rafmacalaba/datause-encoder-data huggingface dataset:
**The Role Of Factoring In the New EU Member States**

will likely already have generated information about the importer’s payment record. In addition, domestic factors in the EU 8 have always factored foreign receivables by participating in alliances such as Factors Chain International or, more recently, by buying credit insurance. Indeed, it may be easier for factors in the EU 8 to develop their business around foreign rather than domestic receivables.

This section first considers the extent to which these advantages have been exploited in the EU 8. It then examines the characteristics of these countries that have shaped the development of their factoring industries, based primarily on a World Bank survey of factors operating in them. The section concludes with some predictions about how the factoring industry may develop in the region, and suggests policy initiatives that may facilitate its growth and improve credit availability more generally.

# **FACTORING MARKETS IN THE EU 8**

To the extent that banks are able to provide financial statement lending, relationship lending, and credit-scored lending, the need for factoring is probably less. Figure 2 shows bank credit to the private sector (as a share of GDP) in the EU 8 and in the 15 countries that made up the European Union before entry of the new members (referred to as the EU 15). Considerably more bank credit is available in the EU 15—suggesting a significant funding gap in the EU 8 and the potential for factoring to fill it.

<!-- Start of picture text --> Figure 2. EU 15 and EU 8 Bank Credit to the Private Sector as Percent of GDP<br>120.00%<br>100.00%<br>80.00%<br>EU 15<br>60.00% EU 8<br>40.00%<br>20.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002<br>Source: World Bank.<br>% of GDP<br><!-- End of picture text --> Figure 3 takes a more detailed look at the EU 8, breaking down sources of domestic financing for the private sector. Although equity markets play a significant role, it is clear that the EU 8 have banking- oriented financial systems. Intermediated finance dominates capital market financing even for large firms. Thus, given that bank credit is insufficient in the EU 8, factoring could provide an important source of financing even for large enterprises.

In recent years the factoring industry has grown significantly in most EU 8 countries, both in absolute terms (Figure 4) and as a percentage of GDP (Figure 5). Poland has the largest volume,

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**THE WORLD BANK**
financial development does indeed improve the productivity of real investment and sustain higher levels of aggregate economic growth (e.g., Levine, 1997).

The quality of a nation’s contracting environment limits the contribution that variations in regulatory structure can make to economic development and macroeconomic growth. Recent adopters of deposit insurance include African and Latin American countries with low levels of financial development and government accountability. Using time-series data for 58 countries, Cull, Senbet and Sorge (2005) find that explicit deposit insurance favorably impacts the level and volatility of financial activity only in the presence of strong institutional development. In institutionally weak environments, deposit insurance appears to undermine the productivity of real investment and retards rather than promotes sustainable financial development.

**_Deposit Insurance and Crisis Management._** Crisis management entails a number

of difficult policy tradeoffs between recovery speed, economic efficiency, and distributional fairness. Due to deficiencies in prior disaster planning, it has become common practice to issue blanket guarantees to arrest a banking crisis. Countries adopting this strategy include Sweden (1992), Japan (1996), Thailand (1997), Korea (1997), Malaysia (1998), and Indonesia (1998). Turkey tried to halt its financial panic in 2000 by guaranteeing not just bank depositors, but all domestic and foreign nondeposit creditors of Turkish banks.

Advocates of using blanket guarantees to halt a systemic crisis argue that sweeping guarantees can be immediately helpful – if not essential – in stopping a spreading flight to quality. However, because blanket guarantees create an expectation of 11
**Figure 10:** Default probabilities

