition models by incorporating quality differentiation across firms: more productive firms – apart from being more efficient - will make use of better-quality, more expensive inputs, and might face higher marginal costs and prices. Baldwin and Harrigan (2011) propose a model in which high quality firms are the most competitive and quality increases with firms' heterogeneous cost. They use product-level U.S. trade data to show that export unit values are positively related to distance.

The main difference between price (Melitz 2003) and quality competition (Baldwin and Harrigan 2011) models is that in price competition models price is the only element upon which firms compete and market entry thresholds are defined in terms of a maximum price<sup>6</sup> . In quality competition models, firms’ competitiveness is based on the quality-adjusted price and market-entry thresholds are based on qualityadjusted price. As per Baldwin and Ito (2011), “ _the lower quality-adjusted prices (unobserved) are_

> 6 Ito (2011).

5
# Abbreviations and Acronyms

|ASMTD|Act on the Submission and Management of Taxation Data|
|---|---|
|B2B|business to business|
|B2C|business to consumer|
|BIPC|bearer‐identifiable prepaid card|
|CFDI|digital tax receipt issued|
|CR|cash register|
|DRM|domestic resource mobilization|
|EC|European Commission|
|ECB|European Central Bank|
|ECR|electronic cash register|
|EID|eligible income deduction|
|ETCR|electronically traceable cash receipt|
|ETP|electronically traceable payment|
|FinCEN|Financial Crimes Enforcement Network|
|HIES|Household Income and Expenditure Survey|
|KNTS|Korean national tax simulator|
|MOFE|Ministry of Finance and Economy|
|NTS|National Tax Service|
|PIT|personal income tax|
|POS|point of sale|
|TIETP|Tax Incentive for Electronically Traceable Payments|
|TIS|Tax Integrated System|
|TLI|total labor income|
|UR|underreporting|
|UW|undeclared work|

iii
from the original consumption data sources. In particular, we focus on the household-level income aggregate dataset (RIGA-H), which provides data on different income sources, such as crop and livestock production, household enterprises, wage employment, transfers, and nonlabor earnings .

For the countries just noted, data on households’ consumption expenditure come instead from the original budget surveys compiled by national statistical bureaus and the World Bank, which can be easily linked

> 2 World Bank, “PovcalNet: An Online Analysis Tool for Global Poverty Monitoring,” database. <u>http://iresearch.worldbank.org/PovcalNet.</u>

> 3 We follow the most recent (2020) World Bank classification that uses the Atlas method to estimate the size of economies in terms of gross national income (GNI) per capita: low income = below US$1,036; lower-middle income = between US$1,036 and US$4,045; upper-middle income = between US$4,046 and US$12,535; and high income = above US$12,535.

> 4 The microdata are available to users upon request. For more see FAO, “The RIGA Database.” <u>http://www.fao.org/economic/riga/riga-database/en.</u>

5
an investigation into prevailing social norms, legal rules, and cultural expectations that may encourage (or limit) agency outside the household.<sup>20</sup> Irrespective of data limitations, our inability to categorize a fiscal policy as “good” or “bad” for gender equality is also partly due to conflicting impacts along these different dimensions. For example, giving benefits to mothers who stay at home to look after their children reduces gender inequality in total incomes while exacerbating gender inequality in labor force participation. In the same vein, generous maternity leave has been shown to discourage women returning to the labor market while voucher systems for childcare have been shown to discourage the use of market-provided childcare services in some contexts (Spiess and Wrohlich 2008).

> 17. These activities should be distinguished from other types of unpaid labor, for example, in subsistence agriculture or labor supplied by household members to a family business.

> 18. Agency is the ability to define goals and act on them; see Donald and others (2020).

> 19. See Bakhtiar and others (2022) for an experimental examination of the processes determining intrahousehold allocations and the impact of those processes on women’s agency.

> 20. When such data is available, or when FIA can be complemented with a discussion of social norms and institutions that limit policy-based progress on gender inequality, a gendered FIA will be much richer and more nuanced. Such information is available from, for example, the Social Institutions and Gender Index; Afrobarometer; the World Values Survey; and Women, Business and the Law. These sources, among others, can be used to complement FIA and triangulate policy-relevant advice.

12
intermediation by charging lower rates on other forms. This effect would not be captured by ex-ante spreads.

For all these reasons, this paper uses ex-post spreads. Specifically, ex-post spreads are narrowly calculated as the difference between interest received on loans divided by (net) loans and interest paid on deposits divided by total deposits. The text figure depicts the behavior of our dependent variable during the sample period (quarterly data are weighted averages of bank-specific net interest spreads, with weights given by banks’ total assets).

<!-- Start of picture text --> Net Interest Spread (%)<br>7.00<br>6.00<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>0.00<br>Source: Author’s elaboration on NBT data.<br>2011q1 2011q3 2012q1 2012q3 2013q1 2013q3 2014q1 2014q3 2015q1 2015q3 2016q1 2016q3 2017q1 2017q3 2018q1 2018q3 2019q1 2019q3 2020q1 2020q3 2021q1 2021q3 2022q1 2022q3<br><!-- End of picture text -->

> 3 Given the structure of the panel dataset, an alternative procedure that could be used is the feasible generalized least squares (FGLS). When comparing the performance of both estimators, the rule of thumb is that the OLS-PCSE estimator is preferable to 1 1 its FGLS counterpart when 2 (N<sup>2</sup> −N) > T (Jönsson 2005). For this paper, 2 (19<sup>2</sup> −19) = 171 > T = 48. 4 Standard tests confirm the presence of autocorrelation, heteroskedasticity and contemporaneous correlation in our panel data. Results are available upon request.

