rafmacalaba/datause-encoder-data huggingface dataset:
# _Government expenditures_

The final factor that we consider to explain movements in the long-run equilibrium REER is a change in the level of government expenditures. An increase in spending on traded goods generates an increase in demand for imports that results in a trade deficit, requiring a real exchange rate depreciation to maintain external balance. In the LIC context, an increase in government investment is likely to capture spending on tradables. Alternatively, an increase in government spending on nontradables is likely captured by consumption expenditures, which is expected to generate an increase in the relative price of nontradables. If the increase in government spending is financed by government borrowing that require subsequent tax increases to finance, aggregate demand may be muted relative to the initial increase. It is the allocation of government expenditure across sectors in the economy is a fundamental determinant of the long-run equilibrium real exchange rate, not unsustainable fiscal policy financed by excessive credit creation. The latter can cause a short-run deviation from the long-run equilibrium real exchange rate, i.e. a misalignment, but it is not considered a driver of the long-run equilibrium exchange rate.

Public expenditures in Tanzania have increased significantly in recent years, contributing to rapid GDP growth (World Bank **Figure 8. Public Investment and Real Effective** 2006). Importantly, the **Exchange Rate** composition of those expenditures has been 300 18 16 increasingly toward 250 public investment. Given 14 the high import content of 200 12 10 public investment, a rise 150 in the share of investment 8 in aggregate spending is a 100 6 4 good proxy for a shift in 50 public spending to 2 0 0 tradable goods. This 1970 1975 1980 1985 1990 1995 2000 2005 compositional shift is consistent with the recent Public Investment (% of GDP) WPI-based REER depreciation of the REER <u>Source: World Bank WDI and reports</u> (Figure 8). Tanzania’s budget is heavily financed by aid inflows for much of the sample under consideration (e.g. 40 percent in 2004) which makes it particularly difficult to disentangle the individual contribution of both aid inflows and government expenditures to movements in the REER in the same empirical specification. The issue of multicollinearity, which is not only limited to the aid and government expenditure variables, is addressed in the next section.

12
2) Fiscal data Cell range G15:X19, Fig. 1 Fiscal data refer to the appropriate coverage of the public sector: total revenues and grants, total expenditure, interest expenditure and the overall balance. Interest payments on domestic debt have to be consistent with the adopted definition of domestic debt. See point 7 below for a discussion of how the FSA tool accommodates both a gross or net definition of interest payments on domestic debt.

Data from this block are used to report a liquidity indicator, gross financing needs, which measures how much cash the government has to have in hand to cover all the claims coming due in the current year (data for the past 10 (5) years are calculated in cells G79:X79; the input sheet). The gross financing need in the current year is measured as the sum of the amortization of foreign and domestic public debt coming due during the current year, the interest expenditure, and the primary deficit also for the current year. To calculate this indicator for the projection period, we use the available projections for external public debt and amortization of external debt from LDB or from the existing macroprojection framework. For domestic debt we allow the user to manually insert data for the projected average maturity of domestic debt. The default assumption in this version of the template is that the average maturity of domestic public debt will remain the same as in the base year (cells S54:X54 from the input sheet).

3) GDP, prices and exchange rates Cell range G22:X29, Fig. 1 This section includes data on gross domestic product in current and constant prices; average and end-of-period exchange rates with respect to the U.S. dollar; and domestic and foreign (US) consumer price index (CPI), average for the period. The average exchange rate is used to convert in U.S. dollar the gross financing need of the government and to simulated domestic debt payments in the alternative scenario. End of period exchange rate is used to estimate the contribution to debt dynamics of real exchange rate devaluation and of the real interest rate on foreign debt. By default, the template reports current WEO projections for the U.S. CPI.

**For ORC only** , the template requires the input of non-oil GDP in current and constant market prices.

4) Monetary data Cell range G32:X34, Fig. 1.

Monetary data include base money (defined as Reserve Money and Currency in circulation) and net foreign assets (NFA) of the Central Bank, end-of-period. The template assumes net debt decomposition, as public debt is net of the Central Bank credit to the government and the Central Bank’s holdings of government paper and of NFAs. By default, NFAs are assumed to earn and interest rate equal to the LIBOR rate on the U.S. dollar: LIBOR rates can be retrieved from the _Intarnational Financial Statistics_ or other financial databases.