<!-- Start of picture text --> AGO ALB ARG ARM AZE BGD BGR BIH BLR<br>0.9<br>0.6<br>0.3<br>BLZ BOL BRA BWA CHN CMR COG COL CRI<br>0.9<br>0.6<br>0.3<br>DJI DMA DOM DZA ECU EGY GAB GEO GHA<br>0.9<br>0.6<br>0.3<br>GTM GUY HND IDN IND IRN JAM JOR KAZ<br>0.9<br>0.6<br>0.3<br>KEN LBN LCA LSO MAR MDA MEX MMR MNE<br>0.9<br>0.6<br>0.3<br>MRT MUS MYS NGA NIC PAK PAN PER PHL<br>0.9<br>0.6<br>0.3<br>PRY ROU RUS RWA SDN SEN SLB SLV SRB<br>0.9<br>0.6<br>0.3<br>SWZ THA TJK TUN TUR TZA UKR UZB VNM<br>0.9<br>0.6<br>0.3<br>2008 2012 2016 2008 2012 2016 2008 2012 2016 2008 2012 2016 2008 2012 2016 2008 2012 2016<br>VUT ZAF ZMB<br>0.9<br>0.6<br>0.3<br>2008 2012 2016 2008 2012 2016 2008 2012 2016<br>Ability Perceptions<br>Probability of default<br><!-- End of picture text -->

# **3.3 The Count Index**

An important element of the analysis is a display of the raw data and organizing the variables by indication of vulnerability or lack thereof. This is important for several reasons, which include (i) transparency about the data used, (ii) serving as an additional filter to spot risks that might not be picked up by the indices, and (iii) serving as a benchmark against other models of vulnerability. Some of these indicators have already been used in standard DSA tables. We rely on thresholds (determined jointly by Bank-Fund staff) to highlight risk in the various indicators (Appendix D highlights the table of indicators).

In total there are 12 indicators for which thresholds are provided: Current account balance (CAB), which has a threshold of 5 percent to GDP; external debt as a share of GDP (threshold equals 60 percent); external private non-guaranteed debt as a share of GDP (threshold equals 20 percent); external gross financing needs as a share of GDP (threshold equals 15 percent); short-term external debt as a share of total external debt (threshold equals 5 percent); gross public debt as a share of GDP (threshold equals 60 percent); external public debt as a share of GDP (threshold equals 30 percent); budget deficit as a share of GDP (threshold equals 4 percent); the primary deficit as a share of GDP (threshold 15
NBIM employed 8 external managers with 8 mandates in 1998, but their number increased steadily over time and reached 50 external managers with a total of 80 mandates in 2006: 22 managers with 35 mandates in the fixed income segment and 28 managers with 45 mandates in equities.

Internal management is divided between enhanced indexing, which follows a passive approach but with some allowance for transactions that respond to special pricing opportunities, and active management, which involves both stock selection and market timing. NBIM also engages in securities lending to augment the income of the fund. It has lending agreements with several global banks.

# **2.6 Investment Performance**

Since the equity allocation has been allowed, the equity portion of the portfolio, including the small environmental fund, has fluctuated within a small range around 40 percent (Table 2.2).

||Table2.2:|AssetA|llocation,|2000-20|06|||
|---|---|---|---|---|---|---|---|
|percent of total assets|2000|2001|2002|2003|2004|2005|2006|
|Equities|39.5|40.9|37.9|42.7|41.0|41.6|40.7|
|FixedIncome|60.5|59.1|62.1|57.3|59.0|58.4|59.3|
|||Sourc|e: NBIM|||||

The investment return, measured in terms of the currency basket in which the various securities are held, amounted in 2006 to 7.9 percent. Over the whole period since 1997, the average return equaled 6.5 percent. The return on equities for the period 1998-2006 reached 7 percent, while bonds produced a lower return of 5.4 percent over the same period. Investment returns calculated in terms of the local currency fluctuated more widely from year to year because of the relative appreciation or depreciation of the local currency but over the whole period the average return expressed in Norwegian crowns was 6.4 percent, almost identical to the return calculated in the currency basket.<sup>18</sup> Equity returns suffered from the bursting of the high tech bubble in 2000 and the sharp market correction of the first few years of the new millennium, but benefited from the substantial rebounding of global equity markets in the past four years. Bond returns have been less volatile but have been lower on average (Table 2.3).

|Table2|.3:AssetRe|turns on|Equities a|ndBond|s,2000-2|006||
|---|---|---|---|---|---|---|---|
|percent|2000|2001|2002|2003|2004|2005|2006|
|Equities|-5.82|-14.58|-24.38|22.84|13.00|22.49|17.04|
|Fixed Income|8.41|5.04|9.90|5.26|6.10|3.82|1.93|
|Total Portfolio|2.50|-2.43|-4.69|12.59|8.94|11.09|7.92|
|||Sourc|e: NBIM|||||

18 It is worth noting that the Government Pension Fund - Norway enjoyed a higher return (measured in NOK) of 11.7 percent in 2006 and an average return since 1997 of 7.1 percent (MOF 2007).

