rafmacalaba/datause-encoder-data huggingface dataset:
# Bankruptcy Reorganisation through Markets:

# Auction based Creditor Ordering by Reducing Debts (ACCORD)

by

## Donald B. Hausch and S. Ramachandran'

## I. **Introduction**

The basic approach in bankruptcy has creditors negotiate with debtors and with each other to reorganize the liabilities if the firm is viable, or to liquidate the firm in an orderly manner if it is not. Courts supervise the process to prevent individual creditors from "grabbing" assets or garnering proceeds out of turn and to prevent holdouts from getting an unfair advantage. Countries differ in their bankruptcy procedures but all have their critics?2 The 1978 U.S. Bankruptcy Code, for instance, allows firms to be either liquidated under Chapter 7 or reorganized under Chapter 11. Two common criticisms of Chapter 7 are that (1) viable firms may be needlessly dismantled, and (2)- sales proceeds would be meagre when the most likely buyers of industryspecific assets are buffeted by the same adverse economic developments as the bankrupt firm (Shleifer and Vishny 1992). Chapter 11 is a structured negotiation between current managers (representing owners, but subject to the principal-agent problem) and the creditors, with the outcome being a different payment stream than what was originally contracted. To limit holdouts stemming from the free-rider problem, courts allow the original debt contracts to be breached if there is substantial, albeit less than unanimous, creditor approval of the revised terms. Chapter 11 is often criticized as (1) too lengthy with high legal and administrative costs, (2) having a pro-management bias, and (3) violating the absolute priority of claims (because secured creditors cannot immediately seize assets and payouts to junior claimants are common despite reductions in the claims of senior claimants).

IUniversity of Wisconsin, Madison and the World Bank, respectively. Readers of an earlier paper should note that the ACCORD scheme described here is simpler than the earlier TADA and uses the private information that creditors reveal about firm values in their bids.

2 Berkovitch and Israel (1999) show that a country's optimal bankruptcy procedure is a function of the ability of creditors to gain inforrnation about the firm and the ability of managers to exploit their private information.

1
Gregorio Impavido For accounting purposes investment portfolios can be classified in two types: (1) for sale; and (2) to hold until maturity. The first type of portfolio is used to finance operations and short term liabilities such as acquisition or operation costs and payment of benefits. Due to the short term nature of this portfolio assets are marked to market. The second type of portfolio is used to back longer term liabilities, the valuation of which is based on a constant discount rate. It is natural to classify in these circumstances all reserves in at least the UPR as held to maturity. Due to the nature of this portfolio, assets are valued at maturity. On average, 90 percent of the portfolio of pension annuity companies is classified as “held to maturity.” Always for accounting purposes, liabilities regarding basic benefits are valued at the fixed technical interest rate defined in regulations which is currently 3.5 percent real.

However, in economic terms companies are required to set up asset liability management policies and hedge interest rate risk by matching assets with liabilities by the use of duration metrics. It was not possible to collect information on the evolution of the average duration mismatch between assets and liabilities. However, it was possible to collect information about the evolution of the average maturity of government and private sector bonds reported in the following two tables:

**Table 13: Average Maturity of Government Bond Portfolio (Years)**

||**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|
|---|---|---|---|---|---|---|---|---|---|
|y <= 1|0.00|0.00|0.38|0.39|0.52|0.23|0.18|0.27|0.26|
|1< y <= 5|3.42|3.09|2.96|2.43|2.24|3.83|3.03|3.32|2.48|
|5 < y <= 10|8.71|7.11|8.01|7.73|6.78|8.29|7.29|7.36|6.73|
|10 < y <= 15|12.36|12.30|12.15|11.63|10.63|13.72|13.18|12.74|12.34|
|y> 15|31.88|30.76|29.74|28.86|26.23|25.77|25.30|24.44|23.90|
|Total Government|6.14|7.51|6.73|13.07|14.71|16.36|14.76|14.88|14.53|
|Total bondportfolio|5.80|7.30|6.50|11.80|13.70|15.00|13.40|13.60|14.20|