5) Other factors Cell range G37:X39, Fig. 1.

Other factors refer to any other variable which have a direct impact on debt dynamics, other than the primary fiscal balance, the contribution of interest rate/growth differential and the exchange rate.

15
# Research Components of World Bank Education Projects, since 1982.

Area ;Country and Loan/Credit Approved

|Area|;Country and Loan/Credit|Approve|d<br>Research Components|Current Status of Studies, including|
|---|---|---|---|---|
|Are|try adpNner|||<br>||Completed Documents/Data|
|ASIA|China CR 1297-CHA|1983<br>1983|of instruction<br>(para.2.26<br>p. <sup>1 3 </sup>Amex 8)<br>Agricultural Education and Research. Estab(ish<br>high priority agricultural<br>objectives and areas<br>of research focus.<br>(ii)<br>manpower studyto assess future<br>agricultural<br>needs and develop manpower<br>plaming strategies.<br>(iii) Pre-investment<br>studies for future project<br>(iv) Tracer studies of agricultural<br>graduates<br>(parss. 3.22-4 pp.29-3<sup>0</sup>).|Studies completed.|
|ASIA|China CR 1671-CHA|1986|Provincial Universities.<br>Specialists to prepare<br>reports on project implementation,<br>mid-term<br>review and 1995 evaluation (para. 3.20 p.31).|Bi-annual<br>meetings of Chinese and foreign experts<br>to review project implementation.<br>Last review 10/89.<br>Reports of evaluation reviews in Asia files "Super<br>-vision reports.|
|ASIA|China CR 1411-CNA|1984|Polytechnic/Television<br>University Project.<br>(i) Tracer studies of graduates (para. 33 p.10)<br>(ii) Pre-investment<br>studies for future projects<br>(para.35 p.11).|Ci) Tracer studies being unzdertaken<br>as part of<br> <br>preparation<br>of completion report.<br> <br>(ii) No pre-investment<br>studies undertaken.|
|ASIA|China LN 2444-CHA|1984|Second Agricultural Education. Tracer survey of<br>graduates and project progress to be reviewed<br>annually (para. 34 p.13).|<br>No information<br>supplied.|
|ASIA|China CR 1551-CHA|1985|Second University Development<br>Project. Three<br>studies: (i) to introduce<br>a system to monitor<br>minimumn<br>University standards,<br>(ii) to study existing national examination to<br>improve its accuracy in predicting academic<br>success,<br>(iii) study of management of overall system of<br>higher education (para. 33d p.11).<br>[also to sponsor 35 unspecified<br>subprojectsl.|Three studies carried out by Chinese experts.<br>Oral reports were given and taken into account<br>when designing new policies and procedures.<br>Bank<br>did not demand the results.|

38
# **Resolution of Corporate Distress: Evidence from East Asia's Financial Crisis**

Stijn Claessens Simeon Djankov Leora Klapper

## **Abstract**

The recent financial crisis in East Asia, across countries with very different institutional characteristics, allows the identification of factors that determine the use of bankruptcy as a means of resolving corporate distress. Using a sample of 4,569 publicly traded East Asian firms, we observe a total of 106 bankruptcies in 1997 and 1998. We find that the likelihood of filing is lower for firms with ownership links to banks and families, controlling for firm and country characteristics. In addition, filings are more likely in countries with better judicial systems. Finally, we find that the interaction between strong creditor rights and a better judicial system increases the likelihood of bankruptcy.

JEL Classification Codes: G33, G34.

The authors are from the World Bank. We thank Magdi Amin, G.K. van der Mandele, and Charles Woodruff for providing us with information on bankruptcy filings, and William Gamble, Patrick Honohan, Larry Lang, Rafael La Porta, Raghuram Rajan, Andrei Shleifer, Michelle White, and seminar participants at the World Bank for comments. Corresponding author: EM Lklapper(a,)worldbank.org, tel. 202 473 8738.
Individually, these points are not novel, but it is their integration that provides new empirical opportunities.