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<!-- Start of picture text --> Fans charts of probabilistic flood shocks. Scenario: RCP 8.5<br>GDP compared to baseline, in percent Capital stock compared to baseline, in percent<br>£00 102<br>00 | |<br>004 | -.02 |<br>-.012 | | -.06 |<br>016 | -.08<br>020 -.10<br>oeRARARRRRRARARRARRRRRARRARRARRESEHAERERERE R a SHR “BERSHMRESERERRERERERRRRARRARARRARRRRRRRRRRRASHR<br>Consumption compared to baseline, in percent Debt/GDP, compared to baseline, in percentage points<br>Fn Be<br>-015 | .002<br>020 .000 |<br>oaRARRARRRARARRRRRRRRRARRARRAMER E GSHEREREREGREER MERERRRARARRARRARRRRRRRRRRRAROESSERQEREROERGER<br><!-- End of picture text -->
beyond observed exchange rates. In this paper, for ease of comparison with the previous section, we stick to verifiability just using exchange rate data.

For the verification of floating regimes, we focus on Brazil, Mexico, Peru, South Korea, and Thailand during specific periods in the 1990s. These countries provide a good opportunity to compare periods of intervention with periods of free-floating. Brazil, Mexico, South Korea, and Thailand suffered exchange rate crises in the 1990s, which forced them to abandon previous exchange rate arrangements and adopt systems officially described as free-floating **by** their respective authorities. Therefore, for these countries we perform the same statistical tests for periods labeled as free floating and for periods during which other regimes were in operation **-** namely periods of managed floating, bands, crawling pegs, and basket pegs. Peru, on the other hand, is an interesting case because despite declaring a free-floating regime for the entire decade, several papers have noted that the Peruvian exchange rate has remained surprisingly steady. (See Calvo and Reinhart, 2000, Hausmann, Panizza, and Stein, 2000, and Edwards and Savastano, **1999,** for characterizations of floating regimes.) As in other cases, the observed pattern calls into question whether the government is in truth following the regime that it says.

Following the methodology used in the previous section, we estimate our basic equation **(1)** for each of these countries over the periods noted in Table 4 and dictated **by** data availability over the 1990s. We allow for a constant rate of crawl, include as regressors the five major currencies **(US** dollar, yen, DM, British pound and French franc), and as before estimate the model **by OLS** in first differences.<sup>3'</sup>

> **3t** As we are only testing the hypothesis of all the weights being equal to zero (a standard goodness of fit test), we are not concerned **by** the potential multicollinearity problem.

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# **_Reforms in the Arab Republic of Egypt, Morocco, and Tunisia_**

Under pressure from social tensions during the Arab Spring, some countries in the MENA region introduced or tightened food price controls in 2011 (Ianchovichina, Loening and Wood 2014). Conversely, however, high oil prices and fiscal pressures encouraged a few MENA countries, including the Arab Republic of Egypt, Morocco, and Tunisia, to reform price controls and related subsidies on energy between 2010 and 2014 (Verme and Araar 2017).<sup>6</sup> Some of the reforms were estimated to have reduced inequality and/or poverty rates (Figure 6.A). The reforms were also associated with improvements in the ease of doing business. Within two years of the reform, enterprises in all three countries reported easier access to electricity (Figure 6.B). The programs, however, differed substantially in their scope, and speed of implementation. They also varied with respect to compensatory transfers to disadvantaged population groups. Morocco reduced the fiscal burden of petroleum subsidies, while at the same time avoiding severe adverse consequences for poverty and inequality. Egypt, however, took a sequential, gradual, approach to reform especially for products such as liquified petroleum gas (LPG), which account for a disproportionately large expense for the poor.

