rafmacalaba/datause-encoder-data huggingface dataset:
WPS4079

# **Banking Services for Everyone? Barriers to Bank Access and Use around the World**

Thorsten Beck, Asli Demirguc-Kunt and Maria Soledad Martinez Peria<sup>*</sup>

## **Abstract**

Using information from 193 banks in 58 countries, we develop and analyze indicators of physical access, affordability and eligibility barriers to deposit, loan and payment services. We find substantial cross-country variation in barriers to banking and show that in many countries these barriers can potentially exclude a significant share of the population from using banking services. Correlations with bank- and country-level variables show that bank size and the availability of physical infrastructure are the most robust predictors of barriers. Further, we find evidence that in more competitive, open and transparent economies, and in countries with better contractual and informational frameworks, banks impose lower barriers. Finally, though foreign banks themselves seem to charge higher fees than other banks, in foreign dominated banking systems fees are lower and it is easier to open bank accounts and to apply for loans. On the other hand, in systems that are predominantly government-owned, customers pay lower fees but also face greater restrictions in terms of where to apply for loans and how long it takes to have applications processed. These findings have important implications for policy reforms to broaden access.

JEL Classification: G2, G21, O16 Keywords: financial development, banking sector outreach, financing obstacles

## World Bank Policy Research Working Paper 4079, December 2006

_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._

> * The authors are with the World Bank’s research department. We thank Jerry Caprio, Stijn Claessens, Xavier Giné, Patrick Honohan, Leora Klapper, Inessa Love, and Susana Sánchez for comments and suggestions. Edward AlHussainy, Andrew Claster, Subika Farazi, Ning Jiang, and Hamid Rashid provided excellent research assistance.
2

> do not advance a new theory, this paper empirically explores one cause of cross-country differences in total factor productivity growth: differences in the level of banking sector development.

> Specifically, this paper examines whether the level of banking sector development exerts a

> causal impact on real per capita **GDP** growth, capital per capita growth, productivity per capita growth and private saving rates. For convenience, we refer to capital per capita growth, productivity per capita growth and private saving as the "sources of economic growth." Recent industry- and firm-level

> research suggests that the level of banking sector development has a large, causal impact on real per capita **GDP** growth [Rajan and Zingales **1998;** Demirgiig-Kunt and Maksimovic **1999].'** Past work,

> however, does not explore the channels via which banks affect economic growth. Thus, using a crosscountry dataset, we assess the causal impact of banks on capital accumulation, private saving rates and productivity growth and trace these effects through to overall per capita **GDP** growth.

This paper improves on the existing literature both in terms of econometric technique and data. First, while King and Levine (1993a) and Levine and Zervos **(1998)** empirically assess the connection between banking sector development and the sources of economic growth, they do not explicitly confront the issue of causality. We use two econometric techniques to control for the simultaneity bias that may arise from the joint determination of banking sector development and (i) capital accumulation, (ii) total factor productivity growth, and (iii) private saving rates. The first technique employs a pure cross-sectional, instrumental variable estimator, where data for **63** countries are averaged over the period **_1960-95._** The dependent variable is either real per capita **GDP** growth, real per capita capital stock growth, productivity growth, or private saving rates. Besides a measure of banking sector development, the regressors include a wide array of conditioning information to control for other factors associated with economic development. To control for simultaneity bias, we use the
Annex 1: Burkina Faso - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**|**50% increase**|**25% increase**|**50% increase**|
|---|---|---|---|---|---|---|---|
|||||**Upper**|**Upper**|**Lower**|**Lower**|
||**Share in**|||**bound**|**bound**|**bound**|**bound**|
|**Food item**|<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|<br>**Impact**<br>**(Cons. only)**|<br>**Impact**<br>**(Cons. only)**|<br>**Impact**<br>**(Cons.& Prod.) **|<br>**Impact**<br>**(Cons.& Prod.) **|
|||||**Burkina National**||||
|**Rice**|3.6|60.2|**46.4**|47.0|47.6|||
|**Bread**|0.7|35.6||46.4|46.4|||
|**Oil, butter**|1.1|74.9||46.6|46.8|||
|**Sugar**|0.9|67.4||46.6|46.7|||
|**Milk**|0.6|18.1||46.4|46.4|||
|All|6.8|91.9||47.5|48.4|||
|||||**Burkina Urban**||||
|**Rice**|2.9|53.7|**19.9**|20.9|21.9|||
|**Bread**|0.4|29.6||20.0|20.1|||
|**Oil, butter**|1.1|72.3||20.1|20.3|||
|**Sugar**|1.0|65.5||20.0|20.2|||
|**Milk**|0.6|15.4||19.9|19.9|||
|All|6.0|91.1||21.4|22.7|||
|||||**Burkina Rural**||||
|**Rice**|4.8|84.8|**52.3**|52.8|53.3|||
|**Bread**|1.1|58.5||52.3|52.3|||
|**Oil, butter**|1.2|85.0||52.5|52.6|||
|**Sugar**|0.7|74.5||52.5|52.6|||
|**Milk**|0.6|28.3||52.3|52.3|||
|All|8.3|94.6||53.3|54.1|||

