rafmacalaba/datause-encoder-data huggingface dataset:
4 of similar goods with a common market, and monetary policy coordination and macro similarities.<sup>5</sup> The financial explanation of spillovers relies on the constraints and inefficiencies in banking sectors and international equity markets. Imperfections in the financial system are exacerbated during a crisis, and such imperfections limit the extent in which financial services can be provided to different countries.<sup>6</sup> The new theories of financial spillovers are based on the existence of bank networks as channels of propagation.<sup>7</sup> Empirically, Rey (2013) provides evidence of a “global financial cycle” in capital flows, asset prices and in credit growth. This cycle co‐moves with the VIX, a measure of uncertainty and risk aversion of the markets.<sup>8 9</sup> The third explanation is based on coordination failure. Existing theories include explanations where spillovers are due to multiple equilibria, herding, learning, and political contagion. The transmission of shocks is related to informational problem that can drive market participants to make decision affecting many countries at once. In addition to investors facing informational challenge, policy makers also often follow each other and often adopt similar macroeconomic policy response. Even when countries have little in common, the behaviors of investors and

> `5` See Gerlach and Smets (1995), Corsetti, Pesenti, Roubini, and Tille (1998), Corsetti, Pericoli, and Stracia, (2003), and Basu, (1998).

> `6` See Goldstein, Kaminsky, and Reinhart (2000) and Kaminsky and Reinhart (2002) for explanations based on common lender and banking and Calvo (2002) for explanation based on financial intermediaries.

> `7` See Allen and Gale (2000) and Elliott, Golub, and Jackson (2014).

8 Further Rey (2013) shows that asset markets in countries with more credit inflows are more sensitive to the global cycle—the latter is not aligned with countries’ specific macroeconomic conditions. The VAR analysis suggests that one of the determinants of the global financial cycle is monetary policy in the center country, which affects leverage of global banks, capital flows and credit growth in the international financial system. Whenever capital is freely mobile, the global financial cycle constrains national monetary policies.

9 See also Bruno and Shin (2013) for evidence of global financial cycle.

(continued)
Policy Research Working Paper 5873

# **Abstract**

This paper proposes two related measures of educational inequality: one for educational achievement and another for educational opportunity. The former is the simple variance (or standard deviation) of test scores. Its selection is informed by consideration of two measurement issues that have typically been overlooked in the literature: the implications of the standardization of test scores for inequality indices, and the possible sample selection biases arising from the Program of International Student Assessment (PISA) sampling frame. The measure of inequality of educational opportunity is given by the share of the variance in test scores that is explained by pre-determined circumstances. Both measures are computed for the 57 countries in which PISA surveys were conducted in 2006. Inequality of opportunity accounts for up to 35 percent of all disparities in educational achievement. It is greater in (most of) continental Europe and Latin America than in Asia, Scandinavia, and North America. It is uncorrelated with average educational achievement and only weakly negatively correlated with per capita gross domestic product. It correlates negatively with the share of spending in primary schooling, and positively with tracking in secondary schools.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at fferreira@worldbank.org and gignoux@pse.ens.fr.

_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
16 intensity, an increase in Saigon river levels, the total Ho Chi Minh City population, and so on. These nine factors were identified during participatory scoping discussions in the two workshops and in discussions with Steering Center for Flood Control. Appendix A describes these factors in more detail.

