What Explains the Price of Remittances ... - The World Bank

What Explains the Price of Remittances? AnExamination Across 119 Country Corridors

Thorsten Beck and Marıa Soledad Martınez Perıa

Remittances are a substantial source of external financing for developing countriesthat influence many aspects of their development. Though research has shown thatremittances are both expensive and price sensitive, little is known about what explainstheir price. Newly gathered data across 119 country pairs or corridors are used toexplore the factors associated with the price of remittances. Corridors with largernumbers of migrants and more competition among providers are found to exhibitlower prices for remittances, when average prices across all types of remittance serviceproviders are considered. Corridors with lower barriers to access banking services andbroader regulation of remittance service providers also have lower prices. Remittanceprices are higher in richer corridors and in corridors with greater bank participationin the remittance market. Few significant differences emerge when results arecompared across banks and, separately, across money transfer operators. However,estimations for Western Union, a leading player in the remittances business, suggestthat its prices are less sensitive to competition. JEL classification: F22, F24.

In 2008, remittances to developing countries reached $328 billion, more thantwice the amount of official aid and over half of foreign direct investment flows(World Bank 2009a). Numerous studies have shown that remittances have apositive and significant impact on economic development along multipledimensions, including poverty alleviation, education, entrepreneurship, infant

Thorsten Beck (corresponding author; [email protected]) is a professor of economics and CentER

fellow and chair of the European Banking Center at Tilburg University. Marıa Soledad Martınez Perıa

([email protected]) is a senior economist in the Finance and Private Sector Development

Research Group of the World Bank. The authors thank Diego Anzoategui and Subika Farazi for

excellent research assistance. They received helpful comments from participants at the Second

International Conference on Migration and Development and the International Conference on Diaspora

for Development, as well as from World Bank colleagues in the Finance and Private Sector

Development Research Group and the Payment Systems Unit. The authors are particularly grateful to

the journal editor and to three anonymous referees for constructive comments and suggestions. This

article’s findings, interpretations, and conclusions are entirely those of the authors and do not

necessarily represent the views of the organizations with which they are affiliated.

THE WORLD BANK ECONOMIC REVIEW, VOL. 25, NO. 1, pp. 105–131 doi:10.1093/wber/lhr017Advance Access Publication May 23, 2011# The Author 2011. Published by Oxford University Press on behalf of the International Bankfor Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions,please e-mail: [email protected]

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mortality, and financial development.1 Hence, understanding the market forremittance transactions can be critical for promoting the development processin many low-income countries.

Remittance transactions are known to be expensive, with estimates averaging10 percent of the amount sent (World Bank 2009b). At the same time, thesecosts are widely dispersed across corridors and range from 2.5 percent to 26percent. Furthermore, case studies have shown that remittance flows are verysensitive to prices and are likely to increase substantially as prices fall. Forexample, Gibson, McKenzie, and Rohorua (2006) estimate a 22 percent priceelasticity in the New Zealand–Tonga corridor and calculate that lowering thefees to the levels found in the most competitive corridors would raise remit-tances by 28 percent. Using a randomized experiment, Aycinena, Martinez,and Yang (2009) estimate that a $1 lower fee in the United States–El Salvadorcorridor would boost remittances $25 a month from an average of $290.

Because remittances are important for economic development and appear tobe sensitive to price, lowering the price has become a priority for policymakers.At the L’Aquila 2009 G-8 Summit, leaders pledged to reduce the price ofremittances by half (from 10 to 5 percent) in five years.2 Yet, little is knownempirically about what explains the price of remittances.3 Are high prices duemainly to factors in the sending or the receiving country? Are high pricesrelated to socioeconomic factors, industry market structure, or government pol-icies and regulations that affect remittance service providers and the mark-upsthey are able to charge? Are there significant differences between banks andmoney transfer operators (MTOs)? Explaining the variation in prices is thus ofinterest for academics and policymakers alike.

Using a new dataset assembled by the World Bank Payment Systems Groupon the price of remittances across 119 country pairs or corridors (RemittancePrices Worldwide database (World Bank 2009b), this article explores the

1. For example, see Adams and Page (2003), Adams (2005), IMF (2005), Lopez-Cordova (2005),

Maimbo and Ratha (2005), and Taylor, Mora, Adams and Lopez-Feldman (2005) for studies on the

impact of remittances on poverty. Studies such as Cox-Edwards and Ureta (2003), Hanson and

Woodruff (2003), Lopez-Cordova (2005), and Yang (2008) find that by helping to relax household

constraints, remittances are associated with improved schooling outcomes for children. Remittances

have also been shown to promote entrepreneurship (see Massey and Parrado 1998; Maimbo and Ratha

2005; Yang 2008; Woodruff and Zenteno 2007). Furthermore, a number of studies on infant mortality

and birth weight have documented that, at least in the Mexican case, migration and remittances help

lower infant mortality and are associated with higher birth weight among children in households that

receive remittances (see Kanaiaupuni and Donato 1999; Hildebrandt and McKenzie 2005; Duryea et al.

2005; and Lopez-Cordova 2005). Aggarwal, Demirguc-Kunt, and Martinez Peria (2010) show that

remittances can have a positive impact on financial development.

2. Paragraph 134, page 49 of the L’Aquila 2009 G8 Summit. http://www.g8italia2009.it/static/

G8_Allegato/G8_Declaration_08_07_09_final,0.pdf.

3. Orozco (2006) and Freund and Spatafora (2008) are the exception, but their data is limited to a

few countries or a few providers. While Orozco’s work focuses exclusively on Latin America, the

Freund and Spatafora study analyzes only the costs of remittances sent from the United States and the

United Kingdom exclusively via MoneyGram or Western Union to 66 countries.

