rafmacalaba/datause-encoder-data huggingface dataset:
WPS3753

# **How Banks Go Abroad: Branches or Subsidiaries?**

Eugenio Cerutti, Giovanni Dell’Ariccia, and Maria Soledad Martínez Pería<sup>*</sup>

# **Abstract**

This paper examines the factors that influence banks’ type of organizational form when operating in foreign markets, using an original database on the branches and subsidiaries in Latin America and Eastern Europe of the top 100 international banks. We find that regulation, taxation, the degree of desired penetration in the local market, and host-country economic and political risks matter. Banks are more likely to operate as branches in countries that have higher corporate taxes and when they face lower regulatory restrictions on bank entry, in general, and on foreign branches, in particular. Subsidiaries are the preferred organizational form by banks that seek to penetrate the local market establishing large and mostly retail operations. Finally, there is evidence that economic and political risks have opposite effects on the type of organizational form, suggesting that legal differences in the degree of parent bank responsibility vis-à-vis branches and subsidiaries under different risk scenarios play an important role in the kind of operations international banks maintain overseas.

# **JEL:** F23, G21

**Keywords:** foreign banks, organizational choice

# World Bank Policy Research Working Paper 3753, 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._ Johns Hopkins University, International Monetary Fund, and World Bank, respectively. We thank Ugo Panizza and researchers at the Inter-American Development Bank for providing us data. We are grateful for comments and suggestions received from Alberto Pozzolo and other participants at the World Bank and Journal of Banking and Finance conference on _Globalization and Financial Services in Emerging Economies._ <u>Contact information: Maria Soledad Martínez Pería, World Bank, 1818 H Street, N.W., Washington D.C., 20433.</u> Phone: (202)458-7341. Fax: (202)522-1155. Email addresses: <u>ecerutti@jhu.edu, gdellariccia@imf.org, mmartinezperia@worldbank.org.</u>
# PRE Working Paper Series

||_Tie_|Author|x|Contact<br>fr<br>_ape_|
|---|---|---|---|---|
|WPS758|Is There Excess Co-Movement of<br>Primary Commodity Prices? A<br>Co-Intef,.ation Test|Theodosios B. Palaskas<br>Panos N. Varangis|<br>August 1991|D. Gustafson<br>33714|
|WPS759|The Profamilia Family Planning<br>Program, Columbia: An Economic<br>Perspective|Jesus Amadeo<br>Dov Chernichovsky<br>Gabriel Ojeda|August 1991|0. Nadora<br>31091|
|WPS760|How Conflicting Definitions of<br>"Manufactures" Distort Output and<br>Trade Statistics|Alexander J. Yeats|September 1991|J. Jacobson<br>33710|
|WPS761|Uncertainty and the Discrepancy<br>between Rate-of-Return Estimates<br>at Project Appraisal and Project<br>Completion|Gerhard Pohl<br>Dubravko Mihaljek|September 1991|P. Lee<br>81950|
delivered during school hours by local teachers, specifically by the tutor in charge of each classroom.<sup>12</sup> The content of the session is based on previous work by Yeager et al. (2016, 2019), Paunesku et al. (2015) and Blackwell et al. (2007).<sup>13</sup> The session comprises three distinct segments of approximately 30 minutes. In the first segment, students were asked to individually read a text designed to introduce students to the concept of growth-mindset (the ‘GYM text’). This was an adaptation of the seminal intervention text in (Blackwell et al., 2007), which explains that the ‘brain works like a muscle’ and that with effort and practice anyone can improve their intelligence (see **Annex 1** ). In the second part of the session, the content of the text is debated by groups of 4 to 5 students (the composition of each group was freely decided by each teacher) and then by the classroom, with the teacher serving as a moderator of the debate. In the last segment of 30 minutes|, students were asked to write individually a reflective letter to a friend/relative describing what they learned.<sup>14</sup>

In a follow-up session, teachers were instructed to carry out two additional activities. First, to choose the best letters (according to their subjective assessment) and to hang all their student letters on the wall, alongside a poster provided by the program and which summarizes the key concepts of the session (see **Annex 1** ). Teachers were asked to leave the letters and the poster on the classroom walls for the remainder of the school year. They were to act as a physical reminder of the sessions and increase the long-term salience of their teachings. Second, teachers were asked to take a picture of the students alongside the letters and the poster and to share this picture by email.