**Table 4.2. Exogenous uncertainties and their range of plausible values.**

|**Uncertain Factor**|**Range of values**|**Relevant sources of data**|
|---|---|---|
|Increase in rainfall<br>intensity over 1980-2000<br>levels|+0% to +60%|IPCC (2012)|
|Increase in Saigon River<br>level over 1990-2000<br>levels|+30 cm to +100 cm|MONRE (2009, 2010); NOAA<br>(2012); Vermeer and Rahmstorf<br>(2009); Discussions with Steering<br>Center for Flood Control|
|Total Ho Chi Minh City<br>population|7.4 M to 19.1 M|Ho Chi Minh City Statistics<br>Office (2011); Ministry of<br>Planning and Investment (2011);<br>World Bank(2010)|
|Geographic distribution<br>ofpopulation|World Bank<br>estimate to current|Ho Chi Minh City Statistics<br>Office(2011);World Bank(2010)|
|Poverty rate|2.4% to 25%|ADB(2010); (Coulthart_et al._<br>2006);UNDP(2010)|
|Average annual<br>economicgrowth|6% to 12%|Hawksworth et al. (2009); ADB<br>(2010);Voice of Vietnam(2001)|
|Geographic distribution<br>of economicgrowth|Equitable to<br>Inequitable|Ho Chi Minh City Statistics<br>Office(2011)._See Appendix A._|
|Population vulnerability|10% to 100%|_Developed in this study. Reflects_<br>_percent of population affected at_<br>_10cm of depth. See Appendix A._|
|Economic vulnerability<br>threshold|0% to 22%|_Developed in this study. Reflects_<br>_economic loss at 10cm of depth. See_<br>_Appendix A._|

# **Policy Levers (L)**

The policies considered in this study represent alternative configurations of an integrated flood risk management strategy that combines infrastructure, adaptation and retreat options. Ho Chi Minh City’s flood control infrastructure, recently constructed as broadly described in the 1999 JICA Master Plan for Drainage Infrastructure (PCI 1999), provides this study’s Baseline strategy. We add to this baseline alternative combinations of four adaptation and retreat policies that seek to reduce exposure and vulnerability. We
|**Model**|**Time Dimension**|**Role of Water in the Model**|**International Trade**|**Contribution to literature**|**Main Findings**|
|---|---|---|---|---|---|
|**Tirado****_et. al.,_2005**||||||
|**Tirado****_et. al.,_2006**||||||
|**Kohn, 2003**|-|tradablegood|Trade Model|Introduces Heckscher - Ohlin -<br>Samuelson framework|both countries are better off when<br>fresh water is internationallytraded|
|**Briand, 2004**|Static|Drinking water modeled as consumer<br>good, primary water is prod. factor<br>andproduced by government|Small<br>open<br>economy,<br>Armington Specification|Introduces<br>drinking<br>water<br>distribution as a separate sector,<br>analyzes drinkingwater vs. irrigation||
|**Finoff, 2004**|Static|Determines salinization and biomass|Small open economy,|First model that links biomass<br>equilibrium to waterpolicies||
|**Boccanfuso****_et. al.,_2005**|Static|Intermediate input.<br>Two water utilities selling water or<br>water suppliers. Price of water is<br>exogenous,|Small<br>open<br>economy,<br>Armington Specification|First implication of Integrated Muti-<br>Household<br>approach<br>to<br>water<br>modeling.<br>Introduced stylized facts of the<br>production behavior of the water<br>sector|Price changes have important general<br>eqb. effects<br>Households are biggest loser from<br>privatization.<br>Rural households lose more.|
|**Diao ****_et. al., _2005**|Static|Production factor|Small<br>open<br>economy,<br>Armington Specification||Water markets are likely to increase<br>the agricultural output significantly.|