**Figure 6. Some impacts of price control reforms**

<!-- Start of picture text --> A. Model-based estimates of impacts of reforms B. Impact of reforms on ease of doing business<br>on poverty and inequality (access to electricity)<br>Percent Index Points (+ = Improvement, both axes)<br>9 Poverty Rate 3 15<br>Inequality (Gini)<br>6 2 10<br>3<br>1 5<br>0<br>0 0<br>-3<br>-1 -5<br>-6<br>t-1 t+1 t+2 t-1 t+1 t+2 t-1 t+1 t+2<br>-9 Egypt (RHS) Morocco Tunisia<br>Egypt Morocco Tunisia<br>Source: Verme and Araar 2017, World Bank.<br><!-- End of picture text --> A. Impacts of reforms on the poverty rate and the level of inequality estimated using SUBSIM model. B. Chart shows the World Bank’s index for ease of getting electricity in year before (t-1) and the two years after (t+1, t+2) energy subsidy reform (World Bank 2019b). Time t=0 refers to 2014 for Egypt and Morocco and 2012 for Tunisia.

In Egypt in July 2014, comprehensive reforms to fuel and electricity prices resulted in a significant rise in gasoline, natural gas, diesel, and electricity prices which contributed to a spurt of headline inflation. Initial price adjustments were followed by stepwise gradual increases to fully eliminate energy subsidies over a five-year period. While the initial price increases themselves are estimated to have raised the poverty rate and inequality, the government has put in place some mitigating

> 6 Djibouti, Egypt, Jordan, Libya, Morocco, Tunisia, and the Republic of Yemen designed and implemented subsidy reform programs. These cases contrast with some other countries in the region, where social tensions during the Arab Spring caused an increased use of food price controls in 2011 (Ianchovichina, Loening, and Wood 2014).

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<!-- Start of picture text --> GVC backward 859.70 64.32<br><!-- End of picture text --> Figure 2: Replication of World Bank WDR 2020 Figure 1.13.

**Figure 1.13** GVCs expanded in both the agriculture and food industries from 1990 to 2015

<!-- Start of picture text --> a. Agriculture GVCs b. Agri-food GVCs<br>60 90<br>80<br>50<br>VNM RWA GHA 70<br>40 ETH KEN 60 HUN TZA<br>50 SRB<br>30 LAO GMB<br>40<br>SSD BGR<br>20 LBN 30 ETH<br>YEM 20<br>10<br>10<br>0 0<br>0 10 20 30 40 50 60 0 10 20 30 40 50 60 70 80<br>Agriculture GVC participation, 1990 (%) Agri-food GVC participation, 1990 (%)<br>East Asia and Pacific Europe and Central Asia Latin America and the Caribbean<br>Middle East and North Africa North America South Asia<br>Sub-Saharan Africa<br>Agriculture GVC participation, 2015 (%) Agri-food GVC participation, 2015 (%)<br><!-- End of picture text --> Figure 3: Replication of World Bank WDR 2020 Figure B2.1.1.

