rafmacalaba/datause-encoder-data huggingface dataset:
# **Benchmarking Financial Systems around the World**

Martin Čihák, Aslı Demirgüç-Kunt, Erik Feyen, and Ross Levine<sup>1</sup>

**Keywords:** Financial sector, financial systems, economic development

## **JEL Classification:** G00, G01, G10, G20, O16

> 1 Čihák, Demirgüç-Kunt, and Feyen: World Bank; Levine: Brown University and the NBER. Aart Kraay was instrumental in sharpening the authors‘ thinking on indices of financial development. The database builds on previous work within the World Bank Group, in particular Beck, Demirgüç-Kunt, and Levine (2000, 2010). The paper also benefited from comments and suggestions by Thorsten Beck, Sergio Schmuckler, Roberto Rocha, Stijn Claessens, Augusto de la Torre, Norman Loayza, and Tunc Uyanik and participants in World Bank seminars. Amin Mohseni, Mauricio Pinzon Latorre, and Subika Farazi provided research assistance. Katie Kibuuka, Diego Sourrouille, Ed Al-Hussainy, Haocong Ren, and Andrea Coppola also helped with compiling parts of the dataset. All remaining errors those of the authors.
**The common feature of all three primary dealer systems in MENA (Egypt, Morocco and Tunisia)**<sup>**36**</sup> **is that only primary market obligations are enforced, while secondary market obligations are not.** This is in part explained by the structural difficulties to trading activity discussed in previous sections: excess liquidity, scarce or unreliable prices in the primary and secondary markets and poor benchmark building issuance policies, Therefore, it is unrealistic to enforce market making obligations as found in advanced markets. In this context, attempting to build an effective primary dealer system can be a source of frustration to both governments and primary dealers. An adequate set of rules and incentives need to be carefully designed and frequently reviewed according to different stages of market development.

**A potential solution in MENA is to re-assess rules so that secondary market obligations are in line with the degree of market development.** This may include initiatives applied in other countries such as price fixing obligations or limited firm quoting obligations, together with a set of incentives that are performance based, such as the publication of league tables for marketing purposes, cash and securities lending facilities at preferential rates, exclusive access to post auction allocations.  Some of these measures are currently being assessed in Egypt.

# _3.5. Investor base_

**An investor base for fixed-income securities, which is as large and diversified as possible, is important for ensuring high liquidity and stable demand in the market.** A heterogeneous investor base with different time horizons, risk preferences, and trading motives ensures active trading and stimulates liquidity, enabling the government to execute its funding strategy under a wide range of market conditions.

**The institutional investor base is poorly and unevenly developed across MENA countries, with a concentration of public debt holdings in the banking sector.** The following subsections describe briefly the current status of the investor base divided by banks, institutional investors (mutual funds, pension funds and insurance companies) and foreign investors.

# _Banks as dominant investors_

**Local banks are the largest domestic investor in EMEs’ sovereign debt.** They held on average around 42 percent of total domestic debt in 2005<sup>37</sup> , whereas in advanced economies they have a less dominant role with only 11 percent of public debt in that

> 36 Egypt ́s program includes fifteen primary dealers that have exclusive access to the auction; in Morocco, the scheme comprises six primary dealers that do not have exclusive access but have as main incentive access to the non-competitive auction; and the model in Tunisia consists of a single primary dealer that is a broker and shares access to the auction with thirteen banks outside the primary dealer arrangement.

> 37 The most recent available data is from CGFS Paper No 28 **“** Financial stability and local currency bond markets”, Committee on the Global Financial System, June 2007.

29
whose legal systems score high on an efficiency index, a greater proportion of firms use longterm external financing. Djankov et al. (2007) find that better creditor protection is associated with higher ratios of private credit to GDP. Qian and Strahan (2007) find that better creditor protection results in more concentrated ownership of syndicated loans, longer maturities, and lower interest rates. Bae and Goyal (2009) find that weaker enforceability of contracts results in smaller loan amounts, shorter loan maturities, and higher loan spreads. More closely related to our work, Haselmann et al. (2010) find that legal reforms increase the supply of bank credit in 12 transition countries. Importantly, they also find that changes in collateral laws have larger impact than changes in bankruptcy laws. However, none of the papers above has investigated the specific reform we are focusing on – the introduction of collateral registries for movable assets.

The rest of the paper is structured as follows. Section 2 discusses the data used in our analysis. Section 3 lays out the empirical methodology. Section 4 presents the empirical results. Section 5 concludes.

