rafmacalaba/datause-encoder-data huggingface dataset:
labor and agricultural value-added. This can give rise to misleading inferences, when one aggregates the household results up to the national level.

An example of this problem arises in two recent studies of the poverty impact of China’s WTO accession. The first is a global CGE analysis in which final demand in China is modeled via a single representative consumer (Ianchovichina and Martin, 2003). They find that China as a whole gains from accession. However, those authors are unable to speak to the poverty issue. Towards this end, a second study (Chen and Ravallion, 2003) takes the price changes from the global CGE study and applies them at the individual household level to elicit the distributional consequences of accession using full detail in the household survey. Thus the authors are able to identify which types of households in which provinces and with which demographics lose and which gain. This is extremely useful information.

However, when the individual household impacts are aggregated up to the national level, China is found to lose, not gain, from WTO accession, thereby contradicting the original macro results from which the household impacts are driven. It is easy to see why this might be the case. Agriculture is hurt most by WTO accession and if this portion of national income is overstated in the survey, then it will take on disproportionate importance in the estimated national impacts – thereby giving the misleading impression of an overall loss. Such contradictions are pervasive in the so-called micromacro synthesis literature, and this highlights the need to reconcile the underlying data bases before undertaking serious policy analysis (Ivanic, 2004).

**_Tax Revenue and Transfer Payments:_** Trade taxes are an important part of total revenue in many developing countries and so the fiscal implications of their reduction or elimination under various trade reform scenarios needs to be given serious consideration (Harrison, Rutherford, and Tarr, 2000). Depending on the choice of tax replacement instrument, the poor may be adversely affected. For example, value-added taxes are a common alternative to tariffs as a revenue-raising instrument. Since the poor have a low propensity to consume imported goods, but a very high average propensity to consume out of current income, this switch may well have an adverse effect on poverty.

Of course, one alternative to tax replacement involves a reduction in government spending. Given the heavy reliance of the poor in many countries on public transfer payments, reductions in this area of the budget could also have adverse consequences for poverty. In general the determinants of 30
# **Table 1. A Review** **<u>of Contingent Valuation Studies on SWM</u>**

|**Authors**|**Country / City**|**Sample / Survey**<br>**Method**|**Project valuated**|**WTP question**<br>**format**|**Reported WTP (per**<br>**household per**<br>**month)**|**Annual WTP**|**Mean annual**<br>**household**<br>**income**|**WTP’s %**<br>**of income**|<br>**Exchange**<br>**rate (for 1**<br>**US$)**|
|---|---|---|---|---|---|---|---|---|---|
|Altaf and<br>Deshazo<br>(1996)|Gujranwala,<br>Pakistan<br>(1990)|In-person questionnaires<br>in 968 households|Municipal waste<br>collection and disposal|Two-step dichotomous<br>choice|8.04 - 11.20 Rs|96.48 - 134.40 Rs|<br>39,600 Rs|0.2-0.3|19.75 Rs|
|Sterner and<br>Bartelings<br>(1999)|Tvaaker,<br>Sweden (1994)|Questionnaires mailed to<br>nearly 600 households|Municipal Waste<br>Disposal, Recycling,<br>Composting|Open-ended|N/A|420 SEK|280,000 SEK|0.2-0.9|N/A|
|Othman (2002)|<br>Kajang and<br>Seremban,<br>Malaysia<br>(2001)|In-person survey in two<br>samples of 582<br>households (582 valid)|Improved waste<br>management|Dichotomous choice|Mandatory separation:<br>22 MYR; w/o<br>mandatory separation:<br>30 MYR|264 - 360 MYR|<br>41,112 MYR|<br>0.6-0.9|3.75 MYR|
|Bluffstone and<br>DeShazo<br>(2003)|Ukmerge,<br>Lithuania|In-person survey in 775<br>households|Improved landfills|Open-ended|N/A|1.70 Euros|2,184 Euros|0.1|0.91 Euros|
|Huang and Ho<br>(2005)|Taichung City,<br>Taiwan, China<br>(2004)|Questionnaires mailed to<br>220 random households<br>(205 valid)|Waste clearance and<br>disposal|N/A|112.97 NT$|1,355.64 NT$|720,000-<br>960,000 NT$|<br>0.14-0.19|About 32<br>NT$|
|Jin et al (2006)|<sup>Macao, China</sup><br>(2004)|In-person survey in 260<br>households (252 valid)|Improved solid waste<br>management program|Two-step dichotomous<br>choice|19.20 MOP|799.29 MOP|219,976.20 MOP|<br>0.4|8.00 MOP|
|Lal and Takau<br>(2006)|Tongatapu,<br>Kingdom of<br>Tonga (2005)|In-person questionnaire|<sup>Solid waste collection</sup><br>and disposal system|Two-step dichotomous<br>choice|13.43 $|161.20$|5,200$ - 10,400$|<br>1.6-3.1|N/A|
|Fonta et al<br>(2007)|Enugu City,<br>Nigeria (2003)|In-person survey in 200<br>households (182 valid)|Collection of solid<br>waste from residential<br>areas|One-step dichotomous<br>choice followed by<br>open-ended question|230.35 Naira|2,764 Naira|151,320 Naira|1.8|About 130<br>Naira|
|Afroz et al<br>(2009)|Dhaka City,<br>Bangladesh<br>(2006)|In-person survey in 480<br>households:(456 valid)|Improved waste<br>collection and<br>recycling system|Two-step dichotomous<br>choice|13 Taka|156 Taka|144,000 Taka|0.1|70 Taka|
|Ichoku et al<br>(2009)|Enugu City,<br>Nigeria (2003)|In-person survey in 200<br>households (197 valid)|Collection of solid<br>waste from residential<br>areas|Payment card|215 Naira|2,580 Naira|151,320 Naira|1.7|About 130<br>Naira|

