rafmacalaba/data-use-mentions-extended huggingface dataset:
.27|12.83|4.31|4.77|2.38|12.87|4.78|
|Timor|1.93|1.63|n.a.|n.a.|3.67|1.01|n.a.|n.a.|
|Turkey|0.78|5.06|11.03|5.70|3.07|0.63|11.01|6.16|
|Uruguay|4.74|7.17|9.11|1.61|7.16|2.58|8.94|1.63|
|USA|3.71|3.47|10.56|5.49|5.00|2.47|10.25|5.87|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

<u>Table 4. Mean hours per day, by marital status, type of activity and sex</u>

|||<br> <br>Wo|<br>men|<br> <br>Married/Livin|<br>gtogether|<br> <br>M|en||
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study|Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study
Policy Research Working Paper 6214

# **Abstract**

The effect of weather shocks on children’s anthropometrics is investigated using the two most recent rounds of the Nigeria Demographic and Health Survey. For this purpose, climate data for each survey cluster are interpolated using daily weather-station records from the national network. The findings reveal that rainfall shocks have a statistically significant and robust impact on child health in the short run for both weight-for-height and height-for-age, and the incidence of diarrhea. The impacts of weather shocks on health are of considerable magnitude; however, children seem to catch up with their cohort rapidly after experiencing a shock. The paper does not find any evidence of nonlinear impacts of weather variability on children’s health, suggesting that a moderate increase in future rainfall variability is not likely to bring additional health costs. Finally, it appears that the impact of these shocks is the same for young boys and girls, which suggests that there is no genderbased discrimination in the allocation of resources within households.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. 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 author may be contacted at eskoufias@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 Bank
that have high betweenness from a topological perspective.<sup>17</sup> Roads with relatively high betweenness centrality include the N1 expressway, Avenue Sergent Moke, Avenue O.U.A., Rte de Matadi, and Avenue de l'Université. It should be noted that this calculation method makes assumptions about the travel patterns in the transportation network which introduces errors in the measurement of critical links/road segments in reality. For example, it is possible that a road segment with high betweenness centrality connects regions with low population density. This measure fails to take into consideration the usage of road segments in reality. Therefore, in addition we adopted another adapted calculation method which is based on travel paths simulations using OD pairs from the JICA travel survey.

Figure 16 shows the results from the adapted betweenness centrality calculation. The main difference between this adapted approach and the conventional one (Figure 15) is the way travel paths are calculated and accounted for. The conventional approach calculates shortest paths between every possible pair of nodes within the network whereas the adapted approach simulates the travel trajectories based on the multimodal transportation model and accounts for public transport waiting times, road speed limits, and designated origin and destination pairs acquired from the JICA commuter travel survey. This adapted approach is able to reflect the actual travel patterns within the network and identify critical links/segments which are most commonly used in reality. Results show that Avenue Du 24 Novembre, east-west sections of the N1 expressway (including Boulevard Triomphal, Boulevard Sendwe, Boulevard Lumumba) have high betweenness centrality. This means that many people rely on these roads for daily commute to work. If there are disruptions along these road segments due to floods or traffic accidents then many commuters will likely experience higher levels of delay due to vehicle speed reduction, congestion, rerouting, etc.

> 17 The conventional betweenness measure focuses on the topology of the network itself and does not account for travel volume and patterns in the network.

20
Board’s _Key Attributes_ for effective resolution frameworks (Box 7), and all offer relevant measures for EMDEs in this context, but also pose implementation challenges in light of institutional capacity constraints and level of economic and financial market development.

This paper is organized as follows. Section 2 describes the conceptual framework of the sovereign‐bank nexus. Section 3 discusses whether the nexus is rising based on data from 140 EMDEs. Section 4 offers six policy implications. Box 5 offers a selected, high‐level overview of relevant developments across regions.

# 2. What is the nexus? A conceptual framework

Conceptually, the sovereign‐bank nexus links the health of the domestic sovereign and the banking system together through two direct and two indirect channels (Figure 1).<sup>1</sup> Depending on national circumstances, the sovereign – bank nexus can act as a shock absorber or amplifier and foreign investors may play a role as well (Box 3). There are two _direct channels_ :

1. _Banks’ direct exposures to the sovereign._ Direct exposures to the sovereign make banks’ balance sheets vulnerable to losses on government assets holdings (e.g., through lending to the public sector and holdings of securities issued by the public sector). Conversely, if banks expand their sovereign exposures, they could help the sovereign to overcome temporary fiscal stress.

2. _Government support to failing banks._ The sovereign is often implicitly expected to support failing banks, particularly those that are systemically important, because it cannot credibly commit ex ante to refrain from doing so during a crisis. This “time inconsistency” bestows funding and other stability benefits on systemic banks. However, it may also lead to excessive risk taking by banks (i.e., moral hazard) and expose taxpayers to losses.

