rafmacalaba/datause-encoder-data huggingface dataset:
# Figure VI-8: Oil and Credit Cycles

<!-- Start of picture text --> Real Oil Prices<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real oil price (y/y)<br>50<br>0<br>-50<br><!-- End of picture text --> _Source: BP historic data_ <!-- Start of picture text --> Saudi Arabia: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<br>10<br>Percent 0<br>-10<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Iran: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<br>10<br>5<br>Percent 0-5<br>-10<br>-15<br><!-- End of picture text --> <!-- Start of picture text --> Kuwait: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<br>10<br>5<br>Percent 0-5<br>-10<br>-15<br><!-- End of picture text --> <!-- Start of picture text --> Bahrain: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<br>20<br>10<br>0<br>Percent<br>-10<br>-20<br><!-- End of picture text --> 54
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Overstaffed and faced with falling revenue, the company faces bankruptcy unless it can create and implement a turnaround plan.

**Figure 7. Telecommunications coverage and the efficient market gap**

<!-- Start of picture text --> Congo, Dem. Rep. of<br>Madagascar<br>Zambia<br>Congo, Rep. of<br>Niger<br>Mozambique<br>Chad<br>Botswana<br>Namibia<br>Lesotho<br>Tanzania<br>Ethiopia<br>Cameroon<br>Cape Verde<br>Burkina Faso<br>Malawi<br>Sudan<br>Kenya<br>Senegal<br>Côte d’Ivoire<br>Benin<br>Ghana<br>Uganda<br>Rwanda<br>Nigeria<br>South Africa<br>0% 20% 40% 60% 80% 100%<br>% of population<br>coverage gap efficient market gap existing access<br><!-- End of picture text --> Source: Mayer and others 2009.

Fixed line density in the Republic of Congo is low and declining. Coverage was 0.41 subscribers per 100 people in 2005, less than half of the resource-rich benchmark. That figure dropped to 0.35 subscribers per 100 people in 2007 from 0.24 in 2008. Moreover, the quality of the fixed line network is rapidly deteriorating, and it is becoming a constraint to expanding and improving Internet services. Much of the telecommunications network—including the national microwave transmission backbone and telephone exchanges in Brazzaville—was destroyed in fighting. Not surprisingly, costs are high. The price of a three 16
experience faster capital accumulation and higher productivity. The supply side equation depends on the following three identities: To choose the estimation strategy, we ran the following standard tests. First, we use the _Hausman test_ to find out the appropriateness of the fixed or random-effects models. With the standard null hypothesis that differences in coefficients are not systematic, the difference between fixed and random effect specification of the same model rejects the null hypothesis (with א<sup>2</sup> =28.69 and >א<sup>2</sup> =0.000) and points us to use a random effect model. We also run the Breusch and Pagan Lagrange multiplier test for random effects with the hypothesis that the Var(u) = 0. The |ppc pp| test statistics (א<sup>�2</sup> = 9218.4 and = 0.000), reject the null hypothesis and direct us to choose a random effects model. |ppc pp| > א<sup>�2</sup>

To identify whether there is any effect of group-wise heteroskedasticity, we test the modified Wald statistics. The null hypothesis we tested is H0: 2 2 . We reject the null ii σσ = σσ ffc ∀ xx 20
its labor force. The aggregate level of land input is measured by its quantity of arable land. All of these variables are taken directly from BESD. Data on another possible measure of aggregate labor input, employment, are unfortunately not generally available for the countries and the time period under study.

The construction of the physical and the educational capital stocks deserves so,..e explanation. The estimation of a time series of physical capital stocks for each country begins with the compilation and/or estimation of a time series of gross domestic investments (or gross domestic **fixed** investments whenever available) for each cotintry for as long a time period as possible.<sup>t</sup> Once such a time series is available, the time series of physical capital stocks can be estimated by the perpetual inventory method given the capital stock of any benchmark year, assuming an annual depreciation rate of, say, S percent.<sup>2</sup> Unfortunately, benchmark year capital stocks are not generally available for the countries in the sample. It has therefore been necessary to estimate a benchmark year capital stock for each country.

