rafmacalaba/data-use-mentions-extended huggingface dataset:
Net_

A combination of securities and commercial banking activities can increase the risk of bank failure (for a more extensive discussion of these issues, see Saunders 1994). While this has at times happened in emerging markets as well as developed countries (e.g., Barings), empirical evidence does not confirm this possibility as a general proposition. An empirical analysis of bank failures in the 1920s in the US for example found that banks undertaking securities activities were no more likely to fail than banks with no connection to the securities business (White 1986). More generally, there is no strong evidence that the combination of financial activities increases risk, and they might well reduce risks. For example, Wall and Eisenbeis (1984), using accounting data at the industry level, find that there was a negative correlation between bank earnings and securities broker/dealer earnings over the 1970 to 1980s period.<sup>9</sup> Nevertheless, safeguards can be necessary to avoid the transfer of explicit and implicit deposit insurance subsidies from the banking part of the institution to the securities part (Kane (1996) and Schwartz (1992) discuss in details how these transfers can occur). These safe-guards can take the form of market value accounting, timely monitoring and disclosure, more risk-sensitive capital requirements, firewalls between different type of operations, strong prompt corrective action procedures, including the closure of insolvent banks, and risk-based pricing of deposit insurance.

9 For a review of this literature see Brewer, Fortier, and Pavel (1989) and Benston (1990).
# **Acknowledgements**

This work is based on the analysis of detailed treasury transaction data from Pakistan and Cambodia. The authors would therefore like to acknowledge the contributions of the State Treasuries and Ministries of Finance of these countries for making the data available and guiding the authors in the interpretation of the results.

The authors are thankful to Leah April, Khuram Farooq, Ayesha Guzman, Lewis Hawke, Minh Nguyen, Ron Points, Sabah Rasheed, Say Sasanith, Nicola Smithers, and Fanny Weiner who have participated in discussions relating to the methodology and made valuable contributions to the debate, which is gratefully acknowledged.

Work on the analysis of the Cambodia data was done during two World Bank missions and one IMF mission in 2017 and 2018. These missions were led by Ms. Leah April, Sr. Public Sector Specialist, World Bank and Mr. Holger Van Eden, IMF Regional Public Finance Advisor, TAOLAM, Bangkok. Mr. Maun Prathna, Director ICT, Ministry of Economy and Finance, RGOC and staff of the ICT Department, MEF, participated in the analysis. Work on Pakistan was done jointly with Mr. Ammar Naqvi, Director General MIS office of the Controller General of Accounts, GoP.

The authors are particularly thankful to Mr. Ismaila Ceesay who has kindly agreed to support and sponsor the publication of this paper. Mr. Ceesay is Governance Practice Manager in the South Asia Region and steward of the global FMIS community.

2
to GDP per capita measured by NUTS-2 regional boundaries.

**However, NUTS-2 regions are large entities, with populations of 800,000 to 3 million people, which hides substantial heterogeneity inside the regions.** The pitfall of the NUTS-2 level as spatial unit for policy design and programming is that this large size often hides substantial variations in economic performance and economic potential. For example, the Emilia-Romagna region (Italy) sees the coexistence of large and wealthy NUTS-3 regions, such as Bologna and Modena, with lagging ones, such as Ferrara and Rimini. In Croatia, a single NUTS-2 region includes the richest city of the country – i.e. Zagreb – and five of the EU’s poorest counties. In Bulgaria, the NUTS-2 region of Yugozapaden includes Sofia Province and Sofia City – that recorded a 10.6% and

> 1 Authors’ elaboration on data available at “https://cohesiondata.ec.europa.eu/overview”.

> 2 Various figures appear in different sources (e.g. European Commission, 2015). The breakdown by year and by fund in each country available under ‘Data on budget commitments’ at <u>https://ec.europa.eu/regional_policy/en/policy/evaluations/data-for-research/</u> indicates the totals cited here.

> 3 Authors’ elaboration of Eurostat data.

> 4 Authors’ elaboration on Eurostat data. The spatial unit of reference corresponds to the level 3 of the _Nomenclature des Unités Territoriales Statistiques_ (NUTS) adopted by the EU.

2
2.5|
|Total change|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

_Source:_ Authors' calculations with data from RIGA for Ghana and Nepal, from household surveys for Bangladesh, Moldova, Romania, Peru, and Thailand, and from SEDLAC (CEDLAS and the World Bank) for countries with income-based measures of welfare. _a/_ FGT0 refers to the Foster,Greer, and Thorbecke (1984) measure of the headcount index, which measures the proportion of the population that is counted as poor. _b/_ FGT1 refers to the Foster,Greer, and Thorbecke (1984) measure of the poverty gap index, which adds up the extent to which individuals on average fall below the poverty line, and 34 expresses it as a percentage of the poverty line.

