rafmacalaba/datause-encoder-data huggingface dataset:
WPS3755

# **The Mix of International Banks’ Foreign Claims:**

# **Determinants and Implications**

Alicia García Herrero and Maria Soledad Martínez Pería ***** Bank of Spain and World Bank

## **Abstract:**

This paper analyzes the determinants and implications for financial stability of the mix of international banks’ claims countries receive. In particular, we distinguish between local claims, extended by international banks through their affiliates in a host (or claim recipient) country, and cross-border claims, booked from outside the host country, typically from banks’ headquarters in their home countries. Using data on U.S., Spanish, and Italian banks’ foreign claims across countries, we find that the share of local foreign claims is primarily driven by the degree of “freedom” in the host banking sector and by business opportunities in the local market. Entry requirements, startup and informational costs associated with international banking also play a role, but their influence is less robust. Finally, we find that the mix of international bank claims has implications for financial stability, since foreign claim volatility is lower in countries that receive a larger share of local claims.

**Keywords** : foreign bank financing, financial FDI, cross-border claims **JEL** : F36, F37, G21

World Bank Policy Research Working Paper 3755, October 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We thank Cristina Luna for her valuable assistance in obtaining and interpreting data from Banco of Spain and Bank of Italy on the Spanish and Italian banks’ foreign claims, respectively. We also thank both central banks for access to these data. We are grateful to Maria del Pilar Casal and Sarojini Hirshleifer for excellent research assistance and to Harald Anderson for providing us data. We received useful comments and suggestions from Gerard Caprio, Asli Demirgüc-Kunt, Enrica Detragiache, Patrick Honohan, Ross Levine, Jesus Saurina, Adrian Tschoegl, and participants at the Stanford Center for International Development (SCID) conference on _Latin American Financial Systems and the Challenges of Economic Growth_ and those at the World Bank and Journal of Banking and Finance conference on _Globalization and Financial Services in Emerging Economies._ Contact author: Maria Soledad Martínez Pería, World Bank, 1818 H St. N.W., Washington, DC 20433, email mmartinezperia@worldbank.org.
**Table 7. Uniform Contribution Rate needed for Multi-Pillar System, With Borrowing to cover Transition Costs**

||1995-2100|1995-2050|2051-2100|
|---|---|---|---|
|No Expanded Coverage||||
|(1) Total (incl. 8.5% D.C.)|19.7|20.4|18.5|
|(2) Transition cost|3.7|6.0|0|
|(3) Basic Benefit|7.5|5.9|10.0|
|Expanded Coverage||||
|(4) Total (incl. D.C)|17.4|16.8|18.2|
|(5) Transitioncost|1.6|2.8|0|
|(6) Basic Benefit|7.3|5.5|9.7|

*See Table 6 for definition of uniform contribution rate for basic benefit + transition. Defined contribution (DC) rate = 8.5%.

**43**
14 Smith (1776) focused on the role of money in lowering transaction costs, permitting greater specialization, and fostering technological innovation. Information costs, however, may also motivate the emergence of money. Since it is costly to evaluate the attributes of goods, barter exchange is very costly. Thus, an easily recognizable medium of exchange may arise to facilitate exchange (King and Plosser, 1986; and Williamson and Wright, 1994). The drop in transaction and information costs is not necessarily a one-time fall when economies move to money, however. Transaction and information costs may continue to fall through financial innovation.

Greenwood and Smith (1996) have modeled the connections between exchange, specialization, and innovation. More specialization requires more transactions. Since each transaction is costly, financial arrangements that lower transaction costs will facilitate greater specialization. In this way, markets that promote exchange encourage productivity gains. There may also be feedback from these productivity gains to financial market development. If there are fixed costs associated with establishing markets, then higher income per capita implies that these fixed costs are less burdensome as a share of per capita income. Thus, economic development can spur the development of financial markets.

# **2. Finance and Growth: Evidence**

## **2.1. Cross-country studies: Financial intermediaries**

We first examine the application of broad cross-country growth regressions to the study of finance and growth. These studies aggregate economic growth over long periods, a decade or more, and assess the relationship between long-run growth and measures of financial development. King and Levine (1993a,b,c) build on earlier cross-country work by Goldsmith (1969). In particular, King and Levine (1993a,b,c) more than double Goldsmith’s (1969) sample
poverty rate that was close to Abidjan, but then rose abruptly between 1986 and 1987. Was the West Forest region so much better off than the East Forest region in 1985 and 1986? If so, what would account for the dramatic decline in per capita expenditures over the four years?

