rafmacalaba/datause-encoder-data huggingface dataset:
WPS4113

# **Location Decisions of Foreign Banks and Competitive Advantage**

## Stijn Claessens and Neeltje Van Horen<sup>*</sup>

## Abstract

While institutional differences have been found to affect country growth patterns, much has remained unexplained, including how economic actors “overcome” institutional weaknesses and how internationalization helps or hinders development. Banking is an institutionally-intensive activity and the location decision of foreign banks provides a good test of how institutional differences are dealt with. Specifically, we examine whether banks seek out those markets where institutional familiarity provides them with a competitive advantage over other foreign competitor banks. Using bilateral data on banking sector FDI in all developing countries and controlling for other factors, we find that competitive advantage is important in driving foreign banks’ location decisions. The findings suggest that high institutional quality is not necessarily a prerequisite to be able to attract foreign direct investment in banking and that there are specific benefits, as well as risks, to international financial integration between developing countries.

## **JEL Classification Codes:** F21, F23, G21

**Keywords:** foreign direct investment, international banking, institutions

## World Bank Policy Research Working Paper 4113, January 2007

_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._

* Claessens is with the World Bank, University of Amsterdam and CEPR, and Van Horen is with the World Bank and the University of Amsterdam. We are grateful to Allaeddin Twebti, Siret Dinc, Matias Gutierrez and Haocong Ren for their help with collecting the data. Financial support for this project by United Kingdom's Department for International Development received through DECRG trade and services project is gratefully acknowledged. Email addresses: <u>sclaessens@worldbank.org and nvanhoren@worldbank.org.</u>
24 POLDIS. We note a significant asymmetry in the behavior of the two variables, with 61% of the variability of POLDIS coming from the variability of MEXDISI.<sup>23</sup>

# Variance Decomposition

<!-- Start of picture text --> Percent MEXDISI variance due to MEXDISI Percent MEXDISI variance due to POLDIS<br>100 100<br>80 80<br>= ~~~~~70<br>60 60.<br>40 40<br>20 20= 30<br>.2 '2 17 2'2 2i 32 3 i27 12i21' 2'2i2 3'i i4'2<br>Percent POLDIS varance due to MEXDISI Percent POLDIS variance due to POLDIS<br>65 65<br>60 61 60<br>55 55<br>50 50.<br>45 45.<br>40 40.<br>39<br>35 25. __ 7§ _ _ 1'2 _ _ VT _ _ 2'2 2'i 3'2 3742 ii'i _ _ _ _ _ _ _ _ _ _35 35 2 . _ _ t _ _ 1'2 17i _ _ _ X _ _ 27i~2 _ _ _ _ ~7 _ 42 47X_ _ _<br><!-- End of picture text --> _Figure 10_ In the following section we present our interpretation of the empirical evidence reported so far. In particular, we introduce _learning spillovers_ as a potential explanation of the link between the sovereign risk premia of different countries. But at this stage we also want to stress how strong these links are: on the basis of our estimates, 38% of a shock to MEXDIS has a permanent effect on POLDIS; in section 6 we will report that the volatility increased for most bonds two to four times after the Mexican crisis. Therefore, in what follows, we maintain the implicit assumption that _learning spillover_ effects cannot by itseyfexplain such excessive responses.

23 We have estimated the ECM for MEXDISI and POLDIS on the period 7/29/94 to 7/28/95, i.e. the period comprising the Mexican crisis and during which the comovements are stronger. The results are confirmed: in particular, the variability of POLDIS is now even more dependent on the variability of MEXDISI (68% versus 61%) and the amount of the permanent effect on MEXDISI due to innovations on both variables has increased relative to the increase in the standard deviations of the residuals (the permanent relative response to MEXDISl innovations has shifted from 0.4 to 0.55, whereas the permanent relative response to POLDIS innovations has shifted from 0.66 to 0.82). Finally, the ECM estimates for MEXDIS (instead of MEXDIS I) and POLDIS for the sample 1/20/95-7/12/96 are substantially the same.
the service area is estimated to amount to 78.3 million m<sup>3</sup> . When the opportunity costs of non-water owned inputs are included, the value of the objective function representing the total annual net return for the service area amounts to $6.4 million; when owned inputs are neglected, the total annual net return is estimated to be a third higher. Table 6.2 presents the results for the four model specifications.

