rafmacalaba/datause-encoder-data huggingface dataset:
Table S2.13 Variance Decompositions, Between- and Within-Firms: Brazil, Costa Rica, and Ecuador

|_a. Brazil_||||
|---|---|---|---|
||Between-frm component<br>(1)|Within-frm component<br>Total <br>(2)|variance<br>(3)|
|1995|58 (0.367)<br>|42 (0.261)<br>100 <br><br>|(0.628)<br>|
|1996|59 (0.364)|41 (0.255)<br>100|(0.619)|
|1997|60 (0.366)|40 (0.247)<br>100|(0.613)|
|1998|58 (0.341)|42 (0.246)<br>100|(0.587)|
|1999|59 (0.337)|41 (0.235)<br>100|(0.572)|
|2000|59 (0.329)|41 (0.230)<br>100|(0.559)|
|2001|58 (0.296)|42 (0.218)<br>100|(0.514)|
|2002|56 (0.275)|44 (0.212)<br>100|(0.487)|
|2003|56 (0.262)|44 (0.204)<br>100|(0.466)|
|2004|55 (0.252)|45 (0.204)<br>100|(0.456)|
|2005|54 (0.230)|46 (0.197)<br>100|(0.427)|
|2006|52 (0.210)|48 (0.191)<br>100|(0.401)|
|2007|52 (0.203)|48 (0.190)<br>100|(0.393)|
|2008|51 (0.201)|49 (0.193)<br>100|(0.394)|
|2009|50 (0.189)|50 (0.188)<br>100|(0.377)|
|2010|49 (0.180)|51 (0.187)<br>100|(0.367)|
|2011|48 (0.179)|52 (0.192)<br>100|(0.371)|
|2012|47 (0.170)|53 (0.190)<br>100|(0.360)|
|2013|46 (0.167)|54 (0.192)<br>100|(0.359)|
|2014|46 (0.162)|54 (0.194)<br>100|(0.356)|
|Change 1995-2001|63 (-0.071)|37 (-0.043)<br>100|(-0.114)|
|Change 2001-2014|85 (-0.134)|15 (-0.024)<br>100|(-0.158)|
|Change 1995-2014|75 (-0.205)|25 (-0.067)<br>100|(-0.272)|
|_b. Costa Rica_||||
|2006|51 (0.216)|49 (0.207)<br>100|(0.423)|
|2007|52 (0.228)|48 (0.214)<br>100|(0.442)|
|2008|50 (0.225)|50 (0.223)<br>100|(0.448)|
|2009|54 (0.249)|46 (0.216)<br>100|(0.465)|
|2010|53 (0.240)|47 (0.213)<br>100|(0.453)|
|2011|54 (0.250)|46 (0.215)<br>100|(0.465)|
|Change 2006-2011|80 (0.034)|20 (0.009)<br>100|(0.043)|
|_c. Ecuador_||||
|2006|61 (0.265)|39 (0.170)<br>100|(0.435)|
|2007|57 (0.232)|43(0.173)<br>100|(0.405)|
|2008|52 (0.194)|47 (0.173)<br>100|(0.367)|
|2009|49 (0.167)|51 (0.175)<br>100|(0.342)|
|2010|47 (0.153)|53 (0.170)<br>100|(0.323)|
|2011|47 (0.145)|53 (0.165)<br>100|(0.310)|
|2012|46 (0.144)|54 (0.169)<br>100|(0.313)|
|Change 2006-2012|99 (-0.121)|1 (-0.001)<br>100|(-0.122)|

_Sources:_ Authors’ calculations based on data from Social Security Records: Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS) in Brazil, Caja Costarricense de Seguro Social (CCSS) in Costa Rica, and Instituto Ecuatoriano de Seguridad Social (IESS) in Ecuador. _Note_ : The table reports the results of the variance decomposition. Cells contain the share of the total variance explained by each decomposition element. The total variance explained by each decomposition element is shown in parentheses.

