rafmacalaba/datause-encoder-data huggingface dataset:
26.1% (22.5%) could not provide any response at all. Panel C shows that only 12.8% of respondents (24.7% of those aged 25–65) correctly chose shares in a single company stock as a riskier investment asset than shares in a unit fund. An important caveat is that the inflation and risk questions asked in the Russian survey differ from those in the U.S. Health and Retirement Study.

As shown in Panel D, a very small number of individuals correctly answered all three questions, i.e., 3.1% of the whole sample (3.4% of those aged 25–65). To the interest and inflation questions 21.8% responded correctly (23.9% aged 25–65). Furthermore, 31.8% gave all incorrect responses (28% aged 25–65) and 12.5% (9% aged 25–65) reply with ―I don‘t know‖ to every question. A remarkable 53.7% of respondents replied with ―I don‘t know‖ to at least one question (48.2% aged 25–65).

# **_3.2 The Demographics of Financial Literacy in Russia_**

Table 2a presents summary statistics of financial literacy in Russia, by demographic characteristics, disaggregated by correct and don‘t know responses. First, the data suggest that financial literacy is negatively related to age: to all three questions, younger groups are more likely to provide correct responses and less likely to indicate that they don‘t know an answer. Second, correct responses are not notably different by gender, although men are much less likely to state that they do not know what the answer is (47.2% of males versus 58.5% of females). Third, individuals with higher education offer a higher number of correct responses (and a lower percentage of don‘t know responses) with respect to all three questions. Finally, we find lower levels of literacy among retired and self-employed individuals, though the latter category might include informal workers.

7
and fourth columns summarize the impact of competition policy on markups using QLME and DLW. The remaining columns summarize the impact of competition policy on profits (the profit ratio), deflated sales, employment, real marginal costs, real intermediate costs per worker, product quality and TFP covariance (or between effects).

Several robustness checks were performed on the aggregate impacts. We produce tables that differentiate the impacts of enforcements and regulations on the productivity distribution of firms in a given market as well as accounting for firm age.

Our sample consists of firms in the same market that were affected by an intervention (treatment group) and those that were not (control group). We cannot fully control for multi-product firms; however, by segmenting ICT into subsectors we get closer to homogeneous product groupings.

We repeat the exercise for both enforcement outcomes and regulatory changes. The direct estimates of _φ_ measure the impact of regulation and enforcement on TFPR but not necessarily quantity total factor productivity (TFPQ). However, since we are also estimating impacts on the dispersion in markups, when all else is held constant the difference between TFRP and the markup approximates the impact on TFPQ; this requires that marginal costs remain fixed.

# **Aggregate ICT**

The first estimates are for aggregate ICT sector. The impact of enforcement reduces the markup dispersion (note that the result is not statistically significant) (Index 3 and 4 of Table 6). TFPR is positive at the aggregate ICT level, but once again not statistically significant. The net impact on TFPQ, holding all else constant, is positive ( _φ_<sup>_w_</sup> 1<sup>_<τ μ_</sup> 1<sup>).This</sup> result seems robust regardless of methodology.

The impact of enforcement on productivity varies by productivity decile. Both the QLME and ACF methodologies show that TFPR significantly increases for the top 25% of productive firms (which we refer to as the leaders). TFRP for the lower 75% of firms is not statistically significant (Index 1 and 2 of Table 7). This result suggests that strong escape competition effects are at play, where leaders in the market upgrade to escape competition while less productive firms are unable to adjust to competitive forces. The effects on the dispersion in markups tend to not be significant except for firms in the bottom 25 percentile—competitive pressure leads to churning at the lower end of the productivity distribution. This supports the escape-competition channel in the sense that leaders are able to maintain market share in spite of competition and competitive forces create churning mainly for productivity laggards.

When the data are further disentangled into firm age and productivity percentiles it appears that leaders again adjust better than laggards to competitive forces (Index 1 and 8 of Table 8). In fact, the results are significant using both QLME and ACF methodologies for all age profiles; the adjustment is largest for middle-aged firms in business for 6–15 years.

