rafmacalaba/datause-encoder-data huggingface dataset:
49 shut down its oil production<sup>67</sup> . UNHCR reported that in 2016, South Sudan’s economic situation deteriorated further and the cost of living rose exponentially<sup>68</sup> .

# Social Service

South Sudan has very high mortality caused by AIDS and high risk of infection of diseases. Education expenditure is low and the literacy rate is also very low (27% in 2009). South Sudan has little infrastructure. According to the CIA Factbook, there are approximately 200 kilometers of paved roads. Electricity is produced mostly by costly diesel generators. Goods and services are mostly imported from surrounding countries.<sup>69</sup>

# **<u>Sudan</u>**

# Security

Similar to South Sudan, Sudan’s government forces have raped, killed civilians, and destroyed hundreds of villages. In September 2016 UN found that violence has displaced up to 190,000 people and many of them are not accessible to humanitarian agencies<sup>70</sup> . Government forces and armed rebels in Southern Kordofan and the Blue Nile continue to be engaged in armed conflict for the fifth year in 2016. Civilians in populated areas were subject to indiscriminate bombing, especially during March through June in 2016. Citizens also face arbitrary detentions, ill‐treatment, and torture. Sudan’s National Intelligence and Security Service is known for detaining activists, students, lawyers, doctors and those who are perceived to be needed in some capacity by the government<sup>71</sup> . Many detainees are facing ill‐ treatment. Females are subjected to sexual harassment by security officers. According to UNHCR, there are 2.7 million people of concern in Sudan (includes refugees, asylum‐seekers, IDPs, returned refugees, returned IDPs, stateless persons, and other concern), a lower number than 2015. About 37,000 refugees returned to Sudan in 2016. At the same time, there were over 2 million IDPs, over 420,000 refugees and over 16,000 asylum‐seekers in other countries.

# Economic Opportunity

Oil output in Sudan has been low due to civil war, poor infrastructure, and low productivity. Compared to South Sudan, Sudan also has fewer oil resources; nevertheless, it still has abundant resources<sup>72</sup> . Sudan ranks 186<sup>th</sup> of 190 states in the World Bank’s Doing Business

> 67 CIA Factbook, South Sudan, <u>https://www.cia.gov/library/publications/the‐world‐ factbook/geos/od.html</u>

> 68 UNHCR, South Sudan <u>http://reporting.unhcr.org/node/2553</u>

> 69 CIA Factbook, South Sudan, <u>https://www.cia.gov/library/publications/the‐world‐ factbook/geos/od.html</u>

> 70 UNHCR, Sudan, <u>http://reporting.unhcr.org/node/2535</u>

> 71 ‐ ‐ Human Rights Watch, https://www.hrw.org/world <u>report/2017/country chapters/sudan</u>

