rafmacalaba/datause-encoder-data huggingface dataset:
system.

The remainder of the paper is organized as follows. Section 2 reviews the process of payment systems modernization in accession countries in the context of the broader structural changes to their banking systems. Section 3 elaborates the main empirical strategy and discusses reduced-form and di¤erence-in-di¤erence estimates used to identify the effect of payment reform on credit. Section 4 assesses the relevance of the two transmission channels. Section 5 provides structural estimates of the relationship between liquidity preference shocks and credit supply shocks. Section 6 concludes the paper.

# 2 Payment Systems and Banking Reforms 3 in Transition Economies

The payment systems of the former Eastern European bloc countries were paper-based settlement systems subject to long transport and processing delays (Sundararajan and Sensenbrenner (1994) and Balino, Dhawan, and Sundararadjan (1994)).

From the mid-1990s, in many economies in the region, signi...cant reforms of payment and settlement systems were introduced as part of the process of ...nancial sector reform, in an e¤ort to improve the speed, reliability and security of payments.

Typically, early reforms relied on readily available technologies to speed up transportation of documents, modify accounting practices, standardize operating procedures, introduce and improve dedicated courier services and expedite large-value payments by use of cable and telegraphic networks. In addition, measures were adopted to address ...nancial risk in payment systems, namely (1) exposure limits; (2) collateralization, (3) loss-sharing arrangements, and (4) shortening of time lags in settlements.

The advanced reforms we focus on in our empirical analysis comprised the introduction of automated arrangements for interbank transactions that o¤er ...nality, a high degree of security, and immediate, same-day, or value-dated settlement.<sup>6</sup> Appendix 1 provides further details on payment system reforms, country-by-country. Major reforms were dated using information contained in the ECB Blue Book on payment systems in accession countries (2002) (available online) as well as national central banks’ annual reports. In almost all cases, across the sample period there is a single clearly identi...able date of introduction of a modern automated interbank payment system. In a small number of countries, multiple systems supporting di¤erent types of interbank transactions were introduced consecutively over the sample period. In these cases, we focus on the date the ...rst of these systems is introduced.<sup>7</sup>

> 6With real time gross settlement (RTGS), payments are settled one by one and in real time – that is, with virtually no delay. Since payments are not netted out, however, gross settlement systems need larger amounts of intraday liquidity to facilitate prompt settlement of payments.

> 7Our results are not a¤ected materially by changes in the dating convention.

6
Table 3 Personality traits by treatment status and t-test results

||Trea|tment|Co|ntrol||
|---|---|---|---|---|---|
||# obs|Mean|# obs|Mean|p value|
|Agreeableness|1478|61.857|1392|60.944|0.1322|
|Conscientiousness|1478|67.896|1392|67.787|0.8736|
|Openness|1478|62.465|1392|62.098|0.6462|
|Emotional stability|1478|69.133|1392|68.671|0.5298|
|Extraversion|1478|57.410|1392|56.983|0.5959|

Mean equality is tested under two side and unequal standard deviation assumptions.

Table 4 Characteristics by treatment status (number of observations)

|Gender|Treatment|Control|
|---|---|---|
|Uninformed|2|4|
|Male|1099|1024|
|Female|329|329|
|Education|Treatment|Control|
|Uninformed|558|522|
|Primary|1|3|
|Secondary|334|302|
|Post-Secondary/Tertiary|585|565|

Gender reported had a large number of uninformed cases: 179 in treatment and 147 in control. The results shown above are gender imputed from their given names in the case of being uninformed.

18
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8 then expect income convergence to take place as they implement policies in tune with EU accession requirements. However, it is also the case that convergence is slow. As shown in Table 2, the four poorest current EU member countries have reduced only by approximately one-half their initial difference with the EU average income per capita during the last 34 years.