**Source** : CNSF

**Table 14: Average Maturity of Private Sector Bond Portfolio (Years)**

||**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|
|---|---|---|---|---|---|---|---|---|---|
|y <= 1|0.00|0.00|0.11|0.14|0.12|0.46|0.36|0.24|0.51|
|1< y <= 5|3.09|3.34|2.14|3.27|4.11|3.95|3.40|2.55|1.97|
|5 < y <= 10|9.65|6.99|6.72|6.50|6.98|7.61|7.29|7.46|6.99|
|10 < y <= 15|13.59|11.80|11.94|11.04|11.93|14.33|12.65|14.05|13.79|
|y> 15|0.00|0.00|16.01|-|19.98|17.39|16.31|23.06|22.13|
|Total Private Sector|3.70|6.53|2.81|5.04|5.89|5.81|4.71|6.94|11.51|
|Total bondportfolio|5.80|7.30|6.50|11.80|13.70|15.00|13.40|13.60|14.20|

**Source** : CNSF

Notice that average maturity of the whole bond portfolio has been increasing in the last nine years from less than six years to more than 14 years. The increase is more

- 18 -
and possible shocks the government may face. The numerical approaches do not provide an explicit analytical solution that would guide one conceptually in the choice of the currency structure for foreign debt, and thus serve as a base for a more judgemental analysis. These approaches provide one with "exact" numbers as an output from the numerical optimization encompassing simulations out of macroeconomic or econometric models and ...nancial accounting. In order to make our literature review comprehensive we broadly group the recent numerical approaches according to the indicators they produce and brie‡y discuss their substance, insights and limitations. The indicators each numerical method produces constitute key inputs into the government’s candidate objective function as their values should be minimized or maximized in accord with government preferences. We look at the following broadly speci...ed indicators: (i) cost at risk (CaR) or value at risk (VaR), (ii) default probability based on speci...ed government’s debt-to-GDP default ratio, and (iii) default probability based on a distress barrier. Although the indicators ultimately look at the same risk we prefer to draw a slight distinction among the three groups with regard to the structures of the numerical approaches.

(i) The ...rst group includes work conducted at, for instance, the Bank of Canada, see Bolder (2002, 2003); Danmarks Nationalbank, see Danmarks National Bank (2006); Korea, see Hahm and Kim (2004); the UK DMO, see UK DMO Annual Review (2006) and Pick (2005); Swedish DMO, see Bergstom and Holmlund (2000); or Peruvian Ministry of Economy and Finance, see Peru Ministry of Economy and Finance (2005). The main indicator of interest that concerns this group is the CaR measure. The latter expresses the risk as the percentile distance, typically between the 50th and 95th percentile of the simulated cost allocation<sup>14</sup> . The larger the percentile distance, the higher the risk of the government liability portfolio. The cost is typically measured as a ratio to GDP and can be expressed either in nominal or real terms. The standard parts of the simulation underlying the CaR computation is a model simulating the paths of the underlying economic/...nancial variables, such as interest rates, output growth, in‡ation, exchange rate etc., and a ...nancial accounting framework for computation of the cost measure for each generated scenario. Since

> 14For a detailed exposition of this method see Danmarks Nationalbank (2000).

15
**School Quality, Labor Markets and Human Capital Investments Long-term Impacts of an Early Stage Education Intervention in the Philippines**<sup>**1**</sup>

Futoshi Yamauchi<sup>2</sup> and Yanyan Liu<sup>3</sup> June 2012 Abstract This paper examines the long-term impacts of improved school quality at the elementary school stage on subsequent schooling investments and labor market outcomes using unique data from a recent survey that tracked students in the Philippines. The empirical results, which are based on a comparison of students who graduated from schools located in adjacent treatment and control areas before and after a school intervention, show significant differences in subsequent schooling investments, migration, and labor market earnings between females and males. That is, females study more (relative to males) and tend to migrate and earn more if they receive high-quality educational investments at an early stage. The above results are consistent with females’ greater incentives to study, driven by their higher returns to schooling, especially after high school completion, observed in the labor market.

Keywords: school quality, labor markets, gender, tracking survey, Philippines JEL classifications: I21, J16 1We would like to thank Chris Barret, Luis Benveniste, Elizabeth King, Aniceto Orbeta, Harry Patrinos and seminar participants at the Philippine Department of Education, the Philippine Institute for Development Study, Japan International Cooperation Agency, Cornell University, and World Bank for useful comments. For collaboration, we are thankful to Yolanda Quijano, Ishidra Afunggol, Fe Gascon, Violy Cordova, Keijiro Otsuka, Judith Borja, Agnes Quisumbing, Juliet Abunyawan, and Felisberta Sanchez; the Bureau of Elementary Education; the Research and Statistics Division; the Antique, Cebu, Ifugao, Iloilo, Leyte, Negros Oriental, Nueva Vizcaya, and Western Samar division offices of the Philippine Department of Education; and the Office of Population Study of the University of San Carlos. We also thank Surajit Baruah for early-stage research assistance. We are responsible for remaining errors.