18
Table 1 - Avian influenza outbreaks, 2003-2006

|**ASIA (High path**|**ogenic)**||**EUROPE (High pa**|**thogenic)**||
|---|---|---|---|---|---|
|Country|1st outbreak|Latest outbreak|Country|1st outbreak|Latest outbreak|
|Afghanistan|02-Mar-06|04-Apr-06|Albania|16-Feb-06|09-Mar-06|
|Cambodia|12-Jan-04|01-Aug-06|Austria|10-Feb-06|22-Mar-06|
|China|20-Jan-04|04-Aug-06|Azerbaijan|2-Feb-06|18-Mar-06|
|Hong Kong||||||
|(China)|19-Jan-04|25-Feb-06|Bosnia-Herzeg.|16-Feb-06|16-Feb-06|
|India|27-Feb-06|18-Apr-06|Bulgaria|31-Jan-06|09-Feb-06|
|Indonesia|02-Feb-04|10-Jul-06|Croatia|21-Oct-05|24-Mar-06|
|Japan|28-Dec-03|05-Mar-04|Czech Rep.|27-Mar-06|19-May-06|
|Kazakhstan|22-Jul-05|10-Mar-06|Denmark|12-Mar-06|22-May-06|
|Korea, Rep.|10-Dec-03|22-Mar-04|France|17-Feb-06|26-Apr-06|
|Laos|15-Jan-04|14-Jul-06|Georgia|23-Feb-06|23-Feb-06|
|Malaysia|19-Aug-04|21-Mar-06|Germany|8-Feb-06|02-Aug-06|
|Mongolia|10-Aug-05|Jun-06|Greece|30-Jan-06|27-Mar-06|
|Myanmar|08-Mar-06|25-Apr-06|Hungary|4-Feb-06|12-Jul-06|
|Pakistan|23-Feb-06|20-Apr-06|Italy|1-Feb-06|19-Feb-06|
|Thailand|23-Jan-04|24-Jul-06|Poland|2-Mar-06|07-May-06|
|Viet Nam|09-Jan-04|15-Aug-06|Romania|7-Oct-05|06-Jun-06|
|**MIDDLE EAST (**|**High pathogenic)**||Russia|15-Jul-05|31-Jul-06|
|Iran|02-Feb-06|02-Feb-06|Serbia-Monten.|28-Feb-06|09-Mar-06|
|Iraq|18-Jan-06|01-Feb-06|Slovakia|17-Feb-06|18-Feb-06|
|Israel|16-Mar-06|30-Mar-06|Slovenia|9-Feb-06|25-Mar-06|
|Jordan|23-Mar-06|23-Mar-06|Spain|7-Jul-06|07-Jul-06|
|Palestine Aut.|21-Mar-06|02-Apr-06|Sweden|28-Feb-06|26-Apr-06|
|**AFRICA (High p**|**athogenic)**||Switzerland|26-Feb-06|11-Mar-06|
|Burkina Faso|01-Mar-06|18-May-06|Turkey|5-Oct-05|31-Mar-06|
|Cameroon|21-Feb-06|28-Mar-06|United Kingdom|30-Mar-06|30-Mar-06|
|Côte d'Ivoire|31-Mar-06|10-Jun-06|Ukraine|2-Dec-05|11-Jun-06|
|Djibouti|06-Apr-06|06-Apr-06|**AMERICAS (Low**|**pathogenic)**||
|Egypt|17-Feb-06|05-Jul-06|USA|11-Feb-04|03-Sep-04|
|Niger|06-Feb-06|25-Apr-06|Canada|19-Feb-04|29-Apr-04|
|Nigeria|16-Jan-06|Aug-06|Mexico|20-Mar-04|15-Jul-05|
|Sudan|25-Mar-06|Apr-06||||

Source: Avian Influenza technical task force, FAO bulletins.

The first HPAI (type H5N1) outbreak was recorded in Korea in December 2003.

By 2004 the same virus had spread to at least 10 Asian countries, including Cambodia, China, Hong Kong (China), Japan, Indonesia, Laos, Malaysia, Thailand and Vietnam. By 2005 and 2006 the virus had further spread into Asia, Europe, the Middle East and Africa.<sup>4</sup> Absence of migratory flyways from infected areas, strict border controls and severe biosecurity practices have largely kept the H5N1 virus outside of the Americas. However, in 2004 and 2005, different strains of avian influenza were also diagnosed in

> 4 The pattern of H5N1 infections in Africa remains elusive as surveillance is especially weak there.