**Table 2: Evolution of GDP per Capita (in US$) among EU Member Countries.**

||**Summe**|**rs and Hes**|**ton Dat**|**a**-**Mark**|**5.6**||||**GNP-PPP**|**Estimate**|
|---|---|---|---|---|---|---|---|---|---|---|
||**1960**||1970||**1980**||1990||**1994**||
||Level||Level||Level||Level||Level||
|**EU High**|8530|Sweden|12018|Sweden|13809|Sweden|16106|Sweden|20270|Belgium|
|**EU Average**|5700||8484||10793||13168||16757||
|**EU Low**|2188|Portugal|3853|Portugal|5723|Portugal|7565|Greece|10930|Greece|
||Level|Share|Level|Share|Level|Share|Level|Share|Level|Share|
|||ofEU||ofEU||of EU||of EU||of EU|
|||Average||Average||Average||Average||Average|
|Greece|2414|42|4824|57|6662|62|7565|57|10930|65|
|Ireland|3918|69|5939|70|8056|75|10837|82|13550|81|
|Portugal|2188|38|3853|45|5723|53|8487|64|11970|71|
|Spain|3620|64|6813|80|8521|79|10802|82|13740|82|

Note: The EU Average is an unweighted mean of all current EU members.

Source: The GNP data for 1994 in PPP terms is from the 1996 World Development Report.

# **III. The Model**

_* Can we make reasonable guesses as to the time it will take CEE countries to converge to a certain threshold; say, for example, 75 percent of the EU average income per capita level?_ _* To what degree can government policy influence the time frame during which income gaps are bridged?_ _* What is the impact of different policy scenarios for an economy's average long-run growth rates?_ To answer these questions we develop and estimate a growth model for a large crosssection of countries, and we then apply the results to CEE countries. Following the above
# **THE CURRENT ACCOUNT AS A DYNAMIC PORTFOLIO CHOICE PROBLEM**<sup></sup>

**Tatiana Didier** _The World Bank_

**Alexandre Lowenkron** _Banco BBM_

## _JEL Classification:_ F21, F32, G11

## _Keywords:_ International Investment, Portfolio Choice, Current Account Balance

>  We are very grateful to Ricardo Caballero, Marcio Garcia, and Guido Lorenzoni for their guidance and support. For valuable comments and suggestions, we thank Nicolas Arregui, Suman Basu, Olivier Blanchard, Martin Evans, Roberto Rigobon, Ken Rogoff, Leandro Rothmuller, Carmen Taveras, Jose Tessada, Cedric Tille, and seminar participants at MIT, Bank of England, Federal Reserve Bank of New York, Federal Reserve Board, Georgetown University, Graduate Institute for International and Development Studies (University of Geneva), University of California at San Diego, University of California at Santa Cruz, University of Houston, and the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank. All errors are our own. Email: tdidier@worldbank.org and alexandrelowenkron@bancobbm.com.br.
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

|Utility|Official<br>standpost price<br>(US$/m<br>3)|Unofficial<br>standpost price<br>(USD/m<br>3)|Official price at<br>4m<br>3|Ratio of<br>unofficial to<br>official price|Ratio of official<br>piped water price<br>at 4m<br>3to official<br>standpost price|
|---|---|---|---|---|---|
|Drakenstein|—|—|0.00|—|—|
|Tygerberg|—|—|0.00|—|—|
|eThekwini|—|—|0.00|—|—|
|Johannesburg|—|—|0.00|—|—|
|SWSC|—|—|0.30|—|—|
|LWSC|0.19|1.67|0.56|9.03|3.02|
|NWSC|—|—|0.25|—|—|

_Source:_ AICD WSS Survey Database, 2007.

43
- 16 -

period, that a slight deterioration in the share of income in the lowest two quintals was to be expected.2Ui 4.06 The interesting aspect of the somewhat surprising improvement in the relative position of the poor is that one of the chief reasons for proscribing market-rate housing finance systems in developing countries is the concern that these systems will not only not serve the poor, but they will also lead to a deterioration in the position of the poor. See, for example, Shelter, (1980), The World Bank. Indeed, in many countries the reliance on the housing finance system to provide "affordable," i.e., very low nominal interest rates, is a direct result of distributional considerations prompted by concerns about the presumed unaffordability of market-rate mortgage credit. Indeed, the ceiling imposed on the amount that mortgage payments can be adjusted for inflation in Colombia is a good example of such a policy.

4.07 We are not suggesting that mortgage indexation was a cause of observed distributional results. Factors other than housing finance policy or financial policy clearly have more to do with the observed trends in income distribution. Nevertheless, the Colombian case is interesting in that it suggests that the development of market-oriented housing finance systems can make a significant contribution to economic growth contemporaneously with an improvement in the position of the lowest income groups. Hence, it does not appear that greater access to mortgage credit by moderate and upper income households is antithetical to the interests of the poor.