20
16 The range of measures included in those NRA and CTE estimates is wide. By calculating domestic-to-border price ratios, the overall estimates include the price effects of all tariff and non-tariff trade measures (positive or negative), plus any domestic price measures (positive or negative), plus an adjustment for the output-price equivalent of direct interventions in farm input markets. Where multiple exchange rates operate, estimates of the import or export tax equivalents of that distortion are included as well. The database is especially well suited to the analysis in this paper because it separately identifies each of the price effects of the different policy instruments referred to above.

The most aggregated summaries of NRA and CTE estimates for covered products for developing and high income countries are provided in Figure 2. Figure 2a supports the widely held view that developing country governments had in place agricultural policies that effectively taxed their farmers through to the 1980s, and that the extent of those disincentives has lessened since then. Indeed since the mid1990s those farmers have enjoyed slightly positive assistance on average. Figure 2b shows the growth of agricultural protection in high-income countries since the 1960s and its reversal on average after the 1980s. Consumers have experienced changes similar to producers in recent years: in developing countries consumers were effectively subsidized for most of the last 50 years although that has lessened since the 1990s, while in high-income countries the implicit taxation of consumers from agricultural support rose until the late 1980s but has fallen since then.

# [Insert Figure 2 about here]

Figure 3 show the trends in NRAs and CTEs, respectively, for the four studied regions of Africa, Asia, Latin America, and Europe’s transition economies. On the production side, Africa is where there has been the least tendency to reduce the taxing of farmers and subsidizing of consumers of farm products. Indeed its average NRA has been negative in all 5-year periods except the mid-1980s when international prices of farm products reached an all-time low in real terms. By contrast, for both Asia and Latin America their NRAs crossed over from negative to positive after the 1980s, while in Europe’s transition economies assistance to farmers has trended upward following the initial shock in the early 1990s. In all four regions, agricultural policies have almost always involved consumer
Figure 4. Most Papers Fit Precision Comparability <!-- Start of picture text --> Funnel plot with pseudo 95% confidence limits<br>0 20 40 60 80 100<br>precision<br>1 2<br>Lower CI Lower CI<br>Pooled<br>0<br>2<br>4<br>6<br>s.e. of precision<br>8<br>10<br><!-- End of picture text --> <!-- Start of picture text --> 1=Precision, 2=Log Sample Size<br>Source: Own estimations<br><!-- End of picture text --> Table 3 shows the results for the heterogeneity tests. There is large heterogeneity across studies which rely on quasi-experimental and structural models-98 and 96 percent respectively, significant at 99 percent level. The simulation and qualitative approaches to evaluate R&D interventions show null heterogeneity. This has to do with the standardized procedures to collect qualitative data and the few alternative methodological approaches that authors have to conduct simulations.