<!-- Start of picture text --> a. Backward integration of<br>electronics and machinery as a<br>share of gross exports<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2005 2010 2015<br>Percent<br><!-- End of picture text --> Throughout the WDR 2020, several figures on GVC-related trade at the world level are reported. These measures can be obtained with `icio` retrieving and then summing the GVC-related trade of each country in the loaded input-output table. Since this could be computationally intensive, we have also released a data set with GVC indicators and the most relevant measures of value-added in trade flows 31
that they could be understood and accurately completed by the women, and to make sure that the questions were culturally appropriate. Second, to maintain data quality, the enumerators visited women on a weekly basis to inspect the diaries and note any problems with them. During these visits, they answered questions about the diaries and continued to train women who were having difficulty.<sup>19</sup> There are several important differences between the two versions of the diaries which will affect our analysis. First, the Round 2 diaries included information on client characteristics, for each transaction. These include the client’s occupation, perceived wealth level, attractiveness, and cleanliness. In addition, they include measures of whether the sex worker thought the client was at high risk of HIV, whether she thought that the client saw a higher than average number of sex workers, and whether the client was circumcised. Unfortunately, these measures are somewhat problematic in that they were collected from the sex worker rather than the clients themselves, so measurement error is a concern. In addition, recording this level of detail for each client was burdensome for women. Consequently, even in the Round 2 data, many observations are missing client information.<sup>20</sup> Finally, to supplement the diary information, we administered a one-time background survey which included questions on demographics, asset ownership, knowledge of HIV/AIDS, attitudes towards sex work, and related issues. In total, the final dataset includes 192 women, 19,041 transactions, and 12,526 sex worker days.

> 19. Since the diaries were self-filled, it was necessary for women to be literate to complete them. Although the majority of the sample is literate, we also made an effort to keep illiterate women in the sample. Every illiterate woman sampled for the project was visited by her peer educator once a day to help her fill out the diary. 20. An important complication this causes is that while we know whether a particular client is a regular or casual, we are not able to match client-specific activities across days.

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16 with which extreme weather events happen (their incidence) and their actual occurrence during the current or previous survey years, a correlation which is most pronounced for floods. Finally, some bunching of events is also observed, with households subjected to a hurricane, often also hit by flash floods, as indicated by the correlation coefficient of 0.55 between the incidence of flash floods and the incidence of hurricanes. Hurricanes thus manifest themselves partly through localized flooding. Riverine floods on the other hand appear negatively correlated with flash floods and hurricanes. Finally, note that a higher incidence of drought does not exclude the occurrence of floods within the same year as indicated by the positive correlation between the incidence of droughts and floods (and hurricanes).

Chart 1: Most people are exposed to droughts, but infrequently; floods affect a sizeable population often, and a large population never; hurricanes pass by most and infrequently affect others.

<!-- Start of picture text --> 1<br>drought<br>0.9 flashflood<br>0.8 riverine flood<br>0.7 tropical storm<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 20 40 60 80 100<br>Population (cumulative %)<br>likelihood of event<br><!-- End of picture text --> The bunching of the disasters underscores the importance of considering their effect separately, but jointly in the regression analysis. Potential persistence in the effect is examined through consideration of both the current and one year lagged events. Finally, to separate out the potential effects of adaptation from the immediate welfare effects from current events, the regressions also control for the frequency with which the different events occurred.

# **5 Welfare Effects of Natural Disasters in Vietnam**

To benchmark the results, Table 1 (column 1) presents the estimated OLS coefficients<sup>36</sup> of a standard consumption model augmented with information on the present and lagged occurrence of

> 36 These were obtained from the pooled 2002, 2004, and 2006 VHLSS data with standard corrections for heteroskedasticity and clustered survey design using the Hubert‐White estimator provided in STATA.
**Figure A5: Impact of U.S. interest rate shocks on EMDE economic activity** A.  Impact of 25 basis point shock on real GDP growth consumption expenditure growth

B. Impact of 25 basis point shock on private consumption expenditure growth <!-- Start of picture text --> Percent<br>Mean 90% confidence interval<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>Mean 90% confidence interval<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> C. Impact of 25 basis point shock on gross fixed capital formation growth D. Impact of 25 basis point shock on real export growth <!-- Start of picture text --> Percent<br>Mean 90% confidence interval<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br>E. Impact of 25 basis point shock on consumer<br>price inflation<br>Percent<br>Mean 90% confidence interval<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>Mean 90% confidence interval<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> E. Impact of 25 basis point shock on consumer price inflation _Source:_ World Bank _Notes:_ Panel local projection models with fixed effects and robust standard errors. See table A3 for details. Models estimated over periods as long as 1997Q2-2019Q4; they exclude observations during global financial crisis (2008Q4-2009Q4) and the COVID-19 pandemic.

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