rafmacalaba/datause-encoder-data huggingface dataset:
Table AI.1 Foreign Currenry Denoinated Assets and Liabilities(USS Nitlion)

|||1906|1987|1988|Source|
|---|---|---|---|---|---|
||TOTAL|||||
|FGt|External<br>Debt|32,789.45|40,932.04|39,592.24|DRS|
|RESc|CB Reservftes|2,596.21|3,245.47|3,671.20|CS|
|RESd|RS Reserves|1,619.00|2,241.00|2,921.00|EIO|
|Ft|Met Externrl Debt|28,372.24|35,445.57|33,000.04|Calculated|
||CENRAL BAK|||||
|FGc|Externnl Debt|8,443.76|10,464.39|6,275.95|Calculated|
|FXbc|FX deposits of Sudget|729.62|990.01|1,096.48|CB|
|FXoc|FX deposits<br>of SEEs|219.32|120.68|218.48|CB|
|FXdc|FX depositsofBS|1,680.81|2,556.76|2,390.46|CB|
|RESc|Reserves|2,596.21|3,245.47|3,671.20|CB|
|Fc|NFAPosition|8,475.30|10,886.37|8,310.17|CB|
||BUDGET|||||
|FGb|Externrl<br>Debt|16,215.96|20,516.05|20,318.68|DRS|
|FXbc|FX deposits<br>at CB|729.62|990.01|1,096.48|CB|
|Fb|<br>NFAPaoition|15486.36|19,5Z6.03|19,222.19|CalcuLated|
||SEEs|||||
|FGo|Externnl<br>Debt|3,767.80|4,249.67|4,332.74|DRS|
|FXoc|FX deposits<br>atCB|219.32|120.68|218.48|CB|
|Fo|<br>NFAposition|3,548.48|4,128.99|4,114.26|Calculated|
||DOUESTIC aS|||||
|FGd|Exten, l Debt|3,590.26|5,396.32|5,931.16|CaLculated|
|FXdc|<br>FX DepositsatC(|<br>1,680.81|<br>2,556.76|<br>2,390.46|cB|
|FXpd|FXdeposits<br>of Private|2,074.00|3,528.00|3,011.00|E10|
|RESd|Reserves|1,819.00|2,241.00|2,921.00|EIO|
|Fd|NFAPosition|<br>2,164.45|<br>4,126.56|<br>3,630.70|CB|
||PRIVATESECTOR|||||
|FGp|ExtermL<br>Debt|77.65|305.61|733.70|Calculated|
|<br>FXpd|Deposits<br>**at**BS|2,074.00|3,528.00|3,011.00|EIO|
|Fp|NFA position|-1,302.35|-3,222.39|-2,277.30|Residual|

Source: See expltation in text 0d right most coltm.

9
# Table 5. Stochastic Frontier Analysis on Select Subpopulations (continued)

_Dependent Variable = Log of Total Maize Grain Production (KG)_

||<br>|**FEMALE-MA**<br>|**NAGED PLOTS**<br>|<br>||**MALE-MAN**|**AGED PLOTS**||
|---|---|---|---|---|---|---|---|---|
||**Ove**|**rall**|**S**|**2**|**Ove**|**rall**||**S2**|
|Highest Membership Grade|MAPS|AFSIS|MAPS|AFSIS|MAPS|AFSIS|MAPS|AFSIS|
|Distance from S1|-0.056***|-0.039**|||-0.084***|-0.073***|||
|Plot Area (Hectares, Logged)|0.935***|0.939***|0.802***|0.960***|1.018***|1.009***|1.020***|1.009***|
|Household Labor Days (Logged)|-0.072|-0.071|0.178*|-0.057|-0.037|-0.043|-0.044|-0.047|
|Hired Labor Days (Logged)|0.016|0.024|0.024|0.005|0.001|0.000|-0.003|-0.003|
|Inorganic Fertilizer (KG, Logged)|-0.008|-0.016|-0.069|-0.038*|0.034***|0.026*|0.003|-0.006|
|KG of Seed Planted (Logged)|0.128**|0.131**|0.224***|0.195***|0.045|0.044|0.070|0.067|
|Intercropping Rate (Logged)|0.058|0.011|0.039|-0.051|0.308***|0.351***|0.306***|0.350***|
|Pure Stand Maize°|0.108|0.153|0.277*|0.222*|0.264***|0.265***|0.288***|0.314***|
|Flowering Season Rainfall (mm, logged)|0.768**|1.038***|0.534|0.878**|0.031|0.448|-0.154|0.220|
|Constant|3.282|1.826|1.784|2.541|6.581***|3.969**|6.968***|4.697**|
|_Technical Inefficiency_|||||||||
|Manager Completed Primary Education°|-0.064|-0.052|18.151***|-0.018|-0.013|-0.009|0.063|0.024|
|Manager Age|-0.008|-0.006|-0.308*|0.018|-0.007|-0.007|†|†|
|Manager Age (squared)|0.000|0.000|0.005**|0.000|0.000|0.000|†|†|
|Manager Received Agricultural Extension Services°|0.205|0.209|3.786*|0.130|0.038|0.061|-0.011|-0.001|
|Dependency Ratio|-0.079|-0.078|-13.581***|-0.057|†|†|†|†|
|Count of Agricultural Assets|-0.031*|-0.034*|-0.490***|-0.044*|-0.012|-0.011|-0.010|-0.006|
|CV of Flowering Season Rainfall (1999-2014)|-0.114|-0.149|†|†|2.344*|1.966|2.970**|2.926**|
|Constant|6.447***|6.335***|-13.098***|5.349***|6.231***|6.399***|5.984***|6.039***|
|_Random Error Term (v)_|||||||||
|Constant|-2.099***|-2.053***|0.029|-1.925***|-1.984***|-1.869***|-1.853***|-1.751***|
|N|324|324|259|286|516|516|375|450|