106 T H E W O R L D B A N K E C O N O M I C R E V I E W




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factors associated with remittance prices in 2009.4 It studies corridors thatinclude 13 major remittance sending countries and 60 receiving countriesrepresenting approximately 60 percent of total remittances to developingcountries. Using data at the corridor level permits bilateral analysis of pricesrather than analysis of prices aggregated at the receiving or sending countrylevel only. Furthermore, unlike previous studies focusing on a certain type ofremittance service provider (usually the largest international MTOs), the analy-sis here considers the largest providers, whatever the type, in each corridor.5

And by averaging across all types of providers and across each type of provider(banks and MTOs) separately, the factors associated with the price of remit-tances can be compared across different types of institutions. Finally, analyzingthe prices charged by Western Union across 98 corridors (80 percent of thesample) alleviates concerns about bias due to differences across firms and thussheds light on the factors correlated with the prices charged by a leading remit-tance service provider with worldwide operations.

The analysis distinguishes three groups of variables that can be associatedwith cross-corridor variation in the price of remittances. One is the impactof government policies—including exchange rate policies, capital controls,and regulation of remittance service providers—that can influence the priceof remittances through their impact on the cost structure of remittanceservice providers. The second is the role of factors that might affect theability of remittance service providers to increase their mark-up, such asextent of competition, market structure, and level of education of themigrant population. The third is the role of socioeconomic characteristics insending and receiving countries that might influence fees through theirimpact on the cost structure of remittance service providers and on provi-ders’ ability to raise the mark-up.

Estimations of the price of remittances across all types of remittance serviceproviders show that prices are consistently lower in corridors with a largernumber of migrants and more competition and in corridors with lower accessbarriers to the banking system and broader regulation of remittance serviceproviders. Remittance prices are higher in richer corridors and in corridorswith a higher share of banks among providers. The prices of sending remit-tance using banks or MTOs are associated with similar factors. Western Unionprices appear to be less sensitive to competition, perhaps a reflection of thefirm’s market power.

This article is related to the literature on the price of banking services. Beck,Demirguc-Kunt, and Martınez Perıa (2008) document large cross-country

4. The original World Bank database for the period analyzed here contains information on 134

corridors. From that total, 13 corridors (where Russia is the sending country) are missing exchange rate

data and 2 other corridors are missing information for some explanatory variables.

5. On average, 8–10 providers are included for each corridor. In some corridors, primarily those

including the United States and Spain as sending countries, the number of providers surveyed exceeds 10.

Thorsten Beck and Marıa Soledad Martınez Perıa 107




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variation in the costs to customers of opening and maintaining bank accountsand in the fees for using automated teller machines and for transferring funds,finding that firms report lower financing constraints in countries with lowercosts of financial services. Freund and Spatafora (2008) and Orozco (2006)also present data on remittance prices, but for few countries and providers andnot at the corridor level.

In a broader sense, this article is also related to the literature on bank inter-est rate spreads (the differences between deposit and lending rates), with higherspreads indicating more expensive banking services. Both institution-specificcharacteristics, such as size and ownership, and market and country character-istics, such as competition and the legal and institutional framework, havebeen shown to be significant predictors of interest rate spreads (seeDemirguc-Kunt, Laeven and Levine 2004; Laeven and Majnoni 2005; andBeck 2007 for a general discussion).

This article is a first exploration of corridor variation in the price of remit-tances and is subject to two important caveats. First, as a pure cross-sectionalanalysis, it is potentially subject to reverse causation and omitted-variablebiases. Hence, only limited, if any, inference can be made on causality.6

Second, the analysis is also limited in scope since it includes data only fromformal providers of remittance services. By some estimates, at least a third ofremittances are sent through informal channels (Freund and Spatafora 2008).These limitations notwithstanding, the article offers interesting evidence thatshould stimulate further data collection and analysis.

The article is organized as follows. Section I describes the data on the priceof remittances. The empirical methodology is in section II and the results are insection III. Section IV summarizes the findings and recommends furtherresearch.

I . D A T A O N T H E P R I C E O F R E M I T T A N C E S

The data on the price of remittances are from the Remittance PricesWorldwide database, a recent survey of remittance service providers conductedby the Payment System Unit of the World Bank in the first quarter of 2009(World Bank 2009b).7 The price of remittances consists of a fee componentand an exchange rate spread component. The World Bank dataset covers 14sending and 72 receiving countries. However, because spread information ismissing for remittances from the Russian Federation and data are missing forsome explanatory variables, the focus is on 119 corridors, at most, including

6. Most of the variables, however, are likely to be exogenous to remittance prices, including

migration flows, distance, and even banking market structure, given the small share of bank profits

stemming from remittances.

7. Since then, the data have been updated, and prices are now available through the first quarter of

2010. However, because data for most of the correlates used in this analysis are not updated with the

same frequency, the panel dimension of the data could not be exploited.





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13 sending countries and 60 receiving countries.8 In most cases, data cover theprices from the main sending location for the corridor in question to thecapital city or most populous city in the receiving market.

Data were collected by interviewers posing as customers and by contactingindividual firms. Within each corridor, the data were gathered on the same dayto control for exchange rate fluctuations and other changes in fee structures. Ingeneral, price data were collected for 8–10 major service providers in each cor-ridor, including the main MTOs and banks active in the market.9 Companiessurveyed in each segment were selected to cover the maximum remittancemarket share possible.10 Since the dataset does not provide information on themarket shares of each provider, it is not possible to compute weightedaverages. Hence, the regression analysis uses both the simple average and themedian prices calculated across all providers in a corridor as dependent vari-ables.11 Results are reported using only the simple averages, however, becauseaverage and median prices are highly correlated (0.96).

Prices based on two amounts are available per corridor: the local equiv-alent of $200 and the local equivalent of $500. Since previous studies havefound that a typical remittance transaction involves sending close to $200,the analyses are based on the prices associated with this amount.12

Furthermore, the prices of sending $200 and $500 (expressed as a percen-tage of the amount sent) are highly correlated (0.91), so the results do notvary significantly.13

Figure 1 illustrates the variation in average prices across the 119 corridors,calculated across all surveyed remittance service providers in each corridor.The average remittance prices are lowest in the Saudi Arabia–Pakistan corridor(2.5 percent of $200) and highest in the Germany–Croatia corridor (25.8percent). Averaged across all corridors and providers, the price is 10.2 percent.