From the outset, the intervention was designed to be highly replicable and scalable. All intervention materials reached schools by parcel delivery (the ‘GYM package’), and no ad-hoc 12 In schools in Peru, tutorial sessions take place weekly over the entire school year. The primary aim of these sessions is to provide a space to help students with their emotional and academic development, and to promote a strong relationship and better emotional understanding between students and teachers. While there are guidelines from MINEDU about the content of these sessions, ultimately it is up to each school and each teacher to define the topics to be covered.

13 Intervention tools were translated into Spanish and adapted to the cultural and curricular idiosyncrasies of the Peruvian context. Instructions for tutor teachers were further developed by the authors. Before the intervention, tools were validated in focus group discussions with local teachers and pupils at GRADE.

14 The importance of reflective writing has been shown in a multiplicity of psychosocial studies (see, for instance, Countryman, 1992), including growth-mindset interventions (e.g., Paunesku et al., 2015).

6
of the goods they produce fluctuate; moreover, larger trade openness also magnifies the output impact of external shocks, particularly the negative ones.<sup>9</sup>

**Figure 10: Trade openness and output growth volatility**

<!-- Start of picture text --> 9<br>TCD<br>8 SYR DZA<br>7 CHN NGA<br>SLELSO<br>6 ARG SDNCMRBDI NERNICZAR TGO COGMWI MRT SYC<br>5 PER VEN TTOCIV DOMBWA GUY<br>CHLURYKEN PNG ZMB<br>432 BRAJPNUSAIND BGDCOLGRCAUSESPGTMMEXFRAGHAITACAFGBRKORHTIPAKMDGMARBFAPRTZAFBENBOLFINNZLCANSWECHEDNKECUSLVAUTPRYNORSENCRIISLHNDPHLTHAIDNEGYLKAHUNISRNLDIRL BELBLZ MYSMLT PAN HKG LUX<br>1<br>0<br>0 0.5 1 1.5 2 2.5<br>Trade Openness<br>Output Volatility<br><!-- End of picture text --> Sources: author’s own calculations using WDI data. Trade openness is measured as the ratio of total trade to GDP for the year 2005.

The impact of trade openness on volatility is particularly large for low and middle-income countries. O’Donnell (2001), for instance, finds that the positive link between trade openness and volatility holds for non-OECD countries, but that the effect is reversed for OECD countries, which tend to have a more diversified production structure. A similar result is establish by Calderón _et al._ (2008), who find that trade openness increases volatility, but show that the result is only relevant for low-income countries. The non-linearity of the relationship between trade openness and volatility can explain why some studies find the opposite result, i.e. that trade openness reduces output volatility (Cavallo, 2005), in particular in high-income countries that have diversified production and where trade openness may act as a shock absorber (Martin and Rey, 2006; Cavallo and Frankel, 2004).

In the last decades East Asian countries have faced a striking increase in trade openness. The average trade volume for non-OECD East Asia is more than one and a half the OECD trade volume (78 versus 45 percent of GDP), and is 20 percent higher than the average trade volume of other non-OECD countries (Figure 11). While this high trade volume strongly contributes to spur East Asian growth, it also makes East Asian countries more vulnerable to external shocks.

> 9 See, among others, Easterly, Islam, and Stiglitz (2000), O’Donnell (2001), Kose _et al._ (2003), Cavallo (2005), Giovanni and Levchenko (2006), Loayza and Raddatz (2006), and Calderón _et al._ (2008).

14
10

# 2.2 Sources of Information

The evolution of the levels and structure of manufacturing industrial activity was taken fr)m the National Accounts series elaborated by the Bank of Mexico for the 1950-1971 period and from two series elaborated by the INEGI, one for the period 1970-1984, the other for 1980-1989. The National Accounts of the Bank of Mexico distinguishes 30 manufacturing sectors, those of the INEGI 131 industrial groups at the subsectoral level.

From the above mentioned information two series of real manufacturing output were constructed: the first with 30 manufacturing sectors ranging from 1950 to 1971, the second with 131 activities, ranging from 1970 to 1989. The second series was obtained by coupling the two INEGI series whose sectoral classifications coincide almost completely. The resulting series are listed in Annexes 1 and 2.