Notes: Standard Errors clustered at EA level; † = insufficient variation for inclusion of variable or high correlation with other covariates; ° = Binary variable; *** p<0.01, ** p<0.05, * p<0.1 22
47 income transfers to induce developed countries into curtailing export cartel activities. However, despite their policy relevance, export cartels have not received much attention in the trade literature. Thus, information on the severity of this problem and thus the costs and benefits of curtailing export cartel activity is somewhat limited.<sup>30</sup>

# **4.3 Behind-the-border measures: Consumer, product and process regulations**

Given the vast number of products, countries and policy instruments involved, designing an international agreement that can specify, in advance, how member countries should behave in every conceivable contingency (a “complete” contract) is simply not economical. Thus, the costs of designing and implementing a viable agreement are likely to shape the nature of the agreement. In this respect, behind-the-border measures are especially problematic as internal policies and regulation are decidedly less transparent than conventional border measures (e.g., tariffs and quotas). Indeed, one can view one of the earliest papers on the theory of trade agreements (Copeland, 1990) as simply treating contracting constraints as exogenous and thus assuming that behind-the-border policies are simply non-negotiable. What Copeland (1990) shows is that, if some domestic policies are non-negotiable, it is still efficient to bind the more negotiable trade barriers as policy-substitution towards less-efficient policies is still globally efficient (since the less-efficient instrument is used less in equilibrium).

In a more recent paper, Horn, Maggi and Staiger (2010), actually endogenize these contracting costs and provide a justification for the differential treatment on NTMs. Specifically, to the extent that internal, or domestic, measures are more costly to contract over (due to being less transparent) than border measures (i.e., trade policies), the benefits of domestic policy cooperation may not outweigh the extensive costs of contracting. However, even if contracting costs are assumed to be symmetric across domestic and trade policy instruments, an optimal agreement may still allow discretion over domestic policies. This is because the underlying rationale for the international agreement is trade-related (the terms-of-trade externality). Thus, while governments may use a domestic policy as a secondary trade barrier when their trade

> 30 For example, while some countries like the US maintain some data on export cartels (see Dick, 1992) many countries do not even track the number of export cartels operating in or from their region.
97 positively correlated, i.e., for each year of contributions the pension increases according to the following table.

|**Table 42CIA: Early **<br>|**Retireme**<br>|**nt and P**<br>|**ension B**<br>|**enefits**<br>||
|---|---|---|---|---|---|
|Retirement Age|57|58|59|60|61|
|Increase inpension(as %wage)|1.68|1.74|1.80|1.86|1.91|
|Retirement Age|62|63|64|65|66|
|Increase inpension(as % wage)|1.97|2.03|2.09|2.15|2.21|

Source: CIA du Canton de Genève (2002), p. 48 The bridging pension equals up to the maximal amount payable under the AHV/AVS. The repayment starts at the ordinary AHV/AVS age or before, on a voluntary basis.