_c/_ FGT2 refers to the Foster,Greer, and Thorbecke (1984) measure of poverty severity, calculated as the poverty gap index squared, which implicitly puts more weight on observations that fall
of being available on a yearly basis independently of the quality of local statistical offices and data gathering. While it comes with its own problems it can shed light on local economic activity where gathering of statistical data is incomplete.<sup>7</sup> This makes it a great fit for measuring growth in a context of civil conflict. Conflict incidence is measured through the number of battle-related deaths from UCDP/PRIO dataset. We run the following regression for country _i_ at time _t_ : where _git_ is economic performance per capita growth of country _i_ in year _t_ , _incidenceit_ is conflict incidence, _μi_ and _ηt_ are respectively country and year fixed effects.

A cross-country analysis as in equation (1) bears considerable potential for both reverse causality and omitted variable bias. Thus, a priori, a convincing causal link is hard to establish. However, here we expect the resulting bias to be small for two reasons. First, the cross-country literature has not found that negative, contemporaneous shocks to growth systematically lead to violence.<sup>8</sup> Second, we have run a large number of robustness checks by adding time trends or lagged growth to our specification and controlling for rainfall shocks directly.<sup>9</sup> The upshot from this is not only that results remain significant but also that the estimated coefficients barely change. This does not mean that a causal link from falling growth to conflict can be ruled out. But it is unlikely to drive the macro relationship we see in the data.

In order to further explore the relationship between violence and country-level output we run two specifications of the model described above. In the first model conflict in country _i_ at time _t_ is defined by any violence, i.e. if at least one battle related deaths occurs. In the second specification, conflict is defined by a higher threshold, by 0.008 deaths per 1000 population.<sup>10</sup> We expect to get different results from the two specifications
effluent concentrations in Indonesian plants is extremely broad, overlapping both U.S. and Indonesian standards. Approximately two-thirds of the plants are in compliance with Indonesian standards, and one-third would be in compliance with U.S. standards.

Thus, evidence from South and Southeast Asia indicates great variation in plants' environmental performance regardless of the state of formal regulation. In Brazil, Seroa da Motta, et al. (1993) have found a similarly broad pattern of variation in BOD removal across Brazilian states. Even in North America, relatively high average performance masks a surprising degree of variation .9 Why should such broad variations exist, even in countries where regulation is weak? Most explanations have relied on anecdotes (Birdsall and Wheeler, 1993) or on work which relates national policies to changes in the balance between "dirty" and "clean" sectors or technologies across countries (Birdsall and Wheeler, 1993; Martin and Wheeler, 1992; Huq et al., 1993). The latter work, however, relies on sector- and technology-specific pollution intensities estimated from U.S. data and does not reflect actual emissions in other countries. More systematic evidence for developing countries is just beginning to emerge. In a recent econometric analysis of BOD emissions from

> 9 Three years ago, a California coastal pulp mill was fined and forced to install abatement equipment after local surfers developed skin lesions from contact with its **untreated** raw effluent in the Pacific. Laplante and Rilstone (1994) note that BOD and SS (suspended solids) pollution from pulp and paper mills in Quebec violates environmental standards at least 38% of the time.

6
. This approach was subsequently applied in World Bank (2007) to produce a regional price index based on the 2002 POF data, and is based on unit-value information provided in the POF survey on food items, as well as a hedonic model of rent. We re-apply the method here, but focus solely on urban areas and construct a Laspeyres price index that captures price differences across city-size categories (and regions). Because of the limited sample size of even the unusually large POF household
we randomized at the village level and we control for village fixed effects, the treatment dummies ( _Dj_ ’s) would be redundant in Equation 2 and therefore are not included.

Due to logistical reasons, the cash transfer program was unexpectedly launched late by Burkina Faso’s government in the 2008-2009 school year. The first cash payment was only made at the end of November/early December 2008, while the school year started October 1, 2008.

This meant most households were not able to enroll their children during the program’s first year as they did not receive the transfer in time to pay school fees due at the start of the academic year. Subsequently, as we will see when we discuss the results, we do not observe any education impacts during the first year of the program. For this reason, we also present a difference-indifferences specification that only includes the baseline Round 1 data and the follow-up Round 3 data from the 2009-2010 school year. In Equation 3, the round indicator is for the second followup survey (Round 3) conducted in June 2010, and the other variables are as defined previously.