Source: Authors’ estimation using country household survey data 20
# **1 Introduction**

This chapter reviews the outcomes of those forcibly displaced due to conflict, violence and persecution and the factors that shape these outcomes.<sup>1</sup> It looks at refugees who flee across borders, as well as at those internally displaced in their own country.

The literature on outcomes of refugees and internally displaced persons (IDP) is nascent but growing. Much of the economics literature has focused on high-income countries because data are more readily available (Becker and Ferrara, 2019; Brell et al., 2020). Yet only onesixth of today’s refugees and nearly no IDPs are hosted in those countries. A descriptive picture of refugee and IDP outcomes in low- and middle-income countries is emerging with the recent surge in representative cross-sectional data (Krishnan et al., 2020; Mejia-Mantilla et al., 2019; Pape et al., 2018). Efforts underway to increase panel data for those forcibly displaced in low- and middle-income countries will allow more rigorous, causal analysis in the future.<sup>2</sup> In analyzing the outcomes of refugees and IDPs in their host communities, the literature has proceeded in two steps. First, it has sought to find the appropriate counterfactual to put the outcomes of refugees and IDPs in perspective, and in doing so has compared the outcomes of refugees and IDPs with other migrants and finally with residents of the host communities. Alternatively, studies have made comparisons with individuals who stayed behind. Where possible, such comparisons have been done over time, but only studies in high-income countries have been able to access panel or longitudinal registry data. Second, the literature has attempted to explain the observed differences or absence thereof, after controlling for observables. Two directions have been pursued. On the one hand, researchers have acknowledged that the conditions leading to the decision to flee create a selection process that in turn greatly influences the extent to which refugees and IDPs have been found to adapt to a new environment among their host communities. Where they settled is also not always random. On the other hand, while refugees, IDPs, migrants in general, and citizens all may live in the same location, they are often not subject to the same conditions

> 1The terms “forcibly displaced” and “forced migrants” are used interchangeably to denote refugees and internally displaced persons who have fled conflict, violence and persecution.

> 2Examples are the Cox’s Bazaar Panel Survey among Rohingya refugees in Bangladesh (World Bank, 2019a) and the Syrian Refugee Life Study (S-RLS) in Jordan (Miguel et al., 2022).

1
4 (3) idj **[AEdi** * EsjD0 , 100 The second shows how much the competitive factor increased or decreased exports above or below that projected purely on the basis of demand changes, Finally, allowance must be made for situations where new export products occur between the initial and end periods. This third index, the diversification factor, equals the actual change in exports minus that predicted by the competition and demand factors. Appendix 1 provides a hypothetical numeric example which illustrates the interrelationships between these indices.

Empirical analyses of sub-Saharan Africa's export performance, along the lines suggested by the above equations, is complicated by the very poor quality of trade data reported by these countries.<sup>2</sup> As an alternative to their use, import statistics for OECD countries in the United Nations COMTRADE Database were used as proxies for the unreliable or missing African data. This implies that, instead of being measured in global terms, Africa's export performance is assessed by its trade with industrial countries. However, in defense of this approach it should be noted that United Nations estimates indicate that approximately 82 percent of Africa's exports go to OECD markets -- 60 percent of all exports go 2A World Bank report by Yeats (1990a) documents the very poor quality of United Nations statistics on African trade. Further evidence relating to this point is provided by Rozanski and Yeats (1994). It should be noted that many of the African countries have not provided the United Nations with trade data since the early 1980s so much of the UN information that is available is based on "estimates' of this exchange. It should be noted that a major attraction of the OECD import statistics used in this study is that these data should not be affected by "unrecorded" trade which is known to be a major problem in many African countries' data.
Although existing studies indicate significant effects on access to credit, income, home investment and fertility (Field 2007; Andrade 2006), this particular study aims at helping to fill an important gap in the literature on property rights and child labor force participation. Furthermore, the results indicate that, unlike employment responses to most welfare programs, which tend to involve an income effect that potentially removes adult households from the labor force, government property titling programs appear to have a different effect -- removing child labor from the labor force.