4
1. Background

# Contents

|1. Background ........................................................................................................................................... 3|
|---|
|2. Trade Linkages between B&R Economies: Gross Trade ...................................................................... 5|
|2.1 Gross Trade Linkages between B&R Economies Revealed by UN Comtrade Data ...................... 5|
|2.2 Gross Trade Linkages between B&R Economies Revealed by TiVA Data ................................. 10|
|3. Origin of Value-Added in Exports of B&R Economies ...................................................................... 13|
|3.1. Concept and Methodology ........................................................................................................... 13|
|3.2. Origins and Destinations of Foreign Value-Added in Exports of B&R Economies .................... 15|
|4. Quantifying Vertical Trade among the B&R Economies .................................................................... 18|
|4.1 Concept and Methodology ............................................................................................................ 18|
|4.2 Backward Linkages ....................................................................................................................... 20|
|4.3 Forward Linkages ......................................................................................................................... 23|
|5. Visualization of Value-Added Network of B&R Economies ............................................................. 27|
|6. Trade Linkages between China and other B&R Economies ............................................................... 31|
|6.1. Gross Trade between China and other B&R Economies ............................................................. 31|
|6.2. China’s Position in GVCs ............................................................................................................ 33|
|7. Potential Benefits from Further Integration ........................................................................................ 36|
|8. Conclusion ........................................................................................................................................... 37|
|References ............................................................................................................................................... 39|
|Appendix ................................................................................................................................................. 41|
|A1: Comparison between EORA and Tiva GVC Indicators .............................................................. 41|
|A2: Network Analysis of Origin and Destination of Value-added using EORA ................................ 45|
|A3: Indicators of Vertical Trade using EORA .................................................................................... 48|
|A4: B&R Economies in the Paper ...................................................................................................... 53|

2
The run-up to COVID-19 does not fit the historical boom-bust pattern, as it is not predicated on mistakes made in a prior economic expansion or an asset price bubble. The common thread is that the historic magnitudes and likely persistence of the downturn in economic activity may expose evolving balance sheet frailties. It is a regressive crisis, within and across countries. Job losses disproportionally hit low-income households and smaller firms that have fewer assets to avert insolvency. EMDEs do not have the fiscal and monetary capacity to respond as vigorously as their wealthier counterparts to counteract the damage that the pandemic and lockdowns are inflicting on their economies. While there are major cross-country differences within this group, EMDEs’ recovery is poised to be more protracted, feeding into the balance sheet frailties.

High leverage on the eve of the pandemic will act to amplify the balance sheet problems of the financial sector in many countries. The IMF has repeatedly flagged concerns about the preCOVID-19 rise in corporate leverage in many emerging markets, where much of the debt is denominated in US dollars. The two largest economies, the United States and China, have highly leveraged corporate sectors. Australia, Canada, and Thailand, among others, have high or record levels of household debt. In Africa, where the NPL ratio was estimated at around 11 percent in 2019, microfinance financial institution portfolios will come under stress as much of their lending is to households with volatile income and no assets.

Since the onset of the pandemic, a broad range of policies have been introduced by governments across the globe to provide liquidity to the many businesses that have been shuttered during the lockdowns and to support households hit by a sudden loss of income and employment.<sup>5</sup> Grace periods in the repayment of existing loans have been granted. Re-contracting of loans in favor of longer maturities or lower interest rates has also been common. The hope is that because the health crisis is temporary, so too will be the financial distress of firms and households. However,

> Reinhart (1999), Bordo, Eichengreen, Kliengbiel, and Martinez-Peria (2001), Reinhart and Rogoff (2008, 2009), Schularick and Taylor (2012), Laeven and Valencia (2013), Bordo and Meissner (2016).

> 5 See World Bank (2020b) for a cross-country database on these policies.

5
frequency at the country level, we would not be able to separate the response of remittances for a specific disaster.