47
**Table 1: Parameter estimates for individual commodities**

||**‐‐‐‐‐‐‐‐‐‐‐‐‐‐**<br>|**‐‐‐‐ ENERGY ‐**|**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**<br>|**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**<br>|**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐‐‐ ME**<br>|**TALS ‐‐‐‐‐‐‐‐‐‐‐**<br>|**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**<br>|**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**Coal**|**N. Gas**|**Oil**|**Aluminum**|**Copper**|**Lead**|**Nickel**|**Tin**|**Zinc**|
|**_Parameter esti_**|**_mates_**|||||||||
||‐0.87|1.49*|1.81***|3.98***|3.67***|1.40*|1.88*|0.72|2.89***|
|y�|(0.64)|(0.80)|(0.37)|(0.39)|(0.67)|(0.76)|(1.03)|(0.79)|(0.24)|
|�|0.08**|‐0.04|‐0.07***|‐0.17***|‐0.18***|‐0.07*|‐0.07|‐0.04|‐0.14***|
|y�|(0.03)|(0.04)|(0.02)|(0.02)|(0.03)|(0.04)|(0.05)|(0.04)|(0.01)|
||0.01|‐0.20***|‐0.41***|‐0.21***|‐0.27***|‐0.07*|‐0.11***|0.00|‐0.22***|
|p�|(0.06)|(0.02)|(0.03)|(0.03)|(0.04)|(0.03)|(0.04)|(0.02)|(0.03)|
||‐0.09***|‐0.16***|‐0.07***|‐0.26***|‐0.13***|‐0.18***|‐0.30***|‐0.21***|‐0.25***|
|ρ|(0.02)|(0.02)|(0.01)|(0.03)|(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|
|**_Key statistics_**||||||||||
|Hausman test|4.81|3.21|4.15|2.40|1.25|2.12|2.28|2.21|10.35|
|_p_‐value|0.19|0.36|0.25|0.49|0.74|0.55|0.52|0.55|0.02|
|Log‐likelihood|1,930|1,917|5,195|964|472|516|‐140|‐204|844|
|Observations|2,898|2,779|3,235|2,525|2,300|2,355|1,555|2,165|2,637|
|Countries|57|60|63|52|49|52|36|49|55|

**Notes** : The dependent variable is the logarithm of consumption of the respective commodity. Three (***), two (**), and one (*) asterisks denote significance of parameter estimates at 1, 5, and 10 percent level, respectively. Standard errors in parentheses. “―” indicates that the corresponding variable was not included in the model.

_— 18 —_
inary analysis of regional growth and decline in a two region case, Northeast Regional Science Review, Vol. 7, pp. 87-101.

Romanoff, E. and Levine, S.H., 1986. Capacity limitations, inventory, and time-phased production in the sequential interindustry model, Papers of the Regional Science Association, Vol. 59, pp. 73-91.

Romanoff, E. and Levine, S.H., 1993. Information, interindustry dynamics, and the service industries, Environmental and Planning A, Vol. 25, pp. 305-16.

Rose, A., 2004. Economic Principles, Issues, and Research Priorities in Hazard Loss Estimation, in: Okuyama,Y. and S. Chang (eds.), Modeling Spatial and Economic Impacts of Disasters, Berlin: Springer, 14-36.

Rose, A., J. Benavides, S.E. Chang, P. Szczesniak, D. Lim, 1997. The regional economic impact of an earthquake: direct and indirect effects of electricity lifeline disruptions, Journal of Regional Science, 37(3), 437-458 Rose, A. and S.-Y. Liao, 2005. Modeling regional economic resilience to disasters: a computable general equilibrium analysis of water service disruptions. Journal of Regional Science, 45:75112.

Rose, A., G. Oladosu and S. Y. Liao, 2007, Business interruption impacts of a terrorist attack on the electric power system of Los Angeles: customer resilience to a total blackout, Risk Analysis 27, 513-531.

Santos, J.R. and Y.Y. Haimes, 2004. Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures. Risk Analysis, 24:1437-1451.

47
and reached nearly 15 percent by the middle of 2009. Nevertheless, Turkey recovered relatively quickly in the aftermath of the Great Recession – achieving consistent growth and reducing unemployment while managing to maintain relatively low inflation through 2011.

The lower panel of Figure 1 shows Turkey’s current account position (as a share of GDP) and its trade-weighted real exchange rate, illustrated so that increases reflect an appreciation of the Turkish lira. Since 2002, Turkey suffered a persistent and rising current account deficit, with import growth outpacing the expansion of its exports. The trend was briefly interrupted at the height of the economic crisis in 2009:Q1 as the collapse in trade flows (significantly reducing both Turkey’s imports and exports, and thus the nominal current account deficit) outpaced the decline in Turkey’s GDP. Turkey’s economic recovery that began shortly thereafter has led to resumption in growth of the current account deficit, which had stabilized in 2011 at roughly 10 percent of GDP. The size of the current account deficit signals a significant trade imbalance concern.