The Pathways for Peace report takes important steps in identifying the delivery of education, health care, water, sanitation, justice and security as “the glue” binding state and society (Milliken and Krause, 2002) via the basic minimum citizens expect in order to accept state authority (Gilley, 2009). However, the report acknowledges the complexity of the relationship between service delivery and legitimacy (Brinkerhoff et al., 2012; Sacks and Larizza, 2012; Stel and Abate, 2014; Mcloughlin, 2015; Fisk and Cherney, 2017). State legitimacy depends on public expectations, which are informed by prior experiences (Nixon et al., 2017), geography, identity, and culture (Sturge et al., 2017). The report acknowledges the Sustainable Development Goals (SDG 16.3) to promote the rule of law and ensure equal access to justice for all (United Nations and World Bank, 2018). The report emphasizes that development approaches must identify _how_ , not only _why_ , justice processes and outcomes discriminate, or are perceived to discriminate, against certain groups (United Nations and World Bank, 2018). Beyond establishment of frameworks identifying how processes treat groups differently, development actors must grapple with how to most equitably deploy finite resources to change that unequal treatment. For example, decisions regarding the equitable deployment of finite resources may be influenced by static (e.g. ethnicity) and dynamic (e.g. socio-economic status) indicators that recognize group differences within a community. Incorporating this data into CBA enables identification of how justice processes treat different groups and the consequences (economic and social) for those groups.

Identifying and communicating the dynamic interactions of factors associated with the individual and net costs and benefits (including their inclusivity) of policy change increases scope for informed citizen participation – enhancing social inclusion and process legitimacy. Social inclusion and process legitimacy constitute two of four (social resilience and social cohesion being the others) clusters of factors comprising the concept of ‘social sustainability’, which is associated with increased per capita income and reduced poverty (Cuesta Leiva et al (2022).<sup>3</sup>

> 3 We note the absence of consensus on the concept or definition of social sustainability. However, we acknowledge the four Cuesta Leiva et al (2022)-identified clusters of identified social sustainability factors. One of those factors is ‘process legitimacy’, described as the extent to which policy and implementation processes’ engagement with local norms and values, and their reconciliation of opposing viewpoints drive perceptions of

3
# **<u>Table 2: Descriptive statistics at block level</u>**

||<br>**Total**|**Outside TC**|**Treatment (T)**|**Control (C)**|
|---|---|---|---|---|
|**Sporadic registration**<br>|||||
|Number of plots<br>|||||
|2000|3.20|2.98|6.23|0.33|
|2010|5.76|5.73|9.23|1.22|
|2015|8.53|8.55|12.45|3.13|
|2019<br>|10.87|10.87|14.26|6.30|
|Area of registered plots m<sup>2</sup>|||||
|2000|4,516|4,415|7,743|746|
|2010|6,915|6,939|10,424|2,045|
|2015|9,730|9,782|13,513|4,331|
|2019|11,883|11,833|15,273|7,604|
|**Systematically registration**|||||
|No. of plots|||||
|2015|16.43|18.65|18.75|0.49|
|2019<br>|16.64|18.89|18.98|0.51|
|Area of registered plots m<sup>2</sup>|||||
|2015|15,349|17,361|17,712|554|
|2019|15,554|17,597|17,928|565|
|**No. of registered transfers**|||||
|2000|0.03|0.03|0.05|0.00|
|2010|0.06|0.06|0.09|0.00|
|2015|0.34|0.36|0.40|0.20|
|2019|0.34|0.34|0.36|0.32|
|**No. of registered mortgages**|||||
|2000|0.01|0.01|0.01|0.00|
|2010|0.08|0.08|0.09|0.03|
|2015|0.19|0.19|0.32|0.07|
|2019|0.16|0.16|0.17|0.15|
|**Structures (based on 2016 imagery)**|||||
|No. of buildings|56|56|75|31|
|Built-up area (m<sup>2</sup>)|4,989|5,003|7,045|2,132|
|Number of blocks<br>|1,932|1,373|321|238|
|Block area m<sup>2</sup>|62,500|62,500|62,500|62,500|

_Source:_ LAA, land administration database and digitized records, 2020 22
frequency of KPI review, and time frame of production targets. The answers to these questions are used to construct a management z-score following the methodology in Bloom and Van Reenen (2007). Despite covering only four of the 16 variables collected in MOPS, the FAT z-score based on this subset of questions accounts for 90.5% of the cross-establishment variance of the original MOPS z-score for Mexican establishments collected by ENAPROCE.