There is considerable heterogeneity in prices even when the same sending orremittance receiving country is considered. Prices of remittances sent from theUnited States vary from 3.7 percent to Ecuador to 14.1 percent to Thailand(figure 2). Remittance prices to India vary from 3.1 percent from Saudi Arabiato 13.3 percent from Germany (figure 3). This variation underlines the

8. The full data are available at www.remittanceprices.org. Data on exchange rate spreads are also

missing for some institutions in Germany, France, and Japan. These institutions are excluded from the

calculations of the average remittances costs from those countries.

9. The number of respondents by corridors varies depending on the number of firms active in the

corridor. Some corridors (like the Spain–China corridor) include only two firms, while others (like the

United States–Mexico corridor) go as high as 18.

10. No more information is provided on how firms were selected. For a discussion of the

methodology, see http://remittanceprices.worldbank.org/Methodology/.

11. A priori, it is not clear how having weighted averages instead of simple averages would change

the estimations. This problem is interpreted as a potential case of measurement error in the dependent

variable, which should not bias the estimates but would affect their efficiency.

12. Freund and Spatafora (2008) use the same amount in their study.

13. These results are available on request.





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FIGURE 1. Average Price of Remittances Sent Across 119 Migration Corridors

Source: Remittance Prices Worldwide database.

FIGURE 2. Average Price of Remittances from the United States to 22Receiving Countries

Source: Authors’ analysis based on data from World Bank (2009b).





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importance of analyzing the price of remittances at the corridor rather than atthe sending or receiving country level.

Remittance prices also vary across provider types (table 1). On average,banks charge substantially higher fees (12.4 percent) than do MTOs (8.8percent). This differential does not control for the fact that banks andMTOs are not active in all corridors and that different banks and differentMTOs are active in different corridors. When the analysis focuses on corri-dors where both types of institutions are active, average prices for banksexceed those for MTOs in 43 of the 63 corridors. Furthermore, when pricesat the provider level are regressed on a set of corridor dummy variables anda bank dummy variable, bank prices average 3 percentage points higherthan MTO fees. Western Union’s prices (10.8 percent) are slightly higherthan the average for all MTOs (8.8 percent). Western Union prices alsoexhibit high cross-corridor variation, ranging from 2.7 percent in the SaudiArabia–Yemen corridor to 29.9 percent in the United Kingdom–Albaniacorridor (figure 4).

I I . E M P I R I C A L M E T H O D O L O G Y

To examine the determinants of remittance prices, the average price of sendingremittances, Pij, is regressed on a set of sending and receiving country

FIGURE 3. Average Price of Remittances to India from Eight SendingCountries






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characteristics and on some corridor-specific variables captured by the matrixX in equation (1):

Pij ¼ a0 þ a1Sending country factorsi þ a2Receiving country factors j

þ a3Xij þ uij ð1Þ

where Pij is the price of sending $200 from country i to country j expressed asa percentage of $200. All explanatory variables are lagged relative to the pricevariable. Since this does not completely rule out reverse causation or endogene-ity bias, the results are interpreted as associations rather than as causalimpacts. Table 1 provides the summary statistics and data sources for each vari-able, and table 2 reports correlations across the main variables.

Government Policies

Equation (1) captures an array of factors that might be correlated with remit-tance prices through their association with the costs faced by remittance serviceproviders and the mark-up providers can charge over their marginal costs.First, it controls for different government policies relating to the exchange rate,

FIGURE 4. Average Price of Remittances sent through Western Union






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table

1.

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text.





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the capital account, and regulation of the remittance market that might influ-ence the costs faced by remittance service providers. It includes a dummy vari-able for receiving countries with pegged exchanged rates (including cases ofcurrency boards, de facto pegged regimes, and no separate legal tender). Lowerexchange rate volatility should be associated with lower prices, by lowering theexchange rate costs and uncertainty faced by providers and, thus, the spreadsthey charge to customers. At the same time, the price of sending remittances isexpected to be higher in countries that impose controls on remittance trans-actions, since controls operate like a tax that is likely to be passed onto recipi-ents. Both the dummy variable for pegged exchange rate regimes and thecapital controls dummy variable are from the International Monetary Fund(IMF 2007). In 39 corridors (almost 33 percent of the sample), the exchangerate is pegged or the economy is fully dollarized, so there is no exchange ratevariability, and in 22 corridors (18 percent of the sample) there are controls ongifts from abroad or remittances.

The analysis controls for the breadth of regulation of remittance service pro-viders in sending and receiving countries by creating an index of regulationthat takes a value of 0–5 depending on whether providers must be registered,must be licensed, are subject to specific safety and efficiency requirements, needto comply with anti–money laundering regulations, or need to comply withlaws and regulations of general applicability. Data for creating the indexes arefrom the 2008 Global Payment Systems Survey conducted by the World Bank(2008). While a broader regulatory framework might make the remittancemarket more transparent and more competitive, greater exposure to regulationscan also increase the costs for regulated institutions, so the impact is ambigu-ous a priori.14 Similarly, greater breadth of regulation might reduce thenumber of service providers, with negative repercussions for competitiveness.The index averages 2.2 among remittance receiving countries and 2.3 amongremittance sending countries.

Remittance Mark-ups

The regressions also include proxies for factors that might be associated withremittance prices because of their effect on the mark-up remittance service pro-viders can charge their customers. The analysis posits that providers can morereadily charge a mark-up if there is little competition in the remittance marketand if customers are not well informed. Two indirect measures of competitionamong remittance service providers are used (direct measures are lacking). Oneis the number of remittance service providers in the database for each corridor.Since the Remittances Prices Worldwide survey tries to cover the most impor-tant providers in a corridor, corridors with more providers are assumed to have

14. Note that the index does not measure the severity of regulations, but only the scope of the

regulatory framework.