72 EIU Country Outlook, Sudan, June 19th 2017
# PRE WorKing Paper Series

||IAL|Author|DL2pQe|Contact<br>for|
|---|---|---|---|---|
|WPSG0/|Abolishing Green Rates: The Effects<br>on Cereals, Sugar, and Oilseeds<br>In West Germany|Donald F Larsor<br>Simon Gl.ice<br>Brent Borrell<br>Merlinda ingco<br>Jonathan Coleman|Malch 1991|D Gustafson<br>337,14|
|WPS608|Cross Country Studies of Growth<br>and Policy Methodological,<br>Conceptual. and Statistical Problems|Ross Levine<br>David Renelt<br>|March 1991|CECMG<br>391 75|
|WPS609|A Sensitivity Analysis of Cross-<br>Country Growth Regressions|Ross Levine<br>David Renelt|March 1991|CLCMG<br>39175|
|WPS610|Can Preslhrpment<br>Inspection Offset<br>Noncompetitive Pricing of Developin<br>Countries' Imports? The Evidence<br>from Madagascar|Alexander J. Yeats<br>g|March 1991|J. Jacobson<br>33710|
|WPS611|Tariff-based Commodity Price<br>Stabilization Schemes in Venezuela|Jonathan R. Coleman<br>Donald F. Larson|March 1991|S. Lipscomb<br>33718|
|WPS612|Education and Productivity in<br>Developing Countries: An Aggregate<br>Production Function Approach|Lawrence J. Lau<br>Dean T. Jamison<br>Frederic F. Louat|March 1991|WDR Office<br>31393|
|WPS613|Price-Wage Dynamics and the<br>Transmission of Inflation in Socialist<br>Economies: Empirical Models for<br>Hungary and Poland|Simon Commander<br>Fabrizio Coricelli|March 1991|0. Del Cid<br>39050|
|WPS614|Accountability in Public Services:<br>Exit, Voice, and Capture|Samuel Paul|March 1991|E. Madrona<br>37496|
|WPS615|Socialist Economic Growth and<br>Political Investment Cycles|Heng-fu Zou|March 1991|A. Bhalla<br>37699|
|WPS616|Optimal Nonlinear Income Taxation<br>for the Alleviation of Poverty|Ravi Kanbur<br>Michael Keen<br>Matti Tuomala|March 1991|J. Sweeney<br>31021|
|WPS617|International Poverty Prolections|Sudhir Anand<br>Ravi Kanbur|March 1991|J Sweeney<br>31021|
|WPS618|Poverty and Development: The<br>_Human Development Report_and the<br>_World Development Report,_1990|Ravr Kanbur|March 1991|J. Sweeney<br>31021|
largest 4 countries in our sample.<sup>13</sup> Regression results for the reduced sample are provided in Table 8. For brevity, only some of the specifications are shown. Table 8 clearly shows that the qualitative nature of the relationship between corruption (overall corruption, incidence and depth of petty corruption) and _Population_ remains intact.

# 3.6 _Additional controls_

Starting with our final baseline specification (column 4, Table 4), we experimented by adding some more controls for extra robustness. The controls include dummy variables for the region (region fixed effects)<sup>14</sup> ; religious affiliation of countries captured by the proportion of population that is Catholic, Muslim and Protestant (omitted category is all other religions)<sup>15</sup> ; a measure of the independence of the judiciary taken from Freedom House’s Economic Freedom of the World database (Judicial independence); and a measure of the functioning of the government based on whether elected officials determine the policies of the government, steps taken by the government to combat corruption and whether the media and public can freely express their views regarding corruption, and if the government is answerable to the electorate between elections and if it works with transparency, taken from Freedom House’s Economic Freedom of the World database (Functioning of the government).<sup>16</sup> There is no qualitative change in our main results due to these controls (Table A3 in the Appendix provides the full results).

> 13 The largest 4 counties excluded have a population of more than 190 million and include Brazil, China, India, and Indonesia. We experimented with alternative samples such as excluding the largest 5 percent and the smallest 5 percent of the countries, but this did not change the qualitative nature of the results discussed in this section.

> 14 The regions are Sub-Saharan Africa, East Asia and the Pacific, Eastern Europe and Central Asia, Latin America and the Caribbean, South Asia, and Middle East & North Africa. The data source for the regions is WDI, World Bank. 15 Data on religions are taken from Maoz and Henderson (2013). The data are available for every 5 years. We use year 2010 values for all the countries in our sample. The data are available at: <u>http://www.thearda.com/Archive/Files/Descriptions/WRPNATL.asp.</u>

> 16 For the two measures from Freedom House’s Economic Freedom of the World, we use year 2013 values for all the countries. Due to missing data for several years, we are unable to match these variables to the year the ES was conducted.

25
# **4.2 Labor Market Scenario**

In theory, the influx of forcibly displaced lead to an increase in labor supply. The effect on the labor market depends not only on the degree of substitutability between the local and the displaced workers but also on the socio-economic characteristics of both the groups. Only if the displaced workers are substitute to local workers, local employment may fall leading to a rise in unemployment and/or a fall in wage. This detrimental effect may well be negated and even exceeded by the humanitarian agencies and the government recruiting local workers to provide and accommodate for the influx of forcibly displaced and the increase in consumer demand which generates a multiplier effect generating new employment opportunities across the economy. However, since this paper concentrates only on the short-term effects, it is not capturing the multiplier effects that takes time to ripple through the economy.