**Table 2 Headcount Index of Poverty by Region**

_(Poverty Line = 75000 CFAFper year in 1985 CFAF)_

|_Region_|_1985_|_1986_|_1987_|_1988_|_1993_|_1995_|
|---|---|---|---|---|---|---|
|Abidjan|0.007|0.014|0.018|0.004|0.051|0.202|
|Other Cities|0.080|0.064|0.059|0.125|0.312|0.286|
|East Forest|0.144|0.095|0.105|0.176|0.389|0.410|
|West Forest|0.016|0.019|0.105|0.193|0.382|0.501|
|Savannah|0.259|0.132|0.248|0.354|0.494|0.494|
|C6te d'Ivoire|0.111|0.069|0.115|0.178|0.323|0.368|

**Table 3 Mean Household Expenditure Per Capita by Region**

|_(in_<br>_1985 CFAFpe_|_r year)_||||||
|---|---|---|---|---|---|---|
|_Region_|_1985_|_1986_|_1987_|_1988_|_1993_|_1995_|
|_Mean expenditure_|_per capita_||||||
|Abidjan|376108|308092|342923|267570|225274|186251|
|Other Cities|261867|258792|218578|158534|125445|118605|
|East Forest|164035|169269|156271|140286|101667|<br>98269|
|West Forest|252047|202475|159314|130142|105986|<br>96247|
|Savannah|142588|150127|126188|112673|86040|91240|
|C6te<br>d'Ivoire|234867|218366|197680|158410|129306|121486|
|_Index ofmean exp_|_enditure per c_|_apita_|||||
|Abidjan|100.0|81.9|91.2|71.1|59.9|49.5|
|Other Cities|100.0|98.8|83.5|60.5|47.9|45.3|
|East Forest|100.0|103.2|95.3|85.5|62.0|59.9|
|WestForest|100.0|80.3|63.2|51.6|42.1|38.2|
|Savannah|100.0|105.3|88.5|79.0|60.3|64.0|
|Cote d'Ivoire|100.0|93.0|84.2|67.4|55.1|51.7|

Grootaert (1995) explains the decline in the West Forest as largely a result of the decline in farm income. His analysis shows that West Forest export crop farmers suffered a decline of about 34 percent in farm income, due in part to the decline in the cocoa and coffee sales between 1985-1988 (table 4). **6** The quantity of cocoa sold declined by 51 percent per farmer, while coffee sales declined by 65 percent. In contrast, farm income of East Forest export crop farmers declined only by 18 percent, compared to a decline in cocoa sales of 11 percent and coffee sales of 33 percent.<sup>7</sup> The relatively better

> 6 The farm income figures reported by Grootaert (1995) in his Table 9 are redeflated using the INS deflators and shown in Table 4.

7 Our calculations of cocoa and coffee sales show a smaller decline in cocoa and coffee than Grootaert's. We omitted several large outliers where we suspected that the unit had been incorrectly coded as tons instead of kilograms, and discarded several observations where the unit of sale was nonstandard. The means are weighted and based on reported sales of all cocoa-growing households in the region. Based on our calculations, the survey data show that sales of cocoa by the cocoa-growing population declined by 3 percent in the East Forest between 5
# **8 Conclusion**

With the continuous increase in remote sourcing data, it has gotten much easier and cheaper to monitor and assess the damages from natural disasters. Joyce _et al._ (2009) and Gillespie _et al._ (2007) have done an extensive review of how satellite images can be used to map natural disasters, while this paper has contributed with providing new techniques that utilize other remote sourced data such as ShakeMaps and ash advisory data.

Throughout, techniques based on freely available data have been used to construct damage indices for different disaster types. Generally the indices can be used in any area of the world, and if calibrated with local data, they could provide an excellent tool for local governments or stakeholders in early disaster assessments.

The indices can be used to get quick damage estimates and inform where to provide relief, as well as in research such as what the authors have done in Skoufias _et al._ (2017), where the indices are used to analyze district budget redistributions following natural disasters.

The main caveat is the indices have not been validated against local level damage data. If one had access to high resolution monetary or intensity data, the estimates would be much more precise.

Table 20 gives an overview of the different data sources and software used. All disasters have used the DMSP global nightlights data to weight the indices based on economic activity. Recently, the VIIRS nightlight data provide an alternative for assessing economic activity or events, as showcased for GDP in China (Li _et al._ , 2013; Shi _et al._ , 2014) and Africa (Chen & Nordhaus, 2015) and for storms and floods in the US (Cao _et al._ , 2013; Sun _et al._ , 2015). If one is interested in events after 2012, the VIIRS data provide higher spatial resolution and track changes by month instead of yearly.