Regarding further research, it will certainly be interesting to apply the same survey in different locations and compare outcome results. Ravallion _et al_ . (2005) argue that the same program works well in one village but fails in another. An example is Bangladesh's _Food for Education Program_ . The program worked well in reaching the poor in some villages but not in others, even in relatively close proximity. Furthermore, it will also add value in keeping tracking of the same households with other surveys to check the consistency and robustness of the results over time.

It is clear that understanding the multiple channels through which land titles influence economic outcome is particularly important given that governments across the world are considering titling programs to address urban informality. In addition, the results have potential implications for understanding labor market frictions in developing countries (Goldsmith, 1995). In places characterized by high levels of residential informality, such as most developing and poor countries, informal property protection may constitute an important obstacle to labor market adjustment. Hence, land titling could be applied as an asset to improve public policy actions that directly impact economic growth.

# **References**

ACEMOGLU, Daron; JOHNSON, Simon & ROBINSON, James A. Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution. _The Quarterly Journal of Economics_ , vol. 117, No. 4, pp. 1231-1294, November 2002.

ALCHIAN, Armen & DEMSETZ, Harold. The Property Rights Paradigm. _The Journal of Economic History_ , vol. 33, No. 1, The Tasks of Economic History, pp. 16-27, March 1973.

ALSTON, Lee; LIBECAP, Gary & SCHNEIDER, Robert. The Determinants and Impact of Property Rights: Land Titles on the Brazilian Frontier. _Journal of Law, Economics & Organization_ , vol. 12, pp. 25-61, 1996.

ANDRADE, Maria T. Direitos de Propriedade e Renda Pessoal: Um Estudo de Caso das Comunidades do Caju. _Revista do BNDES_ , Rio de Janeiro, vol.13, No. 26, pp. 261-274, 2006.

ASSOCIAÇÃO NACIONAL DOS REGISTRADORES DO ESTADO DE SÃO PAULO. _Sistema de Biblioteca. Cartilha dos Registros Públicos_ . São Paulo: versão III, pp.03-05, 2007.

BAHAROGLU, Deniz. World Bank Experience in Land Management and the Debate on Tenure Security. World Bank Housing Research Background - _Land Management Paper_ , July 2002.

BASU, Kaushik & VAN, Pham Hoang. The Economics of Child Labor. _American Economic Review_ , vol. 88, No. 3, pp. 412-27, 1998.

17
In general, the estimated attainment data compares favorably with the census and survey information. The less accurate fit for 1981 is here believed to be due to large measurement errors or the possible manipulation of the census, which took place during the peak of the armed conflict in Guatemala. Consequently, this discrepancy was not smoothed over. Equally, data for 1998 differs slightly from the estimate. This is due to the fact that the survey largely oversamples the urban population of the economy in that year. Given the simplicity of the assumptions of the underlying model, however, the overall results have been found quite satisfactory.

**Table 3. Guatemala: Education Level of Labor Force, 1950-2002** (in percent)<sup>a/</sup>

|Year|Source|No school|Some Primary|Some<br>Secondary|Some Tertiary|
|---|---|---|---|---|---|
|1950|SEGEPLAN (1978)|72.3|24.9|2.3|0.5|
|1964|SEGEPLAN (1978)|60.7|33.4|4.7|1.2|
|1973|SEGEPLAN (1978)|51.7|40.8|6.1|1.4|
|1981|CENSO (1981)|(37.7)|(48.7)|(10.9)|(2.7)|
|1989|ENS (1989)|38.9|47.7|11.4|2.1|
|1994|CENSO (1994)|35.4|47.8|14.1|2.7|
|1998|ENIGFAM (1998)|(30.8)|(50.3)|15.9|3.1|
|2000|ENCOVI (2000)|28.9|48.6|16.5|6.0|
|2002|ENEI 1 (04-05/2002)|26.9|49.3|19.3|4.5|
|2002|ENEI 2 (08-09/2002)|24.7|50.8|19.3|5.2|
|2002|ENEI 3 (10-11/2002)|25.0|48.7|21.0|5.3|

_Source:_ Compiled from census and survey data, ENCOVI and ENEI figures are from UNDP Guatemala. a/ Brackets indicate uncertain figures. Discrepancies are due to rounding.

