rafmacalaba/datause-encoder-data huggingface dataset:
# **Preface**

Rapid advances in technology have led many in the development community to examine how it can be used to lower costs and increase access to financial services.<sup>1</sup> The two case studies presented here focus on the issues that arise when government securities are sold directly to small retail investors using electronic channels, especially the Internet. Governments take this approach in hopes of expanding access to financial services and increasing competition among the savings products offered by commercial banks. Such programs can also be designed to improve debt management and develop debt markets, especially in emerging markets.

Although the two models analyzed in these studies—one from the United States, one from the Philippines—differ in many ways, both show how technology can be used to advance debt management policies and foster financial sector development. The case studies also demonstrate the potential of replicating these systems for other services and business models. The carefully developed systems in both countries suggest that although electronic distribution of securities poses risks, there are also enormous potential benefits—making it an avenue worth pursuing.

## **Why pursue electronic retail distribution of government securities?**

Why would governments want to sell their securities directly to retail investors? And why over the Internet? These questions and this approach have close links to three policy areas: debt market development, public debt management, and overall financial sector development. The implications for each are discussed in greater detail in the case studies but are summarized here.

## _Links to debt market development_

Debt market development may be the area most affected by the electronic distribution of government securities to retail investors. The convenience and low cost of using the Internet to sell these securities greatly facilitate development of the retail investor base by providing direct access to a much greater variety of citizens. The Internet also provides a venue for disseminating basic information on government securities, educating investors. Efforts to expand and educate the retail investor base may depend on other reforms in debt markets, however, as well as on the overall level of development and features of a country’s securities markets.

The case studies suggest that electronic distribution can increase access to securities, especially in countries where telecommunications, Internet, and cellular infrastructure are growing quickly. But active marketing is essential, especially in remote or disadvantaged areas. With advances in technology, development of the retail investor base can be pursued more actively as an alternative to traditional channels involving the marketing of fixed income funds to such investors, often by financial conglomerates.

> 1 See Glaessner, Klingebiel, Claessens, World Bank (2000), (2001), UNCTAD (2002), BIS (2001), OECD (2000)

3
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## **Figure 1. Summary of studies of crude oil price forecast performance**

# **A. Forecasting methodologies evaluated**

<!-- Start of picture text --> Number of studies<br>20<br>16<br>15 12<br>10<br>6 6<br>5<br>0<br>C. Time period for evaluation<br>Number of studies<br>14 12<br>12 11<br>10 8<br>8<br>6<br>4<br>2 0<br>0<br>Futures<br>Machine Multivariate models Univariate models<br>learning models<br>Before 2008 2014 Before After the pandemic<br>September Before July January 2020<br><!-- End of picture text -->

# **C. Time period for evaluation**

Number of studies

# **E. Forecast horizon**

<!-- Start of picture text --> Number of studies<br>16 15<br>14<br>12 11<br>10<br>8<br>6 5<br>4<br>2<br>0<br><3 months <=1 year > 1 year<br><!-- End of picture text --> _Source_ : World Bank.

<!-- Start of picture text --> B. Benchmarks used for evaluation<br>Number of studies<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>25<br>25<br>20<br>15<br>10 8<br>5 4 3<br>0<br>Futures<br>No change Multivariate models Univariate models<br><!-- End of picture text -->

# **D. Data frequency**

<!-- Start of picture text --> Number of studies<br><!-- End of picture text --> <!-- Start of picture text --> 25<br>22<br>20<br>15<br>10 8<br>5<br>1<br>0<br>Daily/Weekly Monthly Quarterly<br><!-- End of picture text -->

# **F. Outcomes of forecast performance evaluation**

<!-- Start of picture text --> Number of studies Machine learning models<br>20 Multivariate models<br>Univariate models<br>15 Futures prices<br>10<br>5<br>0<br>Better Worse Better Worse Better Worse<br>No change is Futures are ... Univariate<br>... models are ...<br><!-- End of picture text --> A. Number of studies that examine the forecast performance of futures prices, machine learning techniques, multivariate models (including structural VARs and Bayesian VARs), and univariate time series models against a benchmark. The two studies that examine both multivariate and univariate models are shown in the category for multivariate models.