The next step was to establish the correspondence between the manufacturing activities of the two series and the 74 industrial ISIC categories reported by Wheeler. For the first series this was not always possible and this must have givten rise to a lower precision in the estimates. For the second series the correspondence could be established in an almost straightforward manner. The correspondence between the National Accounts
Learning from Developing Country Power Market Experiences: The Case of Philippines

|DU|Distribution Utility|
|---|---|
|ECO|Enforcement and Compliance Office (within PEMC)|
|EPIRA|Electric Power Industry Reform Act of 2001|
|ERC|Energy Regulatory Commission|
|FIT|Feed‐In‐Tariff|
|IMEM|Interim Mindanao Electricity Market|
|IPP|Independent Power Producer|
|LTSO|Legally unbundled Transmission System Operator|
|NGCP|National Grid Corporation of the Philippines|
|NPC|National Power Corporation|
|PEM Board|Philippine Electricity Market Board|
|PEMC|Philippine Electricity Market Corporation|
|PLF|Plant Load Factor|
|PPA|Power Purchase Agreement|
|PSALM|Power Sector Asset and Liability Management Corporation|
|RCOA|Retail Competition and Open Access|
|RPS|Renewable Portfolio Standards|
|WESM|Wholesale Energy Market|

iv
Meenakshi, J. V. and R. Ray (2002), "Impact of household size and family composition on poverty in rural India," _Journal of Policy Modeling_ , 24: 539-559.

Newhouse, D., Becerra, P. S. and M. Evans (2017), "New global estimates of child poverty and their sensitivity to alternative equivalence scales," _Economic Letters_ , 157: 125-128.

Olinto, P., Beegle, K., Sobrado, C. and H. Uemastu (2013), "The state of the poor: Where are the poor, where is extreme poverty harder to end, and what is the current profile of the world’s poor?," Economic Premise Note Series No 125, Poverty Reduction and Economic Management Network, The World Bank, Washington DC.

Ravallion, M. (2015), "On testing the scale of sensitivity of poverty measures," _Economics Letters_ , 137: 88-90.

Ravallion, M., Chen, S. and P. Sangraula (2008), "Dollar a Day Revisited," Policy Research Working Paper 4620, Washington DC: World Bank.

Ray, R. (2000), "Household Size and Child Welfare in India," _Economic Political Weekly,_ 35: 3511-3520.

Streak, J. C., Yu, D. and S. Van der Berg (2009), "Measuring Child Poverty in South Africa: Sensitivity to the Choice of Equivalence Scale and an Updated Profile," _Social Indicators Research_ , 94: 183-201.

White, H. and E. Masset (2003), "The Importance of Household Size and Composition in Constructing Poverty Profiles: An Illustration from Vietnam," _Development and Change_ , 34: 105-126.

21
# **Table 4. The effect of victimization on consumption**

|||Consum|ption (ln)||Nonfoo|d (ln)|Food|(ln)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES||||Assistance=0|||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Conflict events|-0.035***|-0.038***|-0.032**|-0.023*<br>|-0.047**|-0.042*|-0.028**|-0.026*|
||(0.012)|(0.011)|(0.013)|(0.013)|(0.022)|(0.024)|(0.014)|(0.014)|
|Observations|2,125|2,075|2,075|1,953|2,125|2,075|2,125|2,075|
|R-squared|0.162|0.304|0.318|0.332|0.107|0.224|0.130|0.277|
|Number of households|717|717|717|674|717|717|717|717|
|Controls|NO|YES|YES|YES|NO|YES|NO|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-waveFE|NO|NO|YES|YES|NO|YES|NO|YES|