In any case, simply employing gross enrollment ratios would overestimate the accumulation of human capital. Gross enrollment ratios are defined as the ratio of total enrollment in the respective schooling level to the population of the age group that is expected to be enrolled at that level. Thus, gross enrollment ratios can exceed 1 and therefore exaggerate the true amount of enrollment when students repeat, which is often the case in Guatemala.<sup>13</sup> In response to this problem and in order to benchmark the estimated educational attainment data with census and survey information, the gross enrollment ratios have been adjusted by a depreciation factor for the respective education level, as reported in Loening (2004).

> 13 The use of net enrollment ratios is hampered by large data gaps. Also, net enrollment ratios introduce large measurement errors if there are under- or over-aged children starting at each level of education, see Barro and Lee (2001). In Guatemala students who start late constitute a significant fraction of total enrollment—in particular for primary schooling.

-19-
Oor 44 912,041 9o1t-n+3 = max eu Wot-n+3 + C1(891) = >n-1i=1 6! (Wr e-nt3ti _ Wo,t—-n+3+i) 1 of 912t-n+3 = max ie : OW1 t-n4+3 + €2(912) — €1(612) = >n-1i=2 6'(Wot_nt3ti _ W1t-n+3+i) 2-%1- |

K Ly Lo F(6o1) = 0 ~~Ly~~ L2 a a
These urbanization rates are roughly in line with the overall rate for least-developed countries over the last 40 years (UN 2015a).

_Urban centers:_ Urban centers in Bangladesh are essentially organized into three levels: city corporations, municipal cities and towns ( _paurashavas_ ), and _upazilla_ headquarters. According to the 2011 census (GOB 2014), Bangladesh had 506 urban centers: six city corporations (Barisal, Chittagong, Dhaka, Khulna, Rajshahi, and Sylhet), 311 municipal cities and towns, and 189 upazilla headquarters. The six city corporations accounted for 34% of the country’s total urban population in 2011.

Since 2011, the number of urban centers has grown, and some urban centers have moved up in level. The number of city corporations has grown from six to 11, with four newly incorporated city corporations: Comila (in 2011), Gazipur (in 2013), Narayanganj (in 2011), and Rangpur (in 2012). Dhaka city corporation was bifurcated into Dhaka North and Dhaka South city corporations in 2011. Based on published census statistics (GOB 2014), we estimate that these 11 city corporations that exist today accounted for 41% of the country’s total urban population in 2011.

# B. _Slum settlements_

Slum settlements are an important phenomenon in urban Bangladesh. According to a 2014 government census of slum settlements, the country had approximately 14,000 of them in all urban areas, with a total slum population of 2.2 million (GOB 2015).<sup>13</sup> Sixty-five percent of slum settlements were located in city corporations, 24% in municipal cities and towns, and 11% in upazilla headquarters or other urban areas. Slum settlements varied substantially in size: on the low end, 27% of slum settlements had fewer than 10 households, whereas on the high end, 7% of slum settlements had at least 100 households. In 2005, an independent census and mapping of slum settlements counted about 9,000 slum settlements in the six city corporations

> 13 A settlement was defined to be slum if it had at least five households and poor housing construction, water supply and sanitation, street lighting, and roads; high population density or crowded housing; and low mean household income status.