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more active firms and, other things equal, to be more competitive.15 Theaverage number of respondents across all corridors is 8, and the number variesfrom 2 in the Spain–China corridor to 18 in the United States–Mexicocorridor.

The second measure is of competition among banks in receiving and sendingcountries. The rationale is that more competitive banking sectors will offercheaper services, including for remittance transactions. This will create pressurefor other providers to lower prices as well. Of course, this implicitly assumesthat banks are important players in the remittance business. Following Panzarand Rosse (1982, 1987), the H-statistic is used to measure the degree of com-petition in the banking sector by calculating the sum of the elasticities ofbanks’ interest revenues to different input prices (see the appendix for a discus-sion of the methodology used to calculate the H-statistic).16 Under perfectcompetition, an increase in input prices raises both marginal costs and revenuesby the same amount and, thus, the H-statistic will equal 1. In a monopoly, anincrease in input prices results in a rise in marginal costs, a fall in output, anda decline in revenues, leading to an H-statistic of less than or equal to 0. WhenH is between 0 and 1, the system operates under monopolistic competition. Anegative relationship is expected between the H-statistic in sending and receiv-ing countries and the price of sending remittances. Data for 1994–2006 fromBankscope database (Bureau van Dijk 2009) are used to compute theH-statistic. Among both remittance receiving and sending countries, theH-statistic averages close to 0.53. But the standard deviation is larger for remit-tance sending countries.

The significance of the relative importance of banks in the remittancemarket in explaining cross-corridor variation in remittance prices is alsoexplored using the share of bank respondents among all remittance service pro-viders in the database. To the extent that, as some have argued, banks viewremittances as a marginal product and are less likely to offer competitive prices(Ratha and Riedberg 2005), a positive correlation is expected between theshare of bank respondents and the average price of remittances. Across the 119corridors, the share of bank respondents varies from 0 percent in the Italy–SriLanka corridor to 100 percent in the South Africa–Swaziland corridor. Onaverage, the share across corridors is 31 percent.

Because data were lacking on the share of the remittance market capturedby each provider, the percentage of bank respondents described above may notreflect the actual importance of commercial banks. Hence, an alternativemeasure, obtained from the Global Payment Systems Survey (World Bank

15. Because in most cases, mystery shoppers were used to gather data on the price of remittances,

the number of respondents should not be affected by the willingness of certain providers to cooperate.

However, it is still possible that in some corridors the number of respondents is small simply because

interviewers had difficulty reaching or locating some providers.

16. Other studies that use this methodology to estimate competition include Bikker and Haaf

(2002), Gelos and Roldos (2004), Claessens and Laeven, (2004), and Levy-Yeyati and Micco (2007).





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2008), is used to test the sensitivity of the findings. The measure indicates thedegree to which central banks consider commercial banks to be significantremittance service providers, on a scale from 1 (least relevant) to 6 (most rel-evant).17 The correlation between this variable and the percentage of bankrespondents is 0.37 and is significant at the 5 percent level.

The financial literacy of remittance senders can also affect mark-ups. Sincefinancial literacy cannot be captured directly, a measure of the education levelof migrants in each corridor is used (migrants with a secondary or tertiary edu-cation as a share of total migrants from the remittance receiving country resid-ing in the remittance sending country). This variable comes from the OECDDatabase on Immigrants and Expatriates (OECD 2010). This variable isexpected to be correlated with financial literacy, and to the degree that finan-cial literacy enables consumers to make better informed choices, prices shouldbe lower. The ratio of secondary and tertiary educated migrants varies from 21percent for Chinese migrants in Italy to 91 percent for Nigerian migrants in theUnited States. Because this variable is available for only 88 of the 119 corri-dors, it is not included in the baseline estimations but is shown as an additionalvariable.

Socioeconomic Variables

Several socioeconomic variables are included that can influence remittanceprices by affecting both costs and mark-ups. One, a proxy for the volume ofremittance transactions within corridors, is the number (bilateral stock) ofmigrants residing in the remittance sending country who are originally fromthe remittance receiving country. These data are from the World Bank (Rathaand Shaw 2007). Unlike the flow of actual remittances, migrant stock is lesslikely to be endogenous to the price variable. A negative relationship is conjec-tured between the stock of migrants and the price of remittances; a highervolume of migrants might imply scale economies and, hence, lower costs forproviders and more competition among them, resulting in smaller mark-ups.18

The number of migrants is negligible in the South Africa–Zambia corridor andexceeds 10 million people in the United States–Mexico corridor. The averageis 379,200 migrants.

GDP per capita is also included, as a proxy for economic development andstandard of living. This variable comes from the World Bank’s WorldDevelopment Indicators database (World Bank 2009c). The cost of goods andservices will be higher in countries with higher standards of living, so remit-tance prices would also be expected to be higher. Countering that tendency,economic development may be associated with greater efficiencies and lower

17. The Global Payment Systems Survey scale uses 1 to indicate most relevant and 6 the least

relevant. The scale is inverted here so that higher values indicate that banks are more important.

18. The presence of more migrants might encourage entry of a larger number of remittance service

providers, leading to more contestability and lower mark-ups.





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costs for financial intermediation (Harrison, Sussman, and Zeira 1999) andthus lower remittance prices. In the sample, GDP per capita for receivingcountries varies from $148 in Malawi to close to $14,000 in the Republic ofKorea. Among remittance sending countries, GDP per capita varies from$3,640 in South Africa to $40,200 in Japan (all prices in U.S. dollars).19

The geographic distribution of the population in sending and receivingcountries might also be an important driver of the price of remittances, as amore sparsely distributed population might be harder to reach, thus raisingtransaction costs for providers. A more sparsely distributed population mightalso increase the pricing power of providers, as geographic access is more diffi-cult for senders and recipients of remittances. The share of rural population inboth sending and receiving countries is used to proxy for the disparity in geo-graphic distribution.20 These data come from the World Bank’s WorldDevelopment Indicators (World Bank 2009c). Among receiving countries, therural population varies from 13 percent of the total in Lebanon to 87 percentin Uganda and averages 48 percent. By contrast, among sending countries, therural population varies from 0 for Singapore to 40 percent for South Africaand averages 21 percent.