Tle procedure use-d in this study Is as follows. Both the ca; tal stocks and gross domestic (fixed) investments of all coult-ies in the sample are assumed to be zero in 1945. Linear interpolation is used to fill out Ehe missing years of data on gross domestic (fixed) investments in 1980 U.S. dollars up to 1960 or the l,rst vear for which data are available. On the basis of the ;;aal series of investments so obtained, the perpetual inventory method is used to generate the time series of capital stocks, assuming that they 'Gross domestic investment is the sum of gross domestic fixed investment and net change in stocks (inventory). It is generally larger than gross fixed investment, though not necessarily so. Tbkuis a time series of capital stocks constructed with a gross domestic investment series is likely to be different from that constructed with a gross domestic fixed investment series. However, as long as the ratio of gross domestic fixed investmen **t** o gross domestic investment is approximately constant for each country within the sample period, the estimation results for the aggregate production function will not be materially affected.

MThe formula used to construct the capital stock is: K, = 0.95 -,., + 1, 9
33

# Annex Table 4. Cost Structure In German Industry (1990)

||||||Share o|f env.|
|---|---|---|---|---|---|---|
|Sector||Costs|Labor<br>Cosrt|Energy<br>in toul<br>investments|Openn<br>*|eu Environmental<br>cosu(1988)|
|||||()|||
|* Man|ufacturer|25.2|2.2|4.8|29|0.7|
||Raw Materials and<br>||||||
||Production Goodl|20.4|4.4|11.3|28|U.S.|
||Iron & Steel<br>|24.7|10.1|13.7|34|na..|
||Chemicals|24.5|3.8|12.9|42|2.2|
||Petroleum|3.2|1.1|15.3|4|1.1|
||Paper<br>|18.0|8.6|10.3||n.s.|
||Metau|16.0|_5.1_|12.3|29|1.6|
|(lal.|iron, basic metals)||||||
||InveamntfGona|30.1|1.1|1.9|38|na|
||Machinery|32.5|1.1|1.4|44|0.3|
||Consumotion Goods|27.2|2.2|2.7|20||
||**_Ceramics_**<br>|43.7<br>|4.9<br>|3.1|33|**n.a**|
||**Glau**<br>|29.6|6.2|6.2|30|**na**|
||Paper<br>|23.2|2.2|2.4|IS|_)_|
||Clotbing<br>|23.0|0.7|2.4|20|_)_0.7|
||Textiles|24.7|2.9|0.6|29|<br>_)_|
||.Food|12.8|1.6|2.7|9|0.4|
|Minina||44.8|10.4|20.7||3.9|
|**E**lectric|itv etc.|13.4|-|18.1||4.2|

Source: Statisiaches lahrbuch, _Germany_ **various** isues, Ausgewahite Ergebnisse zur Umweltokonomischen Gesamtrechnung 1975 bis 1990, Statisches Bundesamt. * openness is defind **as** the share of exports in total sales (%).
Table 13: Falsification Exercise: Determinants of Living in an Extended Family - Albania (2002 - 2012)

|||Dependent|Variable:||
|---|---|---|---|---|
||Livi|ng in an Ex|tended Fa|mily|
||(1)|(2)|(3)|(4)|
|Boy frst|0.0065|0.0247|0.0159|0.0064|
||(0.0131)|(0.0165)|(0.0110)|(0.0159)|
|Boy second|-0.0002|0.0180|0.0092||
||(0.0167)|(0.0156)|(0.0114)||
|Two girls|-0.0187|||-0.0186|
||(0.0228)|||(0.0156)|
|Two boys||-0.0176||0.0004|
|||(0.0227)||(0.0166)|
|Samesex|||-0.0091||
||||(0.0114)||
|R-squared|0.2982|0.2665|0.2982|0.2982|
|Observations|7404|7404|7404|7404|
|Mean dep. var|0.381|0.381|0.381|0.381|
|Survey FE|Yes|Yes|Yes|Yes|
|District FE|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|

_Notes:_ Standard errors clustered at the PSU level are in parentheses. Other covariates in the models are the following: _Age, Age Squared, Age at First birth, Years of Education_ and also indicators for _Boy 1st_ and _Boy 2nd_ . Each model is estimated with survey and district fixed effects. * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%.

46
Nonetheless, as we will see, these advantages come at a cost: not all the effects of aid can be perceived at the project level. This is the reason why the success of projects has to be explained not only by characteristics of the country traditionally included as explanatory variables (such as income per capita or quality of institutions), but also by variables reflecting “external effects” of aid volume on the success of projects, which we now consider.