Turkey’s trade-weighted real exchange rate has undergone brief periods of both appreciations and depreciations since 2006. The lira appreciated considerably until 2008:Q3 and the early stages of the global financial crisis, at which point it depreciated briefly but substantially through 2009:Q1. It then began another sustained period of appreciation through 2010:Q3; at that point it began a period of depreciation that continued through 2011.

There is a substantial research literature examining the relationship between macroeconomic shocks and changes to national trade policy, though most of it is focused on high-income countries.<sup>8</sup> In a recent contribution, Bown and Crowley (2013b) present evidence over the period 1989-2010 that examines the relationship for Turkey and a set of twelve other emerging economies. On average, across these emerging economies, new import protection through flexibilities embodied in its temporary trade barrier policies (discussed in more detail in section 3.2 below) are associated with recent appreciations in the real exchange rate. I.e., for the case of Turkey, this is consistent with a strengthening Turkish lira – that makes imports suddenly more price-competitive with domestic production – subsequently resulting in additional demands for new import restrictions. There is also evidence from emerging economies that new trade barriers are associated with negative shocks to domestic real GDP growth and rising unemployment. The timing of the shocks that Turkey has faced in the Great Recession period, especially

> 8 Knetter and Prusa (2003) and Bown and Crowley (forthcoming) provide evidence on the macroeconomic determinants of these relatively formal instruments of import protection for high-income economies; the latter paper uses quarterly data for the period 1988-2010 and thus covers the inception of the Great Recession.

8
First, income levels and migration influence each other. Low-income levels are among the main causes of migration. On the other hand, extreme poverty may hinder migration as very poor people lack financial and other resources. For example, in Nicaragua, Murrugarra and Herrera (2011) find that the socioeconomic level of a migrant affects his choice of destination, with poorer migrants choosing destinations that have lower transaction costs, and fewer potential gains. While income levels and poverty may influence migration decisions, migration also leads to poverty reduction and changes in the income distribution, especially in origin countries. In Guatemala, Adams (2005) finds that remittances reduce the degree, depth and severity of poverty in the country. In addition, numerous studies examine expenditure or consumption patterns of households with migrants abroad currently or previously (Taylor and Mora, 2006; Yang, 2008; Adams and <mark>Cuecuecha,</mark> 2010). As a result of data requirements and the difficulties involved in identifying migration, research on the dynamics of the relationship between migration and inequality or income growth remains relatively limited (McKenzie and Rapoport, 2007; de Brauw and Giles, 2008; Gibson et al., 2009; Lokshin et al., 2010).

The primary requirement to study migration, poverty and income distribution is an accurate measure of well-being: the preferred measure is consumption, which is an integral part of any multi-topic survey. Deaton and Grosh (2000) provide a detailed description of the issues one faces in measuring consumption, and Deaton and Zaidi (2002) discuss issues related to the computation of a consumption aggregate in household survey data in detail. In order to measure the causal relationship between migration and welfare, one would need to estimate what the migrant household’s per capita consumption level would have been if the migrant had remained within the household.

26
consumable income, after all taxes (direct and consumption taxes), _cash or near-cash_ transfers and subsidies have been accounted for, the estimated poverty rate drops to just less than 24 percent.<sup>45</sup>

**Figure 10. Fiscal Policy’s Impact on Inequality (bars and right axis) and Prefiscal Inequality (dots and left axis), Select Countries/Years**

<!-- Start of picture text --> 0.00 0.0<br>‐2.0<br>0.10<br>‐4.0<br>0.20<br>‐6.0<br>0.30<br>‐8.0<br>0.40<br>‐10.0<br>0.50<br>‐12.0<br>0.60 ‐14.0<br>Gini index<br>Difference in Gini index<br><!-- End of picture text --> _Sources:_ Egypt: based on HIECS 2015 and budget figures from FY14/15 and FY15/16. Georgia: Cancho and Bondarenko 2017. Iran: Enami, Lustig, and Taqdiri 2017. Mexico: Scott 2014. Tunisia: Jouini, Lustig, Moummi, and Shimeles 2018. Armenia: Younger and Khachatryan 2017. El Salvador: Beneke, Lustig, and Andres Oliva 2018. Sri Lanka: Arunatilake and Lustig 2017. Indonesia: Jellema, Wai-Poi, and Afkar 2017. Guatemala: Icefi. 2017a. Jordan: Alam, Inchauste, and Serajuddin 2017. _Note:_ Fiscal impact on inequality is plotted with bars and on the right-hand axis as the difference in Gini Index of the market income distribution and the consumable income distribution. Prefiscal inequality is plotted with dots and measured as the Gini index on the left-hand axis.