# **2.5 The Data**

Our analysis is based on primary data collected from establishments in 15 countries: South Korea, Poland, Croatia, Chile, Brazil (Ceará), Georgia, Vietnam, India (Uttar Pradesh, Tamil Nadu, Gujarat and Maharashtra), Ghana, Bangladesh, Kenya, Cambodia, Senegal, Ethiopia, and Burkina Faso. Several factors were considered in deciding where to implement the FAT survey. We targeted countries on different continents (Asia, Africa, South America, and Europe), with different levels of income, for which there was access to a high-quality sampling frames. In these countries, we collected data from 21,055 randomly selected establishments from the sampling frames. Table 2 shows the distributions of the sample by country, sector, and size groups and Table C.1 provides descriptive statistics. The median establishment in our sample has 9 workers, with an average of 34 workers. 20% of workers have a college degree, 19% of firms were multi-establishments, 18% are part of a multinational firm, 17% are exporters, 18% are 5 years old or younger, and 76% have electricity, computers, and internet access.

## **2.5.1 Sampling**

Our data is representative for a universe of about 2.1 million establishments. The samples are nationally representative for establishments with 5 or more workers. For each country, the sampling frame is based on the most comprehensive and up-to-date establishment-level census data available from the respective National Statistical Office (NSOs) or similar authority. The survey is stratified on three dimensions - sector, firm size, and region - so that we can construct representative measures of technology for aggregates along these dimensions. Sampling weights are based on the inverse probability of selecting establishments within each stratum.<sup>8</sup>

> 8Table A.15 provides information about the distribution of firms by country, sector, and size groups within the universe covered by the FAT survey. For the state of Ceará in Brazil and the Indian states of Tamil Nadu, Uttar Pradesh, Gujarat, and Maharashtra, it is representative at the state level. Section A of the Appendix provides more details on the sampling frame, survey implementation and data collection, and sampling weight.

11
5 working for domestic firms, these firms can benefit from the knowledge and skills that the workers incorporate. An example of this effect is provided by Görg and Strobl (2005), who estimate drivers of productivity for a sample of manufacturing firms in Ghana and identify a productivity premium among firms whose owners worked for MCN affiliates earlier on in their career. Another example is presented by Poole (2013), who uses a matched establishment-worker data set for Brazil and identifies positive productivity effects among domestic firms that employ workers who were previously employed by MNC affiliates.

The third channel consists of inter-firm linkages between MNC affiliates and domestic firms that supply inputs to (or purchase inputs from) the affiliates. These inter-firm linkages are conducive to the creation of productivity spillovers for various reasons. First, MNC affiliates exercise stronger demands on local suppliers regarding the quality of inputs, their cost-effectiveness, specifications, and so on. Even in the absence of technology dissemination, this can lead to productivity effects when domestic firms respond to these stringent demands by improving their performance. Second, input markets are often characterized by frequent contacts and the sharing of information between buyers and sellers, facilitating demonstration effects and unintentional technology flows to domestic firms (UNCTAD 2001). Third, MNC affiliates are often actively engaged in providing support to their suppliers and customers, fostering technology dissemination and productivity spillovers (Jordaan 2016; UNCTAD 2001). An example of this is provided by Jordaan (2017) who presents findings from a survey in northeastern Mexico showing that MNC affiliates provide a variety of types of support to their local suppliers, which according to these suppliers has been very important in generating performance improvements.