# **Table 6.2  Estimates of Net Returns for Representative Farm Model (16,188 hectare)**

||**Without Owned Inputs**|**With Owned Inputs**|
|---|---|---|
|**Description**|**Value/Cost for**<br>**Representative Farm**|**Value/Cost for**<br>**Representative Farm**|
|**Net Return**|**8,367,467**|**6,373,573**|
|Annual Cost of Water Supply|2,067,067|2,067,067|
|**Net Return to Water**|**6,300,400**|**4,306,506**|
|Water Delivery|148 million m3|148 million m3|
|**Net Return per m3 of Water Delivery**|**0.042**|**0.029**|
|Consumptive Use|78 million m3|78 million m3|
|**Net Return per m3 of Consumptive Use**|**0.080**|**0.055**|

71. For the irrigation organization as a whole, a comparison of FDB estimates in net return per acre foot of water delivered and water consumed for each owned-input specification shows:

- When owned inputs are neglected, FDB estimates per unit delivered are again considerably (about 45 percent) larger than when they are included.

- And, of course, FDB for the consumptive use specification are larger (in this case, about 90 percent) than that for delivery—in inverse proportion to their estimated quantity per acre.

72. A comparison of the FDB estimates of table 6.2 with those of table 6.1 shows that the values for corn silage for all model specifications are higher than the values for the irrigation organization as a whole, suggesting that corn silage is a higher value crop than some of the others in the crop mix (in this case, for example, alfalfa).

73. The illustration of the calculations of unit net returns in the Annex, and their link to the estimates for the representative farm model, indicates the close relationship between FDB estimates and the level of 23
Table 2: Effect of Average Temperature Difference on Firm Performance, Accounting for Heterogeneity in GDP and Firm Size

||Small|Small|Small|Medium|Medium|Medium|Large|Large|Large|
|---|---|---|---|---|---|---|---|---|---|
||LIC+LMC|UMC|HIC|LIC+LMC|UMC|HIC|LIC+LMC|UMC|HIC|
|Log(Revenue)|-0.226<sup>_∗∗_</sup>|-0.028|0.123<sup>_∗∗_</sup>|-0.202<sup>_∗∗_</sup>|-0.015|0.141<sup>_∗∗_</sup>|-0.137<sup>_∗_</sup>|0.083<sup>_∗_</sup>|0.245<sup>_∗∗_</sup>|
||(0.065)|(0.037)|(0.055)|(0.06)|(0.036)|(0.054)|(0.072)|(0.049)|(0.061)|
|Log(Rev. per worker)|-0.174<sup>_∗∗_</sup>|0.004|0.139<sup>_∗∗_</sup>|-0.153<sup>_∗∗_</sup>|0.005|0.136<sup>_∗∗_</sup>|-0.113<sup>_∗∗_</sup>|0|0.101<sup>_∗∗_</sup>|
||(0.046)|(0.026)|(0.039)|(0.043)|(0.026)|(0.039)|(0.048)|(0.032)|(0.041)|
|Log(Avg. Wage)|-0.091<sup>_∗∗_</sup>|-0.002|0.065<sup>_∗∗_</sup>|-0.084<sup>_∗∗_</sup>|-0.005|0.061<sup>_∗_</sup>|-0.091<sup>_∗∗_</sup>|-0.032|0.025|
||(0.037)|(0.021)|(0.033)|(0.034)|(0.021)|(0.033)|(0.04)|(0.027)|(0.035)|
|Invest (Binary)|-0.004<sup>_∗∗_</sup>|-0.002|-0.002|-0.003|-0.003|-0.002|-0.004|-0.004<sup>_∗∗_</sup>|-0.003|
||(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.003)|(0.003)|(0.002)|(0.003)|
|Log(Investment)|-0.094|-0.107<sup>_∗_</sup>|-0.117|-0.104|-0.092|-0.083|-0.161|0.03|0.147<sup>_∗_</sup>|
||(0.117)|(0.061)|(0.087)|(0.109)|(0.06)|(0.086)|(0.113)|(0.07)|(0.089)|
|Innovation|-0.024<sup>_∗_</sup>|-0.012<sup>_∗_</sup>|-0.004|-0.021<sup>_∗_</sup>|-0.011|-0.004|-0.008|-0.004|-0.002|
||(0.013)|(0.007)|(0.011)|(0.012)|(0.007)|(0.011)|(0.013)|(0.009)|(0.012)|
|Log(Energy Intensity)|0.023|-0.003|-0.023|0.036|0.015|-0.005|0.165<sup>_∗∗_</sup>|0.161<sup>_∗∗_</sup>|0.126<sup>_∗∗_</sup>|
||(0.053)|(0.034)|(0.054)|(0.049)|(0.034)|(0.053)|(0.058)|(0.044)|(0.058)|