# C. Financia Policy and the Sources of Growth

4.08 Perhaps the most appropriate standard against which to evaluate the usefulness of including monetary balances in the production function is whether it yields any insights about the sources of growth not suggested by the traditional growth accounting perspective. The augmented production function performs well on this score. The inclusion of monetary balances suggests an important channel through which macroeconomic financial policy can affect growth: the channel is the affect of the level of inflation on the competitiveness of the resource mobilization process.

4.09 Since 1984, the last year of our estimation period, the interest rate cap on the mortgage indexation adjustment has been below the rate of inflation. As a result, since that time the mortgages supplied--which now account for 25 percent of financial assets--provide lenders no protection against increases in the inflation rate. If inflation increases, lenders will be unable to increase borrowers' repayment by as much. Hence these instruments will not provide the CAVs a means of competing for deposits. As a result, the CAVs will either (1) be unable to compete for funds with commercial banks and suffer disintermediation; or (2) if the banks do not compete for funds with the CAVs _Iv_ It predicts that the share of income going to the lowest two quintals would decline slightly from 11.4 to 11.2 percent of income. By 1985, in the seven largest cities the households in the lowest two quintals received 12.8 percent. See Reyes (1987).
with the nonfinancial sector in Germany, France, Italy, and Japan-all countries in which insurance companies and pension funds played a very small part in the financial system (at the time). But even in the Netherlands, Sweden, Switzerland, the United Kingdom, and the United States, where institutional investors were more developed, thrift deposit institutions accounted for between one-fifth and one-third of all financial sector liabilities (table 9.1). Also, the share of large commercial banks was not very large even in countries such as the Netherlands and Sweden, where banking concentration was very high.

In the United Kingdom and the United States, buildingasocieties and savings and loan associations played an important role, but credit cooperatives did not emerge, and savings banks accounted for small shares of all resources mobilized (table 9.1). In contrast, savings banks were very large in Germany and Switzerland and only slightly smaller in Italy, Sweden, and France. Credit cooperatives were large in Japan, France, and the Netherlands and to a lesser extent Germany and Italv. Postal savings banks, including postal giros, were more important in France than in Japan, but they were also very large in Italy and the Netherlands. Finally, credit unions were large, at least as formal institutions, only in the United States.<sup>3</sup> Why did thrift deposit institutions emerge in most European countries and the United States and how were they able to grow and acquire suchl importance in the financial systems of so many countries? What was their contribution to financial sector development and most broadly to economic development? And what is their relevance for the financial systems of developing countries, many of which have encouraged the development of similar institutions but with far less satisfactory results? These questions are examined after a description of the development of thrift deposit institutions in different countries.

;. Note: The data in the table are based on published statistics in different countries. If the institutions were very small, statistics were not collected or published. Such was probably the tone of credit unions in the United Kingdom and building societies in France.

4
**Table 13: Number of projects and expected mitigation impact by 2012 by ECA host country**

||Agricu|lture<br>Eeted|Fore|stry<br>Eeted|All Se|ctors<br>Eeted|
|---|---|---|---|---|---|---|
|Host country|Number of<br>projects|xpc<br>mitigation<br>(ktCO2e)|Number of<br>projects|xpc<br>mitigation<br>(ktCO2e)|Number of<br>projects|xpc<br>mitigation<br>(ktCO2e)|
|Albania|0|0|1|161|3|215|
|Armenia|1|377|0|0|8|1,261|
|Azerbaijan|0|0|0|0|7|16,545|
|Belarus|0|0|0|0|0|0|
|Bosnia & Herzegovina|0|0|0|0|0|0|
|Bulgaria|1|56|1|586|38|17,510|
|Croatia|0|0|0|0|0|0|
|Czech Republic|0|0|0|0|59|6,111|
|Estonia|1|89|4|148|15|1,745|
|Georgia|0|0|0|0|5|2,398|
|Hungary|3|1,056|4|2,902|13|9,613|
|Kazakhstan|0|0|0|0|0|0|
|Kyrgyzstan|0|0|0|0|0|0|
|Latvia|0|0|0|0|0|0|
|Lithuania|0|0|0|0|16|8,150|
|Macedonia|0|0|1|0|5|1,859|
|Moldova|1|21|2|1,077|9|3,561|
|Poland|1|700|0|0|24|16,726|
|Romania|0|0|2|866|19|20,875|
|Russia|2|431|8|3,638|134|301,960|
|Serbia|1|47|1|26|6|990|
|Slovakia|0|0|0|0|1|63|
|Slovenia|0|0|0|0|0|0|
|Tajikistan|0|0|0|0|0|0|
|Turkey|0|0|0|0|0|0|
|<br>Turkmenistan|0|0|0|0|0|0|
|Ukraine|6|1,005|1|176|83|111,704|
|Uzbekistan|0|0|0|0|16|13,994|
|Total|17|3,781|25|9,579|461|535,280|