Bank Variables

To measure access to financial services more directly, some estimations alsocontrol for the number of bank branches per capita in sending and receivingcountries.21 This variable is expected to have a negative association with theprice of sending remittances, as higher branch penetration will reduce trans-action costs and increase scale. The ratio of branches per capita averages about6 per 100,000 inhabitants in receiving countries and close to 34 per 100,000in sending countries.

Measures of the costs of accessing formal banking services in both sendingand receiving countries (the minimum amount to open a savings account andthe annual fee to maintain an account) are also included (Beck,Demirguc-Kunt, and Martınez Perıa 2008). Easier and cheaper access is conjec-tured to increase the options for both senders and recipients of remittances andthus to boost competition. The minimum balance to open a savings accountaverages 7.36 percent of GDP per capita in receiving countries and 0.11

19. Regressions were also run that controlled separately for the level of financial development using

the ratio of liquid liabilities to GDP. The results are very similar to those including GDP per capita.

Since these variables are highly correlated—(0.2) among receiving countries and (0.4) among sending

countries—these estimations are not reported, and GDP per capita is included instead as a broader

measure of development.

20. The share of rural population is a better proxy for the effect of service delivery than population

density, which is an average within a country and does not take into account which share of the

population actually lives in more remote areas. The population density variable yielded similar results.

21. These data are from Beck, Demirguc-Kunt, and Martınez Perıa (2007) and can be accessed at

http://go.worldbank.org/EZDOBVQT20. Because these data are not available for all corridors, this

variable is not included in all estimations.





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percent in sending countries; fees average 0.55 percent of GDP per capita inreceiving countries and 0.12 percent in sending countries.

Corridor-specific Variables

Finally, several corridor-specific variables are included that might influence theextent and ease of remittance transactions and, therefore, their costs. These arethe distance between sending and receiving countries (from capital city tocapital city) and a dummy variable for a common language (takes a value ofone if both countries have at least one common language spoken by more than9 percent of the population). These data come from the French ResearchCenter in International Economics (CEPII) Distances database (CEPII 2010).Smaller geographic and linguistic distances might also foster the emergence ofinformal remittance service providers, adding competitive pressure to theformal remittance market. Some estimations also include the log of bilateralexports and imports, a measure of bilateral trade. These data come from theIMF Direction of Trade Statistics (IMF 2009).

Correlations Between Variables in Our Dataset

The average prices of remittances are significantly lower in corridors with ahigher number of migrants, smaller share of rural population, and a commonlanguage (see table 2). Prices are also lower in corridors where competition ishigher and bank participation in the remittance industry is lower. Finally,prices are lower in corridors where sending countries have a broader regulatoryframework for remittance service operators and where minimum balances toopen a savings account and annual fees to maintain them are lower.

Some explanatory variables are highly correlated with others. For instance,GDP per capita in receiving and sending countries is significantly correlatedwith competition among providers, rural population share, branch penetration,cost of using banking services, and extent of bilateral trade.

I I I . E M P I R I C A L R E S U L T S

Table 3, column 3.1 reports the baseline estimation considering average remit-tance prices charged across all providers with variables for which data areavailable for all 119 corridors. Though information on the number of respon-dents and the percentage of banks among respondents is also available acrossall corridors, these variables are not included in the baseline estimations since,as discussed earlier, they might not adequately capture the degree of compe-tition and the importance of banks in the remittance market.

The baseline regression shows that, across all providers in 119 corridors,remittance prices are significantly associated with the number of migrants inthe corridor, the level of income, and the share of rural population in receivingand sending countries. Corridors with a higher number of migrants exhibit





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8

(1.7

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acco

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0.0

2

(20.4

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Min

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**

Contr

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Share

of

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0.0

2

(0.7

3)

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6(2

0.2

5)

Obse

rvat

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119

119

111

119

84

91

89

53

53

105

88

111

R-s

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d0.3

60.3

80.5

60.5

30.5

40.4

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0.2

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50.2

70.3

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10.3

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lower prices, while those with higher incomes per capita and a larger percen-tage of rural population face higher prices. These results are consistent acrossall estimations reported in table 3.

As expected, greater competition among providers (measured by number ofrespondents or the H-statistic for the banking sector) is associated with lowerremittance prices (table 3, columns 3.2 and 3.3). Corridors where banks play alarger role in the remittance market exhibit higher prices (columns 3.4 and3.5). Corridors with broader regulation of remittance service providers in thesending country have lower prices, while the regulatory breadth in the receivingcountry does not seem to matter (column 3.6).

Greater access to and lower costs of banking services are associated withlower prices of remittances (columns 3.7–3.9). In particular, corridors withmore bank branches per capita in the sending country face lower prices, whilecorridors with higher minimum amounts to open accounts and higher annualfees have higher remittance prices.

The results discussed so far are economically as well as statistically signifi-cant. For example, an increase in the number of migrants from the corridor atthe 25th percentile (United Kingdom–China with 56,774) to the corridor atthe 75th percentile (Spain–Colombia with 384,621) is associated with a 2 per-centage point drop in average prices. An increase in competition (as measuredby the H-statistic) in the sending country from the 25th percentile to the 75thimplies a 4.4 percentage point reduction in remittance prices, while an increasein the receiving country is associated with a 1.4 percentage point reduction. Asimilar change in the number of remittance service respondents (from 6, the25th percentile, to 10, the 75th percentile) is associated with a 1.2 percentagepoint drop in prices, while an increase in the scope of remittance regulation inthe sending country implies a reduction of 2.8 percentage points. A comparableincrease in the number of branches per capita in the sending country is associ-ated with a 1.6 percentage point decline in prices. Even stronger, an increase inthe percentage of banks among survey respondents from the 25th (0 percent)to the 75th percentile (50 percent) implies an increase in prices of more than 4percentage points. Note that the average price across corridors associated withthese changes is close to 10 percent, so the effects are considerable.