B. Number of studies that benchmark forecast performance against latest spot prices (“no change”), futures prices, multivariate models (including structural VARs and Bayesian VARs), and univariate time series models. Studies that benchmark against both no-change forecasts and futures prices (9) and against both no-change forecasts and univariate models (2) are shown in the category for no-change benchmarks.

C.-E. Number of studies by end date of sample period (C), data frequency (D), and forecast horizon (E).

F. Number of studies in which benchmark approaches on the x-axis (no-change forecasts, futures, univariate models) had better or worse forecast performance than the approaches listed in the legend (futures prices, univariate models, multivariate models, and machine learning techniques).

20
of 14% in 1990 to 29% in 2007. Both high- and middle-income countries have seen deepening of private bond markets; unfortunately, we do not have data available for low-income countries; but reports on Africa show an uptick in private bond issue activity in the years up to 2008 (Beck, Fuchs and Uy, 2009).

In summary, there has been a deepening of financial systems across the world, with much of the deepening, however, concentrated in high-income countries. This deepening has taken place in banking as much as in stock and bond markets. In section VI, we will explore whether this deepening has been stronger for banks or for markets across different income groups and geographic regions.

# **III. The Banking System – Size, Structure, Efficiency and Stability**

The banking system still constitutes the largest part of the financial system in most countries, especially in emerging and developing markets. We have therefore included an array of indicators, measuring the size, structure, efficiency and stability of banks across countries and over time.

In addition to the indicators discussed in the previous section, the database includes several other indicators of the size of financial intermediaries. Specifically, based on raw data from the IFS, the following three indicators measure the size of the three types of financial institutions relative to GDP:

# - **Central Bank Assets to GDP**

# - **Deposit Money Banks Assets to GDP**

# - **Other Financial Institutions Assets to GDP**

These measures give evidence of the importance of the financial services performed by the three types of financial institutions relative to the size of the economy. The assets include claims on the whole nonfinancial real sector, including government, public enterprises and the private sector. The sum of these three measures would indicate the total claims that financial intermediaries have on nonfinancial domestic sectors, relative to GDP, and thus constitute a comprehensive measure of financial intermediation.

6
# Measuring the Risk of Corruption and Its Price Impact in North Macedonia 20112022

Mihaly Fazekas<sup>1,3</sup> , Aly Abdou<sup>3</sup> , Klea Ibrahimi<sup>1</sup> , Bence Tóth<sup>2,3</sup> , Zdravko Veljanov<sup>1,3</sup>

- 1-Central European University

- 2-University College London

- 3-Government Transparency Institute

This paper is a product of the Macroeconomics, Trade and Investment Global Practice, conducted in the context of the World Bank North Macedonia Public Finance Review (P179795), led by Sanja Madzarevic-Sujster, Senior Economist. The authors thank the World Bank for funding this research project. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at FazekasM@ceu.edu.

_Keywords_ : public procurement, competition, corruption, network analysis, North Macedonia _JEL classification_ : H57; D73; L14
costs and number of days is observed for a 60 percent reduction, but in this case only 7 countries are classified as reformers in the case of costs and 23 for number of days (Table 3).

However, even a small change in the number of procedures, such as a 20 percent reduction, produces significant results. The magnitude and significance of the procedures reform coefficients peak at 40 percent. However, with higher cutoffs, such as a 50 or 60 percent, we misclassify actual reforms of lower magnitude, and hence the size of the coefficient drops (see the discussion of Type I errors in footnote 3). This does not imply that larger reforms are ineffective. It simply suggests that in the case of procedures even smaller reforms are significant, with an optimal reduction of about 40 to 50 percent. For minimum capital requirements we find that a 20 percent reform, as well as 40-60 percent reforms, yield significant coefficients.<sup>15</sup> To gauge the economic magnitude of these results in a way that is comparable with previous literature we convert the increase in new density (our main measure, which is the ratio of new firms to total population) to the percent increase in new registrations.<sup>16</sup> The increase in new registrations varies from country to country, depending on actual pre-reform number of new registrations and the local population. We focus on OECD averages for comparability with previous studies. We find that on average across OECD countries in our sample, a reduction of 50 percent or more in the number of procedures leads to an average increase of 14 percent in the number of new registrations, while similar reductions in the number of days and costs lead to an average 19 percent and 30 percent increase in new registrations of limited liability firms,

> 15 The results on minimum capital are somewhat weaker throughout the paper, perhaps because many countries do not impose minimum capital requirements or because these requirements are not binding. In addition, the changes to minimum capital requirements are often discrete in which they are completely eliminated, rather than reduced. This is visible on the distributions of changes to minimum capital, which has significant density in the left tail of the distribution.