> 1 Sovereign debt is understood to mean the sum of money borrowed by a country’s central government.  However, this section refers to “sovereign” more broadly as a country’s
better understand the characteristics of those who misreport, we also compare mean IPV prevalence for respondents above and below the characteristic median (for numeric variable characteristics) or by their indicator variable characteristics.

# **4.2.2 Results: Respondent characteristics predict misreporting**

We analyze misreporting by respondent characteristics that fall into three overlapping categories. These characteristics include: 1) those most likely to motivate respondents to misreport (to understand why people misreport IPV); 2) variables typically targeted in IPV prevention interventions (to evaluate risks of estimating biased treatment effects); and 3) characteristics frequently cited as IPV risk factors (to assess potential challenges to IPV-related theory that has been informed by analysis of face-to-face survey data). The first two categories of characteristics include perceptions of village gender norms; poverty and vulnerability; marital relationship quality; and personal gender attitudes. Additional IPV risk factors considered include women’s education and literacy, age, labor force participation and other indicators of women’s bargaining power, where theory and empirical evidence are mixed on the relationship with IPV (Bobonis, González-Brenes and Castro, 2013; Buller et al., 2018; Erten and Keskin, 2018; Heise and Kotsadam, 2015; Haushofer et al., 2019; Eswaran and Malhotra, 2011).

Tables 4 and 5 show the relationships between IPV and respondent characteristics by survey method. The first 3 (6) columns show the relevant regression coefficient of interest for Rwanda (Nigeria), and the last 3 columns show the results of chi-squared tests of the coefficient differences between pairs of methods. Each coefficient shown in the tables was estimated in a separate regression. These tables show several significant differences in coefficients across the methods, implying that a number of characteristics are associated with systematic misreporting of IPV. These results are also shown graphically in Figures A2 and A3. We also show the differences in mean IPV prevalence for respondents above and below the median of the characteristic in Tables A9 and A10 and graphically in Figures A4 and A5
14 and urban households, non-staple food expenditures (such as eating out) are likely less robust against income declines than expenditures on staple foods.

Non-staple food expenditures may also be more sensitive to downward income shocks than non-food expenditures. For example, depending on the depth of financial markets, reducing the service stream from existing possessions or housing may take more time to show up in expenditure numbers. Increases in income on the other hand may translate more quickly into purchases of non-staple foods and durables alike (Elbers and Pouw, 2009).

Turning to the non-consumption models, five broad classes of non-consumption asset data are considered. These comprise: 1) geographic indicators such as rural/urban and regional location (proxying a household‘s agro-ecological, economic and institutional assets); 2) household demographic information and 3) educational and employment information such as sector of work by the household head (proxying the quantity and quality of their labor assets); 4) variables on the quality of housing such as presence or absence of electric lighting, permanent roofing material, and private water tap; and 5) ownership of consumer durables such as a bicycle, color television, electric fan, etc. (proxying a household‘s physical assets).

Filmer and Scott (2008) find that asset indices, which are usually composed of housing quality indicators (asset class 4) and consumer durables (asset class 5), are better correlated with consumption measures expressed in per capita terms, the less measurement error there is in the consumption measure, the lower is the transitory component, and the higher is the share of non-food expenditures (i.e. expenditures with a public good component). The inclusion of variables that are more directly correlated
impacts of tourism growth using the Local INterregional Economic Model (LINE) (Zhang and others 2008). However, this method is computationally complex and it is demanding in terms of the data requirements. Many policy questions, especially if the main focus is on short- to medium-term impacts, can also be answered within the context of a SAM model.

This study uses the Social Accounting Matrix (SAM) multiplier model of tourism impacts developed, permitting ex ante assessment of the direction and magnitude of the local economic and poverty impacts of the growth of the tourism sector. This model provides estimates of the disaggregated direct and indirect effects of an increase in tourist expenditures on the local economy and on households with specific income and skill levels. The model translates an increase in tourism expenditures at the aggregate level into regional impacts, using information from surveys of tourist expenditures, consumption, expenditure and employment patters of Panamanian households, and the general structure of the economy.

> 11 I-O tables decompose GDP into a matrix of inputs and outputs. The Tourism Satellite Accounts approach was developed by the World Tourism Organization with a view of implementing a universal measurement methodology for the tourism industry and the tourism sector.