65
**Table 5.A. Model comparison: Bias of aluminum price forecasts**

(U.S. dollars per metric tonne)

|**Approach**|**Horizon**|**Consensus**|**Futures**|**Bivariate**<br>**correlations**|**BVAR**|**OEM**|**Machine**<br>**learning**|
|---|---|---|---|---|---|---|---|
|Consensus|3|-6.1|...|...|...|...|...|
|Futures|3|*|353.8|...|...|...|...|
|Bivariate correlations|3||*|5.8|...|...|...|
|BVAR|3||*||-23.3|...|...|
|OEM|3||*|||15.9|...|
|Machine learning|3||*||||14.2|
|Consensus|6|-2.7|...|...|...|...|...|
|Futures|6|*|354.7|...|...|...|...|
|Bivariate correlations|6||*|10.2|...|...|...|
|BVAR|6||*||-27.9|...|...|
|OEM|6||*|||-0.3|...|
|Machine learning|6||*||||22.4|
|Consensus|9|-2.6|...|...|...|...|...|
|Futures|9|*|362.7|...|...|...|...|
|Bivariate correlations|9||*|113.7|...|...|...|
|BVAR|9||*||-27.5|...|...|
|OEM|9||*|||-9.4|...|
|Machine learning|9||*||||21.8|
|Consensus|12|-1.9|...|...|...|...|...|
|Futures<br>|12|*<br>|370.8|...|...|...|...|
|Bivariate correlations|12|*||235.3|...|...|...|
|BVAR|12||*||-2.2|...|...|
|OEM|12||*|||-7.3|...|
|Machine learning|12||*|*|||13.5|
|Consensus|15|3.9|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15||...|...|-49||...|
|OEM|15||...|...||-4.6|...|
|Machine learning|15||...|...|||7.4|
|Consensus|18|11.8|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|BVAR|18||...|...|-16.9|...|...|
|OEM|18||...|...||6.8|...|
|Machine learning|18||...|...|||4.1|
|Consensus|21|18.9|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21|...|...|...|...|...|...|
|BVAR|21||...|...|11.2|...|...|
|OEM|21||...|...||16.6|...|
|Machine learning|21||...|...|||-5.7|
|Consensus|24|30.1|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24||...|...|22.8|...|...|
|OEM|24||...|...||-7.3|...|
|Machine learning|24||...|...|||-15.7|

_Source_ : World Bank.

_Note_ : Bias is defined as the difference between the actual price and the predicted price. The forecast period is 2015Q12022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate estimates of average bias for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to a t-test (bias).

40
## 3. **German** banks and stock markets - **feature of a univenal bsnklng** system

In Germany's universal l>nking system, banks are allowed to assume all functions that in the USA would only be associated with investment banks: underwriting of equity issues, stock brokerage, mnd portfolio management. These activities themselves shall not be the focus tf this chapter, although they set the stage for the issues to be discussed here: the role of banks in administering stock owners rights in the Board" nd the annual shareholders' meetings. The three ways of the banks to assume a role in corporate governance institutions are to hold significant amounts of shares in the corporation, to act as voting 4ent for small investors in the annual shareholder meetings, or to have a member of the bank hold a seat on the Board directly. In this chapter we shall provide factual information on the extent of bank control over industry by any of the three means, as well as an analysis of the effic-iency or dhawbacks of this arrangeirent.

## 3.1. Equity holdings of German banks

# 3.1.1. Extent and structure of ownership

"German banks own German industry". For a lot of p;actitioners as well as academics, this is a statement that does not deserve further refiLection. However, in Harm (19] we have already provided statistics showing that actual ownership of real sector firms cannot be too significant, because the AG as the only publicly listable corporate charter accounts for only slightly more thin 20% of domestically taxable revenues. Most corporate shares of banks are confined to the AG. Yet, a concern would be whether banks control a major portion of the nation's largest firms, thereby shaping domestic industrial policy not only directly in the large corporations, but also indirectly when the decisions of the large firms trickle down to affect dependent smaller businesses.