# **2. Data**

We use two main datasets to study the impact of the introduction of collateral registries for movable assets on firms’ access to bank finance: the _Doing Business_ and the _Enterprise Surveys_ datasets compiled by the World Bank.<sup>7</sup> The Doing Business dataset contains annual measures of business regulations for local firms in 185 economies since 2004.<sup>8</sup> In particular, this dataset allows us to identify the countries and years when movable collateral registries were introduced. We also use this source to pinpoint countries that undertook other types of reforms that strengthened collateral laws.

> 7 Fleisig et al. (2006) endorse the use of these datasets to study the impact of reforming collateral regimes. 8 The Doing Business dataset  can be found at http://www.doingbusiness.org.

6
the mixed method and the benchmark narrows considerably, from 21.1% (918 million people) to 18.2% (794 million) in 2005. (The $2.00 poverty rates are 39.8% and 36.7% respectively.) In 2005, about two-thirds of the drop in the count of the number of people living under $1.25 a day in moving from the benchmark to the mixed method is due to India.

# **7. Conclusions**

Global poverty measurement combines data from virtually all branches of the statistical system. The measures reported here bring together national poverty lines, household surveys, census data, national accounts and both national and international price data. Inevitably there are comparability and consistency problems when combining data from such diverse sources. Price indices for cross-country comparisons do not always accord well with those used for intertemporal comparisons within countries. In some countries, the surveys give a different picture of average living standards to the national accounts, and the methods used in both surveys and national accounts differ across countries.

However, thanks to the efforts and support of governmental statistics offices and international agencies, and improved technologies, the available data on the three key ingredients in international poverty measurement—national poverty lines, representative samples of household consumption expenditures (or incomes) and data on prices—have improved greatly since global poverty monitoring began. The expansion of country-level poverty assessments since the early 1990s has greatly increased the data available on national poverty lines. Side-byside with this, the country coverage of credible household survey data, suitable for measuring poverty, has improved markedly, the frequency of data has increased, public access to these data has improved, and the lags in data availability have been reduced appreciably. And with the substantial global effort that went into the 2005 _International Comparison Program_ we are also in a better position to assure that the poverty lines used in different countries have similar purchasing power, so that two people living in different countries but with the same real standard of living are treated the same way. The results of the 2005 ICP imply a higher cost of living in developing countries than past ICP data have indicated; the “Penn effect” is still evident, but it has been over-stated.

We have combined the new data on prices from the 2005 ICP and household surveys with a new compilation of national poverty lines, which substantially updates the old lines for the 32
# **1. Introduction**

The severe recession that is hitting the global economy, with very low or even negative growth rates, has caused widespread contractions in international trade, both in developed and developing countries. World Trade Organization (WTO) has forecast that exports will decline by roughly 9% in volume terms in 2009 due to the collapse in global demand brought on by the biggest economic downturn in decades. The contraction in developed countries will be particularly severe with exports falling by 10%. In developing countries, which account for one-third of world trade, exports will shrink by some 2% to 3% in 2009.

The contraction in international trade has been accompanied by a sharp decline in the availability of trade finance. This decline is only partly explained by the contraction in demand: according to a BAFT (Banker’s Association for Trade and Finance) and International Monetary Fund (IMF) joint survey (2009), flows of trade finance to developed countries have fallen by 6% relative to the previous year, more than the reduction in trade flows, suggesting that part of the fall reflects a disruption of financial intermediation. The contraction in value of trade finance has also been accompanied by a sharp increase in its price. Fear that the decline in trade finance and the increase in its cost would accelerate the slowdown of world trade has triggered a number of government initiatives in support of trade finance (Chauffour and Farole, 2009).

The situation is especially worrisome for firms operating in developing countries which rely heavily on trade finance to support both their exports and imports.<sup>1</sup> With a restricted access to financing and an increased cost of financing, these firms may find difficulties in maintaining their production and trade activities.

Trade finance refers to the methods and instruments designed to support importers and exporters throughout the trade cycle. Importers do not want to pay cash in advance for imported goods and services. Consequently exporters have to provide credit and secure financing until they receive payment. Moreover exporters themselves need to finance their own production to support their export sales.

There are various ways for both exporters and importers to finance their trade cycle. This study focuses on inter-firm trade finance,<sup>2</sup> i.e. the finance provided to importers from exporters to buy the goods from overseas, and that provided to exporters, to help

> 1 Emerging market banks report on average a 6% decline in trade finance transactions (IMF/BAFT, 2009). 2 The study will not address issues related to trade finance intermediated by banks _per se_ , although this represents a conspicuous share of trade finance.

