rafmacalaba/datause-encoder-data huggingface dataset:
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Smets, L., 2018. Supporting Policy Reform from the Outside. mimeo

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World Bank, 2006. Development Policy Retrospective 2006. World Bank, Washington, D.C 29
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- June through October averages aver 20—10ON, 20W—I10E, 1900-2013 climatalogy NOAA NCDC Global [istorical Climatology Network data
72. It may be noted that the World Bank has been working with the Chinese authorities to improve China's national accounts. It is believed that China's estimates of GDP in national cuTrency terns would be significantly increased if, following the SNA recommendations, adjustments were made _for_ under reporting and improper valuation. The Bank has made an interim adjustment and raised the yuan estimates of GNP (World Bank, 1994), but this paper has not taken account of these adjustments.

# VIL CONCLUDING REMARKS

73. The purpose of this paper is to estimate a conversion factor which can be used to convert macroeconomic indicators from local currency to US dollars for China based on ICP-type methodology. Since this is a cross-system comparison, many adjustments have been made in order to take the institutional differences between the two countries into account, especially, in service sectors.

74. Some areas can be identified where fiuther research would almost certainly improve this esimate. First of all a new survey of prices supported by the official statistical authorities will provide more information on the yuan/dollar price ratios for all sectors. This may correct some biases in this study due to lack of information, for example, the price differential between urban and rural areas, and the allocation of imported and domestic goods in some categories. It should be stressed that the estimates of PPPs and real income depend on information used in the study. Moreover, the final results are more sensitive to the changes in prices than to the changes in expenditure.

_75._ Additionally, a bilateral comparison between China and the U.S. by the "industry of origin" approach should strengthen the basis for future assessments considerably. This is an ongoing project which could not onlv provide a cross-check with this study but also provide additional information on 32
the effect of taxation on income distribution ( _e.g._ Kakwani (1977); Kakwani et al. (1997); Mahalanobis (1960); Wagstaff and van Doorslaer (2004); Yitzhaki and Slemrod (1991)).

Third, we evaluate the potential gains from geographic targeting in the presence of commune-level estimates. We show that the concentration curve is useful for this purpose as well. We assume that we can target resources only at the stratum level with the DHS alone. Given a fixed budget, we can compare reductions in child malnutrition with stratum-level and commune-level targeting. Alternatively, we can calculate the reduction in resource needs to achieve the same goal if targeting is carried out at a smaller geographic level. In general, the efficiency gain depends on the spatial distribution of malnourished children. We find that the savings in the cost of nutrition programs from commune-level targeting is on average at least two to three times higher than that from stratum-level targeting when the _per capita_ cost of the program is fixed. The efficiency gains can be much higher under alternative assumptions.

This paper is structured as follows: Section 2 reviews the measurement and prediction of nutritional status of children. Section 3 develops the methodology of nutrition mapping. We then discuss the data in Section 4, followed by the results in Section 5. Section 6 discusses the applications of the methodology, and Section 7 concludes.

# **2 Measurement and prediction of child nutrition status**

To measure malnutrition in a non-invasive and inexpensive manner, anthropometry has been widely used among nutritionists and epidemiologists. Among the most commonly used anthropometric measures are weight-for-height, weight-for-age and height-for-age Z-scores. Z-scores measure the number of standard deviations between an individual’s value of the anthropometric indicator and the median of the National Center for Health Statistics (NCHS) growth reference population of the same sex, and in the same age or height group. Deficiency in weight-for-height, weight-for-age and height-for-age Z-scores is respectively called “wast3
36

# Norgaard, Richard B., Suzanne Easton, George Lcdec, and Laurel Prevetti, 1987. "Social Organization for Sustaining Renewable Resources." Paper prepared for the World Bank.

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- Pezzey, John, 1989. "Economic Analysis of Sustainable Growth and Sustinable Development." Working Paper #14, March, 1989, Environment Dept., World Bank.

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- Repetto, Robert (ed.), 1986, _The Global Possible: Resources, Development, and the New Century._ World Resources Institute, Washington, D.C.

- Riddell, Robert, 1981. _Ecodevelopment: Economics, Ecology, and Development: an Alternative to Growth Imperative Models._ Gower, London.