The first task in this chapter is then to assess the extent of banks' stock ownership in the largest firms. Appendix I shows the distribution of ownership among diffcrent groups of owners in the 1argest 100 firms. The table was compiled from various tables in the Monopolkommission's [34,35] 1979 and 1989 reports. The first report was not aimed at giving a complete account of ownersh l) in the largest firms so that the table has a number of missing ends, but the data improved a lot until the most recent report, providing a general picture of the structure of ownership in Germany's large industry.

Until recently surprisingly few companies are majority owned by small investors - the majority of shares seemed to be in the hands of the larger institutional investors, which individually or collectively held the absolute majority in the respective firms. More importantly, the stock ownership of banks is not as pervasive as it is often perceived, and was reduced furthermore during the last decade.

Moreover, the tables compiled by the Monopolkommission revealed that there is no ownership link between the companies in which the banks had a stake, and the companies where other firms of the group of the largest 100 firms had a stake. Not a single firm in 9
2011 PPPs allows one to buy in 2011 the same bundle of goods in as $1.25 did in 2005 at 2005 PPPs.

A higher poverty line of $2.00 per day in 2005 PPPs is also commonly reported by the World Bank, especially for comparative analysis of poverty in middle-income countries, although it is not used for monitoring of global poverty goals. This line was used by Chen and Ravallion (2010) and corresponds to the median value of the sample of national lines used by Ravallion, Chen and Sangraula (2008) to derive the $1.25 line. The median value of the same sample of lines in 2011 PPPs is $3.08 and we therefore consider this as the comparable line to the $2.00 line in 2005 PPPs.

# **_B. Reconsidering the Global Poverty Line – An Alternative, Robust Approach_**

For the last 25 years, the underlying concept of the global poverty line has been that it should reflect some average or typical value of national poverty lines for the poorest of countries (World Bank, 1990; Ravallion, van de Walle and Datt, 1991). The last time the line was estimated, Ravallion, Chen, Sangraula (2009) estimated the line based on selecting the 15 poorest countries from a sample of 75 countries for which they had national poverty lines of any type. While a sample of 75 poverty lines was much larger than what had been used to estimate the dollar-a-day line at 1993 PPPs, it is noteworthy that it is a select sample of countries for which the authors could collect national poverty lines. The authors note for example that Latin America was a region with poor coverage in their sample.

Deaton (2010) critiques this 15-country approach for several reasons, most of which link to the fundamental problem that these countries provide weak support for representing the world’s poor. As one example, he describes how small changes in the composition of the 15-country reference group can result in changing the poverty status of millions of people. He further notes 15
average cross-market premium drops to -1.20 during the crisis, compared to 0.21 in the pre-crisis period. The results in the upper panel of Table 4 show that most stocks indeed experience a significant drop in the cross-market premium during the crisis period. The country with the weakest result is Brazil where only 4 out of 11 stocks show a significant drop in the mean. This result can be explained by the fact that it is hard to detect a clear crisis period in Brazil, as there was a prolonged period of turbulence, but there was only a limited period of severe exchange market pressure. In addition to a decrease in the average mean, the comparison with tranquil times shows that the volatility of the premium increases significantly during a crisis. For example, in Russia the variance increases from 1.67 before the crisis to 38.82 after the crisis. Also in Indonesia, the premium becomes much more volatile, with the variance increasing from 1.20 to 13.62 (Table 4). In Mexico and Brazil volatility increases, albeit at a more modest level.