**Figure 11. Fiscal Policy’s Impact on the Poverty Headcount Ratio**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Market (incl. pensions) Net market Disposable Consumable<br><!-- End of picture text --> _Source:_ Based on HIECS 2015 and budget figures.

_Note:_ The poverty headcount ratios in this figure are based on poverty lines based on the methodology described in Lara Ibarra (2018). The average value is LE 5,748 per person per year, or US$3.98 per day in 2011 PPP.

45 When the government‐cost value of in‐kind services is added to Consumable Income, the poverty headcount rate drops to approximately 12 percent. However, a monetized value of an in‐kind service received does not _directly_ produce an increase in purchasing power (over all goods and services).

29
6 between net farm income or agricultural GDP and farm household welfare is fading, even in some low-income countries (Davis, Winters and Carletto 2009). Frequently, many of the poor, including the rural poor, are net-buyers of staple foods, causing them to be adversely affected—at least in the short run—by increases in prices of staple foods.

Second, the compilation of national household surveys that are comparable for crosscountry analysis has progressed rapidly such that there are now recent surveys for more than 100 countries available at the World Bank. That dataset (www.worldbank.org/prospects/gidd) has already begun to be used in conjunction with the World Bank’s LINKAGE model of the global economy to assess global income distribution issues (e.g., Bussolo, De Hoyos and Medvedev 2008).

Third, the World Bank has recently compiled a very comprehensive new global database that updates and expands substantially our understanding of the distortions to agricultural incentives in developing countries in particular.<sup>3</sup> Those estimates have since been expressed so as to make them usable in national and global economy-wide models (Valenzuela and Anderson 2008). They differ from the usual ones employed by trade modelers of developing country policies in that they are based on direct domestic-to-border price comparisons rather than (as with the GTAP dataset, see Narayanan and Walmsley 2008) on applied rates of import tariffs and other key border measures.

A first attempt to exploit those new methodologies and databases has recently been undertaken to assess the relative impacts on national, regional and global poverty and inequality of agricultural and non-agricultural trade policies at home and abroad. This paper summarizes and draws lessons from the papers that have emerged from that research project. At the outset it should be made clear that agricultural and trade policies are far from the first-best policy instruments for achieving national poverty or income distribution

> 3 That distortions database is documented fully in Anderson and Valenzuela (2008) and is based on the methodology summarized in Anderson et al. (2008) and detailed in Appendix A of Anderson (2009).
41 Table 2 - The Size of the Shocks: Standard Deviations of Estimated Disturbances*

||1980:6-1985:6|1985:12-1988:12|
|---|---|---|
|Inflation|3.85|0.68|
|Wage Growth|7.18|5.55|
|Ml Growth|6.48|7.91|
|M3 Growth|5.08|2.45|
|Credit Growth|4.15|1.57|
|Devaluation (Basket)|4.08|1.53|
|Devaluation (Dollar)|3.57|1.26|
|Controlled Prices' Inflation|5.79|0.96|
|Nominal Interest Rate|1.22|0.20|
|Unemployment|0.49|0.77|
|Industrial Production Growth|6.46|6.29|
|Employment Growth|1.38|1.00|
|Consumption Growth|6.25|3.96|
|Trade Deficit/GDP|0.026|0.024|

* Note: Each entry is the standard deviation of the estimated disturbance from bivariate autoregressions that used four lagged values of inflation and of the other relevant variable and a constant. The figures for inflation are based on autoregressions that had lagged inflation and wage growth as explanatory variables.