- Sies, Ignacy, 1984a. "Developing in Harmony with Nature: Consumption Patterns, Time and Space Use, Resource Profiles, and Technological Choices." in Bernhard Glaeser, ed., 1984, _Ecodevelopment: Concepts, Policies, Strategies._ Pergammon Press, New York.

, 1984b, "The Strategies of Ecodevelopment." _Ceres,_ 17: 4: 17-21, (FAO).

- Sachs, Wolfgang, 1988. "The Gospel of Global Efficiency: On Worldwatch and other reports on the state of the world." _IFDA Dossier 68:_ 33-39 (November-December).

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Thurow, Lester, 1980. _The Zero-Sum Society._ Basic Books, New York.

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Table 2 (continued)

|**Coemodity**Model|Modeling Purposes|Modeling Structure|Enpirica;<br>Scope|**Model**Contribution|
|---|---|---|---|---|
|Energy Demand<br>(1981)|Construct an energy**dmnd**<br>model and projections method,<br>such that the model could<br>easily be linked to World<br>Bank macro projections.|**Dea nd equatios**based on<br>sectoral shares inGDP.<br>Equations stem from production<br>functions erploying labor,<br>capital, energy and raw mterials<br>as factor inputs.<br>21 major<br>regional equations.|world divided into<br>14 regions. Anmal<br>1960-75.|Regional energy demands<br>generated from sub-regiors<br>and country equations.|
|Energy and<br>Petroleun (1964)|Simulate world energy and<br>petroleuc markets, based on<br>OPEC pricing and economic<br>growth assunptions known as<br>the World Energy and Petroleum<br>Model (WEPH).|Competitive marketwhichallows<br>for price-determining behavior<br>of OPEC. Corpetitive equilibriu<br>model forecasting price<br>determination found only in the<br>coal sector.|World including OPEC<br>as a separate region.<br>Annual 1960-78,<br>1985-2000.|Provides integrated outlook<br>of interaction of OPEC prices<br>with world energy market<br>adjustments.|
|Coal (1985)|Simulate coal market impact<br>of asssuptions regarding<br>economic growth, enorgy demands<br>oil prices and nuclear supply.|Competitive world energy market<br>model (WEPN)<br>ihich includes<br>detail on coal market<br>adJustments.|World. Anual<br>1960-78, 1985-2000.|Provides market equilibrium<br>scenarios of coal demand<br>and price adjustments.|
Policy Research Working Paper 9612

# **Abstract**

Official ending is much larger than commonly known, often surpassing total private cross-border capital flows, especially during wars, financial crises and natural catastrophes. This paper assembles the first comprehensive long-run dataset of official international loans, covering 230,000 loans, grants and guarantees extended by governments, central banks, and multilateral institutions in the period 1790–2015. Historically, wars have been the main catalyst of government-to-government lending. The scale of official credits granted in and around WW1 and WW2 was particularly large, easily surpassing the scale of total international bailout lending after the 2008 crash. During peacetime, development finance and financial crises are the main drivers of official cross-border finance, with official flows often stepping in when private flows retrench. In line with predictions of recent theoretical contributions, this paper finds that official lending increases with the degree of economic integration. In financial crises, governments help those countries to which they have greater trade and banking exposure, hoping to reduce the collateral damage to their own economies. Since the 2000s, official finance has made a sharp comeback, largely due to the rise of China as an international creditor and the return of central bank cross-border lending in times of stress, this time through swap lines.

This paper is a product of the Office of the Chief Economist, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sebastian.horn@econ.lmu.de; carmen_reinhart@harvard.edu or creinhart@worldbank.org; and christoph.trebesch@ifw-kiel.de.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
negligible amounts of world trade in relatively minor product categories.<sup>16</sup> Big problems such as textile and apparel imports were handled another way, through the negotiation of ‘voluntary’ export restraint agreements (VERs). The various textile agreements, beginning in 1962, provided GATT sanction to VERs on textiles and apparel. The same method, ‘negotiated’ export restraints or VERs, were used by the developed economies to control troublesome imports into several other important sectors, such as steel in the United States and automobiles in the European Union.