Investment: The investment strategy is focused on long run returns. In 2001, it additionally focused on global diversification of stocks. As a consequence, Swiss shares were reduced and European shares were bought in 2002. Another special characteristic of the investment strategy is the fact that the whole portfolio was actively managed (with only one exception). As a result of the bad performances of actively managed mandates, the pension fund changed this strategy in 2003. Consequently, part of the portfolio was invested in passively managed mandates. The management of all securities is delegated to a specific and specialized institution. As at 31.12.2003 the portfolio contained a high fraction of real estate (33.2 percent) and bonds issued in CHF (18 percent). Foreign bonds (13.9 percent), domestic shares (14.2 percent), foreign shares (14.3 percent), and money holdings (6.4 percent) were the remaining investments. The returns on investment before and after deductions for value fluctuation reserves were 8.39 percent and 5.12 percent, respectively.

# **PF of Credit Suisse Group**<sup>**89**</sup> **:**

_The Pension fund of Credit Suisse Group (Switzerland) ensures its employees and those from companies that are legally or commercially closely associated with Credit Suisse Group. The annuity plan is a defined benefits scheme. The insured is entitled to a retirement pension from the age of 55. A bridging pension until attainment of the ordinary retirement age is only possible if the event of retirement after the 60th birthday. Without providing any reasons the insured can request a lump sum payment of up to 50 percent of the capitalized annual pension. In 2004, the pension fund of Credit Suisse accounted for almost 19,000 insured. Thereof were almost 8,500 retirees. The funding ratio at year–end (2004) was 112.2 percent. The underlying total assets amounted to almost 9 billion CHF._ Early Retirement: Individuals who work for Credit Suisse are eligible for early retirement at age 55. Pension payments that are drawn before attainment of the ordinary retirement age will be reduced for each month according to the following table: 89c.f. Pension Fund of Credit Suisse Group (Schweiz) (2005)
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 31 Hill, D., Bowman, M.J. and Khunda, J.S. (2012) Storm surge barriers to protect New York City. Against the Deluge. Papers from a conference held in Brooklyn, New York, 30-31 March 2009. American Society of Civil Engineers.

Hinkel, J., Lincke, D., Vafeidis, A.T., Perrette, M., Nicholls, R.J., Tol, R.S.J., Marzeion, B., Fettweis, X., Ionescu, C., Levermann, A. (2014) Coastal flood damage and adaptation cost under 21st century sea-level rise. Proceedings of the National Academy of Sciences 111, 3292–-3297. doi:10.1073/pnas.1222469111 Hinkel, J., Aerts, J.C.J.H., Brown, S., Jiménez, J.A., Lincke, D., Nicholls, R.J., Scussolini, P., Sanchez-Arcilla, A., Vafeidis, A., Addo, K.A. (2018). The ability of societies to adapt to 21st century sea-level rise. Nature Climate Change, accepted.

Hoozemans, F.M.J., Marchand, M., Pennekamp, H.A. (1993) Sea level rise: A global vulnerability assessment. Vulnerability assessments for population, coastal wetlands and rice production on a global scale. (Delft Hydraulics / Rijkswaterstaat, The Hague).

IIASA (2016) SSP Public Database, Version 1. Available at: https://tntcat.iiasa.ac.at/SspDb Jonkman, S.N., Hillen, M.M., Nicholls, R.J., Kanning, W., van Ledden, M. (2013) Costs of Adapting Coastal Defences to Sea-Level Rise— New Estimates and Their Implications. Journal of Coastal Research 1212–1226. doi:10.2112/JCOASTRES-D-12-00230.1 Kaneko, S., Toyota, T. (2011) Long-term urbanization and land subsidence in Asian megacities: an indicators system approach. In: M. Taniguchi (ed.), Groundwater and Subsurface Environments: Human Impacts in Asian Coastal Cities, DOI 10.1007/978-4-431-53904-9_13, pp. 249–270.

Lavery, S. & Donovan, B. (2005) Flood risk management in the Thames Estuary looking ahead 100 years. _Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences_ **363** , 1455-1474. (DOI:10.1098/rsta.2005.1579).

Lincke, D. and Hinkel, J. (2018) Economically robust protection against 21<sup>st</sup> century sealevel rise. Global Environmental Change, accepted.

Lowe, J.A., Howard, T.P., Pardaens, A., Tinker, J., Holt, J., Wakelin, S., Milne, G., Leake, J., Wolf, J., Horsburgh, K., Reeder, T., Jenkins, G., Ridley, J., Dye, S., Bradley, S. (2009) Marine and coastal projections, in: UK Climate Projections Science Report. Met Office Hadley Centre and Exeter, UK.

