rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 1: The Russian Venture Company**

<mark>The sole shareholder of OJSC RVC possessing 100 percent of its equities is the Russian Federation represented by the Federal Foundation for Promotion of Small Enterprises in Science and Technology. As a result of two competitions conducted by OJSC RVC in 2007 and 2008 7 venture</mark> foundations were established with a total capitalization of Rub 19.98 billion (US$ 670 million):

1. VTB Venture Foundation (net asset value of Rub 3.09 billion, US$ 104 million);

2. Bioprocess Capital Ventures (net asset value of Rub 2.9 billion, US$ 102 million); <mark>3. OJSC Alliance ROSNO Assets Management (foundation size of Rub 3.06 billion,</mark> US$ 102 million); 4. LLC Maxwell Asset Management (foundation size of Rub 3.06 billion, US$ 104 million); 5. CJSC Leader (foundation size of Rub 3 billion, US$ 102 million); <mark>6. LLC Managing Company North Asset Management (foundation size of Rub 1.8 billion,</mark> US$ 60 million); 7. CJSC Managing Company CenterInvest (foundation size of Rub 2 billion, US$ 67 million).<sup>8</sup> <mark>As of July 1, 2009, two foundations (CEIF VTB-Venture Foundation and CEIF Bioprocess Capital Ventures) financed 14 innovation companies with a total amount of Rub 1.74 billion (about US$ 60 million) while the total number of projects reviewed by all foundations exceeds 1,500. As of August 2010, 10 RVC–backed funds ran a portfolio of 31 companies, with invested capital totaling</mark> Rub 4.5 billion (about US$ 150 million).

<mark>Among the main areas for investments are biomedical technologies, power engineering and energy</mark> saving, ICT systems, software manufacturing technologies, and “critical technologies.” Venture capital investors are forced to invest in mature and/or foreign companies. They are particularly affected by the lack of viable exit strategies due to the underdevelopment of the market for initial public offerings and the lack of depth of financial markets. Statistics from the Russian Venture Capital Association show that 80 percent of investment capital is dedicated to financing restructuring or business expansion and only 20 percent is earmarked for early-stage financing of new companies. Several of the leading Russian funds have searched for an answer by investing in international projects or by copying Western projects, but without any real technological innovation. The best example of this is the fund Digital Sky Technologies, investing in Facebook, ICQ, Vkontakte, Mail.ru, and others.

The problem with investing in Russian start-ups is the lack of investment projects that fit the investment criteria, rather than an absolute lack of sufficient volume of investment capital. Also, Russian technology start-ups lack experience in developing business plans that reflect the fundamentals required for equity investment, and lack access to affordable legal, accounting, and consulting services that are required to prepare an “investable” business plan and to properly protect their IP.

> 8 OJSC: Open Joint Stock Company LLC: Limited Liability Company CJSC: Closed Joint Stock Company CEIF: Closed Equity Investment Fund

18
confined to a small region in Brazil, and one for a non-endemic species, _Lagidium viscacia_ (the Mountain Viscacha), which has a broader distribution across several countries in South America. These examples illustrate the contrasting distribution patterns of species with restricted versus extensive geographic ranges.

To illustrate, effective biodiversity conservation plans depend on accurately identifying regions with species that are either endemic (i.e., species confined to a specific geographic area, often within a single country) or facing high extinction risks. Extinction risk refers to the likelihood that a species will become extinct within a certain time frame and is quantified using various models. In this study, we estimate extinction risk using an ordered-logit model, a statistical technique that predicts categorical outcomes based on multiple variables. For this analysis, a high extinction risk is defined as a score above 80 out of 100, indicating a species with a significant threat of extinction.

Utilizing our newly developed occurrence maps, we identified 267,522 endemic species (44.7% of the total species in our dataset) and 84,627 species whose small occurrence areas (defined as areas smaller than 25 km2) make them highly vulnerable to extinction. Occurrence area refers to the observed geographic range of a species, with smaller ranges often correlating with higher extinction risk due to habitat loss, climate change, or other threats.

In addition, hundreds of thousands of species in our Global Biodiversity Information Facility (GBIF) database have not previously been assessed for extinction risks. To address this gap, we have incorporated location-specific threats (e.g., habitat destruction, pollution, or invasive species) and protection measures (e.g., conservation efforts or legal protections) into the extinction risk estimates for nearly 600,000 species. This includes 510,090 species that have not yet been evaluated for risk by the International Union for Conservation of Nature (IUCN) (Dasgupta et al. 2024b).<sup>6</sup>

# **Geographic Boundaries for Conservation Analysis**

In addition to generating species occurrence maps, we overlaid various geographic boundaries to examine the influence of governance and territorial issues on biodiversity conservation. These boundaries included areas such as non-determined legal status areas, Fragile and ConflictAffected Situations (FCSs), joint regimes of Exclusive Economic Zones (EEZs), and transboundary river basins, all of which present unique challenges for effective conservation. The boundaries for non-determined legal status areas and fragile/conflict states (FCSs) were obtained from the World Bank. The boundaries of joint EEZ regimes were sourced from the Flanders Marine Institute (2019), and the transboundary river basin boundaries were taken from McCracken and Wolf (2019), version 2022-03-07.