(3)

# **5. Empirical Results**

# _5.1. Baseline Balance and Attrition_

In Tables 2a and 2b, we use baseline data to confirm that household, school, and child characteristics are balanced across the treatment groups and between treatment and control. We first present variable means measured at baseline for the control group and each of the treatment arms. In column 6, we estimate regressions of each characteristic on CCT and UCT treatment dummies, as that is the focus of this paper, and then calculate a Wald test of the equality of the UCT and CCT variables. In column 7, we estimate regressions of each characteristic on dummies for the five groups and then calculate an F-test of the joint test that the means of the five groups are equal. In Tables 2a and 2b, results show good balance overall across study arms for school, 15
pattern is found in the available household surveys that allow a comparison of individual nutritional measures with an estimate of the household’s consumption per person, which is clearly the most widely used welfare metric in measuring poverty in developing countries. Adding other household variables—interpreted as either a re-weighting of the DHS wealth index or as supplementary variables—improves the performance of household data in this respect, but we still find that a large share of undernourished individuals are not among those predicted to be undernourished based on household variables. It is clear from this study that to have any hope of reaching undernourished women and children, policy interventions in this setting will either 22
SIDS in table 4 was 61 percent of GDP in 2022, compared to the average of 65 percent of GDP for all EMDEs.<sup>19</sup> Nevertheless, public debt ratios vary greatly among SIDS and current high levels of public debt are identified in the DSAs as major contributors to risks of debt distress in more than a third of SIDS. However, vulnerability to macroeconomic, financial or natural disaster-induced shocks contributes to the risk of debt distress in an even larger number of SIDS, including those with low or moderate public debt ratios.

# **_4.2 The Composition of Public Debt by Creditor_**

Domestic debt comprises 38 percent of total public debt on average for the 34 SIDS in table 5, and external debt 62 percent, although domestic debt is generally larger as a share of total debt for those SIDS with the highest levels of public debt. Domestic debt is mostly held by local financial institutions, especially banks and public provident funds, although for most SIDS, there are no comprehensive data showing a breakdown of domestic debt by type of creditor.

**Table 6 Composition of Public Debt by Creditor; averages for 21 SIDS (% of total)**

|**_Category_**<br>**_(number of_**<br>**_countries)_**|**_PPG debt to_**<br>**_GDP (%)_**|**_Share of tota_**|**_l PPG debt by ty_**|**_pe and creditor_**|**_(%)_**|
|---|---|---|---|---|---|
|||Multilateral<br>debt|Official<br>bilateral debt|Private<br>and<br>other<br>external debt|Domestic<br>debt|
|**_Full sample_**<br
resilience indicators that may be considered alongside other social, financial, economic, and environmental decision factors to inform project selection. That said, the expansive conceptual nature of resilience does not lend to the total quantification of all aspects of resilience. As such, the effort herein attempts to capture a few of the most significant vulnerabilities associated with potential natural disasters in order to at least partially bring resilience considerations into project design and selection processes.

The objective of this paper is to estimate the resiliency benefits for key socio-economic aspects under different road-upgradation scenarios and compare the results with regards to the related costs. The test case used is the East Road of the Malaita Road Network in the Solomon Islands, which has been selected primarily to pilot the methodology and examine its applicability. The parameters selected to measure resiliency were based on the key benefits the road provides to the people living around it – i.e. economic benefits proxied by travel time, access to hospitals and access to markets. In general, access to schools would also be a key parameter, but, in the case of Malaita, it was not considered a key functionality, as most primary students walk to school and most secondary students board at their schools during the week. Therefore, the road disruption causes very marginal interruption to this access. Since it was found that there were severe data constraints in Malaita, the report is based primarily on expert-inputs as well as geospatial data. It considers mainly technical improvements to road upgradation that might impact resiliency. However, it is acknowledged that other improvements on aspects such as maintenance contracts and availability of maintenance/repair funds can also positively benefit the ability of a road asset to recover faster post disruption.

> 20 Ibid.

6
The conceptual framework has been used to investigate the effects of land registration on financial development and economic growth using time series data from Thailand (Byamugisha, 1999). The estimation results indicate that land titling has positive and significant long run effects on financial development while the quality of land registration services, measured by the volume of public expenditure on land registration, has positive and strongly significant effects on economic growth. Land titling was found to have negative and significant effects on economic growth in the current period but positive and significant effects in the long run.

The above empirical findings are country-specific and therefore cannot be automatically generalized to apply to other countries. Further country case studies and cross-country ones are required to generate globally applicable findings and conclusions about the economic effects of land registration on financial development and economic growth.

# **VI. Conclusions**

Economic historians have long documented the role private property rights played in the development of the Western world. Political economists have empirically established the positive relationship between private property rights and economic growth but have done so using proxies as measures of property rights. And agricultural economists and urban real estate specialists have also empirically determined the link between landownership security and land productivity in the sectors of their interest. It should be noted that the use of proxies by political scientists to measure property rights was a source of weakness in their investigations. Similarly, the studies of the agricultural economists and real estate specialists had some particular weakness in so far as they did not account for the land registration impacts that accrue across sectors and those that affect the economy as a whole. This paper has attempted to address this weakness by developing a theoretical and conceptual framework to guide the empirical analysis of the effects of land registration on the economy as a whole. Furthermore, by using a direct measure of private property rights (land registration), our study aims to overcome the weakness of the earlier property rights studies which used proxies to measure private property rights. Empirical results from the application of our economy-wide land registration framework indicate
from our administrative records of program take-up. This includes information on which firms we assisted to get business registration certificates and a TPIN, as well as information on attendance at the bank information sessions and on which firms signed up for business bank accounts at the conclusion of these sessions.