Table 3. Heterogeneity Test

|**Tests for Heterogenei**|<br>**ty Between Evalu**|**ation Meth**|**od Groups**|
|---|---|---|---|
||**eeroene**|||
|**Type of Evaluation Model**|**gy**<br>**Statistic**|**P-value**|**I-squared ***|
|Structural model|1041.1|0.000|98.6%|
|Quasiexperimental|236.9|0.000|94.9%|
|Qualitative|3.0|0.394|0.0%|
|Simulation|1.9|0.588|0.0%|
|Overall<br>_* Variation in ES attributabl_|1731.5<br>_e to heterogeneity_|0.000<br>|97.9%|

21
effects of economic growth. In other words, were two countries to grow at the same rate, poverty would come down faster in a country following rural nonfarm diversification and secondary town development than in a country following rapid metropolization. The findings thus suggest that rural diversification and less concentrated urbanization lead to more inclusive growth patterns.

The results in columns 3 and 4 of Table 5 further show that these empirical regularities are robust to controls for political regimes as well as population growth.<sup>25</sup> Given that the results are also controlled for differences in initial conditions (such as land inequality, institutional and political arrangements) through the inclusion of country specific dummies, this is a striking result. Yet it resonates with the findings from the 1991/4-2010 tracking case study from Tanzania as well as those from the comparative historical analysis of the evolution of inequality and growth in Taiwan, China, and South Korea in relation to their spatial development during 1965-1990 (Otsuka, 2007).<sup>26</sup> During 1984-2002, Suryahadi, Suryadarma, and Sumarto (2009) also found growth in rural and urban services in Indonesia to be equally important as growth originating in agriculture for poverty reduction, highlighting the importance of the development of the middle for poverty reduction in this transforming economy.<sup>27</sup> Clearly, the poverty reducing effect of diversifying into rural nonfarm and secondary town activities on poverty reduction can be substantial.

Turning to the channels, the estimation of equation (1) is repeated though this time using share weighted changes in the share of the middle and the metropoles, i.e. equation (2) (Table 6, columns (1) and (2)). These represent in effect the percent change in the non-agricultural

> 25 This is in addition to being robust against different measurement issues, including different metrics for poverty (poverty gaps, percentage point changes) and metropoles (750,000 inhabitants instead of 1 million as cut-off), as well as the decomposition of growth in its sources (agriculture and non-agriculture) and non-linearity in the population shares (Christiaensen and Todo, 2013).

> 26 Taiwan, China, and South Korea experienced for example a similar per capita GDP growth of 7.1 percent between 1965 and 1990.  Both countries also started at similar levels of inequality (a Gini of about 0.32). Yet throughout the subsequent decades inequality has been lower in Taiwan, China, and higher in South Korea. Taiwan’s economic development has been based on the development of more labor intensive small and medium enterprises located in rural and suburban areas, while South Korea’s development has been led by more capital intensive urban based, large enterprises.

> 27 Similar to Datt and Ravallion’s (1996) analysis of Indian data, they also relate the effect of urban service growth on poverty to the more labor-intensive, low capital, lower skilled part of the urban service sector.

20
# **I. Introduction**

The substantial positive returns to education for both individuals (e.g. Psacharopoulos and Patrinos, 2004) and countries (e.g. Barro and Lee, 2010) are a fairly established fact in the literature of the economics of education. It comes as no surprise, then, that policy makers and academicians are so interested in understanding the factors that can influence the quantity and quality of education.

While the academic literature on these factors is vast, most of it focuses on developed countries, and while there is some literature on developing countries, only recently have studies using data from countries in Eastern Europe and the Former Soviet Union (henceforth, _transition countries_ ) started to appear. The recent surge in this literature can be explained by the fact that performance measures for these countries have become available, both through the participation of these countries in international assessments of student learning like the Organisation for Economic Cooperation and Development (OECD)‟s Programme for International Assessment (PISA), the Trends in International Mathematics and Science Survey (TIMSS) and Progress in International Reading Literacy Survey (PIRLS), and through the introduction of centralized nation-wide assessments in several transition countries.