Thus, the results indicate that, during crises, the cross-market premium becomes more volatile and continues to oscillate around zero, while the average premium drops. This implies that markets do not segment during crises. However, the risk associated with swapping the underlying stock for the DR (and vice versa) increases, due to exchange rate, transfer, and convertibility risks. Moreover, the typical decline in liquidity in periods of financial distress tends to reduce traders’ inventories and adds to price volatility, inhibiting immediate arbitrage. This is in line with the findings in Levy Yeyati, Schmukler, and Van Horen (2008b), where we document, using an event-study approach, that trading costs increase (e.g., bid-ask spreads widen) in crisis times. The negative cross-market premium suggests that these risks are more pronounced for the underlying stock and, as a result, investors demand a discounted price for this stock.

21
Joseph, T., Y. Nyarko, and S.-Y. Wang. 2015. _Asymmetric Information and Remittances:_

- _Evidence from Matched Administrative Data_ . NBER Working Paper 20986. Cambridge, Mass.: National Bureau of Economic Research.

Kapiszewski, A. 2006. _Arab Versus Asian Migrant Workers in the GCC Countries_ . Paper

- presented at the United Nations Expert Group Meeting on International Migration and

- Development in the Arab Region. UN/POP/EGM/2006/02, 15-17 May 2006, Beirut.

- Kinnan, C. 2014. _Distinguishing Barriers to Insurance in Thai Villages_ . Northwestern

- University, Department of Economics, Evanston, Il.

- Lucas, R. E. B., and O. Stark. 1985. “Motivations to Remit: Evidence from Botswana.” _Journal of Political Economy_ 93 (5), 901–18.

- McKenzie, D., and H. Rapoport. 2007. “Network Effects and the Dynamics of Migration and Inequality: Theory and Evidence from Mexico.” _Journal of Development Economics_ 84 (1), 1–24.

- ———. (2010). “Self-Selection Patterns in Mexico-US Migration: The Role of Migration Networks.” _Review of Economics and Statistics_ 92 (4), 811–21.

- McKenzie, D., J. Gibson, and S. Stillman. 2013. “A Land of Milk and Honey with Streets Paved with Gold: Do Emigrants Have Over-Optimistic Expectations about Incomes Abroad?” _Journal of Development Economics_ 102, 116–27.

- Osella, F., and C. Osella. 2000. “Migration, Money and Masculinity in Kerala.” _Journal of the Royal Anthropological Institute_ 6 (1), 117–33.

Peter, K. B. 2010. “Transnational Family Ties, Remittance Motives, and Social Death among

- Congolese Migrants: A Socio-anthropological Analysis.” _Journal of Comparative Family Studies_ 41 (2), 225–43.

35
[42%].<sup>2</sup> This range is typical of cross-section (sometimes averaged over 5- year periods) estimates of aggregate trade volumes where trade costs are given by: Where, the set<sup>_z_</sup> _ijm_ (m=1,...,M) includes binary dummy variables (usually invariant through time, such as sharing a common border, a common language, etc.) capturing other barriers to trade than distance. These costs enter log-linearly in the “traditional” gravity equation: and the distance effect is given by the estimate θ = αρ , with α < 0 being the trade elasticity to trade costs _tij_ . As discussed by AvW (2004), and as illustrated in the above typical estimates of θ<sup></sup> , these appear to be implausibly high.<sup>3</sup> This lead AvW to conclude that distance is in fact capturing other barriers to trade (e.g. NTBs, information barriers, and contracting Costs and insecurity) not appropriately controlled for in the set _m_ of dummy variables<sup>_z_</sup> _ij_ .<sup>4</sup>

> But the real “puzzle” is that estimates of θ<sup></sup> coming from more recent data yield larger estimates. These results imply that distance has exerted a more powerful (negative) effect on the volume of trade in recent times. This is clear from figure 1 below reproduced from a recent meta-analysis of 1,467

> elasticity estimates θ<sup></sup> compiled from gravity model estimates reported in over 100 published papers (see Disdier and Head, 2008). This figure plots the elasticity estimates against time and fits a kernel smoother through the

θ 2 The general formula is: _M_ 1 / _M_ 0 = ( _D_ 1 / _D_ 0 )

> 3 In the theory-based gravity equation, the elasticity of trade to trade costs depends on the

> elasticity of substitution in consumption, σ according to α = (σ − 1 ) . Since σ has to be estimated separately (as reported by Anderson and Van Wincoop, 5<σ<10), the elasticity of trade to distance will, in fact, depend on the ease with which goods can be substituted across suppliers. As we discuss below, the composition of trade would then appear to matter.