8
we expose subjects to two dictator games. Religious preferences are measured by providing subjects with a small endowment (400 tokens; 333 PKR; 3.2 USD) and asking them whether they would like to donate any part of this endowment to a local mosque. Social preferences are measured by providing subjects with a small endowment (400 tokens; 333 PKR; 3.2 USD) and asking them whether they would like to donate any part of this endowment to a local (educationfocused) charity, The Citizen’s Foundation (details above). The measure of the religious and social preferences is simply the number of tokens donated to the mosque and charity, respectively. It is important to note that the order in which subjects saw the two dictator games were randomized, such that one set read the description of the charity (and made donations to the charity) first, followed by the mosque, while the order was vice versa for others. Importantly, the dictator games were implemented before the investment decisions. Both dictator decisions were incentivized and were paid with the donations added to the final amount donated to the mosque/charity.

Note that religious preferences are notoriously difficult to measure, particularly due to differing motives, practices, experiences, etc. For this reason, we also use two survey-based measures: the Duke University Religion Index (DUREL) (Koenig and Bussing, 2010) and the Hoge Intrinsic Religiosity Scale (Hoge, 1972). Our incentivized measure is significantly and positively correlated with both survey-based measures ( _p<0.05_ in both cases). Survey based measures are very noisy, and prone to bias, so we restrict our analysis to the incentivized measures. Our results do not change when we use the survey-based measures, though our significance levels naturally drop due to the noisy nature of these alternative measures.

Finally, we measure risk preferences, financial literacy, and previous (real world) investment experience. Risk preferences are measured using seven hypothetical choices between a gamble with a 50% chance
9th International Conference on Agricultural Statistics_ , World Bank, Washington DC. Lark, T. J., R. M. Mueller, D. M. Johnson and H. K. Gibbs. 2017. "Measuring Land-Use and Land-Cover Change Using the U.S. Department of Agriculture’s Cropland Data Layer: Cautions and Recommendations." _International Journal of Applied Earth Observation and Geoinformation_ 62: 224-235. Leasure, D. R., et al. 2023. "Nowcasting Daily Population Displacement in Ukraine through Social Media Advertising Data." _Population and Development Review_ 49 (2): 231-254. Lin, E., C. D. Sprunger and J. Hwang. 2021. "The Farmer's Battlefield: Traditional Ecological Knowledge and Unexploded Bombs in Cambodia." _Agriculture and Human Values_ 38 (3): 827-837. Lin, F., et al. 2023. "The Impact of Russia-Ukraine Conflict on Global Food Security." _Global Food Security_ 36: 100661. Lobell, D. B., et al. 2020. "Eyes in the Sky, Boots on the Ground: Assessing Satellite‐ and Ground‐Based Approaches to Crop Yield Measurement and Analysis." _American Journal of Agricultural Economics_ 102 (1): 202-219. Matasov, V., A. V. Prishchepov, M. R. Jepsen and D. Müller. 2019. "Spatial Determinants and Underlying Drivers of Land-Use Transitions in European Russia from 1770 to 2010." _Journal of Land Use Science_ 14 (4-6): 362-377. Meyfroidt, P., et al. 2016. "Drivers, Constraints and Trade-Offs Associated with Recultivating Abandoned Cropland in Russia, Ukraine and Kazakhstan." _Global Environmental Change_ 37: 1-15. Miguel, E. and G. Roland. 2011. "The Long-Run Impact of Bombing Vietnam." _Journal of Development Economics_ 96 (1): 1-15. Munteanu, C., et al. 2017. "Nineteenth-Century Land-Use Legacies Affect Contemporary Land Abandonment in the Carpathians." _Regional
or “upper-middle” income economies.<sup>5</sup> And 60 percent of economies are now classified as high or upper-middle-income economies, compared to just 35 percent in 1990. However, World Bank income thresholds are only adjusted for inflation—the threshold for the “high income” classification has remained unchanged in real terms since 1990. Therefore, they do not imply convergence to the frontier but rather a broad-based absolute improvement.

Historical episodes of convergence towards the frontier are rare (Durlauf et al., 2005; Johnson and Papageorgiou, Johnson and Papageorgiou; Rodrik, 2011). Full convergence to the frontier requires sustained high productivity growth over many decades. Just nine economies transitioned into the top quartile of incomes between the 1950s and the post-crisis period.<sup>6</sup> Of these, Equatorial Guinea and Oman benefited from oil and gas exploration; Japan, Cyprus, and Portugal were already close to the highest quartile in 1950; the Republic of Korea, Hong Kong SAR, and Singapore were “Asian Miracle” economies, with their success attributed to a number of factors, including high education levels, strong governance, and industrial policies that included export promotion (Leipziger and Thomas, 1993).