2Correspondence: The World Bank, 1818 H Street, NW, Washington DC 20433; Email: fyamauchi@worldbank.org; Phone: 202-458-4262 3International Food Policy Research Institute; Email: y.liu@cgiar.org 1
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single subperiod of the survey year: the more expenditures are concentrated, the less significant is the distortion.

At the same time, by using nominal expenditure we avoid the distortion induced by the fact that the _temporal deflator may not correctly reflect the dynamics of relative prices_ during the survey year. A correct estimate of the price of commodity _j_ should be given by: j where CPIt is a temporal price index specific to commodity _j_ . If all prices increase at a uniform j h j h growth rate, then CPIt = CPIt and uvj (CPIt ) = uvj (CPIt). However, if the price of commodity _j_ j h h j increases more than the general price level, then CPIt > CPIt ⇒uvj (CPIt) > uvj (CPIt ) and this implies that by using the real consumption aggregate to calculate unit values _we overestimate the market price_ for commodity _j_ .

The general recommendation is that if _i)_ the dynamics of the general price level during the survey year is sustained, and _ii)_ the official temporal CPI can be assumed to capture the dynamics of the most important commodities consumed by the households, the analysts should use the strategy “CPI first, SPI next”, that is, first adjust the nominal consumption aggregate for intra-survey temporal price variation and, then use the adjusted expenditures to estimate a spatial price index that can be used to further adjust households’ expenditures.

# **6 An empirical illustration**

In this section, we illustrate the empirical implications of choosing a single index (SI) versus multiple index (MI) approach in spatial (and temporal) price adjustment, using data from the Islamic Republic of Iran’s 2019 Household Expenditures and Income Survey (HEIS).

# **6.1 Construction of single and multiple price indexes**

Similar to most expenditure surveys, the Islamic Republic of Iran’s HEIS only collects information on expenditure and quantities consumed for food items. Unit values obtained from reported food expenditures and quantities allow for the estimation of a survey-based food spatial price index. On 25
Bank and Gaza have strong export concentration in other row materials, such as stone and glass (50% of total export of Lebanon) or vegetable or animal products (40% of total export of the West Bank and Gaza).

To better understand the determinants of exports for firms in the MENA region, we rely on the workhorse tool of gravity models. First introduced by Tinbergen (1962), gravity equations analyze the determinants of bilateral trade flows taking into account geographic distance between trading partners. The general form of a structural gravity model follows the one introduced by Anderson and van Wincoop (2003). After controlling for size (proxied by GDP), bilateral trade between exporter _i_ and importer _j_ depends on bilateral trade barriers between _i_ and _j_ , relative to the product of their multilateral resistance terms, i.e. the average trade barrier each country/region has with the rest of the world. Bilateral trade barriers may be determined by various factors, including trade agreements, institutions, geographic proximity, cultural similarities and historical bonds (Head and Mayer, 2014; Dhingra et al., 2017).

We develop a gravity model to study the determinants of exports, by the traditional bilateral trade barriers, but also taking into account the role of innovation and GVC participation, with the following equation: P i 1 i 2G i 1 i 2 i ii 3ii ii ii i i ln (TTe IIee ) = αα + αα + αα + ββ iiI PPI IIP + ββ GG + γγ TTiieeI IIddTT + γγ ddPPc PPIIccI cc ii are importer, exporter, and sector fixed effects respectively. + γγ ddPPI iic iiIIcc + εε i is a binary variable taking value 1 if a firm in sector k and country i is an innovator, and ii ii ii G i is a binary variable taking αα , αα , αα iiI PPI IIP value 1 if the firm is a GVC participant. i , i , i are proxies of bilateral GG trade barriers indicating the log-weighted distance between country i and country j, whether they share TTiieeI IIddTT ddPPc PPIIccI cc ddPPI iic iiIIcc their official language and whether they have a common border.