_Note:_ Dependent variables are household log total consumption and consumption split into food and nonfood consumption. In column 4, households that report having received assistance have been removed from the sample. Data used are from the three waves of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 36
# **Table 4. Prevalence of impoverishing health expenditures and impact of drug and hospital expenditures**

|||**Total**<br>**consumption**<br>**expenditures**|**After total health**<br>**expenditures**|**_IHE cases_**|**After spending**<br>**on drugs**|**_IHE cases_**|**After**<br>**hospitalization**<br>**expenses**|**_IHE cases_**|
|---|---|---|---|---|---|---|---|---|
|Q1|N|2,417,393|2,417,393|_0_|2,417,393|_0_|2,417,393|_0_|
||% (CI)|100|100|_0_|100|_0_|100|_0_|
|Q2|N|2,418,463|2,418,463|_0_|2,418,463|_0_|2,418,463|_0_|
||% (CI)|100|100|_0_|100|_0_|100|_0_|
|Q3|N|440,216|917,212|_476,996_|787,396|_347,180_|455,582|_15,366_|
||% (CI)|18.2 (17.4; 19.1)|38.5 (37.5; 39.6)|_20.3_|32.8 (31.8; 33.8)|_14.6_|18.9 (18.0; 19.7)|_0.7_|
|Q4|N|0|0|_0_|2,760|_2,760_|335|_335_|
||% (CI)|0|0|_0_|0|_0_|0|_0_|
|Q5|N|0|292|_292_|0|_0_|0|_0_|
||% (CI)|0|0|_0_|0|_0_|0|_0_|
|Total|N|5,276,072|5,753,360|_477,288_|5,626,012|_349,940_|5,291,773|_15,701_|
||% (CI)|43.7 (43.2; 44.1)|47.7 (47.2; 48.2)|_4.0 (3.9; 4.2)_|46.6 (46.1; 47.0)|_2.9 (2.7; 3.1)_|43.8 (43.3; 44.3)|_0.1 (0; 0.2)_|

IHE: impoverishing health expenditures; Q1-Q5: quintiles 1, 2, 3, 4, and 5; CI: confidence interval Note: CIs could not be calculated for IHE cases by quintile because quintiles were calculated before and after health expenditures 16
significant, which suggests that country-specific unce)tainty indeLd tends to depress secondary market prices.

While LDC loan prices have dropped, the secondary market trading volume has risen substantially. Using data provided by jalomon Brothers, Merrill Lynch and the IMF, the World Bank has managed to piece together some estimates of trading volume since 1984 for a number of debtor countries, as reflected in Table 7. Data for the years 1984, 1985 and 1986 represent only debt conversion transactions, defined as debt for equity swaps, domestic debt swaps and debt repurchases. In 1987 the heaviest secondary market trading was in Mexican bank debt at $ 4.8 billion, followed by trading in Chilean debt. Overall trading volume grew rapidly ftom a mere estimated $ 2 billion in 1984 to a projected trading volume of $ 35 billion for all of 1988.

As an additional measure of liquidity, one can relate the estimated volume of trading in a country's debt to the total of its commercial bank obligations. Such measures of relative trading activity are reported in Table 8. Apparently in 1987 Chilean debt was traded most intensively with trading equalling 26 percent of indebtedness . Oddly enough, the last column of the table shows that during the first half of 1988 56.8 per cent of Bolivian bank loans changed hands, which no doubt reflects the Brazilian debt buy-back of March 1988. Relative trading in Jamaican loans during the first half ci 1988 was also quite robust at 20.3 per cent.

To some extent secondary market volumes reflect and parallel the debt conversion and debt buy-back schemes put in place by the various debtor countries. The pick-up of trading of Argentinian bank debt in 1987, for instance, may be ia anticipation of Argentina's debt conversion project 7
<!-- Start of picture text --> Figure 2.1. Aggregate payment flows in SDGSIM<br>Factor domestic wages and rents private savings<br>Households<br>Markets<br>indir taxes<br>gov cons and inv Government Private<br>interm input demand<br>Investment<br>Financing<br>Activities<br>Domestic<br>Commodity Rest of<br>Markets imports World<br>domestic demand<br>exports<br>foreign wages and rents<br>private investment<br>dir taxes lending<br>trnsfr+interest<br>factor demand private consumption<br>trnsfr-interest<br>lending<br>trnsfr-interest FDI<br>lending<br><!-- End of picture text --> Source: Authors’ elaboration.

