rafmacalaba/data-use-mentions-extended huggingface dataset:
# 4. Previous Costing Studies

A scoping review undertaken in parallel with this Working Paper (Siekmans et al, forthcoming) did not identify any published systematic reviews that bring together the evidence for LMIC on the economic costs of unhealthy diets. Candari and colleagues (2017) conducted a review of the literature to estimate the economic costs associated with unhealthy diets and low physical activity. They identified a total of six studies from Australia, China, the United Kingdom, and the United States that varied widely in their definition of unhealthy diets, including single food items, a list of specific food groups, and a dietary pattern compliant with national dietary recommendations. They reported a wide range of cost estimates that were sensitive to both the diet measure and study methodology.

Since that review, several new studies have been published that estimate the burden of unhealthy food choice by quantifying the impact of individual dietary factors (dairy products, salt, sugar, sugar-sweetened beverages, ultra-processed foods) or poor-quality diets (measured by healthy eating indices) on diet-related NCD outcomes (cardio-vascular diseases, diabetes, chronic disease). For LMIC, several studies estimate the cost (mortality and DALYs) of cardiovascular disease or the NCD burden attributable to the dietary risk factors, using the Global Burden of Disease dataset for 204 countries (Liu et al. 2022; Qiao et al. 2022; Zhang et al. 2023) or in specific country contexts (Brazil, Mexico) (Dávila-Cervante, 2020; Machado et al. 2022). Four studies (three in Brazil, one in Costa Rica) estimated the costs associated with excess salt/sodium consumption, one using the Global Burden of Disease dataset (Guedes et al. 2022) and the others assessed health care costs associated with hypertension (Nilson, da Silva et al. 2020; Nilson, Metlzer, et al. 2020; Vega-Solano et al. 2023). An additional eight studies were found that estimated costs (usually mortality and DALYs) associated with specific dietary risk factors such as diets high
# 5.3 Accounting for density/crowding externalities

An additional aggregate e¤ect of immigration may stem from its impact on the aggregate scale of production. On one hand, the existence of a ...xed factor in production (such as land) would cause aggregate decreasing returns. On the other hand, the e¢ ciency of production may be increased by an increase in employment density due to "agglomeration externalities" as in Ciccone and Hall (1986). In general agglomeration externalities/crowding e¤ects can be modeled by assuming that the TFP is also as function of the aggregate scale of production (Qt). An extension of expression 10 above can be stated as follows: In this expression, the crowding e¤ect of labor force size on land, assuming a share of land in production of 0:03 in rich countries (see Ciccone and Hall 1986) would give � = �0:03 . On the other hand, incorporation of the positive density externalities estimated by Ciccone and Hall (1986) would lead to a positive value of � = 0:06:<sup>14</sup> We explore the implication of such e¤ects for the impact of immigration, keeping the values of the other parameters at the basic speci...cation.

Figure 8 illustrates the estimates in the benchmark case and compares with either type of externality. Since the values of the estimates for the parameter � and the e¤ect of emigration in changing total employment (and its density) are small, the e¤ects of introducing this externality (either positive or negative) are also minimal. The presence of an agglomeration externality would marginally increase the positive average wage e¤ects of immigrants, while the crowding externality would marginally reduce it but the di¤erences are on the order of a small fraction of a percentage point.

# 5.4 Accounting for employment rates

While the model is based on employment the empirical analysis is based on working-age population data
advertisements and websites, operating complaints hotline, receiving complaint statistics from financial institutions, conducting consumer interviews and focus groups, and engaging in mystery/incognito shopping. Rather, the vast majority—67 percent—of those with the authority to enforce financial consumer protection monitor compliance through on-site inspections. This is not surprising, as compliance with consumer protection regulations is addressed as part of business conduct supervision which uses financial examination as the main tool. Yet this approach may not be sufficient to fully capture the scope and nature of the violations and direct contact with the user of services. Other methods mentioned above can enhance monitoring effectiveness of the responsible agency. Moreover, monitoring the trends in customer complaints can provide insights to the regulators for necessary adjustments in rules and regulations as financial products constantly evolve.

# **3.3 Dispute Resolution Mechanisms**

In retail financial transactions, especially those involving low-income individuals, power imbalances between providers and users of financial services are substantial. An individual is unlikely to initiate and go through a legal process when subjected to unfair treatment due to insufficient resources and/or understanding. A modern financial consumer protection framework relies on two key mechanisms to address this concern. First, it involves financial institutions establishing an effective mechanism to receive and resolve customer complaints. Second, in case the complaint is not resolved within a reasonable time or the customer is not satisfied with the outcome, the complaint may be referred to a third party independent dispute resolution mechanism such as an ombudsman or a mediation service.