**Table 1: Summary statistics for developing countries**

|Variable|Obs.|Mean|Standard<br>deviation|
|---|---|---|---|
|Remittance as a share of GDP|3,974|3.4%|7.9%|
|Private debt as a share of GDP|3,976|0.7%|2.6%|
|Portfolio equity as a share of GDP|3,661|0.1%|0.5%|
|Emigrants as a share of origin country population|4,995|9.2%|12.1%|
|Per capita GDP (constant 2000 US$)|4,035|1,469|1,530|
|Number of people affected per 100,000 population|2,142|4,148|12,295|
|Disaster damage as a percentage of GDP|898|0.004%|0.02%|

Source: Authors’ calculations using International Emergency Disasters Database (EM-DAT) and World Development Indicators (WDI), World Bank.

We utilize reported measures of the total amount of direct damage (DDAMAGE) and the total number of people affected (DAFFECTED) for the years 1970- 2006 for all countries on which data is reported in EM-DAT. The literature on the macroeconomic impact of natural disasters has used similarly aggregated variables (see Noy 2008).

# **_3.2 Empirical strategy and estimation_**

This section will attempt to provide more systematic cross-country evidence using data on all available countries on the possible existence of this ―countercyclical‖ or compensatory effect of remittance flows in the context of natural disasters at the aggregate level.

The cross-country regression is estimated for the following specification: where _Yit_ is the remittances as a share of GDP. The disaster variable is disaster cost as share of GDP in the previous year, or people affected as share of population in the previous year. We include an interaction term for the stock of emigrants and the disaster variable in a country in a given year. Other controls include per capita GDP, region fixed effects and time trend. We introduce lagged remittances as an additional explanatory variable to account for the observed persistence of remittance flows over time.

8
2100]. These estimates, plotted in Figure 1, suggest that there exist a positive relationship between SLR and the increase in global mean surface temperature, but also a time component, related to the substantial inertia of the physical processes involved.

<!-- Start of picture text --> 4<br>3.5<br>3<br>2.5 Avg increase in<br>temp 2055<br>2<br>Avg increase in<br>1.5<br>temp 2090<br>1<br>0.5<br>0<br>0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7<br>SLR (in meters)<br>Figure 1 . Global mean surface temperature change (°C) and global mean sea level rise (m)<br>Increase in Temperature (°C)<br><!-- End of picture text --> To better understand the nature of the relationship between the global mean SLR, the increase in the mean global temperature and time, we ran a series of regressions, finding that the following equation provides a satisfactory fit for the relationship:

# _SLR_ =[( _α_ + _βΔt_ )( _T −_ 2000)]

where Δ _t_ is the change in average global temperature with respect to the baseline [1985-2005], and _T_ is the year period. A panel estimation of equation (1) gives a value for the α coefficient of 0.000954281, whereas the corresponding value for β is 0.003421296.

To account for the vertical land movement ( _V_ ), equation (1) can be modified as follows:

# _aSLR_ =[( _α_ + _βΔt −V_ )( _T −_ 2000)]

where _aSLR_ is the _adjusted_ sea level rise. Data on VLM by country have been retrieved from the SONEL database (www.sonel.org).

For example, the adjusted SLR associated with an increase in temperature of +1°C and VLM of +0.001 m/yr (rising) at the year 2050 is:

# 0.16878=[(0.000954281+0.003421296 _x_ 1 _−_ 0.001)(2050 _−_ 2000)]

that is, about 0.17 meters.

Using the DIVA v2.04 model, Arnell et al. (2014) provide estimates of the percentage loss in the coastal wetland for 16 macro-regions and 3 single countries. These estimates, reported in Table 1, are associated with a future global mean SLR of 0.16 m, predicted by the HadCM3 climate model under the A1b SRES scenario.

**Table 1** . % change in coastal wetland at 0.16 m of SLR by macro-region (from Arnell et al. 2014)

|**Region/country**|**% change in coastal**|**Region/country**|**% change in coastal**|
|---|---|---|---|

4
--. 4-

underdeveloped and relative prices are distorted. To assist its borrower countries in this regard, the Bank will need to develop and d.isseminate an operational approach _ta_ div.estit..re decisorns, %which should t nen be rout ne'; applied in a manner similar to the wav cost/benefit analvsis is new being applied to project evaluation.34/ D. Learning From Experience 70. As ..ted, empirical evidence regarding the impact of attempted reforms on the performance of PE is relacively scarce. Similarly the effectiveness of some of the reform components remains largely un-analyzed. At this stage, sufficient time has elapsed to permit a systematic assessment of the experience to date. The most important aspects deserving further invescigation include: the outcome of PE reforms, the ex-post performance of divested firms, the efficiency ccnsequences of involuntary resettlement of labor, and the effectiveness of program contracts on the performance of PEs at the micro level. This sub-section elaborates on these issues.