9
**Table 3.3: Average Interest Rates and Inflation, 1975-2003**

|Year|3-month|6-month|12-month|Spread<br>12m-3m|Inflation <br>Rate|12m Real<br>Rate|
|---|---|---|---|---|---|---|
|1975|9.00|9.25|9.50|0.50|13.2|-3.23|
|1976|9.25|9.50|9.75|0.50|14.0|-3.69|
|1977|7.75|8.00|8.25|0.50|12.2|-3.56|
|1978|8.92|9.17|9.42|0.50|12.7|-2.93|
|1979|11.08|11.33|11.58|0.50|19.4|-6.51|
|1980|13.75|14.00|14.25|0.50|24.3|-8.10|
|1981|13.75|14.00|14.25|0.50|23.9|-7.80|
|1982|14.75|15.00|15.25|0.50|21.1|-4.79|
|1983|14.75|15.00|15.25|0.50|20.3|-4.19|
|1984|17.27|17.52|17.77|0.50|18.7|-0.76|
|1985|17.08|17.54|18.38|1.30|19.3|-0.76|
|1986|17.00|17.50|18.50|1.50|23.0|-3.64|
|1987|17.25|17.75|19.00|1.75|16.3|2.35|
|1988|16.25|17.13|19.21|2.96|13.7|4.85|
|1989|16.42|17.29|19.04|2.62|13.6|4.78|
|1990|18.50|20.17|23.00|4.50|20.5|2.07|
|1991|18.79|21.79|23.33|4.54|19.3|3.37|
|1992|17.69|20.15|21.71|4.02|15.9|5.04|
|1993|18.23|20.23|21.23|3.00|14.5|5.86|
|1994|17.57|18.17|19.21|1.64|10.9|7.52|
|1995|14.40|15.01|15.49|1.09|8.9|6.02|
|1996|11.92|12.13|12.80|0.88|8.2|4.25|
|1997|10.04|9.94|10.31|0.27|5.5|4.51|
|1998|12.03|11.87|11.51|-0.52|4.7|6.48|
|1999|--|--|8.85|--|2.7|6.01|
|2000|--|--|6.12|--|3.1|2.93|
|2001|--|--|4.08|--|3.4|0.66|
|2002|--|--|3.49|--|3.6|-0.10|
|2003|--|--<br>Sourc|2.91<br>e: Bank of|--<br>Greece|3.6|-0.68|

- The attractiveness of government securities was reinforced by their preferential tax treatment. While interest from bank deposits was subjected to tax at the rate of 15 percent in January 1991, interest income from government securities continued to be exempt from tax.

- Distribution of securities was effected through several channels. Investors could purchase and sell government securities directly at the Bank of Greece or through a large network of authorized agents that included all credit institutions, members of the Athens Stock Exchange, and the Deposits and Loans Fund.

- Authorized agents were offered attractive commissions for placing government paper. For treasury bills these included both a sale and renewal commission (since some

29
**Table 8: Farm Size and incidence of improved seed use**

||**Maize**|**Teff**|**Wheat**|
|---|---|---|---|
||**Panel**<br>|**A: All obser**<br>|**vations**<br>|
|Cropped area (α1)|0.037**|0.001|0.037|
||(2.510)|(0.091)|(1.397)|
|Cropped area squared(α2)|-0.002|-0.000|0.008|
||(-1.028)|(-0.012)|(1.641)|
|Commercial farm dummy|0.350***|0.408***|0.306**|
||(7.155)|(10.142)|(2.295)|
|Cropped area * commercial (β1)|0.077***|0.059**|0.149**|
||(2.991)|(2.111)|(2.389)|
|Cropped area sq. * commercial (β2)|-0.009**|0.012|-0.033***|
||(-2.176)|(1.261)|(-3.844)|
|Constant|0.315***|0.014|0.091***|
||(14.547)|(0.988)|(3.068)|
|Number +of observations<br>|1,760|1,228|547|
|R<sup>2</sup>|0.249|0.236|0.224|

_Note:_ Woreda fixed effects included throughout. Cropped area is in logs, denominated in hectares.

22
**Figure 6. Trade in Sub-Saharan Africa within Regional Economic Communities, 2017**

_(percent of regional trade)_ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>COMESA CEMAC ECOWAS SADC<br>2016 EXPORTS 2016 IMPORTS<br><!-- End of picture text --> _Source: Direction of Trade Statistics, International Monetary Fund._

# **5.3.2 Regional trade agreements: The AfCFTA**

The African Continental Free Trade Agreement (AfCFTA) has been signed by member countries of the African Union to bolster trade within the region by eliminating tariff on most goods, liberalizing trade in services, reducing (or eliminating) non-tariff barriers to intra-regional trade and creating a continental single market with free labor and capital mobility (Figure 7). Once operational, it has been estimated that the AfCFTA will establish a market of 1.2 billion people (60 percent of which are below the age of 25 years) with a combined GDP of US$ 2.5 trillion (IMF 2019).

The AfCFTA has the potential to enhance connectivity across markets in the region—especially the linkages between smaller economies and larger markets. It can also be a risk management mechanism to protect Sub-Saharan African countries from trade tensions outside the region and/or economic downturns in the world’s larger markets. A larger effective domestic market acts as insurance against disruptions to global trade associated either with global volatility or with contraction in global demand.

37