# _Taking into account the decreasing returns of aid_

Decreasing returns can be assumed for aid as for other expenditures. However there also could be some threshold below which the marginal return is increasing, giving some legitimacy to the big push thesis. In macro economic works examining the aid and growth relationship, decreasing marginal returns to aid have been examined through a possible negative coefficient on the squared aid level (Collier and Dollar 2001) and have been a matter of debate (Hansen and Tarp, 2001), while few evidence have been made of increasing returns (with the exception of Gomanae et al. 2003 who found the existence of a threshold below which aid would have no effect). In our micro economic study of the determinants of the success of projects, as in Dollar and Levine (2005) decreasing (increasing) returns will be captured through a negative (positive) coefficient of the aid level. Possible thresholds of successively increasing, then decreasing returns could be tested though the coefficients on the level of aid and its square value.

# _The negative impact of instability evidenced both at the macro and micro level_

There is now a relative consensus about the negative effects of economic instability on growth and poverty reduction. Both aspects are considered in Guillaumont (2006); main evidences on the effects on growth include Ramey and Ramey (1995), Hnathovska and Loayza (2004). At a micro economic level, risk averse agricultural producers might refuse to adopt more profitable but riskier techniques, or can forgo educating their children following a negative shock. At a macro economic level, the main channels through which exogenous sources of economic instability hinders growth include its transmission to the real exchange rate, to public finance, resulting in chronic inflation and public indebtedness, or to the instability of public investment, leading to a lower average rate of return (Guillaumont et al. 1998).

Instability has been found to increase poverty not only through lower growth, but also through its effect on income distribution. (Agenor 2001, Laursen et al. 2004, Guillaumont 2006, Guillaumont and Korachais 2006). In most of these macro economic papers, reference is made to 5
and we discuss several sensitive issues regarding, for example, what data should be made public and when, and the relationship between PCRs and private credit bureaus. The main focus of the paper is, however, on how PCRs can be used to improve the quality of credit analysis by financial institutions and to strengthen bank regulation and supervision. We present multicountry evidence of the importance of credit information sharing and on the importance of including positive and not just negative information. We also discuss the potential role of PCRs in banking regulation in the context of the proposed new capital accord known as Basel II.

The plan of the paper is as follows. In the next section we briefly discuss the development of PCRs across the globe, in Latin America and in more detail in the three countries where we conduct our empirical research. In section III we present the empirical analysis on the gains to information sharing and in section IV we present a policy discussion. This is organized around the various roles of credit bureaus. In section V we then discuss the main design parameters of PCRs relevant to using a PCR as a complementary tool to aid in the implementation of the Basel II proposals. Section VI concludes.

# **II. On the Development of Public Credit Bureaus**

Public credit registries (PCRs) contain information on the performance of borrowers in a financial system and are administered and maintained by either the central bank or bank supervisor. According to survey data collected by the World Bank, almost 60 countries have PCRs.<sup>5</sup> The region with the highest coverage of public credit registries is Latin America, where 17 countries have established PCRs, including all the largest economies (Argentina, Brazil, Chile, Colombia, Mexico).

The first countries to establish public credit registries were in Western Europe – Germany in 1934 followed by France in 1946. By the mid-1960s, three other European countries – Italy, Spain and Belgium – had also established PCRs. Early adopters included the former French

> 5 This data is based on the 2002 survey of PCRs conducted jointly by the Credit Information Group in the Vice Presidency for Financial Services (FSE) and by the Doing Business Unit in the Vice Presidency for Private Sector (PSD) as well as from previous FSE surveys on this topic. Not all countries responded to the 2002 survey, especially in Africa and Asia, so the actual number of PCRs is likely to be slightly greater than 58.