The inclusion of non-determined legal status territories is critical because these regions often face unclear or disputed governance, which can hinder effective conservation efforts. These

> 6 <u>Global Biodiversity Species Extncton Risks Data</u>

8
Summary statistics for the CDS sample are in the Appendix, Table A2. The characteristics of these banks are quite similar to those of the large bank sample of the previous sections. The median change in the CDS spread over the pre-crisis period was minus six basis points, while during the crisis period the median increase was 167 basis points. The increase in the spread was even more pronounced in the quarters after the Lehman bankruptcy (246 basis points). As in the case of stock returns, the dispersion of spread changes also increased sharply during the crisis.

The empirical model is similar to that used to explain corporate CDS spreads by Ericsson, Jacobs, and Oviedo (2009), which is in turn inspired by the corporate bond spread models of Campbell and Taksler (2003) and Cremer, Driessen, Maenhout, and Weinbaum (2004). In a simple linear regression framework, changes in the CDS spread are regressed on changes in the bank’s leverage, defined as the book value of the bank’s debt divided by the sum of the book value of debt plus the book value of equity), the change in stock price volatility, and changes in the yield on the risk-free asset.<sup>14</sup> According to the theory, an increase in the default probability (an increase in the CDS premium) should be increasing in the bank’s leverage and in the variability of its expected future cash flows (proxied by equity volatility), and it should be decreasing in the risk-free interest rate. Since we are interested in the role of capital, we add to these three variables various lagged measures of bank capital, as in the previous sections. We also allow the coefficients of the capital ratios to differ between crisis and non-crisis periods. Finally, in these regressions we control for region/time fixed effects rather than country/time fixed effects because we do not have a sufficiently large number of banks per country. Of course, we expected better capitalized banks to experience a smaller increase in the CDS premium during the crisis than weakly capitalized banks.

The regression results are in Table 11. The risk-free interest rate and leverage are significant with the expected sign, while volatility of equity has the right sign but is not significant. However, higher bank capital does not seem to lead to a smaller increase in the CDS spread during the crisis: the coefficient of Tier 1 capital does turn from positive to negative as the crisis begins, but it is not significantly different from zero. Somewhat oddly, Tier 2 capital has a positive and (marginally) significant coefficient in the regression in which assets are not risk-adjusted. One potential reason for these results may be the small sample size and the lack of liquidity of the CDS market following the Lehman bankruptcy. However, when we estimate separate coefficients for the post-Lehman period, we do continue to find no significant results for capital.

> 14 The bank’s leverage (ratio of debt to assets) should not be confused with its leverage ratio (ratio of book capital to assets).

12
(2017) provides evidence from India using a randomized control trial in conjunction with a quasi-experiment and finds that replicating the mean effect of the information nudge in electricity consumption through tariff changes alone would require at least a 12.5 percent increase in the price. Comparing these policy instruments can shed light on the debate about what best motivates households’ uptake of renewable energy and energy conservation behavior.

# **3. Methodology**

# **_3.1  Baseline Data_**

Baseline data was collected from a survey of a stratified random sample of 810 rural households from 45 study sites in Ethiopia. Sites were selected purposively from the three largest regional states of Ethiopia (Amhara, Oromia and Southern Nations, Nationalities and Peoples (SNNP) regional states) to maintain heterogeneity in site characteristics while also targeting sites that are not connected to the grid and not targeted by governmental and nongovernmental organizations for dissemination of solar lanterns. There were 18 randomly selected households in each of the 45 sites. As noted in the experimental design section below, all 810 households were subjected to the randomized field experiment.

To gather information about observable characteristics of our sample households, we also conducted a baseline survey including: (1) socioeconomic characteristics (including demographic characteristics and wealth), (2) fuel used by households for lighting and the corresponding equipment, (3) knowledge and perceptions of the environmental, health and other effects of using biomass and solar lanterns, and (4) social networks. As the survey was applied to a random sample of households in our study sites, interviewed households will be regarded as our experimental population.

# **_3.2  Experimental Design_**

After we collected data on baseline characteristics of households, we conducted an information provision experiment to promote the adoption of solar lanterns. This was followed by elicitation of the individuals’ WTP using the BDM bidding method. The incentivecompatibility of the method leads us to believe that their stated WTP is a very good approximation of their actual WTP. The following is a summary of our experimental design.