In terms of the development of their educational systems, transition countries may be considered to lie somewhere between developed and developing ones. Transition countries typically have had mandatory basic education and enjoy very high levels of school attendance. As a result, so the issue is not how to get children to attend school, as it is in a big part of the developing world. Instead, transition countries face the challenge of how to modernize and reform their educational system so as to provide quality education at an affordable price.

One possible opportunity to realize such quality enhancing reforms might come from taking advantage of the so-called “demographic dividend”, i.e. the sizeable demographic changes that took place in most of the transition countries, especially in the last 20 years. In fact, after the fall of the Soviet Union, transition countries have faced an unprecedented demographic shock, with increasing mortality and emigration, but also with a serious drop in fertility. This negative shock to fertility has translated in an increasingly smaller number of school-aged children, considerably reducing school size and class size over time (Berryman, 2000). In addition, given that this drop 2
# SUPPLEMENT

## I. METHODOLOGY AND DATA

We leverage the growth of international assessments to construct globally comparable learning outcomes. These tests are derived from assessments conducted in the United States since the 1960s such as the SAT and the National Assessment of Educational Progress (NAEP). The tests are psychometrically designed, standardized assessments of cognitive skills. Since the 1990s, international assessments have been conducted by organizations such as the OECD. Two high profile examples are PISA and TIMSS which covered 71 and 65 countries in 2015. These assessments enable credible global comparison of learning across countries and over time. Earlier analyses focus mainly on OECD countries and cover few developing countries. This limits the distribution of countries represented and has implications for our understanding of the link between human capital and economic development.

We include 164 countries, two-thirds of which are developing countries, by linking international assessments to their regional counterparts. Regional assessments cover much of Sub-Saharan Africa and Latin America. Thus, through construction of a linking procedure between international and regional assessments we quantify the difference between them, adjust for this difference, and then place learning outcomes from regional assessments on a global scale.

## _A. The Linking Procedure_

The central intuition behind the construction of globally comparable learning outcomes is the production of a linking function between international and regional assessments. This function can be produced for countries that participate in a given pair of assessments and captures the difference in difficulty between the two assessments. This linking function can then be used to place scores for countries that only participate in regional assessments on the international scale. This enables construction of globally comparable learning outcomes.

We use multiple methods to produce globally comparable scores. Our primary approach uses regression when multiple countries participate in assessments being compared. When only one country participates, we use linear linking. Both methods adjust test scores by a constant as well as relative standard deviations across tests. These approaches build on a literature comparing scores across different tests (Kolen and Brennan 2014) as well as a more recent work linking aggregate level scores across states in the United States (Reardon, Kalogrides and Ho 2019).

The conversion can be implemented by regressing mean scores from countries that partake in a regional and international assessment to derive α and β and produce a linking function between assessments: μYi = α+ βμXi + εi where μ denotes the mean scores, X is a regional assessment, Y is an international assessment and _i_ denotes countries that have scores on both assessments. We can then convert scores from 23
# Public Sector "Debt Distress" in Argentina, 1988-89

# by Paul Beckerman*

|1|Introduction|1|
|---|---|---|
|2.|Argentine inflation in the 1970s and 1980s|_5_|
|3.|The role of Argentina's Central Bank|11|
|4.|The Primavera Plan|17|
|_5._|The BB Plan|19|
|6.|Common characteristics of the Primavera and BB Plans|22|
|7.|Stabilization measures of December 1989 and January 1990|24|
|8.|Concluding observations|25|
|9.|Appendix: Some technical issues regarding Central Bank losses and<br>borrowing requirements|27|
|Ref|erences|35|
|Tab|les|37|
|Gra|phs|48|