In contrast, no robust association is found between remittance prices andmeasures of exchange rate stability or the presence of capital controls on remit-tances (columns 3.1 and 3.10). Similarly, the distance between sending andreceiving countries, the extent of bilateral trade, and whether countries share acommon language are not correlated with remittance prices (columns 3.1 and3.12).22 Finally, the share of educated migrants does not have a significanteffect (column 3.11).

22. If common language is replaced with a dummy variable for whether the receiving and sending

countries have colonial ties, the main results do not change and the dummy variable for colonial ties

tends to be positive and significant. These results are available on request.





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Using alternative indicators for several variables, such as the Parson andothers (2007) data on bilateral migration and a Barro and Lee (2001) measureof educational attainment, yields similar findings.23 Also, running theregressions for median instead of average prices does not change the results sig-nificantly; neither does using prices based on sending $500 instead of $200.The results are not reported here but are available on request.

Overall, the estimations have good predictive power. The R-squared for thebaseline regression (table 3, column 3.1) is 0.36 and varies from 0.25 (column3.11) to 0.56 (column 3.3), depending on the additional controls included.Similarly, the estimations are reasonably good at predicting the differencebetween extreme observations (the difference between the corridors with themaximum and minimum prices). Depending on the estimation, the share of theactual difference between the maximum and minimum prices that is predictedby the estimations varies from 0.23 (column 3.11) to 0.41 (column 3.5).

Finally, partial plots of remittance prices against the variables found to beconsistently significant (log of migrants, log of GDP per capita in sendingcountry, share of rural population in sending country, number of respondentsin the corridor, H-statistic for bank competition in the sending country, shareof bank respondents, index of importance of banks among remittance serviceproviders, and index of regulation of remittance service providers) show thatthese variables do a good job of predicting prices and that the correlations arenot driven by outliers (figure 5). The log of migrants appears to be an excep-tion, with large outliers for the South Africa–Zambia and South Africa–Angola corridors (top left corner of figure 5). However, when these two out-liers are removed, the log of migrants remains significant at the 1 percent leveland the other results in the baseline estimations do not change significantly.

Next are the factors that influence remittance prices across service providertypes. Table 4 shows separate estimations for average prices among banks(columns 4.1–4.4), MTOs (columns 4.5–4.8), and Western Union (4.9–4.12).To save space, only some of the specifications shown to be significant in theregressions for all providers (see table 3) are reported here; others are availableon request. In columns 4.1–4.4, the dependent variable is the average priceacross all bank respondents in a corridor. Since there are corridors wherebanks do not play a significant role in the remittance market (and so were notincluded in the database), the sample size is smaller than that in table 3. Mostof the results discussed so far hold when the sample is restricted to banks. Inparticular, a larger number of migrants, lower levels of per capita income inthe receiving country, and a smaller share of rural population are still

23. The correlation between the World Bank bilateral migration data and the Parson and others

(2007) data is 0.66, and results do not change when the Parson and others data are used. These results

are available on request. Barro and Lee’s (2001) average years of schooling of the population over 25

for the receiving country was used. Results remain unchanged. The results using the data on the

education of migrants are presented here, since those data more directly relate to the population that

conducts remittance transactions.





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FIGURE 5. Partial Plots of Selected Regressors against Remittance Prices

Source: Authors’ analysis based on data described in the text.





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TA

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1.3

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31.6

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3)*

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4

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(1.4

6)

(0.1

4)

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8)

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4)

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8)

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9)

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tance

0.1

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92.6

92

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0.2

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0.2

82

0.2

82

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82

0.3

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(1.1

3)

(1.2

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(1.1

4)

(20.7

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(20.6

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(20.6

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(0.2

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(20.5

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(20.5

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(20.7

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(20.3

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0.7

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0.8

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1.7

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0.2

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12

0.2

02

0.3

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1.1

42.6

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(20.5

7)

(20.9

3)

(22.4

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*(0

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(0.2

0)

(20.2

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(0.7

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(20.2

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(20.3

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(21.0

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2

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**

(2.4

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*(0

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H2

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0.6

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0.2

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26

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associated with lower prices, as is broader regulation of remittance service pro-viders in the sending country. As before, a higher share of banks amongrespondents and higher minimum balances to open accounts are positively cor-related with prices. The measures of competition are no longer significant atthe 5 percent level, a result likely due to the lower number of observations.24

Most of the earlier findings are confirmed when the sample is restricted toMTOs (columns 4.5–4.8 of table 4). A larger number of migrants and greatercompetition in the banking system are associated with lower prices, whilehigher levels of income and bank participation are associated with higherprices. A larger share of rural population is associated with higher remittanceprices among MTOs, but regulation of remittance service providers and costsof opening bank accounts are not significantly associated with remittanceprices among MTOs.

Columns 4.9–4.12 of table 3 show results for the prices charged by WesternUnion, one of the world’s largest MTOs, active in 98 corridors of the sample.Focusing on one financial institution permits controlling for any bias arisingfrom differences in institutions across corridors (composition bias), even withinthe group of banks and MTOs. The price data for Western Union confirm thata larger number of migrants and lower GDP per capita in the receiving andsending countries are associated with lower prices. In addition, exchange ratestability (as a result of pegged rates or dollarization) is also correlated withlower prices. Contrary to previous estimations, however, none of thecompetition-related indicators enter significantly, which could be due toWestern Union’s dominant position in the remittance business across most cor-ridors.25 Similarly, the share of rural population is generally not significantlyassociated with remittance prices across corridors for Western Union.