71. Outcome of PE Reforms: With a few exceptions, most Bank assessments of PE reform either address the degree of compliance with conditionality, the effectiveness of the instruments used, o. the problems encountered in implementation. Rarely has the effect of PE reform been assessed in terms of outcomes; e.g. profitability, productivity, exports. budgetary impact, crowding out the private sector etc.

72. As noted, it may not have been possible in the past to measure the effect of PE reforms in terms of outcomes, in principle because of the brief period during which PE reforms have been attempted on a sectoral basis. It now appears, however, that sufficient time has elapsed, at least in a few cases, to permit such a study. The objectives would be to detail the outcome of attempted reforms, and provide insights a.dd feedback from experience to improve future attempts at reforming PEs. Notably, a study of the nature described here is underway for three Latin American countries; Chile, Argentina and Mexico. It would be desirable to mount similar regional studies in the rest of the Bank.

73. Ex-Post Performance of Divested PEs: Divestiture as a device for improvIng the contribution of PEs to a country's economic development has increasingly been used by developing and developed countries. It has been supported by the Bank in 35 countries in over 70 PE reform operations. 74. The intensified use of divestiture rests on two empirically verifiable propositions; first, it will improve productive efficiency and, second, it will reduce the budgetary burden of PEs. there has been no systematic empirical verification of these propositions, especially drawing upon the experience of LDCs, nor has there been an attempt to produce an

**34/** A theoretical welfare-based approach to divestiture decisions has been developed by Jones et. al. (Forthcoming). Further work may be necessary, however, to make the approach more operational and adaptable to various modes of divestiture and varving country circumstances.
2002 and 2005. Similar enterprise surveys have been conducted by the World Bank in many countries in other regions, but so far they have been done only on a country-bycountry basis, rather than region-wide every three years as with BEEPS.

The World Economic Forum’s (WEF) “Executive Opinion Survey” is another cross-country survey of firm managers. In the 2005 survey, a total of 10,993 responses were received, ranging from 22 for Mauritius to 473 for Russia. Cross-country rankings on several corruption questions (see Appendix B5) from this survey are published for 117 countries in WEF’s annual Global Competitiveness Report (Lopez-Claros, Porter and Schwab, 2005). Ratings are computed as the simple average of all executives’ responses. Another organization, the IMD, uses a nearly identical methodology, but using somewhat different survey questions, in its World Competitiveness Yearbook (IMD, 2005). The IMD executive survey is conducted in many fewer countries than the WEF survey, and it includes fewer questions on corruption. The IMD also discloses less information than the WEF on the size and composition of its sample of executives in each country.

The WEF and IMD executive opinion surveys differ from the BEEPS (and the World Bank’s other firm surveys) in several important respects. First, the sample in each country is selected with a preference for executives with international experience, who tend to be from larger and exporting firms. Second, the questions are designed to elicit “the expert opinions of business leaders” on corruption and other issues, and focus much less than BEEPS on firms’ experiences. The WEF, for example, asks about diversion of public funds – an issue on which few firms would have direct knowledge. Third, the WEF and IMD surveys are designed solely to produce country-level measures of the business climate. The BEEPS (and other World Bank firm surveys) is designed for firm7
<!-- Start of picture text --> Kaplan-Meier survival estimates<br>0 5 10 15 20 25<br>Duration<br>Not competitive bidding Competitive bidding<br>Kaplan-Meier survival estimates<br>0 10 20 30<br>Duration<br>Not OGM sector OGM sector<br>1.00<br>0.75<br>0.50<br>Survival rate<br>0.25<br>0.00<br>1.00<br>0.75<br>0.50<br>Survival rate<br>0.25<br>0.00<br><!-- End of picture text --> 40