Alternatives do exist for choice of software if one wants everything to be open source and free. Instead of ArcGIS, packages such as QGIS and RGDAL for R are potential alternatives. R and Python are used more and more for spatial data and can often provide the necessary tools to do the modeling. Instead of statistical software such as Matlab and Stata, R and Python once more provide excellent alternatives.

Table 20: Disasters and the Data Sources and Softwares used

|Disaster Type|Data Sources|Software Used|
|---|---|---|
|Flood|Hydro1K<br>GLCC<br>FAO Digital Soil Map<br>TRMM<br>GDAS|GeoSFM (ArcView 3.3)<br>Python<br>Stata<br>ArcGIS|
||USGS ShakeMaps||
||USGS Building Inventory|Python|
|Earthquake|World Housing Encyclopedia<br>Urban Extent Map (CIESIN)<br>GESI|Stata<br>ArcGIS|
|||Python|
|Volcanic Eruption|Ash Advisory Data<br>OMI/AURA Satellite Images|Matlab<br>Stata<br>ArcGIS|
|||Python|
|Tsunami|DFO Inundation Map|ArcGIS<br>Stata|

30
|**Tabl**|**e 4: A**|**gricultur**|**al Trade Prot**|**ection Regressions − Specific**|**Tariffs**<br>||
|---|---|---|---|---|---|---|
|**Year Dep. Var**|**.**|**Constant**|**M/Y**<br>**X/Y **|**%PACEXP RANKPACEXP**|_N_<br>_R_<sup>2</sup>|Adj. _R_<sup>2</sup>|
|1993 SPECT|est|0.232|−0.046 1.281|1.488|31 0.311|0.234|
||_t_|**2.058**|**−2.978**<br>**3.258**|1.460|||
|1993 SPECT|est|0.049|−0.044 1.246|0.450|31 0.334|0.260|
||_t_|0.285|**−2.913**<br>**3.259**|**1.769**|||
|1996 SPECT|est|0.261|−0.006 0.144|2.564|33 0.218|0.137|
||_t_|**2.820**|−0.354 0.553|**2.621**|||
|1996 SPECT|est|0.064|0.001 0.036|0.567|33 0.192|0.109|
||_t_|0.428|0.085 0.140|**2.393**|||
|1999 SPECT|est|0.242|−0.015 0.387|3.227|38 0.136|0.060|
||_t_|**2.248**|−0.669 0.789|**2.021**|||
|1999 SPECT|est|0.083|−0.006 0.194|0.521|38 0.162|0.088|
||_t_|0.557|−0.271 0.409|**2.298**|||

Notes: 1. See note to Table 2. 2. SPECT=Specific Tariff.

Similar inferences may be drawn about specific tariffs (SPECT) from the estimates reported in Table 4. The association of SPECT with lobbying spending is found for both %PACEXP and RANKPACEXP (except in 1993 when the coefficient on %PACEXP is borderline significant). Curiously, in the 1993 models of SPECT, M/Y and X/Y have signs that are contrary to expectation. For example, X/Y has a positive coefficient, implying that the higher is the export-output ratio, the higher is the specific tariff on that product. It is likely that tariffs are protecting imports of similar products and thus promoting exports of those products. This is an instance of tariffs as export promotion, an idea that was set forth in Krugman (1984).

Table 5 uses data on agriculture tariffs on imports from the European Union for those goods that also benefited export subsidies in the US. These data are from Hoekman, Ng, and Olarreaga (2002).<sup>23</sup> Specifically, Table 5 models the average US tariff (during 1995-1998) on products that also benefited from a US export subsidy. The extra variable that appears in these models is the corresponding European Union average tariff (AVGEU15). That variable is included in order to examine whether US tariffs retaliate against EU tariffs for these goods. The clearest inference from Table 5 is that lobbying spending, whether measured by %PACEXP or RANKPACEXP, is powerfully associated with the imposition of US tariffs on products whose exports are subsidized in the US. This finding is robust for each of the four

> 23 I am grateful to Francis Ng for providing the data and its documentation.

51
pattern of adjustment to the fall in demand and job losses: in Hungary the reaction was mainly labor force withdrawal, whereas in Poland it was mainly active search for new jobs. These differential responses can be accounted for by differences in labor market institutions (e.g. benefit systems).