Except for those especially sanctioned by the textile arrangements, VERs were clearly GATT-illegal.<sup>17</sup> However, while VERs violated GATT legalisms, they accorded well with its ethic of reciprocity and their negotiation controlled for the possibility of chain reaction of import restrictions from one country to another, as had been disastrous in the 1930s.

The reality of power politics was at play, but compensation was involved. Reduced export volumes were compensated by higher prices, the result being often a net gain for exporters.<sup>18</sup> In some instances the quid pro quo was foreign aid or some other non-trade consideration.

Though VERs were clearly GATT-illegal, they prevailed through the 1970s and the 1980s as the predominant mechanism for managing troublesome imports. (Figure 3.1) Their use came to an end with the Uruguay Round Agreement on Safeguards. Its Article 11 includes an explicit prohibition and a procedure for notification and phasing out of those in place. One might speculate that the reasons behind this elimination included:

- The increasing weight of developing countries in world trade and their increasingly active role in the GATT system

- The growing realization in developed economies that a VER was a costly form of protection<sup>19</sup>

- The long-term legal pressure of the GATT rules

- The availability of an attractive, GATT-legal alternative, antidumping.

# **Antidumping ascends**

Antidumping was a minor instrument when the GATT was negotiated, and provision for allowing antidumping measures was included with little controversy. In 1958, when the GATT Contracting Parties first canvassed themselves about

> 16 1980 statistics show that actions taken under Gatt Article xix covered imports valued at US$ 1.6 billion, while total world trade was at the same time valued at US$ 2,000 billion (Sampson, 1987, p. 145).

> 17 Gatt, 1994, p. 494.

> 18 Had imports been restricted to the same volume by a tariff, the scarcity value of the restriction would have been collected by the importing country.

> 19 For example, Hufbauer and Elliott (1994) found that of the welfare loss placed on the U.S. economy from all forms of protection in place in the early 1990s, over 83 percent of that loss came from VERs .

10
below real GDPjo , which means that they are more productive than indicated by the PWT and have low terms of trade. That group has lower GDP per capita on average than the former group, but still includes some wealthy countries such as Hong Kong, for which real GDP is two-thirds je that of real GDPjo , due to low export prices from Hong Kong.<sup>15</sup> An alternative way to split our sample is by oil and non-oil exporters, since we expect the relationships for the two sets of countries may be different.<sup>16</sup> In Figure 2 we plot the terms-oftrade and GDP per capita levels for the set of 126 non-oil exporting countries, together with the regression line. Generally, there is a positive relationship: countries with higher per-capita GDP also have higher terms of trade. The slope coefficient on GDP per capita is significantly positive (at the 5% level). This terms-of-trade increase is driven mainly by an increase in export price levels, as import price levels are relatively stable (see Table 3). However, GDP per capita explains only 5% of the variation in the term of trade. In contrast, we find no significant relationship for our set of 25 oil exporters (see Figure 3). This appears to be due to the fact that export prices in these countries are driven by movements in the global oil market that are common to all.

The pattern shown in Figure 2, whereby countries with higher per-capita GDP have higher terms of trade, carries over to the comparison of real GDP<sup>e</sup> and real GDP<sup>o</sup> . In Figure 4, a comparison of the two is given for the full set of countries, together with the 45 degree line. In o column (9) of Table 1 the difference between the two is given as a percentage over real GDPj . The magnitude of this difference depends on the openness of a country and its terms-of-trade. For example, although India had a particularly low terms-of-trade in 1996, the difference between real GDP and real je GDP is only 4%, due to its low share of exports and imports in jo GDP. Alternatively, big differences can be found for small open economies such as Bermuda, Ireland, Israel, Malta and Singapore in which real GDP was at least 30% than real je GDP due jo to advantageous terms-of-trade. Countries such as the Bahamas, Hong Kong, Moldova, Macao,

> 15   An number of electronic products exported from Hong Kong have lower unit-values than those exports from other countries. An example is electronic microcircuits (SITC 7764), which sell for $1.09 from Hong Kong, but $1.43 from Japan and $1.74 from the U.S.