Menéndez, M., Woodworth, P.L. (2010) Changes in extreme high water levels based on a quasi-global tide-gauge data set. Journal of Geophysical Research 115. doi:10.1029/2009JC005997 Mooyaart, L.F. and Jonkman, S.N. (2017) Overview and Design Considerations of Storm Surge Barriers. Journal of Waterway, Port, Coastal, and Ocean Engineering, 143, doi:10.1061/(ASCE)WW.1943-5460.0000383
# **Table 7.B. Model comparison: Root mean squared error of lead price forecasts**

(U.S. dollars per metric tonne)

|**Approach**<br>|**Horizon**|**Consensus**|**Futures**|**Bivariate**<br>**correlations**|**BVAR**|**OEM**|**Machine**<br>**learning**|
|---|---|---|---|---|---|---|---|
|Consensus<br>|3<br>|153.8<br>|...<br>|...|...|...|...|
|Futures|3|*|103.7|...|...|...|...|
|Bivariate correlations|3|||105.7|...|...|...|
|BVAR|3|*|*|*|252.9|...|...|
|OEM|3||||*|94.5|...|
|Machine learning|3||||*||133.8|
|Consensus|6|156.9|...|...|...|...|...|
|Futures|6|*|99.8|...|...|...|...|
|Bivariate correlations|6|*|*|303.5|...|...|...|
|BVAR|6|*|*||266.5|...|...|
|OEM|6|||*|*|104.7|...|
|Machine learning|6|||*|*||125.8|
|Consensus|9|164.5|...|...|...|...|...|
|Futures|9|*|97.1|...|...|...|...|
|Bivariate correlations|9|*|*|300.2|...|...|...|
|BVAR|9|*|*||336.8|...|...|
|OEM|9|||*|*|131.2|...|
|Machine learning|9||*|*|*||149.9|
|Consensus|12|176|...|...|...|...|...|
|Futures|12|*|94.7|...|...|...|...|
|Bivariate correlations|12||*|241|...|...|...|
|BVAR|12|*|*|*|358.1|...|...|
|OEM|12||*||*|166.5|...|
|Machine learning|12||*||*||208.1|
|Consensus|15|187.8|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations<br>BVAR|15<br>15|...<br>*|...<br>...|...<br>...|...<br>360.7|...|...<br>...|
|OEM|15||...|...|*|204.4|...|
|Machine learning|15|*|...|...|||278.3|
|Consensus|18|194|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations<br>BVAR|18<br>18|...<br>*|...<br>...|...<br>...|...<br>338.8|...<br>...|...<br>...|
|OEM|18||...|...||224.4|...|
|Machine learning|18|*|...|...|||331.3|
|Consensus|21|195.1|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations<br>BVAR|21<br>21|...<br>*|...<br>...|...<br>...|...<br>352.7|...<br>...|...<br>...|
|OEM|21||...|...||236|...|
|Machine learning|21|*|...|...|||364.3|
|Consensus|24|204|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24|*|...|...|370.8|...|...|
|OEM|24||...|...|*|200.2|...|
|Machine learning|24|*|...|...||*|379.2|

_Source_ : World Bank.

_Note_ : The forecast period is 2015Q1-2022Q1. 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 average RMSE estimates for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to an F-test (RMSE).

45
# **1 Introduction**

Malnutrition remains a major public health concern in most developing countries. The World Health Organization (2002) estimates that about 3.7 million deaths among young children worldwide were related to malnutrition in 2000. Similarly, Pelletier et al. (1994) estimate that about one-half of childhood deaths in developing countries are caused by undernutrition. Malnutrition has also been associated with mortality and morbidity in later life, delayed mental development, decreased cognitive and behavioral functioning throughout childhood and adolescence and poorer performance in school (de Onis et al., 2000; Galler and Barrett, 2001; Glewwe et al., 2001; Shariff et al., 2000).

In Cambodia, almost half of the children under five are malnourished as measured by the height-for-age or weight-for-age indicator (National Institute of Statistics et al., 2001). Given the grave consequences of malnutrition, the issue deserves serious attention. However, as with many other developing countries, the resources available for improving children’s nutritional status are severely limited in Cambodia. Hence, efforts must be made to allocate the available resources in an efficient manner. In particular, geographic targeting, or targeting according to locational information, is often easy to administer and implement, and can be quite effective when malnourished children are concentrated in certain locations. However, the formulation of an effective geographic targeting policy requires knowledge of the location of malnourished children, information which is often not readily available. The Cambodia Demographic and Health Survey (CDHS) 2000 data provide information on child nutritional status at the level of 17 strata, a level that is more aggregated than the provincial level.<sup>1</sup> Hence, this information is too aggregated to be useful for formulating targeting policies.