Surprisingly, only around one-third of the countries in the survey report the existence of laws or regulations that set standards for internal complaint resolution mechanisms for financial institutions. These standards include certain processes to be implemented, standards on the timely handling of complaints, and accessibility. The fact that there are no clear standards on handling and processing complaints in most countries raise doubts on the ability of the supervisory agency to monitor and assess how well financial institutions manage these complaints.

More than half of the countries in the survey (
Despite the collection of data from firms of all sizes we limit our study to firms with 5 or more persons engaged because of comparability issues across the two firm censuses that we use (see Kerr and McDougall (2019) for more detail). Since informal firms are included the data we use still covers a much larger fraction of employment than the WBES. Of the firms and workers surveyed in the 2003 NIC firm data that we use, only around 14% of firms employing 52% of total workers would have been eligible for the WBES, by reporting being registered with the Registrar General’s department.

# 3 Data used in the Study

## 3.1 Ghanaian Firm Census Data

This study uses data from two firm censuses undertaken by the Ghana Statistical Service (GSS) to create a district‐industry panel to estimate agglomeration economies in Ghanaian manufacturing. The two censuses used are the National Industrial Census (NIC) of 2003 and the Integrated Business Establishment Survey (IBES) of 2014. Both were collected at the plant level<sup>3</sup> and include data collected on economic activity, employment and location in phase 1. As discussed above, the censuses covered all non‐household‐based firms with a fixed location, and firms located in houses if these had a sign indicating the presence of a firm, whether formal or informal.

For the analysis below, we create a panel of district‐industries using the 110 districts in Ghana in 2003 and 13 sub‐sectors within manufacturing. Average district size is 2200km<sup>2</sup> whilst median size is 1350 km<sup>2</sup> . The standard deviation is roughly 2500, ie there is substantial variation in sizes of districts. The means are equivalent to circles with radii of 27 and 21km respectively. This makes the district mean 50% larger than the mean area of the “ _zones d’emploi_ ” used in Combes (2000) and 100% larger than the mean of the
26 Keyantash, John, and John A. Dracup, 2002, The Quantification of Drought: An Evaluation of Drought Indices, _Bulletin of the American Meteorological Society,_ 83 **‐** 8: 1167‐1180.

Klotzbach, Philip and William Gray, with Uma Shama, Larry Harman, and Daniel Fitch. No date. Methodology from "United States Landfall Probability Webpage". Downloaded from http://www.e‐transit.org/hurricane/methodology.doc on August 30, 2008.

Lehner, Bernhard, Kris Verdin, and Andy Jarvis, 2008, Hydrosheds Technical Documentation: Version 1.1. Downloaded at http://hydrosheds.cr.usgs.gov/index.php.

Loayza, N., E., Olaberria, J., Rigolini, and L., Christiaensen, 2009, Natural Disasters and Growth – Going Beyond the Averages, _World Bank Policy Research Working Paper 4980_ .

- Lopez, Ramon, 2009, Natural disasters and the dynamics of intangible assets, _Policy Research Working Paper Series 4874_ , World Bank: Washington D.C.

McKee, T. B., N. J. Doesken, and J. Kleist. 1993. “The Relationship of Drought Frequency and Duration to Time Scales”, preprints for the Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179–184.

Mitchell, T. D. and P. D., Jones, 2005, An Improved Method of Constructing a Database of Monthly Climate Observations and Associated High‐Resolution Grids, _International Journal of Climatology,_ 25:693‐712. CRU TS2.1 data available at http://www.cru.uea.ac.uk/~timm/grid/CRU_TS_2_1.html.

Mueller, Valerie, and Daniel, Osgood, 2009, Long‐Term Impacts of Droughts on Labour Markets in Developing Countries: Evidence from Brazil, _Journal of Development Studies_ , 45‐10: 1651
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Benin. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, water and sanitation—and different policy areas—including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Benin, making it possible to benchmark the country’s infrastructure situation against that of its African peers. Given that Benin is a poor but stable country (per capita income of $690 in 2008), two sets of African benchmarks will be used to evaluate Benin’s situation: nonfragile low-income countries and middle-income countries. Detailed comparisons will also be made with immediate regional neighbors in the Economic Community of West African States (ECOWAS).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, it was necessary to standardize the indicators and analysis so that everything was done on a consistent basis. That means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

Between 2002 and 2006 Benin’s growth rate, at 3.3 percent per year, slowed from the rates of 4.9 percent per year registered between 1997 and 2001. Both levels are less than the 7 percent that would be needed to significantly reduce poverty. Nevertheless, the overall net contribution of infrastructure to Benin’s growth in the early 2000s was the highest, at 1.6 percentage points, of
Using a regression discontinuity design combined with three rounds of household survey data collected over a period of six years, we find strong markers for the program’s success in registering land in the targeted program area. Land registration, however, does not translate into agricultural investments or increased credit taking. Instead, treated households decrease their amount of agricultural labor, accompanied by only a small reduction of agricultural production and no changes to productivity. In line with this result, we observe that households decrease their sharecropped land holdings amid a surge in land valuations. The analysis also uncovers important within-household differences in how women and men respond differentially to the program. There appears to be a general shift to non-farm economic activities, and in particular women’s business profits increase considerably.