**Table 1  Employment, unemployment and labor force participation, 2001**

||Employment<br>Rate<br>Unemployment<br>rate|Labor force<br>participation<br>rate|
|---|---|---|
|**CEE**|**59.3**<br>**11.0**|**67.1**|
|Bulgaria|50.6<br>18.1|61.9|
|Czech Republic|65.5<br>7.3|70.7|
|Estonia|62.0<br>9.1|69.3|
|Hungary|56.6<br>5.6|60.1|
|Latvia|60.4<br>12.8|68.8|
|Lithuania|59.9<br>13.1|69.6|
|Poland|51.5<br>19.9|64.6|
|Romania|57.6<br>7.0|63.4|
|Slovakia|56.8<br>18.6|69.9|
|Slovenia|63.4<br>6.0|67.8|
|**SEE**|**45.7**<br>**19.1**|**56.8**|
|Bosnia and Herzegovina|40.1<br>16.4|48.0|
|Croatia|53.7<br>14.4|62.9|
|Macedonia, FYR|40.4<br>31.9|59.8|
|Serbia and Montenegro b)|48.5<br>13.8|56.3|
|**BRU**|**67.9**<br>**9.6**|**72.6**|
|Belarus|-<br>-|-|
|Russia a)|75.8<br>8.0|82.5|
|Ukraine|60.0<br>11.1|62.7|
|**Other CIS**|**56.9**<br>**11.0**|**63.7**|
|Armenia|-<br>-|-|
|Azerbaijan|-<br>-|-|
|Georgia b)|56.8<br>12.3|65|
|Kazakhstan c)|63.6<br>9.3|70.1|
|Kyrgyzstan|-<br>12.5|-|
|Moldova d)|50.4<br>9.8|56.0|
|Tajikistan|-<br>-|-|
|Uzbekistan|-<br>-|-|
|**Memorandum**|||
|OECD|65.1<br>6.9|69.9|
|EU15|64.3<br>7.7|69.7|

- Data not available

Note: Labor Force Survey data Definitions: Working-age population: 15-64 Labor force = employed plus unemployed (aged 15+) Labor force participation rate = labor force 15-64 as a percentage of working age population Employment rate = employed 15-64 as a percentage of working age population Unemployment rate = unemployed as a percentage of the labor force 7
# C. Empirical Estimates of the VSL

# _Hedonic Wage Studies_

Empirical estimates of the VSL most frequently come from hedonic wage studies, which estimate compensating wage differentials in the labor market, or from contingent valuation surveys in which people are asked directly what they would pay for a reduction in their risk of dying. The premise underlying compensating wage differentials is that jobs can be characterized by various attributes, including risk of accidental death. If workers are well-informed about risks of fatal and non-fatal injuries, and if labor markets are competitive, riskier jobs should pay more, holding worker and other job attributes constant. In order to estimate compensating wage differentials empirically, an equation is estimated to explain variations in the wage received by workers as a function of worker characteristics (age, education, human capital) and job characteristics, including risk of fatal and non-fatal injury (Viscusi, 1993). In theory, the impact of a small change in risk of death on the wage should equal the amount a worker would have to be compensated to accept this risk. For small risk changes, this is also what the worker should pay for a risk reduction.

For the compensating wage approach to yield reliable estimates of the VSL, it is necessary that workers be informed about fatal jobs risks and that there exists sufficient competition in labor markets for compensating wage differentials to emerge. To measure these differentials empirically requires accurate estimates of the risk of death on the job—ideally, broken down by industry and occupation. The researcher must also be able to include enough other determinants of wages so that fatal job risk does not pick up the effects of other worker or job characteristics. For example, since data on risk of injury are usually collected at the industry level, it is important to control adequately for other sources of inter-industry wage differentials.

A typical hedonic wage equation is of the form (8), in which the log of the wage is regressed on fatal risk of death on the job, non-fatal job risk, worker characteristics— including age, race education and years of experience and union status—as well as job characteristics, including industry and occupation.<sup>21</sup> where

> <sup>_w_</sup> _i_<sup>:</sup> worker _i '_ s  wage rate

α : constant _xim_ : personal and job characteristics, worker _i_

> 21 The extent to which industry and occupation dummy variables can be included in the equation is limited by the nature of the risk data.  If fatal job risk is estimated at the 3-digit industry level, then 2-digit industry dummy variables can be included in the equation, but not 3-digit dummies, to avoid exact collinearity among right-hand-side variables.