Such informational constraints are among the central issues concerning the formulation of targeting policies (Ravallion and Chao, 1989; Kanbur, 1987). This study aims to overcome

> 1In Cambodia, there are four administrative divisions, which are province, district, commune and village in descending order of aggregation. On average, they have about 89,000, 12,000, 1,300 and 160 households respectively. Each stratum has on average 125,000 households.

1
WPS4089

# **THE STRUCTURAL DETERMINANTS OF EXTERNAL VULNERABILITY**<sup>*****</sup>

Norman V. Loayza _World Bank_ Claudio Raddatz _World Bank_

## **_Abstract_**

This paper examines empirically how domestic structural characteristics related to openness and product- and factor-market flexibility influence the impact that terms-oftrade shocks can have on aggregate output. For this purpose, it applies an econometric methodology based on semi-structural vector auto-regressions to a panel of 90 countries with annual observations for the period 1974-2000. Using this methodology, the paper isolates and standardizes the shocks, estimates their impact on GDP, and examines how this impact depends on the domestic conditions outlined above. We find that larger trade openness magnifies the output impact of external shocks, particularly the negative ones, while improvements in labor market flexibility and financial openness reduce their impact. Domestic financial depth has a more nuanced role in stabilizing the economy. It helps reduce the impact of external shocks particularly in environments of high exposure --that is, when trade and financial openness are high, firm entry is unrestricted, and labor markets are rigid.

### World Bank Policy Research Working Paper 4089, December 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * This paper was prepared for the conference “The Growth and Welfare Effects of Macroeconomic Volatility,” Barcelona, March 17-18, 2006.  We thank Luis Servén, Paolo Mauro (our discussant), Jim de Melo (the WBER editor), Romain Ranciere and Jaume Ventura (conference organizers), as well as other conference participants for useful comments.  We are grateful to Koichi Kume for editorial assistance.  Loayza: <u>nloayza@worldbank.org.  Raddatz: craddatz@worldbank.org.</u>

1
contractibility of inputs provided by suppliers have a positive relationship with vertical FDI, provisions that improve the contractibility of headquarter services are almost always uncorrelated with FDI.

Our work fits in the broader research effort aiming at understanding the relationship between international trade and institutions (see, Nunn and Trefler (2013) and WTO (2013) for recent surveys). Our findings complement a number of recent works in this area. In particular, Bernard, Jensen, Redding, and Schott (2010) and Nunn and Trefler (2013) have empirically investigated how contractual frictions affect intrafirm trade. The difference between these studies and our analysis are twofold: first we employ firm-level information to measure vertical FDI, rather than focusing on intra-firm trade. This allows us to expand the analysis to countries other than the United States, for which intra-firm trade data are not always publicly available. Second, we focus on changes in contractibility determined by deep agreements rather than by domestic institutions or by other technological determinants of contractibility. Recent empirical work has also looked at the relationship between international agreements -PTAs and bilateral investment treaties (BITs)- and FDI (among others, Blanchard and Matschke , 2012; Baltagi, Egger, and Pfaffermayr , 2008; Egger and Merlo , 2012).<sup>5</sup> Overall, these studies show that trade and investment agreements affect and are affected by FDI/offshoring. Aside from the use of a new measure for vertical FDI, our work adds to these findings by focusing on the depth/content of trade agreements, which allows disentangling an important channel through which trade institutions affect the ways goods are traded internationally.

The rest of the paper is organized as follows. Section 2 presents the theory of how PTA provisions are related to the international organization of production. Section 3 describes the methodology used to assess the depth and composition of trade agreements and to measure vertical FDI. The empirical analysis and the key findings of the paper are presented in Section 4. Concluding remarks follow.

> 5 Blanchard (2007) and Blanchard (2010) present formal models of FDI and trade agreements. Differently from our work, these models study the implications of international investment for trade/tariff negotiations.

5
Appendix Table 1. Descriptive Statistics

|Variable|Data Source|Mean<br>ASEAN|Standard Deviation<br>ASEAN|
|---|---|---|---|
|Net Gini|Solt (2009)|0.43|0.05|
|Market Gini|Solt (2009)|0.48|0.08|
|3rd Quintile Income Share|Brueckner et al. (2015)|0.14|0.01|
|4th Quintile Income Share|Brueckner et al. (2015)|0.21|0.01|
|Ln GDP per capita|Heston et al. (2012)|7.38|1.73|
|Investment/GDP|Heston et al. (2012)|0.23|0.12|
|Share of Population Tertiary Education|Barro and Lee (2010)|0.05|0.06|
|Share of Population Secondary Education|Barro and Lee (2010)|0.10|0.08|

23