2
# **<mark>Economic Growth, Convergence, and World Food Demand</mark> and Supply**

Emiko Fukase<sup></sup> and Will Martin<sup></sup> <mark>Key words – cereal equivalents, convergence, income growth, food security, global. JEL Codes: C53 O13 O47 Q11.</mark> Acknowledgements: The authors would like to thank participants at presentations on versions of this work at International Food Policy Research Institute (IFPRI), North Carolina State University, the Agricultural and Applied Economics Association meetings, the Annual Conference of the Australian Agricultural and Resource Economics Society, and the World Statistics Congress of the International Statistical Institute. We also would like to thank Francisco Ferreira, Hanan Jacoby and Michael Toman for support and Jonathan Nelson for useful comments. We are grateful to the CGIAR Research Program on Policies, Institutions, and Markets (PIM) and the World Bank Strategic Research Program (SRP) for partial support of this work.

>  World Bank. e-mails: emikof2@aol.com, efukase@worldbank.org.

>  International Food Policy Research Institute. e-mail: w.martin@cgiar.org.
- 10 -

Table 3 Share of Various Traditional<sup>Earmarked</sup><sup>**Funds**in Total Earmarked lundsa 1970-86</sup><sup>**a/**</sup> (percentages)

||1970|1975|1980|1981|1982|1983|1984|1985|1986|
|---|---|---|---|---|---|---|---|---|---|
|Ltional Level|44.7|65.2|72.2|72.4|74.7|70.8|71.9|73.5|74.7|
|Tax onImpo.ts<br>C.I.F.|1.3|4.1|8.1|7.5|8.4|6.8|8.5<br>|8.5<br>|10.5<br>|
|<br>Caja Agraria|0.0|0.0|0.0<br>|0.0<br>|0.0<br>|0.0<br>|1.1<br>|1.1<br>|1.2<br>|
|IFI|0.0|0.0|0.0|0.0|0.0|0.0|1.1|1.1|1.2<br>|
|PROEXPO|1.3|4.0|7.3|6.7|7.5|6.0|6.3|6.3|8.1|
|GasolineTax<br>|14.2|8.9|20.8|21.1|18.1|15.3|13.5|14.8|13.8|
|Fondo Vial Nacional<br>(FVN) 752|1C.7|6.7|15.6|15.8|13.5|11.5|10.5|9.9|9.7|
|Fondo de Caminos||||||||||
|Vec!nalea (FCN) 102<br>|1.4|0.9|2.1|2.1|1.8|1.5|1.2|2.0|1.6|
|Ferrocarrilea<br>Nacional<br>102<br>|1.4|0.9|2.1|2.1|1.8|1.5|1.2|2.0|1.6|
|Corp. Fin de<br>Tranaporte<br>52|0.7|0.4|1.0|1.1|0.9|0.8|0.6|0.9|0.8|
|Tax on International<br>Travel|0.7|n.a.|1.1|1.5|1.9|1.7|1.5|1.4|1.0|
|42 Tax Coffee<br>Exports|19.1|12.9|4.4|3.2|2.7<br><sup>3</sup>|2.6<br>|2.8<br>|3.3<br>|5.3<br>|
|Fondo<br>Nacional<br>del<br>Cafe<br>|n.a.|n.a.|3.7|2.7|,.|2.2|2.3|2.8|4.4|
|Fedecafe,<br>zonas||||||||||
|<br>cafeterias|n.a.|n.a.|0.7|0.5|0.4|0.4|0.5|0.5|0.9|
|2Tax onTourism|0.0|0.7|0.4|0.5|0.5|0.5|0.1|0.1|0.1|
|6ITax on Movie<br>Theater Tickets|0.0|0.0|0 0|0.0|0.0|0.0|0.0|0.0|0.1|
|ontributions and<br>Participations|2.8|2.2|1.5|1.0|1.6|1.9|1.9|3.0|_2.1_|
|pecial Funds|0.1|0.5|0.1|0.1|0.1|0.4|0.6|1.2|2.4 b/|
|Revenue Sharing|6.4|35.9|35.7|37.7|41.4|41.7|43.1|41.2|38.9|
|<br>Tax Transfer|0.0|23.4|23.8|27.4|28.3<br>|29.9<br>|28.7<br>|25.9<br>|22.5<br>|
|SalesTax Allowance|6.4|12.5|11.9|10.3|13.1|11.8|14.3|15.3|16.5|
|mnartment Level|46.7|29.5|23.4|23.1|21.0|23.8|23.5|22.7|23.2|
|inicival Level|8.6|5.3|4.4|4.5|4.3|5.4|4.6|3.8|2.1 c/|

See footnote to Table 2 for definitions. Includes some miscellaneous reallocations under<sup>Law 55.</sup> Understated because of missing data.