I V. C O N C L U S I O N S

This article on 119 migration corridors finds that remittance prices are associ-ated with a number of factors. First, the number of migrants is negatively andsignificantly associated with the price of remittances across different samplesand providers. This seems to suggest an important volume effect that worksthrough scale economies and lower costs for providers or through higher com-petition in a larger market leading to a lower mark-up. Second, remittanceprices are higher in corridors with higher income per capita, which couldreflect higher prices of nontradable goods, such as services, in general. Third,competition and market structure matter, except in the case of Western Union.Corridors with a larger number of providers and countries with more

24. This is established by rerunning the regression for the average fee across all providers for the

same sample as used in table 4.

25. This could be due to the fact that Western Union might have been operating longer in some

corridors than other firms. Also, Western Union might have better network coverage than other

providers in some countries.





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competitive banking sectors exhibit lower prices, although prices are higher incorridors with a higher share of banks among providers. Fourth, bankingsector outreach, as measured by branch penetration and cost barriers, is associ-ated with lower remittance prices. Finally, a broader regulatory framework forremittance service providers in the sending country is associated with lowerremittance prices, especially among banks.

Several factors were not found to be consistently correlated with remittanceprices, In particular, exchange rate stability, capital controls, and financial lit-eracy. However, this might be due to the use of imperfect variables to capturethese policies.

While this article offers some interesting findings on an important topic, it isonly a first exploration into what drives remittance prices. Future researchshould be able to exploit panel variation to get deeper into the issues, whilealso addressing some of the limitations of the data and analysis.

RE F E R E N C E S

Adams, Richard H. 2005. “Remittances and Poverty and Investment in Guatemala.” In International

Migration, Remittances and the Brain Drain, ed. M. Schiff, and C. Ozden. Washington DC:

World Bank.

Adams, Richard H., and John Page. 2003. “International Migration, Remittances and Poverty in

Developing Countries.” Policy Research Working Paper 3179. World Bank, Washington DC.

Aggarwal, Reena, Asli Demirguc-Kunt, and Marıa Soledad Martınez Perıa. Forthcoming. “Do Workers’

Remittances Promote Financial Development?” Journal of Development Economics.

Aycinena, Diego, Claudia Martinez, and Dean Yang. 2009. “The Impact of Remittance Fees on

Remittance Flows: Evidence from a Field Experiment among Salvadoran Migrants.” Department of

Economics, University of Michigan, Ann Arbor.

Barro, Robert J., and Jong-Wha Lee. 2001. “International Data on Educational Attainment: Updates

and Implications.” Oxford Economic Papers 53 (3): 541–63.

Beck, Thorsten. 2007. “Efficiency in Financial Intermediation: Theory and Empirical Measurement.” In

Bernd Balkenhol, ed., Microfinance and Public Policy: Outreach, Performance and Efficiency,

New York, Palgrave, MacMillan.

Beck, Thorsten, Asli Demirguc-Kunt, and Marıa Soledad Martınez Perıa. 2007. “Reaching Out: Access

to and Use of Banking Services across Countries.” Journal of Financial Economics 85: 234–66.

———. 2008. “Banking Services for Everyone? Barriers to Bank Access and Use around the World”.

World Bank Economic Review 22 (3): 397–430.

Bikker, Jacob A., and Katharina Haaf. 2002. “Competition, Concentration, and Their Relationship: An

Empirical Analysis of the Banking Industry.” Journal of Banking and Finance 26 (11): 2191–214.

Bureau van Dijk. 2009. Bankscope Database. www.bvdinfo.com/ProductsContainer/Bankscope.aspx.

CEPII (French Research Center in International Economics). 2010. Distances database. CEPII, Paris.

http://www.cepii.fr/anglaisgraph/bdd/bdd.htm.

Claessens, Stijn, and Luc Laeven. 2004. “What Drives Bank Competition? Some International

Evidence.” Journal of Money, Credit, and Banking 36 (3): 562–83.

Cox-Edwards, Alejandra, and Manuelita Ureta. 2003. “International Migration, Remittances, and

Schooling: Evidence from El Salvador.” Journal of Development Economics 72 (2): 429–61.

Demirguc-Kunt, Asli, Luc Laeven, and Ross Levine. 2004. “Regulations, Market Structure, Institutions,

and the Cost of Financial Intermediation.” Journal of Money, Credit, and Banking 36 (3): 593–622.





Dow

nloaded from

http://elibrary.worldbank.org/reference/matches?id=http://referencestore.ingenta.com/content/reference/0258-6770-FFE83BD39D65B3C4656DEA070A0E0997-22-3-397--

Duryea, Suzanne, Ernesto Lopez Cordova, and Alexandra Olmedo. 2005. “Migrant Remittances and

Infant Mortality: Evidence from Mexico.” Inter-American Development Bank, Washington, DC.

Freund, Caroline L., and Nikola Spatafora. 2008. “Remittances, Transaction Costs, Determinants, and

Informality.” Journal of Development Economics 86 (2): 356–66.

Gelos, R. Gaston, and Jorge Roldos. 2004. “Consolidation and Market Structure in Emerging Banking

Systems.” Emerging Markets Review 5 (1): 39–59.

Gibson, John, David J. McKenzie, and Halahingano Rohorua. 2006. “How Cost Elastic Are

Remittances? Evidence from Tongan Migrants in New Zealand.” Pacific Economic Bulletin 21 (1):

112–28.

Hanson, Gordon H., and Christopher Woodruff. 2003. “Emigration and Educational Attainment in

Mexico.” School of International Relations and Pacific Studies, University of California, San Diego.

Mimeo.

Harrison, Paul, Oren Sussman, and Joseph Zeira. 1999. “Finance and Growth: Theory and New

Evidence.” Finance and Economics Discussion Series, FEDS Working Paper 99-35. Federal Reserve

Board, Washington, DC.

Hildebrandt, Nicole, and David McKenzie. 2005. “The Effects of Migration on Child Health in

Mexico.” Economia 6 (1): 257–89.

IMF (International Monetary Fund). 2005. World Economic Outlook 2005. Washington, DC: IMF.

———. 2007. Annual Report on Exchange Arrangements and Restrictions. Washington, DC: IMF.