First, we provided information as follows. Interviewed individuals in each village were randomly divided into two groups (treatment and control groups) and placed in two separate rooms. Each group consisted of 9 respondents. The treatment group was provided information on the private and environmental benefits of the solar lantern, which might not be obtained from 9
as a source of these financial resources. Public investment, financed by public debt, can directly improve the economy’s productive capacity by increasing the marginal product of private capital and labor. As time progresses, this could generate positive effects on private investment and private consumption (Leduc and Wilson, 2013). Izquierdo et al. (2019) find that countries with a low initial public capital stock (as a proportion of GDP) have significantly higher public investment multipliers than countries with a high initial public capital stock. Eden and Kraay (2014) report that in low-income countries, on average, <mark>an extra dollar of government investment raises private investment by roughly two dollars and output by 1.5 dollars</mark> .

On the other hand, public debt can be problematic if it is used for unproductive ends. More precisely, if the economic and social returns to fiscal spending are lower than the realized interest rates paid for debt, the debt burden relative to national income will tend to grow over time. Even when the economic and social returns to fiscal spending exceed the interest rate on the debt used to finance it, governments still need to be cautious about borrowing more when debt is already high. As debt rises, it creates fear that governments will be unable to repay, resulting in higher risk premia and higher long-term real interest rates (Reinhart et al., 2012). The tools at the disposal of governments, such as counter-cyclical fiscal policy, are also severely weakened. Elevated debt reduces creditworthiness, restricting access to further financing and curbing the ability of economies to roll over (that is, refinance maturing) debt. High debt levels may be monetized, leading to inflation, currency depreciation, capital flight, and possibly debt or financial crises (Boskin, 2020). All of these could cause private firms to reduce investment. This channel is referred to as the “crowding out” effect of public debt. The impact of high public investment on private investment therefore remains an open empirical question.

This paper examines the impact of public debt on private firms’ investment using the World Bank Enterprise Surveys, which provide a large sample of firms in many developing countries. Our contributions are threefold. First, we find that higher public debt significantly and robustly reduces private investment on the extensive and intensive margins. In other words, firms are less likely to invest, and they invest less when public debt rises. Firm panel estimations reveal that a given firm is 0.1 percent less likely to buy land and 3
# **Trade Policy and Market Power: Firm-Level Evidence**

Alan Asprilla, Nicolas Berman, Olivier Cadot, and Melise Jaud<sup>_∗_</sup> University of Lausanne, Switzerland; Aix-Marseille University, CNRS, EHESS, Centrale Marseille, AMSE, France, and CEPR, UK; University of Lausanne, Switzerland, CEPR, UK, and FERDI, France; World Bank, U.S.A. and University of Lausanne, Switzerland _JEL classification_ : F12, F13, F14, D40, F31. _Keywords_ : Trade policy, non-tariff measures, tariffs, exchange rate, price discrimination.

> _∗_ Olivier Cadot passed away in March 2019. Olivier was a profoundly kind and thoughtful friend and a remarkable researcher. Working with him has been a privilege and an incredibly joyful adventure. We are grateful to George Alessandria, three referees, as well as Paulo Bastos, Richard Newfarmer, Frederic Robert-Nicoud, and to seminar participants at CERDI, Nice Sophia-Antipolis, the Central Bank of Uganda, the World Bank, the ERF Annual Conference, the ETSG conference and the University of Lausanne for useful comments and suggestions. Special thanks to Caroline Freund for detailed advice. Jingjing Xia provided excellent research assistance. An early version of this paper was prepared as a background paper for the World Bank’s “Champions Wanted” regional study and draws on prior research undertaken with support from the International Growth Center. Support from France’s Agence Nationale de la Recherche under “Investissement d’Avenir” grant ANR-10-LABX-14-01 and from Switzerland’s NCCR under WP6, as well as from the governments of Norway, Sweden and the United Kingdom through the Multi-Donor Trust Fund for Trade and Development are gratefully acknowledged. Melise Jaud appreciates financial support from the Swiss National Science Foundation. The project leading to this publication has received funding from Excellence Initiative of Aix-Marseille University - A*MIDEX, a French “Investissements d’Avenir” programme. The opinions expressed in this study are those of the authors and do not necessarily represent the views of the World Bank, its Board of Directors, or the governments that it represents. Please address correspondence to: Melise Jaud, World Bank Group, 1818 H street NW, Washington, DC 20433. E-mail: mjaud@worldbank.org.
10 which are OECD member countries;<sup>4</sup> 13 of Europe’s transition economies, five of which are OECD member countries;<sup>5</sup> and six other high-income OECD member countries: Australia, Canada, Japan, New Zealand, the Republic of Korea, and the United States. The OECD member countries that are not included in the focus countries sample are Belgium, Greece and Luxembourg (for which NRA estimates are not available and Mexico (a recent and much poorer member).