Table 6. Gravity estimation: determinants of exports

|Dependent variable:_ln(exports)_|(1)|(2)|(3)|
|---|---|---|---|
|Innovative firm|0.455***||0.180|
||(0.173)||(0.172)|
|GVC participant||1.617***|1.615***|
|||(0.170)|(0.181)|
|Distance (log)|-0.870**|-0.799**|-0.862**|
||(0.341)|(0.328)|(0.343)|
|Common border for trading partners|-0.540|-0.525|-0.579|
||(0.442)|(0.408)|(0.426)|
|Common official/primary language|0.297|0.448|0.448|
||(0.361)|(0.350)|(0.357)|
|Observations|3,164|3,355|3,129|
|R-squared|0.401|0.451|0.459|

_Note:_ OLS regressions with sampling weights. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Only the top five export destinations are used for the estimation. The regression control for sector, exporting country, and importing country fixed effects. Distance, common border and common language between trading partners are based on BACI data.

In our analysis, firms in Enterprise Survey are matched to BACI data at the industry level, which provides disaggregated data on bilateral trade flows for more than 5,000 products (that were 15
|||||Rest of||**App**|**endix Tab**|**le 2. Emi**|**ssions Red**|**uctions(**|**percent)**||Middle||||Low and||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||EU27<br>with<br>EFTA|United<br>States|Japan<br>|<br>high<br>income<br>|Rest of<br>high<br>income|Brazil|China|India|Russia<br> <br>E|Rest of<br>ast Asia|Rest of<br>South<br>Asia|Rest of<br>ECA|<br>East and<br>North<br>|Sub-<br>Saharan<br>Africa|Rest of<br>LAC|High<br>income<br>countries|<br>middle<br>income<br>|World<br>total|
|||||nnex 1|||||||||Africa||||countries||
||||||||Relative to|Business|as Usual(B|AU)in 2|020||||||||
|NBTA17|-30.0|-33.5|-15.8|-34.4|1.7|1.3|0.6|0.8|0.6|1.6|1.2|1.2|1.7|3.0|1.6|-28.4|1.0|-9.3|
|BTAFU|-30.0|-33.5|-15.8|-34.4|0.9|0.6|-1.3|-0.5|-0.9|0.1|-1.3|-2.2|-2.0|1.5|0.6|-28.5|-1.0|-10.6|
|BTADU|-30.0|-33.5|-15.8|-34.4|0.6|0.8|-0.3|-0.2|-0.1|0.9|-0.2|-0.4|0.0|2.2|0.8|-28.5|0.0|-10.0|
|BTADE|-30.0|-33.5|-15.8|-34.4|0.6|0.8|-0.3|-0.2|-0.1|0.9|-0.2|-0.4|0.0|2.2|0.8|-28.5|0.0|-10.0|
|BTAFR|-30.0|-33.5|-15.8|-34.4|1.1|0.6|0.0|0.4|0.1|1.0|0.4|0.1|0.5|2.3|1.0|-28.4|0.3|-9.8|
|BTADR|-30.0|-33.5|-15.8|-34.4|0.2|0.6|-1.7|-1.6|-1.4|0.0|-1.6|-2.1|-2.4|0.7|-0.3|-28.5|-1.5|-10.9|
|||||||||Relati|ve to 2005||||||||||
|NBTA17|-17.0|-17.0|-17.0|-17.0|74.2|37.6|183.5|156.5|125.3|74.4|121.7|124.6|35.3|82.5|73.3|-11.4|122.5|56.9|
|BTAFU|-17.0|-17.0|-17.0|-17.0|71.7|36.6|177.0|150.3|120.8|71.6|115.7|117.4|29.8|78.4|70.1|-11.6|117.2|54.1|
|BTADU|-17.0|-17.0|-17.0|-17.0|74.2|37.6|183.5|156.5|125.3|74.4|121.7|124.6|35.3|82.5|73.3|-11.4|122.5|56.9|
|BTADE|-17.0|-17.0|-17.0|-17.0|73.1|36.8|182.0|155.0|124.3|73.4|120.5|122.5|33.8|81.4|71.7|-11.5|121.1|56.2|
|BTAFR|-17.0|-17.0|-17.0|-17.0|72.8|36.5|178.3|153.2|121.9|71.8|116.4|117.1|30.4|79.9|71.5|-11.5|118.2|54.7|
|BTADR|-17.0|-17.0|-17.0|-17.0|72.4|36.8|181.2|153.7|123.7|73.1|118.8|121.1|33.0|81.1|72.0|-11.5|120.4|55.8|