* World Bank. The author gratefully acknowledges comments by Werner Baer, Suman Bery, Donald Coes, Roque Fernandez, Dale Gray, James Hanson, Ricardo Lago, Millard Long, William Mayville, Paul Meo, Richard Newfarmer, Martha Preece, Peter Scherer, Eduardo Somensatto, William Tyler, and John Welch on earlier drafts. The writer is particularly indebted to Ricardo Lago for discussions of the game-theory approach briefly discussed in section 6.
Table 2: First stage

||Mean PM2_._5|Mean PM2_._5|Mean PM2_._5|
|---|---|---|---|
|Winter inversion, low|2.3865<sup>_∗∗∗_</sup><br>|2.4005<sup>_∗∗∗_</sup><br>||
||(0.4930)|(0.4931)||
|Summer inversion, low|-1.2599||-1.5167|
||(1.4048)||(1.4015)|
|N|8,823|8,823|8,823|
|**Relative Efects:**||||
|100% winter days as inversion_→_X% ∆PM2_._5|5.16|5.13||
|**F-test of inversions:**|12.47|23.70|1.17|
|**Weather controls:**||||
|Temperature|Y|Y|Y|
|Precipitation|Y|Y|Y|
|Relative humidity|Y|Y|Y|
|Surface pressure|Y|Y|Y|
|Wind speed|Y|Y|Y|
|Wind direction|Y|Y|Y|
|**Fixed efects:**||||
|Fiscal year|Y|Y|Y|

Notes: The outcome is the first difference within a district of the annual average of PM2 _._ 5 measured at the district level during a fiscal year from April to March and weighted by population. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversions are calculated comparing surface temperature to temperature in the two levels immediately above the surface. If either of these levels has a higher temperature than the surface we define it as an inversion. Winter refers to inversions between October and March. Summer refers to inversions between April and September. All inversion data and weather controls are population weighted averages across districts. The unit of observation is the district year. In all columns we control for the first difference of the number of days in one of fifteen precipitation bins, the number of days the mean temperature is in one of twenty-five bins, a second order polynomial of relative humidity and surface pressure, four bins of wind speed, ten wind direction bins, and interactions of relative humidity and the square of relative humidity with all of our temperature bins. All columns include fiscal year fixed effects. In all columns we cluster standard errors at the district level. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

19
Tanzania which suggests that what Zhou Enlai reportedly said about the impact of the French Revolution may also apply to the consequences of AGOA: it is too early to say.

Understanding the precise reasons for the heterogeneous response to trade preferences remains a challenge. The few instances of export growth seem to have combined market access with domestic reforms that improved access to imported inputs through low domestic tariffs, lightened the regulatory burden and enhanced access to infrastructure (in some countries through the creation of effective SEZs). Further research is needed in this area.

# **<mark>References</mark>**

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Chipman, John. 1970. “External Economies of Scale and Competitive Equilibrium,” _The Quarterly Journal of Economics_ 84(3): 347-385.

Cirera, Xavier and Cooke, Edgar. 2015. “Trade Preferences,” in O. Morrissey, R. Lopez and K. Sharma (Eds.) _Handbook on Trade and Development_ , Edward Elgar.

<mark>Collier, Paul and Venables, Anthony. 2007. “Rethinking Trade Preferences: How Africa Can Diversify its Exports,”</mark> _<mark>The World Economy</mark>_ <mark>30(8): 1326-1345.</mark> <mark>Correia, Sergio. 2015. “Singletons, Cluster-Robust Standard Errors and Fixed Effects: A Bad Mix,” Duke University mimeo.</mark> Dasgupta, Partha and Stiglitz, Joseph. 1988. “Learning-by-Doing, Market Structure and Industrial and Trade Policies,” _Oxford Economic Papers_ 40(2): 246-268.

Defever, Fabrice and Ornelas, Emanuel. 2015. “Trade Liberalization and Third-Market Effects,” mimeo.

<mark>De Melo, Jaime and Portugal-Perez, Alberto, 2014. “Preferential Market Access Design: Evidence and Lessons from African Apparel Exports to the US and the EU,”</mark> _<mark>The World Bank Economic Review</mark>_ <mark>28(1): 74-98.</mark> <mark>Edwards, L., and Robert Lawrence, 2016. “</mark> AGOA Rules: The Intended and Unintended Consequences of Special Fabric Provisions <mark>,” in:</mark> _<mark>African Successes, Volume III: Modernization and Development</mark>_ <mark>, pp. 343-393, National Bureau of Economic Research.</mark> Ethier, Wilfred. 1982. “National and International Returns to Scale in the Modern Theory of International Trade,” _American Economic Review_ 72(3): 389-405.

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