In summary, productivity growth improved for a broad set of EMDEs starting around 2000 but has not yet led to a material reduction in productivity gaps with advanced economies. In some cases, these improvements only partially unwound previous productivity growth underperformance,

> 5See also _Special Focus 2.1_ (World Bank, 2019b)

> 6This statement relies on convergence in income per capita instead of labor productivity, allowing a sample of 137 economies since 1950, compared to 103 since 1970 for labor productivity. The Maddison Project database of income per capita for 137 economies since 1950 provides a much wider coverage than the labor productivity database used throughout this chapter – the productivity dataset falls to 49 economies for the same period of data. Output per capita provides a less precise measure of productivity, not accounting for changes
# **4.2 Random assignment and treatments**

Upon being selected for the baseline survey, firms were randomly assigned by computer to either a control group (297 firms) or an information treatment group (757 firms). The first two columns of Table 2 show that these groups are similar on baseline observables, confirming that this in-thefield randomization produced a balanced assignment. The control group were just administered the baseline survey, while the information treatment group were instructed to use the computer tablet of the interviewer to access a _Business Services Marketplace_ .<sup>4</sup> Upon logging into this marketplace, the entrepreneur was asked to look through a list of providers in their city in each of three categories: HR providers, accounting firms, and marketing firms. The marketplace contained information for 35 business service providers in Lagos, and 25 in Abuja, with 7 to 15 providers in any given provider type-city category. They were then instructed to short-list and rank their top three providers in each category. This was an incentivized choice, since they would have the option to go into a draw to win funding that they could use to spend on their top choice among this list.

Firms were further randomized into one of four groups that were provided with different information on the platform when making this choice: (1) 187 firms were allocated to an _information only_ treatment group, where they were shown information about the business service providers such as their name, logo, tagline (e.g. “We are client-focused with over 10 years of experience in recruitment and selection”), and contact information. This group were not given any independent information about provider quality; (2) 187 firms were allocated to a _ratings only_ group, which supplemented the information with a star rating, based on the mystery shopper ratings<sup>5</sup> ; (3) 183 firms were allocated to a _ratings plus negative comments_ group, which provided the star ratings and any negative comments from the mystery
trade for thousands of years,<sup>3</sup> but emigration on such a large scale is a relatively recent phenomenon. The first Kerala Migration Survey reported that nearly 1.4 million migrants from Kerala were in GCC countries in 1998 and that their annual remittances exceeded Rs. 130 million. This increased sharply to 2.4 million migrants by 2013, with annual remittances increasing to Rs. 710

> 2 <u>https://www.arabianbusiness.com/banking-finance/400036-wkd-uae-saudi-arabia-set-for-near-40bn-talent-shortage-timebomb http://saudigazette.com.sa/article/535108</u>

> 3 Kerala may have been engaged in trading activities as early as 3000 BC with Sumerians and Babylonians (Menon 2007). Phoenicians, Greeks, Egyptians, Romans, Jews, Arabs, and Chinese were attracted by a variety of commodities, especially spices and fabrics. Initially foreigners migrated to Kerala. Jews and trading communities from the Middle East settled in Kerala at the end of the first century. During the 15th century the Chinese had used Kerala as a trading base with the Middle East and East Africa. European arrival started with the Portuguese when Vasco da Gama arrived in Calicut in 1498 followed by the Dutch and the British. The British arrived in the 17th and 18th centuries with the East India Company.

3
countries is associated with slower financial development and growth and lower quality financial intermediation (La Porta, Lopez-de-Silanes, and Shleifer, 2002 and Cole, 2009b).

# **5. Firm Financing in Developing Countries**

In this section, we focus on access to finance issues at the firm-level taking into account the institutional constraints that firms in developing countries operate under, as discussed in the previous section.

# **_5.1 Financing Constraints_**

The studies in section 4 explored the broad intuitional causes of financial constraints faced by firms in developing countries. The more recent wave of studies has focused on describing the specific mechanisms by which institutions shape financial constraints of firms. Financial constraints affect firms in both developed and developing countries and are an active area of research in corporate finance. One strand of this literature has tried to infer financing constraints indirectly from investment-cash flow sensitivities (e.g. Fazzari, Hubbard, and Petersen, 1988; 2000)<sup>18</sup> or the propensity of firms to save cash out of incremental cash flow (e.g. Almeida, Campello, and Weisbach, 2004). Most of these studies are based on large firms in developed economies which have limited implications for financing constraints of firms in developing countries, many of which face a broader set of constraints and for which there is less available financial data. Hence, below we focus only on studies that use more direct measures of firm financing constraints.

Beck, Demirguc- Kunt, and Maksimovic (2005) provide the first direct evidence of firms‟ perception of financing constraints and how they impact firm growth. They use a size-stratified survey of over 4000 firms in 54 countries, where firms reported on a scale of 1 (no obstacle) to 4 (major obstacle) the extent to which financing, legal, and corruption problems presented obstacles to the operation and growth of their businesses They use country random effects regressions to assess the impact of the three obstacles on firm growth controlling for various firm and