> 16 We calculate the increase in new registrations by taking actual new density in a country, add the reform coefficient, which shows an increase in new density as a result of a reform, for example at 50 percent reform we obtain 0.23 for procedures or 0.35 for days and 0.57 for costs (Table 6). Then we multiply these new densities by population to obtain  new registrations after the reform and calculate a percent change in new registrations.

19
**Table 5: Intrahousehold heterogeneity of impacts by gender and age: all eligible households** **<u>Fixed Effects Model</u>**

||||N|umber of hour|s per week chi|ld between 6|and 15 years wo|rked in :||
|---|---|---|---|---|---|---|---|---|---|
|COEFFICIENT|nr obs/hh|non-<br>agricultural|agriculture &<br>livestock|agriculture|livestock|chores|domestic<br>work|all econ<br>activity|all work|
|male*itt|4253/1594|-0.00231|-1.179**|-0.824*|-0.374**|-0.0395|-0.223|-1.256**|-1.518**|
|||(0.11)|(0.47)|(0.45)|(0.17)|(0.25)|(0.27)|(0.49)|(0.63)|
|female*age1015*itt|4253/1594|0.209|0.575|0.264|0.311|-0.180|0.663*|0.707|1.191|
|||(0.182)|(0.488)|(0.447)|(0.197)|(0.302)|(0.350)|(0.511)|(0.723)|
|male*age1015*itt||-0.0281|-2.786***|-2.014***|-0.805***|-0.154|-0.0823|-2.840***|-3.076***|
|||(0.133)|(0.784)|(0.725)|(0.215)|(0.330)|(0.234)|(0.794)|(0.906)|
|male*itt||0.137|0.743|0.476|0.267|-0.0584|0.217|0.775|0.933|
|||(0.132)|(0.542)|(0.498)|(0.226)|(0.380)|(0.364)|(0.591)|(0.883)|
|P-value female*age1015*itt = male|*age1015*itt|0.586|0.001|0.015|0.000|0.909|0.088|0.001|0.000|

# **<u>Random Effects Model</u>**

||||N|umber of hour|s per week chi|ld between 6|and 15 years wor|ked in :||
|---|---|---|---|---|---|---|---|---|---|
|COEFFICIENT|nr obs/hh|non-<br>agricultural|agriculture &<br>livestock|agriculture|livestock|chores|domestic<br>work|all econ<br>activity|all work|
|male*itt|4253/1594|-0.0580|-1.741***|-1.296***|-0.413***|0.229|-0.142|-1.755***|-1.478**|
|||(0.11)|(0.48)|(0.46)|(0.15)|(0.22)|(0.24)|(0.52)|(0.61)|
|itt||0.192**|-0.104|-0.288**|0.177|-0.707**|0.0735|0.00238|-0.747|
|||(0.090)|(0.23)|(0.12)|(0.19)|(0.35)|(0.25)|(0.26)|(0.63)|
|female*age1015*itt|4253/1594|0.181|-0.0594|-0.0295|0.0467|-0.255|0.810**|0.0364|0.805|
|||(0.151)|(0.291)|(0.221)|(0.201)|(0.285)|(0.329)|(0.344)|(0.614)|
|male*age1015*itt||0.0502|-3.405***|-2.851***|-0.672***|-0.0134|0.163|-3.199***|-3.011***|
|||(0.114)|(0.817)|(0.782)|(0.184)|(0.336)|(0.194)|(0.824)|(0.914)|
|female*itt||0.0864**|-0.0664|-0.269***|0.151|-0.558*|-0.399*|-0.0165|-1.208**|
|||(0.0434)|(0.201)|(0.103)|(0.166)|(0.321)|(0.213)|(0.219)|(0.543)|
|male*itt||0.106***|0.117|0.0591|0.153|-0.471|-0.161|0.0917|-0.489|
|||(0.0384)|(0.255)|(0.183)|(0.144)|(0.305)|(0.176)|(0.314)|(0.494)|
|||0.529|0.000234|0.00199|0.00407|0.253|0.204|0.000384|0.00115|
|P-value female*age1015*itt = male|*age1015*itt|0.529|0.000|0.002|0.004|0.253|0.204|0.000|0.001|