Notes: Each row represents a different regression of a given firm performance variable on the model specified in equation (1). Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. We evaluate the estimated model at average firm size and GDP per capita within each group of firms listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs and LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.<sup>_∗_</sup> (p<0.10),<sup>_∗∗_</sup> (p<0.05)
17 While less than 10 per cent of the estimated world needs of ORS were produced in 1980, t6e picture dramatically improved in the 1980s. According to UNICEF, today 112 developing countries have :ograms which promote the use of ORT; global production of WHO/UNICEF ORS formula has reached 300 mn liters a year; almost 60 per cent of the developing world's under five children now have access to a source of ORS; almost one quarter of children with diarrhea are being treated with ORS or other appropriate fluids such as rice water, soups, gruels, fruit juices; the number of deaths caused by diarrheal dehydration is steadily falling. ORT may be preventing 750,000 to 1 mn deaths a year.<sup>12</sup> Other diseases, such as measles, cholera, poliomyelitis, tuberculosis, tetanus, or whooping cough, owe their decline to immunization. Immunization data collected or generated by the World Health Organization (WHO) have not become generally available until the 1980s, as most vaccination programs did not start until the 1970s. Vaccination against measles does not appear to start anywhere until the 1980s. Before that, only a handful of countries collected vaccination data on a somewhat regular basis (Sri Lanka, Costa Rica, Chile).

Furthermore, the quality of the existing data is not systematically trustworthy. Some countries have distribution figures, which are not vaccination figures. Between the moment the vaccine leaves the central headquarters of the health ministry in the capital, and the moment it reaches the arm of the child -- if it does -- many things may have gone wrong, making 12UNICEF (1989), op.cit. p.8.
For the Compustat sample we consider a slightly different empirical specification due to the different nature of the data, where _yjst_ denotes either R&D expenses over total assets or cash holdings over total assets for firm _j_ in sector _s_ , and year _t_ . _Xjst_ is the matrix of firm-level control variables, _Xt_ the vector of macro-level control variables, _Ij_ is a vector of firm fixed effects, and _εjst_ an independent and identically distributed error. The main coefficient of interest ( _β_ 1) measures to what extent a firm in the U.S. adjusts its R&D activity in periods of higher inflation, after controlling for all fixed firm attributes, for changes in the firm-specific control variables, and for changes in aggregate economic activity. We also report the results from the analogous specification to (15) where we control for industry instead of firm fixed effects. In that case, _β_ 1 measures whether establishments operating in the same two-digit industry are, on average, less likely to innovate when rises.

The results, reported in Table 6, show that U.S. firms indeed hold more cash when they invest in R&D.<sup>23</sup> A 1% increase in corporate cash holdings is associated with an increase of 1.5 pp in firms’ share of R&D in total assets (column 1), corresponding to an average increase in firms’ R&D intensity by 6%. The positive correlation between corporate cash holdings and R&D investment is significant across firms operating in the same two-digit sector (column 1) and within firms over time (column 2). The results in columns 3 and 4 further confirm that inflation raises the (opportunity) cost facing firms holding such liquidity. A 1 pp increase in inflation reduces firms’ liquidity holdings by 5.5% (liquidity is reduced by 0.6 pp relative to an average across firms of 11%). Moreover, the results are robust if we restrict the sample to firms that actually report positive R&D investment (column 5). Taken together, columns 1 to 5 of Table 6 confirm our earlier findings from the WBES sample and thus further corroborate the theoretical model’s central predictions.

In analogy to the WBES results for developing countries, we find that U.S. firms reduce their

> 23Our finding is consistent with Opler et al. (1999) who show that U.S. firms with higher growth opportunities, approximated by their market-to-book value and R&D expenses, hold on average more liquid assets (cash and marketable securities) relative to total assets.

26
groupings obtained by educational attainment, by land ownership, and by occupational status.

Figure 3 shows the growth incidence curves for the poorest thirty percent of all households under the three scenarios. The curve for the deep Doha scenario lies slightly above the BaU curve. The full liberalization reform also shifts the whole curve upwards, however this shift is larger than that of the Doha case, and it seems to favor the poorest among the poor; in other words, full liberalization appears to induce an additional propoor distributional shift.