The first waves of quantitative FDI spillover studies were concerned with the econometric estimation of intra-industry (horizontal) spillovers, linking the presence of MNC affiliates to productivity of domestic firms operating in the same industries. The evidence on the general prevalence of these intra-industry spillovers is very mixed and inconclusive, ranging from negative to insignificant to positive (Görg and Greenaway 2004; Smeets 2008). Furthermore, findings from studies that apply meta-analysis regression techniques on large sets of empirical estimates show that on average estimates of intra-industry spillovers are insignificant or economically not important (Havranek and Irsova 2011; Irsova and Havranek 2013).

More recently, studies have started to incorporate estimating for the presence of interindustry (vertical) productivity spillovers between industries. These spillovers can arise through backward linkages between MNC affiliates and domestic input suppliers and forward linkages between MNC affiliates supplying inputs to domestic client firms. Following the original contribution by Javorcik
**Table 2: HDI in Main Capitals of Brazil**

|**City**|**HDI**<br>**1991**|**HDI**<br>**2000**|**Population**|
|---|---|---|---|
|Sao Paulo|0,804|0,841|10,434,252|
|Rio de Janeiro|0,798|0,842|5,857,904|
|Salvador|0,751|0,805|2,443,117|
|Belo Horizonte|0,791|0,839|2,238,526|
|Fortaleza|0,717|0,786|2,141,402|
|Brazil|0,696|0,766|137,953,959 (urban)|

|Source:_Atlas do Desenvolvimento Humano no Brasil_, UNDP.|
|---|

In fact, the most developed neighborhood in Fortaleza has a HDI comparable to that of industrialized countries (0.916), while the least developed neighborhoods have a level of development comparable to that of countries in sub-Saharan Africa (0.338).<sup>14</sup> The three neighborhoods included in the survey— Autran Nunes, Edson Queiroz, and Pirambú— were among the poorest in Fortaleza (see Table 3). The following three sections describe each neighborhood in more detail.

|**Table 3: HDI and P**<br>|**opulation in**<br>|**Autran Nune**|**s, Edson Queiro**<br>**Total P**|**z, and Pirambú**<br>**opulation**|
|---|---|---|---|---|
|**Neighborhood**|**Region**|**HDI**|**Male**|**Female**|
|Autran Nunes|III|0.380|10,418|10,905|
|Edson Queiroz|VI|0.500|9,590|10,701|
|Pirambú|I|0.391|8,844|9,609|

Souce: _Secretaria de Planejamento e Orçamento/ Prefeitura de Fortaleza_ and Census 2000.

# **Pirambú**

Pirambú is located in the northern part of Fortaleza. This shantytown was developed mainly by people migrating from rural areas in Ceará and other northeastern states in search for higher standard of living, employment, and other opportunities. As Pirambú grew bigger and expanded

> 14 Source: _Dados da Defesa Civil do Município_ .

10
three sub-sectors. The share of harmonized standards for both clothes and fabrics increases markedly over the sample period, from an admittedly low baseline in both cases (Tables 5-6 and Figures 3-4). However, the opposite dynamic is apparent for fibers: the share of harmonized standards rises slightly between 1995 and 1998, but then falls markedly for the remainder of the period (Table 6 and Figure 5). The contrast between the three sub-sectors at the end of the sample period is also interesting: for fibers, over 80% of standards are harmonized with ISO standards, while the figure is just 20% for clothes and a little under 30% for fabrics.

It is important to stress that at this stage, the World Bank data covers Community-level standards only. Since the standard setting body in each EU Member State is required to translate such norms into local standards, our data therefore also capture part of the standardization activity of each individual country. However, we do not currently have data on country-specific standards in EU Member States. There are two main reasons for this. First, data availability in Perinorm varies considerably from country to country. Without any simple cross-check, such as the CEN online catalog, it is difficult to be sure that a dataset of national standards is in fact capturing all relevant information. Second, standardization at the country level has been ongoing for a much longer time span that at the regional level. As a result, it is necessary to go much further back in history in order to make a reliable assessment of the total stock of standards in force at any given time. Not unexpectedly, Perinorm’s coverage becomes more patchy the further back one goes (Moenius, 2000), thus rendering it particularly difficult to obtain accurate stock information for those countries with a long history of standardization.

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