> 4 Based on σ=8, they estimate that the overall border barriers to trade amount to around 50% .

4
# 1. **Introduction**

There are over 300 million new cases a year of malaria in the world, resulting in over 1 million deaths. Of these, 90 percent of the cases, and 97 percent of the deaths occur in Sub-Saharan Africa (World Health Organization 2002). Reducing the burden of malaria figures high on the agenda as the poorest nations around the world commit themselves to reaching the Millennium Development Goal of halving of child mortality by 2015. Malaria is frequently characterized as being intimately linked with poverty. For example, the World Health Organization's recent _Report on Infectious Diseases_ argues forcefully that these diseases, and malaria in particular, are both a consequence of poverty as well as an obstacle that keeps people in poverty (World Health Organization 2002).

Much of the evidence on economic aspects of malaria focus on the relationship between malaria and GDP growth as derived from cross-national data. Additional work focuses on the loss of productivity associated with malaria among adults and the resulting loss in welfare. This paper uses individual and household level data to empirically explore the associations between household wealth and the incidence and treatment of fever-as an indicator of malaria-among children in Sub-Saharan Africa. The data used are from Demographic and Health Surveys collected in the 1990s from over 20 countries in which malaria is prevalent. The approach used is to assess first the broad geographic associations - and test whether these are significant across two broad regions: Western and Central Africa, and Eastern and Southern Africa. Next, the approach is to explore the association between fever (and treatment) of a particular child and the level of wealth in the household and cluster in which he or she lives.

The analysis reveals a positive, but weak, association between reported fever and poverty. The geographic association becomes insignificant, however, after controlling for mother's education. There is some evidence that higher levels of wealth of other households in the cluster in which the household lives are associated with lower levels of reported fever in Eastern and Southern Africa.

Poverty and the type of care sought-or not sought-for an episode of fever are significantly associated: wealthier households are substantially more likely to seek care in the modern health sector. In Western and Central Africa those from richer households are more likely to seek care from all types of sources: government hospitals, lower-level public facilities such as health clinics, as well as private sources. In Eastern and Southern Africa the rich are primarily more likely to seek care from private facilities. In both regions there is substantial use of private facilities-usage that increases with wealth. Like the incidence of fever, treatment seeking behavior is strongly associated with the level of wealth in the cluster in which the child lives.

I
Timilsina et al. 2019). In the existing CGE models, the AEEI are selected arbitrarily to adjust the baseline to satisfy the modeler’s judgement or policy makers’ expectation.

# **3. Data and Parameters**

In this paper, the benchmark year for the CGE model is 2012, and 31 SAMs of 31 provinces that are suitable to the structure of the multi-regional CGE model have been constructed as the database. Firstly, we constructed a 2012 multi-regional input-output (MRIO) table covering 31 provinces of China based on the published 2012 input-output tables of the 31 provinces (NBS,2016). Secondly, 31 macro SAMs of the 31 provinces were compiled to provide a consistent macroeconomic data framework. Thirdly, based on the 31 macro SAMs, 31 disaggregated SAMs are developed for the multi-regional CGE model. The provincial SAMs are available at World Bank’s Development data hive<sup>4</sup> .