_<u>Figure 3: Growth incidence curves for the BaU and Trade scenarios, poorest 30 percent of all households</u>_ <!-- Start of picture text --> Growth Incidence Curve<br>0 10 20 30<br>Cumulative population ranked by per capita income<br>BaU Doha<br>Full<br>2.2<br>2<br>1.8<br>1.6<br>Annual per capita income growth in %<br>1.4<br><!-- End of picture text --> Source: Authors’ calculations.

Table 13 shows the sectorally disaggregated results. Inequality for all households falls due to decreased inequality among agricultural households and lower inequality increase among non-agricultural households, although inequality between these two groups may have risen somewhat. Despite declining inequality and slightly higher per capita income growth, poverty reduction for agricultural households barely changes and this is due, as shown below, by the lower migration levels induced by the trade shocks. Indeed, in the deep Doha scenario the reduction in the population share of agricultural households is only very slightly below that achieved in the BaU. More remarkable is the additional poverty reduction for non-agricultural households that can largely be explained by the lower increase in inequality, as per capita income growth is only marginally higher.

43
Cameron, A.C., and Trivedi, P. K. (2005) Microeconometrics: Methods and applications. Cambridge University Press, New York.

CEPAL (2009). Contribución de los servicios energéticos a los objetivos del desarrollo del milenio y a la mitigación, de la pobreza en América Latina. CEPAL, Santiago de Chile.

Comisión Reguladora de Energía (CRE). Estadísticas. Precio de Gas Natural: Usuarios Finales. CRE, Mexico.

Gertler, P., Fuchs, A., and Sturdy, J. (2009). Evaluacion de impacto del componente energetico del programa de desarrollo humano Oportunidades. Sedesol, Mexico.

Jamasb, T., and Meier, H. (2010). Househod energy expenditure and income groups: Evidence from Great Britain. EPRG Working Paper 1003. University of Cambridge, Cambridge.

Joyeux, R., and Ripple, R.D. (2007). Household energy consumption versus income and relative standard of living: a panel approach. Energy Policy, 35(1), 50-60.

Jumbe, C.B. (2004). Cointegration and causality between electricity consumption and GDP: Empirical evidence from Malawi. Energy Economics, 26, 61-68.

Kanagawa, M. and Nakata, T (2007). Analysis of the energy access improvements and its socio-economic impacts in rural areas of developing countries. Ecological Economics, 62, 319-329.

Khandker, S.R., Barnes, D. F., and Samad, H.A. (2012). Are the energy poor also income poor? Evidence from India. Energy Policy, 47, 1-12.

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**Figure 3. The results chain: From competitiveness to greater value for trade**

|**INCREASING COMPETITIVENESS AND OPENNESS (DIRECT OBJECTIVES)**<br>Technical assistance for trade policy and regulations<br>Suppression/reduction of obstacles to trade at the border<br>Suppression/reduction of cost-increasing, trade-distortive or discriminatory measures beyond the border<br>Mainstreaming and promotion of trade, trade integration and investment<br>Economic infrastructure<br>Improvement of the accessibility/connectivity of the market (telecoms and transport)<br>Improvement of other domestic infrastructure and basic services<br>Productive capacity building<br>Improvement of the legal/regulatory environment for business<br>Improvement of the organization and performance of markets<br>Increase in productivity, production and innovation capacities<br>Trade-related adjustment<br>Adjustment to tariff and price fluctuations<br>Restructuring of industries/sectors facing a trade shock<br>Provision of safety nets and training opportunities for workers affected by trade<br>Other forms of adjustment<br>Facilitation of the movement of productive capacities<br>Enforcement of trade-related rights and obligations<br>Promotion of responsible business/investment principles and practices|
|---|
|**INCREASING TRADE AND INVESTMENT (INTERMEDIATE OBJECTIVES)**<br>Development of an open, rule-based, predictable and non-discriminatory trading system<br>Increased competitiveness and attractiveness for foreign investment<br>Increased exports/export market shares and foreign reserves<br>Diversification of exports and imports<br>⇒**Increased participation and consolidation of global value chains**⇐<br>Reduction of trade costs and prices of imports/inputs<br>Reallocation of production capacities to more competitive and higher value-added segments|

## **INCREASING THE VALUE FOR TRADE (FINAL OBJECTIVES)**

Direct and indirect job creation Increased level and predictability of income Economic and social upgrading Diffusion of technology and knowledge Better and more sustainable use of resources Political and economic stability Source: OECD, 2012a