> 5 See Brock and Rojas-Suarez (2000) for a detailed discussion of the definitions of interest rate spreads.

9
law and order and then international certification. Similarly, the biggest contributor via the structural effect is market size, followed by secondary education attainment, and then law and order. Thus, our results underline the importance of human capital and institutions along with some of the other factors for closing the productivity gap between the UMICs and the HICs.

Interestingly, several factors fail to make a significant contribution to the productivity gap via the endowment or the structural effect. Examples include the age of the firm, foreign ownership of the firm, years of top manager experience, the gender of the top manager, regulatory burden experienced by the firms, corruption, etc. Some of these factors such as corruption and regulatory burden experienced by the firms are only poorly correlated with labor productivity in the UMICs and the HICs while others contribute roughly equally to labor productivity in the two income groups.

The rest of the paper is organized as follows. In Section 2, we discuss the data and base regression. We introduce the labor productivity decomposition methodology in Section 3. Section 4 provides a discussion of the decomposition analysis followed by robustness checks in Section 5. Section 6 concludes the paper.

# **2. Data and Base Regressions**

# _Data source_

The main data source we use is firm-level surveys conducted by the World Bank’s Enterprise Surveys (ES). The ES are nationally representative surveys of formal (registered) private firms with five or more employees. The surveys cover manufacturing and services firms but exclude extractive industries and agriculture. The sampling methodology used is stratified random sampling with the region (within the country), sector, and size as the strata. The use of a 6
The objectives of this study were to (1) identify the drivers of THE, (2) estimate the prevalence of CHE and IHE, and (3) identify the drivers of and potential financial risk protection (FRP) against CHE and IHE through subsidies in Guinea.

# **Methods**

# **Study design and population**

We conducted a retrospective cohort study based on the 2018-2019 Guinea Living Standards Measurement Study (LSMS).<sup>[11]</sup> This survey is a monitoring and evaluation tool for Guinea's National Economic and Social Development Plan. It was conducted in two phases to take into account variations in consumption related to the country's two main seasons (the rainy season and the dry season). In the first phase, households were interviewed for three months during the rainy season, from July to September 2018, and in the second phase, households were interviewed for three months during the dry season, from April to June 2019. In order to have a nationally representative sample of the population, for both phases, stratified cluster sampling was used and each of the two collection phases involved half of the sample. The strata represented the type of area (urban or rural) in each of Guinea's eight administrative regions. Because the Conakry region was a purely urban area, there were a total of 15 strata. The clusters (enumeration areas) represented specific geographic areas (villages and communities in rural areas and neighborhoods in urban areas). The LSMS was conducted by the National Institute of Statistics of Guinea with technical support from the World Bank's Poverty and Equity Global Practice.

Analyses were conducted at the individual level. All individuals included in the LSMS were considered eligible. However, we excluded those for whom there was no consumption expenditure.

# **Study data**

The LSMS included 20 sub-databases, each focused on a specific theme such as health, employment, social nets, and welfare. We obtained the 20 de-identified
The 2005/06 household budget survey (HBS) indicates that, in the survey year, 35 percent of the Yemeni population lived under the national moderate (expenditure) poverty line, with a much higher rate in rural areas (40 percent as opposed to an urban rate of 21 percent). Disparities between different governorates are also strong, with poverty incidence highest in the Amran governorate (64 percent), and lowest in Al Mahrah (9 percent). The 2005/06 rate was significantly lower than the 1997/98 rate of 40 percent, generated by a comparable household budget survey. The reduction was remarkable in urban areas, with the poverty rate falling by over one third, while the rural rate remained stubbornly high at above 40 percent. However, according to more recent estimates, the poverty rate is once more on the increase, reaching 42.4 percent in 2010, primarily as a result of increased food prices (MOPIC, 2011).

Analysis carried out based on data from the 2005/06 HBS also demonstrates that the substantial amounts spent on fuel subsidies only partially benefited the poor, with around 80 percent of the benefits accruing to the non-poor, while the high cost of health care discourages the poor from seeking care (Government of Yemen and others, 2007). The survey also confirms that public health care services and the increasing share of education expenditures allocated to the tertiary sub-sector do not target the poor. There are also major leakages in the public transfer schemes targeting the poor.

Trends over the last few years in primary education point to an increase in the gross enrolment rate (GER) in basic education from 58 percent in 1997/98 to 66.5 percent in 2003/04, with adult literacy reaching 50 percent by 2006. Despite substantial progress in girls’ education, the GER for girls only reached 51.5 percent in 2003. These average rates of school enrolment in the country hide serious geographic disparities. Boys and urban children enjoy greater education opportunities and higher enrolment