15
In the ECD project, this refers to the improvement of the child readiness through an eight-week curriculum module in Grade 1 that incorporates innovative and participatory approaches and complementary health and nutrition inputs (iron supplementation and deworming) to first graders. This program includes the review and improvement of the ECE curriculum, support for training of teachers in the new Grade 1 curriculum, the reproduction and distribution of teaching materials, training of trainers and teachers and the distribution of iron supplements and deworming tablets for Grade 1 entrants. Managed by the Department of Education.

**_Day Care Program Parent Effectiveness Services Program_**

A range of services to provide early education to children age 3-5 years, including the provision of day care centers.

A range of services to enhance parental involvement in child care and development and teach parents more efficient ways of childrearing and what the children need for their physical and mental development. The Child Development Worker conducts workshops on these services.

In the ECD project, this refers to the upgrading of the PES program with the provision of the mother and child book (that records the child growth from birth to age six) and distributing the parents’ ECD manual. In the context of the LGU, the PES includes the training of the child development worker as the key PES provider and responsible for community-based parent education. Managed by the DSWD in coordination with the Department of Education (incorporated into the Teacher Child Parent Program) and the DOH (incorporated into the health education programs).

**_Day Care Mom Program_**

A range of services and support provided by day care moms to provide childminding services to children under three years old.

26
# Appendix D: AHP and PROMETHEE Input Tables

_Table D. 1. Pairwise comparison between short- and long-term impacts using the AHP scale (please fill only the blank cells)_

||**Short-term impacts**|**Long-term impacts**|
|---|---|---|
|**Short-term impacts**|1||
|**Long-term impacts**||1|

_Table D. 2. Pairwise comparison between short-term “Energy/Environmental” and “Economic/Social” criteria using the AHP scale (please fill only the blank cells)_

||**Energy/Environmental**|**Economic/Social**|
|---|---|---|
|**Energy/Environmental**|1||
|**Economic/Social**||1|

_Table D. 3. Pairwise comparison between long-term “Energy/Environmental” and “Economic/Social” criteria using the AHP scale (please fill only the blank cells)_

||**Energy/Environmental**|**Economic/Social**|
|---|---|---|
|**Energy/Environmental**|1||
|**Economic/Social**||1|

51
basic equation _(5)_ by<sup>separatingthe promotioneffort (P)intheseveral categoriesdefined</sup> by Wells and Wint,<sup>2001; thatare image-building,investor servicing,investment</sup> generation, and policy<sup>advocacy.Theseactivitieshave been extensivelydetailed by these</sup> two authors and will<sup>not be explainedin thispaper (see AnnexA for a briefsummary).</sup> Here it suffices to indicate<sup>that theyare generallywell known bymanagersof investment</sup> promotion agencies<sup>andexpertsin thatfield.</sup> A third and final extension<sup>is to incorporatea number of internalcharacteristicsof the</sup> promotion agencies<sup>asexplanatoryvariables into equation(5).In doingso, we follow</sup> again Wells and Wint,<sup>2001.In particular,theseauthors haveargued thatsuch</sup> effectiveness rnight be<sup>strengthened when the agency benefits from the involvement of</sup> the private sector and<sup>a focusmandate on investment promotion.</sup>

# **III. Definition of Variables**<sup>**and Sources of Data**</sup>

This section discusses,<sup>first, theselection of variablesused for the applicationof the</sup> model developed above.<sup>Then, we briefly describe someof themethodologicalchoices</sup> that we had to face.

We used as a first indicator<sup>total gross FDI inflowsas definedby UNCTAD,whichoffers</sup> a more complete country's<sup>coverage thanthe IMF or the World Bank's database. This</sup> indicator presents the<sup>double advantageto be relativelyhomogenousacrosscountriesand</sup> relatively well used<sup>in theeconomic literature,thereforefacilitatingcomparisons.It may</sup> be however biased for<sup>our purposeof measuringthe IPA effectivenesssince many</sup> agencies have reported<sup>that they do not have the main responsibilityfor privatizationand</sup> for the mining sector<sup>--twoimportant channels of FDI worldwide.For thisreason, using</sup> the UNCTAD data,<sup>we adjustedgrossFDI flows by the amountof Merger&</sup> Acquisitions (M&A)<sup>in each country.Theidea is to capturemore accuratelythe FDI</sup> flows that are under<sup>the direct control of most IPAs.</sup> 9