The second source of data is the baseline and four rounds of follow-up surveys. The interventions took place from June to September 2012. Four rounds of follow-up surveys were conducted after the interventions. The first follow-up survey took place between November 2012 and March 2013, on average 4 months after the interventions. The second follow-up survey took place between November 2013 and March 2014, on average 16 months after the interventions. The third follow14
37 HIV/AIDS prevention or reproductive health programs. The few that have been well evaluated suggest that community level approaches can be effective in changing violencerelated attitudes and behaviors. The following programs exemplify this approach: _Program H_ (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Greene and Murphy, 2003; Guedes, 2004). The initiative includes four components: a) training professionals to work with young men in the area of health and gender-equity using a set of manuals and videos; b) social marketing of condoms; c) promoting health services; and d) evaluating changes in gender norms. In 2002, PROMUNDO and Horizons began a two-year evaluation to measure the effectiveness of two different approaches, compared to a control site. Researchers have developed a "Gender-Equitable Men" (Leichert) scale with 24 items for measuring attitudes. Methods include pre- and post-tests as well as a six-month follow-up community-based survey. In addition, they are gathering qualitative information among men and their female partners. Preliminary results suggest that the program has been successful at increasing gender equitable norms and reducing behavior that put men at increased risk of HIV/AIDS (Pulerwitz, Barker et al. 2004).

_ReproSalud_ (Peru) was lauched by the NGO Manuela Ramos in 1995 as a USAID-funded rural reproductive health program. ReproSalud used participatory rural appraisal (PLA) to help women's groups identify women's reproductive health needs and to organize community meetings to design strategies to address those needs. Domestic violence and forced sex within marriage emerged as an important problem in those communities. In response, ReproSalud organized workshops for women and men on gender issues, carried out community awareness campaigns and established a microcredit program for women. By 2002, ReproSalud had reached over 123,000 women and 66,000 men. Qualitative and quantitative evaluation data
- “...buyers do not allow any ‘Made in Bangladesh’label on the [garment] product ...they [requested] label these very products as ‘Made in India’... ”

- –Prabir De, “Non-Tari¤ Measure Study: Bangladesh, India and Nepal,”2016

“Five individuals and two domestic honey-processing companies have been charged with federal crimes... The charges assert that the Chinese-origin honey was misdeclared as other commodities upon importation into the United States and transshipped through other countries to evade anti-dumping duties.” –World Customs Organization, Illicit Trade Report, 2012 “Professional Fraud Facilitators... provide a complete fraud package to EU importers and Chinese exporters, with delivery of goods to EU port, change of container in transit country and documented new origin for goods (new B/L, invoices, origin certi...cates).”

- European Anti-Fraud O¢ ce, Customs Origin Fraud and Professional Fraud

- Facilitators, 2014

# 1 Introduction

In international trade, importing and exporting countries often record starkly di¤erent customs statistics for the same shipments, mostly with imports signi...cantly larger than exports. For example, in 2018, the US recorded US$563 billion in overall imports from China, while China recorded US$480 billion in exports to the US.<sup>1</sup> The result is a trade discrepancy of nearly US$83 billion, or 15 percent. Not only is the presence of such trade discrepancies puzzling, it also distorts the true picture and may lead to public misperception and ill-informed policy decisions. At a detailed product level, the discrepancies in customs statistics are even

> 1These ...gures are from national statistics of the US and China.

1
.02)|(0.05)|(0.04)||
|Share of non-drinkers|-0.05***<br>(0.01)|-0.05***<br>(0.01)|-0.05**<br>(0.02)|0.00<br>(0.01)||
|Share of smokers|0.11***<br>(0.01)|0.12***<br>(0.01)|0.12***<br>(0.04)|0.06<br>(0.04)||
|Mean PM2_._5|0.52***<br>(0.05)|0.47***<br>(0.02)|0.47***<br>(0.06)|0.45***<br>(0.05)|0.44***<br>(0.04)|
|R2|0.59|0.59|0.59|0.47|0.47|
|AICc|3995.20|3983.77|3983.77|2242.19|2227.05|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 The residuals are checked for spatial autocorrelation, and significant residual clustering was still found. Spatial autocorrelation is not easily addressed in count data with standard regression implementations, hence the results are simply from a mis-specified model. For this reason, the models that relax distributional assumptions