Source Risoe (2011) and authors‘ calculations 50
indices. The SA SQI, which is not normalized on the sample, exhibits a weaker relationship with subjective indicators than the WA SQI, particularly when household fixed effects are included. This is likely explained by the fact that the WA SQI is normalized on the sample and, therefore, is more a measure of relative soil quality.

# _3.2.2 Spectral Analysis & Geospatial Data_

To provide further confidence in the value of conducting spectral soil analysis on plot-level soil samples, a brief comparison is made with publicly available geospatial data. Admittedly, comparison may be made with more than one source of geospatial data. However, the AfSIS data have among the highest resolutions currently available in public data sets (250m) for Ethiopia (for details see Hengl et al, 2015). They also may be the most comparable to the LASER data in a methodological sense considering both are conducted by the ICRAF. For these reasons, the comparisons made here may present an upper bound of comparability, at least in this particular context. Values were extracted from the AfSIS geospatial data set using the GPS coordinates of the specific plots. Comparison is made between organic carbon content (percent) as measured by plot-level spectral testing and that indicated in the AfSIS map.

Table 7 summarizes the mean organic carbon content observed in LASER and AfSIS. In the full sample, the difference in means between the two data sets is statistically different from zero at the 1 percent level. Although the magnitude of the difference may be immaterial depending on the research question of

**_Table 7._** _Soil Organic Carbon (%) in LASER and_

||LASER<br>Orga|AFSIS<br>nic Carbo|Difference<br>in Means<br>n (%)|
|---|---|---|---|
|All Top-Soil||||
|Mean|3.350|3.476|***|
|Standard Dev.|1.216|1.192||
|Correlation||0.586||
|N||1674||
|EAs with lowest ~~2~~5|% variance|||
|Mean|2.775|2.884|**|
|Standard Dev.|1.200|1.045||
|Correlation||0.715||
|N||412||
|EAs with highes~~t 2~~|5% variance|||
|Mean|3.739|3.767|-|
|Standard Dev.|1.418|1.189||
|Correlation||0.387||
|N<br>*****|*|429||

*** p<0.01, ** ~~<u><mark>p<</mark></u>~~ 0.05, * p<0.1 Difference in means: T-test

interest, it is important to note that the correlation between the two measures is only 0.586.

Concerns with the use of geospatial data are often related to their (in)ability to capture variation in soil properties within small areas. Indeed, a closer look at the correlation between the spectral analysis and the geospatial data reveals that the correlation falls when limiting the sample to the EAs with the highest quartile of variance in carbon content (as measured by spectral analysis). In EAs with the highest variance, correlation is only 0.387, while in EAs with the lowest 23
bring about Pareto gains.

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tion is desirable under any realistic parameterization. Second, it establishes that, when increasing tax revenues is not feasible, the optimality of diverting funds from government investment projects to redistributive programs depends on the rate of return to government investment, the administrative cost of transfers, the elasticity of labor supply and the social aversion to inequality.

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18
# **Financial Globalization: A Glass Half Empty?**

Facundo Abraham Sergio L. Schmukler<sup>*</sup>

**JEL Classification Codes:** F21, F36, F61, F62, F63, F65

**Keywords:** capital flows, developing countries, emerging markets, institutional investors, international capital markets, international integration

> * This paper was prepared for the <u>Handbook of Finance and Development, edited by Thorsten Beck and</u> Ross Levine. We are grateful to Thorsten Beck, Ross Levine, and Luis Servén for their very useful comments. Research support came from the World Bank’s Development Economics Department, the Knowledge for Change Program (KCP), and the Strategic Research Program (SRP). The authors work at the World Bank Research Department.

Email addresses: facundoabraham@gmail.com, sschmukler@worldbank.org.