In addition to the SAM, there are two types of parameters that should be estimated before the CGE model can be used for policy analysis: one is a share parameter, such as consumer expenditure share, and the other is an elasticity parameter, such as the elasticity of substitution in the production function. The share parameters can be estimated from the SAM using a calibration method that assumes the benchmark year represented by the SAM is an equilibrium solution for the CGE model, but the elasticity parameters need to be obtained based on the relevant literatures (Bao, Q., et al, 2013; Zhai Fan and Hertel, T.W, 2006; Zhang, Z., 1998). The CO2 emission factors from fossil fuels are calculated based on IEA statistical data (2014).

The BAU scenario is constructed using assumptions on labor supply growth and projected real GDP growth, as shown in Table 2.

**Table 2** The Labor supply growth rate and GDP growth rate from 2012 to 2030

|Year|Labor supply growth rate|Average annual GDP growth rate|
|---|---|---|
|2012-2015|2.70%|8.51%|
|2015-2020|4.60%|6.56%|
|2020-2025|0|5.49%|
|2025-2030|0|5.47%|

_Note: Values for 2012-2015 are actual or observed values. The projected GDP is set at a moderate level based on a review of relevant literature(Li Shan Tong, 2010;_ Johansson A., et al _.,2013; OECD,2012) .Labor supply_

> 4 https://datacatalog.worldbank.org/

33
In July 2009, the FSS asked banks to voluntarily strengthen their mortgage loan risk management. In October 2009, the request was extended to non-banking financial companies.<sup>26</sup> The recommendations called for lowering the LTV and raising the DTI ratios for properties in certain zones of the Seoul metropolitan area, which were no longer classified as overheated speculative districts or speculative designation zones.<sup>27</sup> At that time, there were only three speculative districts in Seoul where the LTV ratio requirement remained at 40 percent.

As a result of these changes, the LTV limit was lowered from 60 percent to 50 percent in the following cases (Table 6):

- For apartment units with mortgage maturities of less than 10 years.

- For apartment units with mortgage maturities of more than 10 years but with a collateral value over KRW600 million.

- For non-apartment, detached units with mortgage maturities of less than 3 years.

Table 6. Change in the LTV Ratio for Banks in the Seoul Metropolitan Area, 2009

|||Apartm<br>|ent units<br>|Detache<br>|d units<br>|
|---|---|---|---|---|---|
|||Before|After|Before|After|
|Not more than 3 years||60% or less|**50% or less**|60% or less|**50% or less**|
|3–10 yrs||60% or less|**50% or less**|60% or less|60% or less|
|More<br>Collateral|more than 600Mil won|60% or less|**50% or less**|60% or less|60% or less|
|than 10 yrs<br>value|600 Mil won or less|60% or less|60% or less|60% or less|60% or less|
|Payment in installments|(10 yrs or more)<sup>a</sup>|70% or less|70% or less|70% or less|70% or less|

_Source_ : FSS Press Release, July 2009.

- a. Refers to loans with fixed interest rates (and a grace period of one year or less, DTI of 40% or less), having a plan to sell the  loan to the Korea Housing Finance Corporation or a plan for self-securitization.

The strengthened LTV and DTI regulations have subdued the upward trend of housing prices. Since early 2010, despite signs of economic recovery, several restraining factors have held this rising trend in check. These factors include anxiety about overvaluation of housing prices and the amount of unsold housing (FSR, April 2010).

This series of prudential measures seem to have generally protected the quality of loans. This is evaluated by using the average LTV ratio as an indicator of banks’ mortgage loan quality. At the end of July 2009, the average LTV ratio was 47.1 percent, compared to 74.9 percent in the United States (FSR, November 2009).

.

> 26  The currently applicable LTV ratio of 70 percent for non-banks (mutual finance companies, savings banks, and credit-specialized financial companies) was strengthened to 60 percent, starting in October 2009.  The currently applicable LTV ratio of 70 percent or less in regard to non-apartment detached units will remain in force unchanged. 27  The strengthened LTV limit excluded mortgage loans that have a combined total of less than KRW50 million from all lenders, mortgage loans for relocation, and balloon payments and mortgage loans for unsold residential units.

14