4
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- 18 -

**Table 17: Asset Management Fees (% charged on average assets), 1998 – 2004**

|**Fund**|**Sponsor**|**1998**<br>**(%)**|**1999**<br>**(%)**|**2000**<br>**(%)**|**2001**<br>**(%)**|**2002**<br>**(%)**|**2003**<br>**(%)**|**2004**<br>**(%)**|
|---|---|---|---|---|---|---|---|---|
|Dimenzió|EMP|0.00|0.93|0.71|0.37|0.27|0.29|0.30|
|HONVÉD|EMP|0.11|0.70|0.56|0.54|0.49|0.24|0.34|
|Postás|EMP|0.09|0.52|0.33|0.37|0.36|0.33|0.33|
|Vasustas|EMP|0.03|0.95|0.40|0.35|0.22|0.17|0.25|
|VIT|EMP|0.00|0.00|0.00|0.00|0.00|0.00|0.05|
|AEGON|FIN|2.25|2.93|1.68|1.68|1.36|1.43|1.37|
|AH|FIN|0.93|0.99|1.41|0.91|1.18|0.78|1.19|
|Budapest|FIN|1.15|1.34|1.21|1.16|1.17|1.02|1.00|
|CS|FIN|0.79|0.96|1.09|1.08|0.97|1.28|1.06|
|ERSTE|FIN|0.17|0.71|0.71|0.72|0.98|1.41|1.44|
|ING|FIN|1.03|0.90|0.94|0.82|0.90|0.86|0.98|
|MKB|FIN|0.00|0.00|0.68|0.53|0.53|0.65|0.64|
|OTP|FIN|1.12|1.50|0.78|0.94|0.71|0.79|0.87|
|Quaestor|FIN|0.00|0.00|0.01|0.00|0.01|0.01|0.09|
|UNIQA|FIN|-|0.80|1.90|1.86|1.83|1.34|1.21|
|Aryankor|INDEP|0.34|0.59|0.62|1.05|1.19|0.58|0.57|
|Életút|INDEP|0.00|0.53|0.74|0.25|0.20|0.21|0.29|
|Évgyűrűk|INDEP|0.16|1.15|0.58|0.65|0.54|0.57|0.50|
|Weighted Average|MKT|1.13|1.42|1.06|1.03|0.95|0.92|0.97|
|Weighted Average|EMP|0.05|0.62|0.44|0.37|0.32|0.23|0.28|
|Weighted Average|FIN|1.29|1.51|1.14|1.09|1.00|1.00|1.05|
|Weighted Average|INDEP|0.21|0.94|0.60|0.81|0.84|0.57|0.53|

Source: HFSA raw data 40. This segmentation in asset management fees can be ascribed to the governance structure of MPFs and weak transparency provisions in the regulation of the second pillar. As previously mentioned, MPF boards are _de facto_ controlled by their sponsors, which define the fees that will be charged by the service providers within the group. Members do not _de facto_ vote with their voices, as they typically do not go to general assemblies and vote for the boards, but could “vote with their feet”, i.e., switch to the funds generating higher rates of return and charging lower fees. However, the lack of information on critical data on rates of returns and fees have limited competition and promoted the segmentation previously discussed. The potential advantages of a hybrid pension system that includes low cost institutions have not been effectively explored.

41. The lack of transparency in the Hungarian second pillar has been due not only to the insufficient disclosure of returns and fees but also to the mixed fee structure that creates problems of comparability (i.e., operating fees charged on contributions and asset management fees charged on assets). In 2004, MPFs were required for the first time to publish net and gross rates of return, but operational fees and asset management fees are still disclosed as a function of two different numerators. This does not facilitate comparability of fees across funds for the average consumer.
Table 3: Calibration of Distortions in China's Initial and Terminal Stationary Equilibrium.

|||China's C|ase Study||
|---|---|---|---|---|
||Value in<br>1998|Source/Target|Value in<br>2011|Source/Target|
|Productivity-<br>Elasticity of<br>Distortions, _ν_|0.578|Regression coecient<br>_log_(_TFPR_) on<br>_log_(_TFPQ_), Annual<br>Surveys of Industrial<br>Production 1998|0.36|Projected regression<br>coecient of<br>log (_TFPR_) on<br>log (_TFPQ_) for 2011|
|Scale-Parameter<br>of Distortions, _ZI_|10.05|Capital-output ratio<br>1998|32.07|Capital-output ratio<br>2011|
|_τ _<sup>_π_</sup>|0.4|Earnings Share top 1%<br>richest Households,<br>World Inequality<br>Database|0|Assumption|
|||Average rm size in||Assumption based on|
|_fc_|48.1|China relative to the<br>U.S. in 1998, equal to<br>3.1|48.1|persisting entry<br>Barriers Brandt et al.<br>(2020)|

Note: The data for the estimation of regression coecients between _log_ ( _TFPR_ ) and _log_ ( _TFPQ_ ) stems from the Annual Survey of Industrial producers for the years 1998-2005. The capital-output ratios are drawn from the Penn World Table Database, version 10.0 Zeileis 2021, Feenstra et al. 2015. The earnings data for China in the World Inequality Database draws from Piketty et al. 2019.

pervasive entry distortions that still characterize China's economy (Brandt et al. 2020). The resulting paths of the productivity-elasticity of idiosyncratic distortions and the prot taxes are plotted in gure 5.1.