Durce: Estimates based on Table<sup>A2 in Statistical Appendix.</sup>
Figure C3: Poverty rates across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Poverty rate (%)<br>Poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the distributions of poverty rates over different geographic areas in 20 countries. WorldPop 250m is used for the DOU and DB methods. The dashed lines represent the average national poverty rate in the sample.

Figure C4: Comparison of urban poverty rates between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>60 60 MWI 60 MWI<br>MWI<br>50 50 50<br>NER NER<br>40 40 40<br>UGA UGA UGA<br>TZA TZA TZA<br>30 NER 30 30<br>TCD TCD<br>ETH ETH<br>20 ETH 20 LSO 20 LSO<br>BFATCD LSOAGO BFA AGO BFAAGO<br>10 VNMGABEGYGINSENCOLMRTCIVGNBBGDGHA 10 VNMGABEGYGINSENCOLMCIVRTGNBBGDGHA 10 VNMGABEGYCOLMRTGINSENGHACIVGNBBGD<br>10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60<br>Urban poverty rate (%) Urban poverty rate (%) Urban poverty rate (%)<br>Official definition Official definition DOU<br>DOU DB DB<br>Urban poverty rate (%) Urban poverty rate (%) Urban poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

72
# 1. **Why this survey?**

There exists a huge number of anecdotal evidence that uncertainty on laws, policies and regulations hurts private sector development in many LDCs. Typical examples are the study by De Soto (1989) on the problems of infornal firms in Peru, the description by Klitgaard (1990) of the uncertainties for doing business in Equatorial Guinea, or the analysis of institutional uncertainty in Nicaragua by Borner, Brunetti and Weder (1995). Such case studies show that unpredictable state action can have large costs in terms of economic development. If the private firms cannot be sure which regulations apply in the near future, whether private contracts are unarbitrarily enforced, or whether their property will be protected against violence private firms typically react by cutting back on long-term investment. The considerable sunk costs of most investment projects create large disincentives against binding any resources to long-term investment projects if the firm operates in uncertain environments. The theory of irreversible investment (e.g. Dixit and Pindyck 1994) has reinforced the argument that such uncertainties are particularly costly in terms of aggregate investment.

Given the case study evidence and the theoretical arguments on investment irreversibility, the reliability of government activity should be at the forefront of an analysis of the sources of differences in economic development. Due to lack of adequate data, this is not the case. Research and data on the sources of development is mainly based on broad cross-country data sets that allow direct comparisons of government policies and other country characteristics. Such comparable cross-country data on the degree of reliability of government activity has, however, not been available.<sup>3</sup> This survey's aim is to fill that gap by creating a comparable, quantitative data set on different aspects of the degree of institutional uncertainty as perceived by private entrepreneurs. In contrast to case study work, this data is created for a broad cross-section of countries making it possible to calculate individual indicators that can be used in standard cross-country analysis.

> **3** Researchers have worked with some measures of political instability or policy volatility derived from political handbooks. The problem is, however, that such objective indicators measure crude aspects of policy instability but they do not grasp uncertainty. What matters for the private investment decision is not objective instability but subjectively perceived _uncertainty._ For a more detailed discussion of alternative proxies for policy uncertainty and their potential problems see Brunetti, Kisunko and Weder (1997).

1 1
Table A2: Ghana - Educational attainment of children and their parents by five-year birth cohort