_Notes:_ **_NBTA17:_** _Industrial countries alone reduce emissions by 17% and take no trade policy action;_ **_BTAFU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in imports;_ **_BTADU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in domestic production;_ **_BTADE:_** _Industrial countries alone reduce emissions by 17% with import tariffs on all merchandise imports and rebates on all merchandise exports based on carbon content in domestic production;_ **_BTAFR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in imports;_ **_BTADR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in domestic production_ 39
Figure 8: Counterfactual Measures of GDP per Capita <!-- Start of picture text --> 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Year<br>AL HC K/Y WAP LFP<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>Counterfactual GDP per Capita (2011 USD)<br>1500<br><!-- End of picture text --> Note (1) Each line represents the counterfactual path of GDP per capita from the isolated growth of each variable. Note (2) ”AL”: Productivity growth of labor-augmenting technology, ”HC”: Human capital growth, ”K/Y”: Capital deepening, ”WAP”: Changes of working-age population share, ”LFP”: Changes of labor force participation rate.

17
|**Table 17.**QMLE parameter estimates of a fractional Pr<br>with IV and inclusion ofDiDifvrand its inte<br>|obit forP3(Participation)<br>raction withP2andP1<sup>+</sup>|
|---|---|
|Variable<br>MENA<br>|OECD|
|P2(Provision)<br>3.07<sup>∗∗</sup><br>(1.55)<br>|−11.37<sup>∗∗∗</sup><br>(1.03)|
|P1(Protection)<br>3.34<sup>∗∗∗</sup><br>|−1.13<sup>∗</sup>|
|(0.46)<br>|(0.58)|
|rf_rp<br>−0.57<sup>∗∗∗</sup><br>|−0.63<sup>∗∗∗</sup>|
|(0.13)<br>|(0.10)|
|rf_iu<br>0.39<sup>∗</sup><br>|0.51<sup>∗∗∗</sup>|
|(0.21)<br>|(0.08)|
|rf_hdi<br>−1.60<sup>∗∗∗</sup><br>|3.82<sup>∗∗∗</sup>|
|(0.39)<br>|(0.49)|
|LvD 3_DiDifvr<br>1.87<sup>∗∗∗</sup><br>|−4.31<sup>∗∗∗</sup>|
|<br>(0.42)<br>|(0.45)|
|<br>LvD 3_DiDifvr× P2 × P1<br>−11.95<sup>∗∗∗</sup><br>|16.88<sup>∗∗∗</sup>|
|<br>(3.25)<br>|(1.67)|
|<br>p�21<br>−3.59<sup>∗</sup><br>|8.64<sup>∗∗∗</sup>|
|(1.86)<br>|(1.27)|
|p�22<br>−2.93<sup>∗∗∗</sup>|3.26<sup>∗∗∗</sup>|
|(0.53)|(0.65)|
|Constant<br>−1.95<sup>∗∗∗</sup>|1.99<sup>∗∗∗</sup>|
|(0.40)|(0.39)|
|Wald statistic<br>192.75<sup>∗∗∗</sup><br>|3357.26<sup>∗∗∗</sup><br>|
|Obs.<br>112<br>|350<br>|
|+Dependent variable:P3; Standard errors in parentheses obtained using 5<br>for MENA data: Domestic credit to private sector (% of GDP), Exports of g<br>of goods and services (annual growth rate), FDI (net outflows, % of GDP),<br>data: Tax revenues (% of GDP), Military expenditures (% of total govern<br>capita;<sup>*</sup>:p< 0.10,<sup>**</sup>:p< 0.05,<sup>***</sup>:p< 0.01.|00 bootstrap replications; Instruments<br>oods and services (% of GDP), Imports<br>and real GDP; Instruments for OECD<br>ment expenditures), and Real GDP per|

# **6. Conclusion**

The objective of this paper was threefold. The first was to suggest a way to translate a conceptual framework recently proposed by Loewe et al. (2021) to analyze the social contract in the MENA region into one that lends itself to data-fitting and regression analysis. In the same spirit as these authors, we focused on the extent to which a government protects its citizens, provides them with social benefits, and allows them to participate in the country's decisionmaking process. Given the economic, social, and political pathways of the countries in the region since WWII, in particular, at the turn of the century (Heydemann, 2020), we opted to employ an econometric modeling approach built on a potential causal connection from provision and protection to participation.

36
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31