# 5.3 Benchmark Reforms: Idiosyncratic Distortions vs Entry Distortions

We begin the quantitative analysis by exploring the transition dynamics implied by reforms that either dismantle entry or idiosyncratic distortions. The objective of this exercise is to more clearly illustrate the mechanisms driving the transition depending on the type of reforms. Figure 5.2 illustrates the dynamics of _TFP,_ average size, the 26
23 Table 8: Volatility of the Real Exchange Rate: 1975-1988

# Coefficient of Variation

|East Asia||
|---|---|
|Korea|0.095|
|Singapore|0.091|
|Thailand|0.106|
|Malaysia|0.094|
|Average|0.097|
|Latin America||
|Argentina|0.247|
|Brazil|0.114|
|Chile|0.274|
|Colombia|0.230|
|Mexico|0.210|
|Peru|0.154|
|Uruguay|0.181|
|Average|0.201|

Source: World Bank data.
6 Once the government set the wage bill, the workers' role was to set individual wages within the firm. The wage scale was determined by a referendum of employees." Theoretically, pay depended also -on supervisors' assessments of work quality and fulfillment of work norms, but in reality the wage scale was decisive. In comparison to capitalist firms, Yugoslav firms had extremely compressed wage scales. For example, in one establishment with several thousand workers, the pay of the highest paid manager was 4.54 times that of the lowest paid laborer. To put that range in perspective, the pay range of entry-level positions in U.S. state governments is of similar magnitude to the pay range across all experience groups in the self-managed Yugoslav firm, and U.S. state governments have relatively compressed pay at the upper range of skills.' In the same Yugoslav firm, a worker with 20 years of job tenure earned just 6.2 percent more than a worker with no job tenure. In contrast, Topel (1991) found that a U.S. worker with 20 years of job tenure was paid almost 34 percent more than a novice worker.

As in other socialist economies, Yugoslav workers had strong job security. In fact, job security in Yugoslavia was constitutionally guaranteed. Except for extremely rare cases of bankruptcy, workers could be fired only for breaching work discipline or refusing job reassignment Even such dismissals were hindered by a judicial system that clearly sided with workers. Moreover, to prevent unemployment, government pressed frms to hire. Unlike the rest of Yugoslavia where hiring was mandated by law, only informal hiring pressure was applied to Slovenian firms. Similar to other socialist economies and in contrast to the rest of Yugoslavia, unemployment was negligible in Slovenia until the late 1980s.

6The theorefical foundation for such a scale was sought in Marx's concept of 'degree of complexity of labor' (number of units of simple labor that is required fora unit of complex labor), the concep **t** hat finns' internal documents elaborated in detail by providing numerous evaluation criteria.

7The data on the Yugoslav firm is tken from -Vodopivec (1992). Data on pay ranges in U.S. state governments are from Orazem, Matila and Weikwm (1993), Table 3.
This is not to say however that the aggregate indicators cannot be used to make cross-country comparisons. To the contrary, there are a great many pair-wise country comparisons that do point to statistically significant, and likely also practically meaningful, differences across countries. Our 2007 Control of Corruption indicator for example covers 208 countries, so that it is possible to make a total of 21,528 pair-wise comparisons of corruption across countries using this measure. For 65 percent of these comparisons, 90% confidence intervals do not overlap, signaling quite highly statistically significant differences across countries. And if we lower our confidence level to 75 percent, which may be quite adequate for many applications, we find that 74 percent of all pair-wise comparisons are statistically significant. In sum, the likelihood that a comparison between any given pair of countries does exhibit a reasonably significant difference in governance performance is close to three-quarters.

The benefit of improved precision of aggregate indicators with increased data availability over time can also be clearly seen from this calculation. Consider our 1996 Control of Corruption indicator, which was based on a median of only four data sources per country, as opposed to a median of 11 sources in 2007, implying substantially higher margins of error in 1996. Of the 11,781 possible pair-wise comparisons in 1996, only 46 percent are significant at the 90% confidence level, and only 59 percent at the 75 percent confidence interval (versus 65 percent and 74 percent respectively, in 2007).

We also emphasize that the WGI are unusual in that we generate and report these margins of error, which allow an explicit assessment of the significance of observed cross-country and over time differences in estimates of governance. Although rarely explicitly disclosed -- or even acknowledged-- all other measures of governance are subject to margins of error as well, which in our past work we have shown to be at least as large as those we calculate for our individual and aggregate indicators.<sup>16</sup> This underscores the need for caution in making cross-country comparisons with any type of governance indicator.

> 16 See Kaufmann, Kraay, and Mastruzzi (2004).

19