|Cohort|Average years<br>|||||
|---|---|---|---|---|---|
||of schooling|No schooling|Primary|Secondary|Tertiary|
|Son||||||
|1944-1948|6.4|46.1|12.0|28.0|13.9|
|1949-1953|7.9|34.7|11.9|37.8|15.6|
|1954-1958|8.5|28.9|13.2|43.2|14.7|
|1959-1963|8.1|31.5|13.6|40.5|14.5|
|1964-1968|8.3|28.4|13.6|46.2|11.9|
|1969-1973|8.1|27.4|13.8|48.3|10.6|
|1974-1978|7.6|25.2|14.2|48.8|11.8|
|1979-1983|7.7|21.8|14.1|52.8|11.4|
|1984-1988|8.5|16.0|14.7|56.0|13.2|
|1989-1993|8.9|8.8|14.1|72.3|4.8|
|Daughter||||||
|1944-1948|2.6|69.8|15.0|12.9|2.3|
|1949-1953|4.0|60.8|12.5|21.2|5.6|
|1954-1958|5.4|47.7|16.1|29.9|6.3|
|1959-1963|5.0|51.9|14.1|29.4|4.6|
|1964-1968|5.4|47.3|15.7|31.8|5.3|
|1969-1973|5.3|46.7|16.4|32.4|4.5|
|1974-1978|5.1|41.8|18.1|36.2|3.8|
|1979-1983|5.3|37.4|19.0|38.7|4.9|
|1984-1988|6.7|28.4|16.9|45.8|8.9|
|1989-1993|7.7|18.1|16.0|61.9|4.1|
|Mother||||||
|1944-1948|0.5|95.8|1.3|2.5|0.4|
|1949-1953|0.6|94.3|1.8|3.2|0.7|
|1954-1958|0.7|93.1|2.5|3.9|0.6|
|1959-1963|1.0|90.8|2.8|5.4|1.0|
|1964-1968|1.5|86.1|4.2|8.0|1.7|
|1969-1973|1.9|82.4|4.9|11.1|1.6|
|1974-1978|2.5|77.7|5.1|15.1|2.2|
|1979-1983|3.0|73.4|6.7|17.3|2.6|
|1984-1988|3.9|64.8|8.4|23.5|3.3|
|1989-1993|4.2|59.8|11.1|25.6|3.6|
|Father||||||
|1944-1948|1.6|87.8|1.1|8.8|2.3|
|1949-1953|1.7|86.3|2.0|9.5|2.2|
|1954-1958|2.3|81.9|3.0|11.7|3.5|
|1959-1963|2.7|79.0|2.5|14.2|4.3|
|1964-1968|3.6|72.1|3.1|19.6|5.2|
|1969-1973|4.1|69.5|2.5|20.5|7.5|
|1974-1978|4.6|65.3|2.9|23.9|7.9|
|1979-1983|5.2|60.5|4.0|26.4|9.1|
|1984-1988|6.3|51.6|4.9|32.4|11.1|
|1989-1993|6.6|46.1|7.9|35.3|10.7|

34
and the Cognitive Reflection Test (CRT). Our measure of locus of control indicates children think they are unable to control what happens in their lives. More specifically, a decrease in locus of control indicates that students feel they can manage their experiences, thus demonstrating an increase in self-efficacy. Raven is a measure of abstract reasoning and a nonverbal estimate of intelligence. It is implemented as a set of matrices in progressive order. Finally, CRT is a test designed to measure if an individual tends to automatically choose an initially incorrect response and then engage in deeper reasoning to find a correct answer.

During the third stage, right after the students finished the battery of psychometric tests, we obtained emotional response intensity for negative and positive stimuli in terms of valence locus. Here, we exposed students to alternate series of images selected to elicit positive and negative emotional responses in order to estimate poststimuli valence indexes.

Then, using this response intensity measure and the valence-at-resting-state index from the first stage, we estimated emotional responsiveness as the difference between both levels. Specifically, the positive (negative) valence difference indexes measure the variation in valence index recorded in the third stage when the stimulus was positive (negative) net of the individual’s baseline resting state valence index recorded in the first stage. Both differences can be interpreted as a lower level of overreaction of participants—they become more phlegmatic or cold headed—or that individuals move toward a greater withdrawal behavior or attitude.

# **5.3 Attrition Analysis**

The final sample, after filtering the EEG data and accounting for attrition, is 308 valid EEG recordings of students; that is, an average attrition share of 49%. In this subsection, we present some checks to verify that this attrition rate was not correlated with the intervention. Our argument is that attrition was caused mainly from the quality of the data recordings. For example, long, dense, or unclean hair, and/or freezing computers were the most common troubles for the Matlab toolbox in logging the EEG recordings.

As the first check, we compare the share of nonvalid EEG recordings between T and C groups and find there are no statistical differences among them. (See the “Missing Share” row at the end of Table A7). Therefore, their missing status is not correlated with the treatment.

How different are the attrited and nonattrited subsamples? We provide information on means of variables for each subgroup in Table A8 in the Appendix. Estimations indicate that both groups are similar in most of the baseline variables except for the number of boys and area of residence (both are statistically different at 5%) and a category of household composition and mother’s education. However, after adjusting the tests for differences by the number of hypotheses we tested and FWER, 28