Over time, production growth is determined by growth in total factor productivity (TFP) and factor employment (made possible by better utilization of existing stocks and/or growth in stock supplies). TFP growth is made up of two components, one that responds positively to growth in government infrastructure capital stocks and one that, unless otherwise noted, is exogenous. Growth in private capital stocks is endogenous, depending on investment and depreciation. Public investment and capital stock changes are determined by policy. For other factors, the growth in employable stocks is exogenous. For labor and natural resources (with sector-specific factors for natural-resource-based sectors), the projected supplies in each time period are exogenous. For natural resources, they are closely linked to production projections. For labor, the projections reflect the evolution of the population in labor-force age, labor force participation rates, and educational attainment. In the labor market, unemployment is endogenous – the model includes a wage curve (a supply curve) that is upward sloping until full employment is reached, at which point it becomes vertical -- see Figure 2.2.

3
<!-- Start of picture text --> Number of episodes = Banking crisis<br>8 = Currency crisis<br>= Debt crisis<br>6<br>4<br>2<br>0<br>1960-1979 1980-1999 2000-2018<br><!-- End of picture text --> <!-- Start of picture text --> Percent of total<br>mEAP mECA @LAC =MNA @&SAR &SSA<br>100<br>80<br>60<br>20<br>0<br>Banking Currency Debt<br>Percent of episodes<br>60<br>40<br>20<br>0<br>All Banking Currency Debt<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes<br>= Advanced economies<br>0.12 = EMDEs<br>=LICs<br>0.08<br>0.04<br>0.00<br>1960-1979 1980-1999 2000-2018<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes TM 1960-1979<br>6 TM 1980-1999<br>5 = 2000-2018<br>4<br>3<br>1<br>)<br>EAP ECA LAC. MNA_ SAR _ SSA<br><!-- End of picture text --> <!-- Start of picture text --> Index, t=100<br>108 = Not associated with crisis<br>= Associated with crisis<br>106<br>104<br>“<br>102 *<br>100<br>t+1 t+3<br><!-- End of picture text -->
<!-- Start of picture text --> Household i 3-5<br>CopingMechanisms wy Pre-Harvest... Up to 3 months... months 3 months plus...<br>. ,<br>Rains * Eat less- + Use savings and + Reduce food = - Sell productive<br>Fail preferred food borrow infake assets<br>+ Other work = Sell non-<br>productive assets<br>Current w.<br>Disaster Assess Appeal Funding<br>Response<br>Rains Fail Response<br>Timeline<br>(months)<br>-3 -2 -1 Harvest 1 2 3 4 5 6 7 8 9<br>1 Clarke/Hill, Cost-Benefit Analysis of the African Risk Capacity Facility, 2012<br>The way disaster response could work:<br>als,<br>Afri oe<br>ow. ay Ane SCALE UP<br>we MODELLING SAFETY NETS<br>Capacity<br>RAINS FAIL — Estimate ARC<br>impact PAYOUT RESPONSE<br>imel ine a 9 a<br>{months} 3 -2 -1 Harvest 1 z 3 4 5 6 7 8 )<br><!-- End of picture text -->
|Manager Controls Income from||0.007|0.624***||-0.002|0.699***|
|---|---|---|---|---|---|---|
|Plot||(0.005)|(0.233)||(0.002)|(0.148)|
|Total Days Worked by HH Men||0.149***|0.082||0.087***|-0.001|
|||(0.055)|(0.065)||(0.011)|(0.026)|
|Total Days Worked by HH||0.001|-0.292***||-0.000|-0.137***|
|Women||(0.010)|(0.076)||(0.001)|(0.025)|
|Total Days Worked by Men||0.035**|-0.028||0.009***|-0.004|
|(non-HH)||(0.014)|(0.024)||(0.002)|(0.005)|
|Total Days Worked by Women||0.003|0.060**||0.002*|0.014|
|(non-HH)||(0.004)|(0.030)||(0.001)|(0.009)|
|All Harvest Sold||0.016*|0.035*||0.005**|0.004|
|(1=Yes)||(0.009)|(0.018)||(0.002)|(0.009)|
|Plot of Land uses||0.020|-0.048||0.085***|0.024|
|Urea/Phosphates/NPK/DAP||(0.015)|(0.040)||(0.008)|(0.019)|
|Plot of Land uses||0.053***|-0.035||0.074***|0.051**|
|Pesticides/Fungicides/Herbicides||(0.013)|(0.040)||(0.008)|(0.020)|
|Constant|||-5.762**|||0.306|
||||(2.775)|||(0.768)|
|Observations|2,142|2,142|2,142|11,435|11,435|11,435|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

53
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