> 16 Oil-exporters are defined as those having export shares of mineral fuels of at least 25%. Data on mineral fuel exports is taken from the World Bank, _World Development Indicators_ .

18
Table A.14: Robustness checks

||**Sought company**<br>**to visit the ADC from**<br>**Non-husband/MIL**<br>(1)|**Visited the**<br>**ADC with**<br>**Non-husband/MIL**<br>(2)|**Visited**<br>**the ADC**<br>(3)|**Visited**<br>**any clinic**<br>(4)|**Use modern**<br>**FP method**<br>(5)|**Number of**<br>**close peers**<br>**in village**<br>(6)|
|---|---|---|---|---|---|---|
|Own Voucher|0.099|0.065|0.208|0.179|0.058|-0.050|
|_Robust (p-value)_|(0.000)|(0.016)|(0.000)|(0.000)|(0.116)|(0.499)|
|_Clustered (p-value)_|(0.001)|(0.024)|(0.000)|(0.003)|(0.103)|(0.393)|
|_WC Bootstrap (p-value)_|(0.001)|(0.008)|(0.000)|(0.000)|(0.132)|(0.385)|
|_MHT Correction (p-value)_|(0.026)|(0.178)|(0.000)|(0.003)|(0.243)|(0.508)|
|BAF Voucher|0.291|0.079|0.190|0.125|0.059|0.133|
|_Robust (p-value)_|(0.000)|(0.005)|(0.000)|(0.006)|(0.106)|(0.114)|
|_Clustered (p-value)_|(0.000)|(0.048)|(0.000)|(0.025)|(0.050)|(0.060)|
|_WC Bootstrap (p-value)_|(0.000)|(0.019)|(0.000)|(0.036)|(0.040)|(0.095)|
|_MHT Correction (p-value)_|(0.026)|(0.178)|(0.000)|(0.003)|(0.243)|(0.508)|

_Notes:_ p-values in parentheses. _Robust_ represents p-values from robust standard errors. _Clustered_ represents p-values based on standard errors clustered at the village level. _WC Bootstrap_ reports the p-values based on wild clustered bootstrapped standard errors, obtained from the _boottest_ command in Stata. _MHT correction_ denotes p-values obtained after correcting for multiple hypotheses testing using the _mhtexp_ command in Stata. MIL denotes mother-in-law and FP denotes family planning. Variable definitions are in Online Appendix.
# **Figure 25. Distribution by Size of Firms Exporting Services.**

<!-- Start of picture text --> 3% 4%<br>1%<br>28%<br>64%<br>Less than 10000 10.000 to 45.000 45.0000 to 450.000<br>More than 450.000 N.a.<br><!-- End of picture text --> Source: RUSLANA.

49
# _3.2 Fiscal space_

Fiscal space can be created from four potential sources: increased domestic revenue, more efficient public spending, increased public borrowing and the mobilization of budget grants from external donors. Increased revenue mobilization, although an important long term objective of fiscal policy in Tajikistan (Brownbridge and Canagarajah, 2008) is unlikely to be feasible in the short term without raising tax rates, which would be counterproductive in the context of the economic crisis because it would entail a negative fiscal impulse. Improving expenditure efficiency is also an important long term objective but requires institutional reforms and capacity building in the public service and so has limited short term relevance.

**Table 5**

# **Tajikistan Fiscal Data, percent of GDP: average 2003-06, 2007 and 2008**

||Average 2003-06|2007|2008|
|---|---|---|---|
|Revenues|18.4|20.5|20.5|
|Grants|1.6|1.1|0.8|
|Total Expenditure|21.3|27.8|27.5|
|Current Expenditure|13.8|13.6|12.4|
|Capital Expenditure|7.4|14.1|13.9|
|Fiscal Balance|-1.3|-6.2|-6.1|
|Net External Financing|2.8|6.6|6.4|
|Net Domestic Financing|-1.4|-0.7|-0.7|
|Public and Publicly Guaranteed Debt|||31.5|
|External PPG debt|33.7*|33.4|30.4|
|Domestic Debt|||1.1|
|NPV of External PPG Debt|30.6*||18.8|

16