The quarterly data of PNADC (Continuous National Household Sample Survey) from 2012 to 2020 is used to look into the labor market consequences of the Venezuelan influx in Roraima. The survey sample of PNAD is representative of the states and the federal level of Brazil and aims to monitor dynamism in the labor force. Unfortunately, it does not collect information on nationality or immigration status and as a result, this paper focuses on the overall labor market variables and not solely on the impact on native community. Figure 8 shows that the working age population, defined as those above the age of 14 years, increased from 372,000 in last quarter of 2017 to 383,000 in the first quarter of 2018 – an increase of about 11,000, which is about a 3% increase, suggesting a labor supply shock in Roraima due to forcibly displaced Venezuelans. This shock in working age population of this magnitude in the first quarter of 2018 is only being witnessed in Roraima as Figure A5 reveals and most of the other northern states see a slight decline in the population in 2018, adding evidence that the influx of forcibly displaced Venezuelans are responsible for the population jump up in Roraima. It further shows a jump up in total number of unemployed from about 22,000 to about 25 thousand in the first quarter of 2018 to about 27,000 in the second quarter to about 33,000 in the third quarter of 2018. Underemployment increased from 10,500 to about 12,400 in the first quarter of 2018 to about 15,000 in the second quarter to 18,000 in the third quarter of 2018, suggesting that in the short-term, the labor market in Roraima is finding it hard to cope leading to higher unemployment and underemployment. Total employment also shows a downward trend for most of 2018 decreasing from 214,000 to about 213,000 in the second quarter to about 212,000 in the third quarter of 2018. The fall in the number of employed also seems precipitous in the second quarter of 2020 due to lockdown related to COVID-19 pandemic. The diagram also shows that pre 2018 while unemployment was increasing briskly, employment was rising, while underemployment was constant, suggesting that labor market experienced a shock in 2018 that lead to a change in trend.

13
<!-- Start of picture text --> 20 20<br>0 0<br>−20 −20<br>n i<br>−40 j = 30 days −40<br>n i j = 75 days τ s = 7 days<br>−60 n i j = 90 days −60 τ s = 14 days<br>n i τ s = 30 days<br>j = 120 days<br>−80 n i −80 τ s = 60 days<br>j = 180 days τ s = 90 days<br>−100 −100<br>0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8<br>Time (years) Time (years)<br>Change in VA (%) Change in VA (%)<br><!-- End of picture text --> Fig. 4. Dynamics of production in the Louisiana economy, for five values of the parameters _n_<sup>_i_</sup> _j_<sup>,which</sup> describe the size of inventories (left panel), and for five values of the parameter _τs_ , the characteristic time of the inventory restoration (right panel).

and final demand compete for scarce output, and final demand increases because of reconstruction needs. If _τs_ is small, then interindustry demands increase rapidly as soon as inventories are below their target level, and sectors capture more of the available production than with a large _τs_ . So, the parameter _τs_ also describes the possibility to concentrate available production toward interindustry demands (at the expense of final demand). With a small _τs_ , more of the production is channeled toward other sectors allowing to keep producing and increasing total output.

# _4.3.3 Overproduction capacity, αmax and τα_

Sensitivity analyses are also carried out on _αmax_ and _τada_ , i.e. the adaptation maximum and timescale (see Eq. (6)). These two parameters were found extremely important in H08, and it is still the case in the new version. With no overproduction, i.e. with _αmax_ = 0 or _τα_ = + _∞_ , total output losses are equal to $37 billion. With _τα_ = 1 year, output losses lie between $37 billion with no overproduction to $2 billion with _αmax_ = 1 _._ 5. Output losses are even negative, with net gains of $5.3 billion, with _αmax_ = 2. The left-hand side panel of Fig. 5 shows the dynamic 33
about 26.4 thousand in 2050. In comparison, the Eurostat LFP scenario contributes about 0.18 percent to the growth rate per year. In terms of income per capita, the results under the Eurostat is similar to the baseline case, mainly because the dierences in the population size estimations towards the end of the projection horizon. The GDP growth impact of the retirement age scenario is an interesting case. Between 2020 and 2025, which is the time frame where the transition from the old retirement age to new retirement age actively increases the number of workers due to the reform, there is a substantial boost to the GDP growth, about 0.3 percentage point per year. However, once the reform is completed, the growth rate of the labor force is solely determined by the demographic dynamics again. Therefore, the GDP growth, in this case, is very similar to the ones under the baseline for the remaining projection period.