The database contains annual estimates of nominal rates of assistance (NRAs, positive or negative) and consumer tax equivalents (CTEs) for key farm products. The NRA and CTE estimates in the database are at the commodity level and cover a subset of 39 agricultural products in the OECD. These so-called covered products account for around three-quarters of total agricultural production over the period studied. The database identifies the extent to which each commodity in each country each year is import or export dependent or a nontradable (which may change over time). For the 34 focus countries, the database contains around 16,000 consistent estimates of annual NRAs to the agricultural sector and the same number of CTEs between 1955 and 2007. Since not all countries have estimates for the 1950s, we report estimates below starting from 1960.

The range of policy measures incorporated in the NRA estimates in the database is wide. By calculating domestic-to-border price ratios, the estimates include assistance provided by all tariff and nontariff trade measures at each country’s border, plus any domestic price support measures (positive or negative), plus an adjustment for the output-price equivalent of direct interventions on inputs. Where and when multiple exchange rates operated, estimates of the import and export tax equivalents of that distortion are included as well. The range of measures included in the CTE estimates includes both domestic consumer taxes and subsidies and trade and exchange rate policies, all of which drive a wedge between the price that consumers pay for each commodity and the international price at the border.

Analytical narratives of agricultural policies for the last five decades in the 34 OECD countries are provided in Anderson (2009). This book reports on the data in the Distortions to Agricultural Incentives database, and contains case studies for specific regional groupings. The book reports on measures such as unweighted and

> 4 Austria, Denmark, Finland, France, Germany, Iceland, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the UK.

> 5 Bulgaria, **Czech Republic** , Estonia, **Hungary** , Latvia, Lithuania, **Poland** , Romania, Russia, **Slovakia** , Slovenia, **Turkey** and Ukraine (OECD member countries in bold).
Sandford, C. (ed.), (1995) _Taxation Compliance Costs: Measurement and Policy, Fiscal Publications, Bath._ Sandford, C., Godwin, M. and Hardwich, P. (1989) _Administrative and Compliance Costs of Taxation, Fiscal Publications, Bath._ South African National Planning Commission (2010): “Development indicators” available at _<u>www.thepresidency.gov.za</u>_ USAID (2008), “Formal SMME Tax compliance Survey Report: Prepared for National Treasury Republic of South Africa”, available at: <u>http://pdf.usaid.gov/pdf_docs/PNADR986.pdf</u> Wooldridge, Jeffrey M. (2002) “Econometric Analysis of Cross Section and Panel Data” _The MIT press_ 56
induced a damage of $70.3 million in the polders and related water regulators in coastal Bangladesh. Past experience has identified “overtopping” of an embankment of a polder to be the most important factor responsible for polder’s damage during cyclones.<sup>44</sup> Comparison of projected surge heights and heights of existing embankments of polders indicate an additional 15 polders are likely to be overtopped by 2050 in a changing climate,<sup>45</sup> and **the estimate of additional potential damage is $17.3 million** .

To sum up, Table 5 presents itemized additional potential damage and additional potential loss for an average 10 year return period cyclone induced inundation in a changing climate by 2050 without adaptation. **The estimates indicate if a 10 year return period cyclone (likely to be more intense than current cyclones with climate change) hits Bangladesh, additional potential damage is likely to be $2.437 billion and additional potential loss $2.123 billion** (See Table 5) **.**

# **3.4 Mapping existing Coastal Protection in Bangladesh and gap analysis** <u>:</u>

Bangladesh has an extensive infrastructure such as polders, cyclone shelters, early warning and evacuation system to protect coastal regions.

# a) _<u>Coastal Polders</u>_ <u>:</u>

In the early sixties and seventies 123 polders, of which 49 polders are sea facing, were constructed to protect low lying coastal areas against tidal flood and salinity intrusion. Bangladesh Water Development Board (BWDB) maintains an extensive database of coastal polders, and it includes information on length, location, construction year and cost for each polder.<sup>46</sup> See Figure 3 for the lay out map of the costal polders (See Figure 3).

Geographic Information System (GIS) software has been used to overlay the best available spatially‐disaggregated data on polders in the derived vulnerable coastal zone of 44 When water surge overtops an embankment, rapid and deep scours start to form on the country side slope of the embankment. The process rapidly weakens the structure and leads to its collapse.

> 45 Computation has been explained in details in the next section.

46 In addition, information on riverbank and shoreline protection and re‐sectioning of embankment works has been extracted from the Coastal Embankment Rehabilitation Project (CERP) and South‐ Eastern Zone‐Chittagong, South‐Western Zone‐Faridpur, Southern zone‐Barishal maintained by the BWDB. It includes information on the ongoing projects, construction period and cost.
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