Our results show that one key channel through which secure land rights can improve smallholders’ welfare is through gains in allocative efficiency of productive resources. Resources, in the form of land and labor, spent on agricultural production are reduced and switched (especially for women) into non-farm enterprises. Combined with the increase in household wealth, this evidence provides a suggestion that improvements in land security through land registration may contribute to structural transformation.

22
Skilled Labor|0.92|0.78|0.47|0.81|1.28|1.08|1.16|1.14|0.91|0.96|0.99|0.76|<br>0.76|
|**Exports inpercent**|**of imp**|**orts**||||||||||||
|Natural Resource|139|149|103|130|155|166|175|148|162|192|159|175|<br>156|
|Unskilled Labor|167|160|123|136|124|144|144|142|144|141|144|130|<br>130|
|Capital Intensive|34|33|42|50|60|72|77|49|36|45|37|34|<br>36|
|Skilled Labor|46|39|21|42|59|58|85|74|56|63|64|48|<br>48|
|All abovegoods|75|79|69|83|92|102|117|97|87|99|90|83|<br>82|

Source: Based on EU Data from UN COMTRADE Statistics.

The aggregates, however, do not allow determining the extent to which the reduction in distortions generated by economic reforms has triggered the process of relocation of resources to new lines of production. A different picture emerges from the analysis of export growth and changes in shares in EU external imports at four-digit SITC Rev. 2 level. Table 25 presents the composition of Bulgarian EU-oriented exports of products whose share in EU imports grew at least at an average annual growth of 30 percent amounting at a minimum to 2.85-fold increase in this share in 2003 over 2000. 141 four-digit SITC items, accounting in 2003 for 16 percent of Bulgaria
Figure 4. Has Filed a Complaint for Any Reason with the Police or Any Institution <!-- Start of picture text --> β<br>0.118<br>0.099<br>0.094<br>0.087 0.088<br>0.08<br>N = 1481 N = 1369 N = 965 N = 710 N = 516 N = 659<br>Full Sample Male Female<br>Has PEP−RAMV Doesn’t have PEP−RAMV<br>.14 .14<br>.13 .13<br>.12 .12<br>.11 .11<br>.1 .1<br>.09 .09<br>.08 .08<br>.07 .07<br>.06 .06<br>.05 .05<br><!-- End of picture text --> _Notes:_ The figure presents the propensity to file a complaint with the police or any official body. We present results for all, male, and female heads of households using the VenRePs Survey collected by Ib ́a ̃nez et al. (2024).

women. Particularly, female victims of a crime reported the event 11.8 percent of the time if they had the PEP but only 9.4 percent of the time if they did not have the PEP. The difference in reporting rates between PEP holders and nonholders is only statistically significant for women, as documented in Appendix Table C1.

# **VI IMPACTS OF THE PEP ON CRIMES COMMITTED**

To complement our main results, we also study the effects of the PEP on crimes committed by Venezuelan migrants. For this purpose, we employ data from Colombia’s Office of the Attorney General as described in the data section. We use the same 2SLS specification described in section IV to evaluate the effects of the regularization on the rate of total crimes directly
were about 9 percent higher in the Bureau of Economic Analysis data relative to the data of the Bureau of Labor Statistics. In addition, they find that the two data series consistently grew apart from 1992 to 2000. As underlined by Garner and Short (2001), the reason for this mismatch may be that respondents in both cases have mistaken and biased ideas about the market values of their dwellings or that the reported rental equivalence values are likely to be capturing variations in housing and neighborhood quality that hedonic approaches do not capture. Corradin, Fillat, and Vergara-Alert 2017 make an argument that is similar to the latter point. Moreover, owners may express above-market evaluations of their dwellings because of a special attachment for specific features of their homes, especially if they designed or built the homes. Heston and Nakamura (2009) call this the owner pride factor. Van der Cruijsen, Jansen, and van Rooij (2018) provide a review of possible behavioral reasons beyond the bias of homeowner valuations. They compare the self-assessed homeowner valuations with appraisals of the same housing administrated by the municipality to which the housing belongs for tax purposes. They find that the median respondent overestimates the true value by 11 percent.