To see the scal implications of alternative labor force dynamics and associated dierences in the real economy, we rst investigate the impacts on public expenditures. As Panel (c) of Figure 9 shows, the high labor force scenario leads to a small increase in public expenditures for healthcare when compared to the baseline scenario. This is mainly because per capita GDP grows faster under this scenario; and this eect is magnied under greater than unitary elasticity of healthcare expenditures with respect to per capita income. Other alternative scenarios, retirement age and Eurostat labor force, also increase the healthcare expenditures, however, these impacts are small compared to the high labor force scenario. A similar outcome is observed in the case of budget transfers to the pension fund. Panel (d) shows that the higher labor force participation signicantly reduces the transfers budget transfers to the pension fund as a share of GDP. There are several factors behind this outcome including the addition to the number of contributors, and relatively smaller rate of growth in real wages, which is used as a base for pension benets, and higher GDP levels in the denominator. In comparison, retirement age scenario has a very small impact on transfers, whereas the Eurostat labor force projections have a moderate negative impact which is more pronounced towards the end of our projection horizon.

Panel (e) in the same gure shows the evolution of primary decits over the projection horizon. As expected, the dynamics under high labor force scenario provide a relatively strong improvement in the primary balances. By the end of the projection horizon, the primary decit is projected to be 0.75 percent of GDP under this scenario. Compared to 0.83 percent under the Eurostat LFP scenario, and about 0.91 under the baseline and retirement age scenarios, this presents a more favorable case. Dierences across these cases mainly arise from the expenditure side. As shown in the Panel (f), these primary decit projections are translated into lower debt to GDP ratio for the high LFP scenario (43.7 percent), and the Eurostat LFP scenario (49.4 percent) in comparison to the baseline and 26
Policy Research Working Paper 5489

# **Abstract**

This paper is about the importance of the information in Public Credit Registries (PCRs) for supporting and improving banking sector regulation and supervision, particularly in the light of the new approach embodied in Basel III. Against the backdrop of the financial crisis and the existence of information data gaps, the importance of complete, accurate and timely credit information in the financial system is evident. Both in normal times and during crises, authorities need a device that allows them to look at the universe of credits in a detailed and readily way. And more importantly, they need to develop tools that exploit as much as possible the information therein contained. PCR databases contain individual credit information on borrowers and their credits which makes it possible to implement advanced techniques that measure banks’ credit risk exposure. It allows optimizing the prudential regulation ensuring that provisioning and capital requirements are properly calibrated to cover expected and unexpected losses respectively. It also permits validating banks’ internal rating systems, performing stress tests and informing macroprudential surveillance. In this respect, it is envisioned that the existence of a PCR will be a key factor to enhance the supervision and regulation of the financial system. Furthermore, the extent, accuracy and availability of the information collected by the authorities will determine the usefulness of the PCR as part of their toolkit to monitor the potential vulnerabilities not only on a microprudential level, but also on a macroprudential one.

This paper—a product of the Finance & Private Sector Unit, Latin America and the Caribbean Region—is part of a larger effort in the department to strengthen credit reporting systems in Latin America and the Caribbean under the Western Hemisphere Credit Reporting Initiative (WHCRI). Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at mggirault@bcra.gov.ar and jhwang1@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 42 -

devaluation in late 1984, the Baht stabilized at about Bt25 per dollar, showing only small changes around this value-the average monthly variation during this period was an appreciation of 0.82 cents of a Baht per month, while the coefficient of variation of the exchange rate was only 0.0253 (see Box 2).

The inflation rate in Thailand dunrng this period also remained stable at about 0.38 percent per month, on average, with a standard deviation of about 0.45 percent. In this domestic environment, wit:h an average international inflation rate at about 0.18 percent per month-measured by the US WPI-and a depreciating dollar _vis-a-vis_ other major currencies, the real effective exchange rate in Thailand depreciated steadily until the first quarter of 1995. The latter lrend reversed when the US dollar began appreciating _vis-a-vis_ the yen and other major currencies in international markets in the second quarter of 1995 (see Figure 17).