# **_A source of opportunities_**

The “second unbundling” is a source of opportunities for developing countries. A greater division of labor and segmentation of production at a global scale allows more countries to benefit from trade. The participation in GVCs allows (as well as is the result of) greater competitiveness, better inclusion in trade and investment flows, as well as socio-economic upgrading, with more and better 6
## **2.1.2 Deep Trade Agreements Database**

Against the trend of increasing restrictions by the main trading nations, many countries continue to pursue deeper trade integration and international cooperation. Even those countries implementing protectionist measures have continued to discuss, sign and implement “deep” trade agreements, i.e. those agreements that affect domestic policy areas beyond trade, such as the international flows of investment and labor, and the protection of intellectual property rights and the environment. Fortunately, the Deep Trade Agreements (DTA) database reports extensive information on the exact provisions included in each trade agreement signed up to 2023. As many as 61 percent of PTAs that were signed before 1995 covered fewer than 10 policy areas, while in the decade from 2013 to 2023, this figure dropped to just 8 percent.

For our purposes, following the spirit of Mattoo et al. (2022), we are interested in a specific aspect of the “depth” of a preferential trade agreement, namely the extent to which it regulates subsidies. Table 2 reports 12 questions that are included in the DTA database that can be used to construct a suggestive measure of the depth of an agreement with respect to subsidies.<sup>5</sup> . We define the variable _PTA_ _~~D~~ epth_ as a measure going from zero to (potentially) one, representing the share of affirmative responses to the 12 questions listed in table 2.<sup>6</sup> The median value of _PTA_ _~~D~~ epth_ is 0.16, and the range is from zero to 0.75. Interesting examples include the EU (0.58), CPTPP (0.33), USMCA (0.25), NAFTA (0.16), and RCEP (0.00).

# **2.2 Empirical Strategy**

The dependent variable is the growth rate of trade from origin country _i_ to destination country _j_ of product _k_ (included in sector _s_ ) between year _t_ and _t −_ 1.<sup>7</sup> The choice of a specification in growth rates is motivated by two factors. First, the main explanatory variable of interest is a flow measure of newly announced 5Examples of these questions are “Does the agreement prohibit or regulate subsidies distorting trade or competition (within domestic, export or third markets)?” or “Does the agreement prohibit or regulate local-content subsidies?” 6 Formally, we define where _Subsidies_ _~~C~~ overage_<sup>_q_</sup> is the _q_<sup>_th_</sup> question in Table 2 referring to the PTA between countries _i_ and _j_ entered into force at time _t_ .

> 7For ease of interpretation, the dependent variable is already multiplied by 100: ∆ _TRADEijkst_ = 100 _∗_ ( _lnEXPijks,t − lnEXPijks,t−_ 1).

7
|**Table 2: Data Availability by T**|**reatment St**<br>Before|**atus and Ti**<br>During|**ming**<br>A|fter Subsid|y|
|---|---|---|---|---|---|
||Subsidy|Subsidy|Year1|Year2|Year3+|
|**Panel A: Data on Operating S**|**tatus Availab**|**le**||||
|Control Group|0.958|0.951|0.983|0.986|0.976|
|Wage Subsidy Treatment|0.980|0.956|0.976|0.964|0.972|
|p‐value|0.148|0.787|0.596|0.099|0.799|
|**Panel B: Data on Having a Pa**<br>|**id Worker Av**|**ailable**||||
|Control Group|0.839|0.951|0.965|0.962|0.962|
|Wage Subsidy Treatment|0.888|0.956|0.948|0.920|0.956|
|p‐value|0.102|0.787|0.333|0.040|0.748|
|**Panel C: Data on Profits Avai**|**lable**|||||
|Control Group|0.808|0.944|0.965|0.965|0.962|
|Wage Subsidy Treatment|0.832|0.956|0.944|0.920|0.956|
|p‐value<br>Notes: Before Subsidy refers<br>intervention. P‐value is from<br>control and wage subsidy tre<br>are available for at least one<br>Profits and Workers are set t|0.467<br>to round 3 d<br>t‐test of equ<br>atment. Prop<br>survey round<br>o zero for fir|0.529<br>ata betwee<br>ality of resp<br>ortions sh<br>during the<br>ms which a|0.241<br>n baseline<br>onse rate<br>own indica<br>specified<br>re closed d|0.024<br>s and<br>s between<br>te that da<br>timing wi<br>own.|0.748<br>ta<br>ndow.|

38