_NOTE:_ Standard errors corrected for clustering at community level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variables corrected for gender-specific age trends by regression of outcomes in control and on series of age dummies for each gender. All estimates include a gender dummy, and interactions for gender and age. All households with at least 2 children between 6 and 15. age1015 is a dummy variable = 1 for children between 10 and 15. Fixed effects model controls for household fixed effects, random effects model controls for household random effects.

25
(f) The dwelling is not improvised, and the walls are made of brick (or better).

Based on the HIECS 2004/2005 data, 6.2% of the Egyptian population met all of these basic needs, while 14.2% had three or more unmet basic needs. Defining the poor as those with at least three unmet basic needs, we see that only SFD health and potable water projects were targeted to villages with disproportionately high levels of this type of poverty.

**<u>Table 18.  Poverty Rates in Areas Served by SFD Projects</u>**

||Headcount po|verty rate (%)|t-statistic for<br>difference in|% of household<br>unmet ba|s with at least 3<br>sic needs|
|---|---|---|---|---|---|
||No SFD<br>project|With SFD<br>project|poverty rates|No SFD<br>project|With SFD<br>project|
|Education|19.8|15.0|14.2|14.5|10.1|
|Wastewater treatment|19.6|12.8|9.9|14.2|13.2|
|Environment|19.5|20.1|-1.7|14.2|13.8|
|Roads|19.5|21.8|-5.0|14.3|13.2|
|Health|19.5|22.1|-6.0|14.2|14.7|
|Microcredit|18.8|23.1|-18.1|14.1|14.9|
|Potable water|18.2|30.2|-38.6|13.5|19.5|
|Microcredit*||45.6||||
|Anyintervention|18.6|21.6|-15.6|14.2|14.3|

Note: * Based on household-level data. All other figures are based on village/ward-level data from HIECS 2004/2005. _Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

These numbers likely understate the extent to which SFD programs reach poor households if, as is plausible (although not certain), the actual beneficiaries of SFD programs in a given village are disproportionately poor. In the case of microcredit it is possible to identify the households that avail of the credit, using the HIECS 2005/2005 household data. The relevant numbers are set out in Table 19, and they show that the poorest 10% of the population account for 24.9% of all microcredit loans; furthermore, the headcount poverty rate among microcredit users was 45.6%, or far above the national rate of 19.6% (in 2004-05). Without further information on the size of loans, we cannot tell what proportion of the _value_ of microcredit goes to the poorest households, but the indications are that microcredit is very highly targeted to the poor. However, it must be noted that these numbers refer to all microcredit, and not specifically to SFD-supported microcredit, which individuals cannot identify separately, mainly because SFD support for microcredit operates indirectly via NGOs and financial intermediaries.

# **Conclusions**

In our examination of the Egyptian Social Fund for Development, we have focused on three key questions: How large is the impact of the SFD interventions? Have the benefits been commensurate with the costs? And have the programs been targeted successfully to the poor?

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 33 of 36_
**Table 5: Household bargaining**

||Household<br>decision<br>making|Business<br>decision<br>making|Hours worked<br>by other<br>family|No children,<br>no elderly<br>|Spouse<br>Present at<br>Interview|
|---|---|---|---|---|---|
||Adjusted|Profits||Adjusted|Profits|
||FE|FE|FE|FE|FE|
||(2)|(3)|(4)|(5)|(1)|
|Treatment Amount|8.01***|8.41***|0.44|7.60|7.77***|
||(3.11)|(2.99)|(1.26)|(4.50)|(2.83)|
|Amount*Female|-8.14|-8.62**|-2.09|-8.93|-8.53**|
||(4.47)|(4.24)|(4.96)|(5.93)|(3.97)|
|Spouse present at interview|||||-4.33*<br>(2.33)|
|Spouse present* female|||||2.17<br>(2.75)|
|Decision making power in household|-0.85|-1.40||||
|/ business|(1.76)|(1.48)||||
|Observations|3486|3486|3799|1534|3697|
|Number of Firms|332|332|365|152|365|
|Notes:||||||