———. 2009. Direction of Trade database. DOT Online. IMF, Washington, DC. http://www2.

imfstatistics.org/DOT/help/DOThelp.htm.

Kanaiaupuni, Shawn Malia, and Katharine M. Donato. 1999. “Migradollars and Mortality: The Effects

of Migration on Infant Survival in Mexico.” Demography 36 (3): 339–53.

Laeven, Luc, and Giovanni Majnoni. 2005. “Does Judicial Efficiency Lower the Cost of Credit?”

Journal of Banking and Finance 29 (7): 1791–812.

Levy-Yeyati, Eduardo, and Alejandro Micco. 2007. Concentration and Foreign Penetration in Latin

American Banking Sectors: Impact on Competition and Risk. Journal of Banking and Finance 31

(6): 1633–47.

Lopez Cordova, Ernesto. 2005. “Globalization, Migration, and Development: The Role of Mexican

Migrant Remittances.” Economia 6: 217–49.

Maimbo, Samuel Munzele, and Dilip Ratha. 2005. Remittances: Development Impact and Future

Prospects. Washington, D.C.: World Bank.

Massey, D., and E. Parrado. 1998. “International Migration and Business Formation in Mexico.” Social

Science Quarterly 79: 1–20.

OECD (Organisation for Economic Co-operation and Development). 2010. Database on Immigrants

and Expatriates. OECD, Paris. http://stats.oecd.org/Index.aspx?lang=en#.

Orozco, Manuel. 2006. “International Flows of Remittances: Cost, Competition and Financial Access

in Latin America and the Caribbean—Toward an Industry Scorecard.” Inter-American Dialogue,

Washington, DC.

Panzar, John C., and James N. Rosse. 1982. “Structure, Conduct, and Comparative Statistics.”

Economic Discussion Paper 248. Bell Laboratories, Murray Hill, NJ.

———. 1987. “Testing for ‘Monopoly’ Equilibrium.” The Journal of Industrial Economics 35 (4):

443–56.

Parsons, Christopher, Ronald Skeldon, Terrie Lousie Walmsley, and L. Alan Winters. 2007.

“Quantifying International Migration: A Database of Bilateral Migrant Stocks.” Policy Research

Working Paper 4165. World Bank, Washington, DC.

Ratha, Dilip, and Jan Riedberg. 2005. “On Reducing Remittance Costs.” Development Prospects

Group. World Bank, Washington, DC.

Ratha, Dilip, and William Shaw. 2007. South–South Migration and Remittances. World Bank Working

Paper 102. Washington, DC: World Bank. http://go.worldbank.org/U4RXL56V20.





Dow

nloaded from

http://elibrary.worldbank.org/reference/matches?id=http://referencestore.ingenta.com/content/reference/0378-4266-FE2830122B4AA2ACE6BC14BFAB62CCB6-29-7-1791--

Taylor, J. Edward, Jorge Mora, Richard Adams, and Alejandro Lopez-Feldman. 2005. “Remittances,

Inequality, and Poverty: Evidence from Rural Mexico.” Working Paper 05-003. Department of

Agricultural and Resource Economics, University of California, Davis.

Woodruff, Christopher, and Rene Zenteno. 2007. “Migration Networks and Microenterprises in

Mexico.” Journal of Development Economics 82 (2): 509–28.

World Bank. 2007. Data on Access to Financial Services. World Bank, Washington, DC. http://econ.

worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:21546633�page

PK:64214825�piPK:64214943�theSitePK:469382,00.html

———. 2008. Payment Systems Worldwide, a Snapshot: Outcomes of the Global Payment

Systems Survey 2008. Washington, DC: World Bank. http://siteresources.worldbank.org/

INTPAYMENTREMMITTANCE/Resources/Global_Survey_Book.pdf

———. 2009a. Migration and Remittances Website. http://go.worldbank.org/SSW3DDNLQ0.

———. 2009b. Remittance Prices Worldwide database. World Bank, Washington, DC. http://

remittanceprices.worldbank.org.

———. 2009c. World Development Indicators database. World Bank, Washington, DC. http://data.

worldbank.org/data-catalog/world-development-indicators.

Yang, Dean. 2008. “International Migration, Remittances, and Household Investment: Evidence from

Philippine Migrants’ Exchange Rate Shocks.” Economic Journal 118 (528): 591–630.

A P P E N D I X : O B T A I N I N G T H E P A N Z A R A N D R O S S E ( 1 9 8 7 )H - S T A T I S T I C

Based on the Panzar and Rosse (1987) methodology and following the empiri-cal strategy pursued by Classes and Laeven (2004), the H-statistic is obtainedby estimating equation (A1):

LnðPitÞ ¼ ai þ b1 lnðW1;itÞ þ b2 lnðW2;itÞ þ b3 lnðW3;itÞ þ g lnðZ;itÞþ dDþ eit

ðA1Þ

where P is the ratio of gross interest revenues to total assets (proxy for banks’output price); W1 is the ratio of interest expenses to total deposits and moneymarket funding (proxy for input price of deposits); W2 is the ratio of personnelexpenses to total assets (proxy for input price of labor); W3 is the ratio ofother operating and administrative expenses to total assets (proxy for inputprice of equipment/fixed capital); Z is a matrix of controls including the ratioof equity to total assets, the ratio of net loans to total assets, and the logarithmof assets; D is a matrix of year dummies; ai denotes bank-level fixed effects; idenotes banks; and t denotes years. Annual balance sheet and income state-ments from Bureau van Dijk’s Bankscope database (Bureau van Dijk 2009)were used to calculate the H-statistic for each sending and receiving countrybanking sector during 1994–2006.

The H-statistic equals b1 þ b2 þ b3, the sum of the input price elasticities oftotal revenues. Conceptually, the statistic measures the responsiveness of bankrevenues to input prices. An H-statistic less than or equal to 0 is a sign of amonopoly, H equal to 1 indicates perfect competition, and H between 0 and 1indicates monopolistic competition.





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What Explains the Price of Remittances ... - The World Bank