**Figure 17. Real Effective and Nominal Exchange Rate**

<!-- Start of picture text --> 28~~~~~~~~~~~~~~~~~~~<br>-2~~~~~~~~~~~~~~~~~6<br><!-- End of picture text --> <!-- Start of picture text --> 200<br>iw<br>24<br>20<br>140~1<br>12D ~ S16<br>4F F : i14<br>100 1<br>- _ u REER _ Yen/USS_ - _Nominal (yeaf<br>Average)<br><!-- End of picture text --> Intemational market conditions during 1985-95 facilitated achieving high economic growth in Tihailand-based on a sustained development of exports-by maintaining a stable nominal exchange rate, despite domestic inflation being higher than the relevant international one (U.S.). Indeed, the strategy adopted by the monetary authorities in Thailand until the devaluation of the Baht in July 1997, was to maintain a stable nominal exchange rate, although they systematically implemented small changes to its value wheniever there were signals that the differential between the domestic and foreign inflation rates was widening and that the real sector was growing too fast-which could lead to potential ove:rheating. These results are obtained by estimating the BoT exchange rate :reaction function: i.e., by regressing the change in the exchange rate parity (dependent vaiiable) on the domestic inflation rate, the external (U.S.) inflation rate, and 53 Average values are calculated between January 1985 and May 1997.
Estimating the Economic and Distributional Impacts of the RCEP Figure 22. Total volume Vietnam exports (LHS) and imports (RHS), percentage change of productivity kick scenario relative to baseline, 2035

|Motor vehicles and parts|Trade||
|---|---|---|
|<br>Textiles|Tourist services||
|<br>Wearing apparel and leather|Wearing apparel and leather||
|Meat products|Public administration and...||
|Tourist services|Communications||
|Electrical equipment and...|Wood and paper products||
|Trade|Textiles||
|Food and beverages|Non metallic minerals||
|Chemical, rubber, plastics|Other manufactures||
|Other transport equipment|Fossil fuels||
|Metals|Utilities||
|Other manufactures|Air transport||
|Wood and paper products|Food and beverages||
|Air transport|Motor vehicles and parts||
|Animal products|Financial services and...||
|Crops|Road transport||
|Petroleum, coal products|Business services||
|Road transport|Water transport||
|Water transport|Construction||
|Communications|Chemical, rubber, plastics||
|Other extraction|Metals||
|Construction|Electrical equipment and...||
|Business services|Basic pharmaceuticals||
|Basic pharmaceuticals|Other transport equipment||
|Non metallic minerals|Crops||
|Financial services and...|Petroleum, coal products||
|Public administration and...|Meat products||
|Fossil fuels|Animal products||
|Utilities|Other extraction||
|Total agriculture|Total agriculture||
|Total natural resources|Total natural resources||
|Total manufacturing|Total manufacturing||
|Total services|Total services||
|Total|Total||
|-10<br>-5<br>0<br>5<br>10<br>15<br>20||0<br>20<br>40|

Source: World Bank staff calculations using ENVISAGE model In most sectors, intra-RCEP exports grow faster than exports to rest of the world. Figure 23 shows the value change in trade between the productivity kick scenario and the baseline, by sector, in 2035. The exception is wearing apparel and leather, where exports to the rest of the world increase by US$18 billion (the highest exports increase for the rest of the world), while exports to RCEP members increase by US$5 billion. The sector expanding the most in terms of exports within RCEP member countries is electrical equipment and machinery, with an increase of US$19.5 billion compared to the baseline.

All imports from RCEP member countries register a faster growth, with wood and paper products (US$22 billion), utilities (US$9 billion), tourist services (US$8.5 billion) and trade (US$7 billion) expanding the most. Imports from the rest of the world see a contraction in some sectors, namely electrical equipment (US$2.5 billion), textiles (US$1.2 billions), and metals (US$1.1 billion), which are now being replaced by imports from sources within RCEP member countries due to a more liberalized market.