Robust Standard errors in parentheses, clustered by enterprise *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. Decision making power in the household is measured as the sum of 4 purchase decisions in which the owner is involved. Decision making power in the business indicates the owner makes input and equipment purchase decisions without input from his/her spouse.

**Table 6: How do the characteristics of male and female owners differ?**

||Males<br>**Mean**|Females<br>**value**|**T-test**<br>**p value**|
|---|---|---|---|
|Ever had a formal loan|0.23|0.23|0.99|
|Number of Wage Workers|0.55|0.87|0.00|
|Asset Index|-0.20|0.20|0.04|
|Years of Education|8.57|9.41|0.01|
|Digitspan Recall|5.88|5.61|0.06|
|Entrepreneurial Self-efficacy|31.51|30.79|0.22|
|Polychronicity|7.76|7.32|0.06|
|In Self-employment to care for children|0.40|0.49|0.09|
|In Self-employment for business growth|0.31|0.35|0.55|
|Risk Aversion|0.37|-0.07|0.01|

38

-  -
employment definition. We split the sample to productivity quartile within industry‐year and take the model quartile for each firm.

Third, we attempt to measure the geographic span of spillovers by constructing the variables at different levels of geographic aggregation. Transferring tacit knowledge, for example, may require face‐to‐face interactions through personal relationships or labor mobility. Supplier‐buyer relationships may operate via somewhat longer distances. The previous regressions include county‐level measures, which are relatively broad geographic aggregates. In contrast, microregions are very narrowly defined, often interpreted as local labor markets. Recall that there are 20 counties and 174 microregions in Hungary. Consequently, knowledge spillovers via labor mobility are likely to show up at the microregion level, while broader supplier‐buyer relationship may be present at the county level.<sup>26</sup> Table 7 presents two sets of regressions. In the first and third columns, we include only the microregion‐ level measures. This exercise is the same as our main regression, but the explanatory variables are calculated at a more detailed level of geographic aggregation. In the second and fourth columns we include the microregion‐level measures together with the county‐level measures.

Microregion‐level variables have very similar coefficients to county‐level ones, suggesting that the channels at small distances are not systematically different. Importantly, the positive association between HGF share in buyer industries is positively associated with productivity growth both at the microregion and the county levels. The smaller point estimates and weaker statistical significance may reflect the noisier nature of the microregion‐level measures, which can result from strong granularity at this fine level of aggregation.

This exercise also serves as a robustness check. Our results are robust to different geographic aggregation levels, and the forces we quantify act both at very short and somewhat longer distances.

# **Table 7 Microregion‐level measures**

||||**Productivity (**<br>|**3‐year change)**||
|---|---|---|---|---|---|
||**Level**|**Laborpro**|**ductivity**|**TF**|**P**|
|HGF Downstream share|microregion|0.026***|0.012***|0.030***|0.013***|
|||(7.635)|(3.330)|(8.012)|(3.508)|
|HGF Upstream share|microregion|0.005*|0.002|0.004|0.002|
|||(1.778)|(0.547)|(1.143)|(0.683)|
|HGF Intra‐industry|microregion|‐0.008***|‐0.003|‐0.014***|‐0.008**|
|share||(‐2.659)|(‐1.024)|(‐4.009)|(‐2.301)|
|HGF Downstream share|county||0.029***||0.036***|
||||(6.671)||(6.699)|
|HGF Upstream share|county||0.015***||0.009*|
||||(3.744)||(1.750)|
|HGF intra‐industry|county||0.010***||0.020***|
|share||||||
||||(2.977)||(5.062)|

26 Note that HGFs in the microregion are already included in the county‐level measure, hence their effect is captured to some extent by those variables.

16