Sometimes, the same survey supplies enough information to check the accuracy of homeowner selfassessments. Van der Cruijsen, Jansen, and van Rooij (2018) and Gao and Liang (2019) compare the two sets of values reported by respondents in, respectively, the DNB Household Survey in the Netherlands and the China Household Finance Survey: the self-assessed current value and the purchase price for the same dwelling, which is updated using an evaluation of the fluctuations in housing prices in the same neighborhood or region over time. Both contributions find that households systematically overestimate their home values. Benítez-Silva et al. (2015) exploit the longitudinal panel structures of the Health and Retirement Study and the American Housing Survey and are able to compare the transaction prices at a given time and self-reported housing values before the sales of the same housing. They conclude that
Policy Research Working Paper 11048

# **Abstract**

The composition of flora and fauna in low-lying coastal regions worldwide is being altered by sea-level rise in a changing climate, favoring saline-tolerant species. These shifts are projected to have significant implications for nature-based livelihoods, resource availability, market prices, and the food and nutrition security of coastal populations, particularly those with limited choices and affordability. The vulnerabilities arising from these changes underscore the critical need for adaptation planning to build resilience. In the southwest coastal region of Bangladesh, rising sea levels and upstream changes in freshwater flux are intensifying riverine salinity, with annual flux dynamics driving substantial salinity changes and providing insights into future trends as high-salinity water encroaches further inland. In this study, river salinity monitor data were combined with fish sales records from nearby wholesale markets to evaluate the magnitude, spatial distribution, and fishing impact of salinity changes throughout 2023. Significant impacts on fish quantities were observed, and analysis of associated child health data revealed that salinity-related health challenges persist despite steady poverty reduction. Econometric analysis of fish catch records demonstrated that salinity changes differentially affect the availability of fish species with varying salinity tolerances, reflecting the interplay of species-specific salinity aversion and fishers’ adaptive strategies to optimize profitable catches. These findings highlight the importance of complementing technical assessments of species-specific salinity tolerances with empirical salinity and catch data to improve projections of salinity impacts on fish consumption in affected regions. The results provide actionable insights into the dynamic interactions between environmental change, ecological responses, and human adaptation in coastal settings.

This paper is a product of the Development Research Group and the Development Data Group, Development Economics and the Environment Global Department. 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 sdasgupta@worldbank. org. A verified reproducibility package for this paper
# **5. Results**

Due to differences between life and non-life insurance markets, we separate the analysis between life and non-life IP. In Table 2 we present the estimates of Equation 1 using all countries in our sample. Estimates in odd-numbered columns do not include country time trends, while in even-numbered columns we include these trends. In some cases, especially when we use data with smaller coverage of countries and time (i.e. Financial Reform data and Findex data), including country time trends may be too demanding due to small sample sizes, so our preferred specifications do not include country time trends. In all columns we include GDP per capita in both linear and non-linear fashion.

In Columns 1 and 2 of Table 2 we regress IP on GDP per capita. In Columns 3-10, we add the other variables of interest, one at a time (always controlling for GDP and GDP<sup>2</sup> ). We prefer to add each one at a time for two reasons: (i) as mentioned in Section 4, since income per capita, the governance index and our measures of financial markets development are arguably correlated, we want to avoid collinearity issues; and (ii) the geographical and time coverage of the Abiad, Detragiache and Tressel (2010) and of the Findex data is far more limited. In Columns 3 and 4 we add our governance index, while in Columns 5-6, 7-8 and 9-10 we include domestic credit to private sector (as % of GDP), the Financial Reform Index, and the number of account holders (as % of population) from Findex, respectively. Due to sample size restrictions, our preferred specification when using the last two variables does not include country time trends. Appendix Table 2 shows analogous estimates using total (sum of life and non-life) IP.

In order to quantify the magnitude of our estimates, we ask what the predicted increase in IP would be if a country were to move from the 25<
imagery sources as described in panel B in Table 1, district fixed effects, a binary indicator for urban villages, and log village-area. “Low-resolution public” indicators refer to the low-resolution and public indicators as defined in Table 1. “All public” indicators refers to all the public imagery based indicators, including the high-resolution imagery based indicators described in Table 1. “Proprietary texture” indicators include very high-resolution texture features based on Digital Globe from Engstrom et al. (2017) as defined in Table 1. “All” imagery refers to all public and proprietary texture indicators, and object identifiers, such as cars, roofs etc., as defined as in Table 1. The out-of-sample _R_<sup>2</sup> , Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for the LASSO based Poisson regression are from tables 8-10.

25