rafmacalaba/datause-encoder-data huggingface dataset:
6

**Table 2. Instances of Graduation and Reverse Graduation in the Sample**

|**Country**|**Fiscal year of last IDA**<br>**credit on initial**<br>**graduation**|**‘Reverse graduates’ - fiscal year**<br>**reentered**|
|---|---|---|
|Albania|FY2008||
|Angola|FY2014||
|Armenia|FY2014||
|Azerbaijan|FY2011||
|<br>Bolivia|FY2017||
|Bosnia and Herzegovina|FY2014||
|<br>Cameroon|FY1981|FY1994|
|China|FY1999||
|Congo, Republic of|FY1982|FY1994|
|Côte d'Ivoire|FY1973|FY1992|
|Egypt|FY1981, FY1999|FY1991|
|<br>Georgia|FY2014||
|Honduras|FY1980|FY1991|
|India|FY2014||
|Indonesia|FY1980, FY2008|FY1999|
|Nicaragua|FY1981|FY1991|
|Nigeria|FY1965|FY1989|
|<br>North Macedonia|FY2002||
|Papua New Guinea|FY1983|FY2003|
|Philippines|FY1979, FY1993|FY1991|
|Serbia|FY2008||
|Sri Lanka|FY2017||
|Syria|FY1974|FY2017|
|Vietnam|FY2017||
|Zimbabwe|FY1983|FY1992|

To be candidates for graduation under the policy, countries should be over the GNI per capita threshold and creditworthy. China is an exception, graduating at the end of FY1999 because it was considered creditworthy, despite its per capita GNI being only about 90 percent of the IDA threshold at the time. GNI per capita is sometimes adjusted retrospectively, and the currently available (‘ex post GNI’) data may not accurately reflect the income data that were available when graduation decisions were made (‘contemporaneous GNI’). This may be true particularly for the earlier part of the sample period. By including all country years for which GNI per capita (measured ex post) exceeds 80 percent of the operational cutoff, we err on the side of including too many instead of too few graduation candidates. If we included only observations in which ex post GNI per capita exceeds the operational cutoff, we would very likely be dropping some cases where contemporaneous GNI exceeded the cutoff. Note that this 80 percent rule also ensures that China’s graduation is not excluded from the sample.

# _Estimation method_

The estimation method follows Heckelman, Knack, and Rogers (2012). The dependent variable is graduation status, taking on the value of 1 for country-year observations corresponding to having
_Figure A6: Decomposition of changes in disposable income of those severely affected by income groups_ <!-- Start of picture text --> AT BE BG CY CZ<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>DE DK EE EL ES<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>FI FR HR HU IE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>IT LT LU LV MT<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>NL PL PT RO SE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>SI SK<br>1 2 3 4 5 1 2 3 4 5<br>Market incomes Automatic stabilisers New work compensation schemes - earnings part New work compensation schemes - compensation part Other COVID policies<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0-10<br>-20<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0-10<br>-20<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0-10<br>-20<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>-100<br>-20<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0-10<br>-20<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>-100<br>-20<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br><!-- End of picture text --> Note: Severely refers to losses in market income of more than 11 percent (Scenario A vs. D). Income groups defined based on Scenario D.

Source: Own calculations based on EUROMOD I4.0+.

27
# **RIGHT TO EDUCATION: FORCED MIGRATION AND CHILD EDUCATION OUTCOMES**

JUAN F. VARGAS<sup>_¶_</sup> AND SANDRA V. ROZO<sup>Π</sup> JEL Codes: F22, I25 Keywords: Migration, Education, Colombia, Venezuela We thank Juan Blyde, Mat ́ıas Busso, Ana Mar ́ıa Ib ́a ̃nez and Anna Maria Mayda for their advice and thorough discussion. We also thank Dante Contreras, M ́onica Garc ́ıa, Marisol Rodriguez and seminar participants at the Inter-American Development Bank’s Workshop _Understanding the Impacts of Migration in Latin America and the Caribbean_ for helpful comments. Esteban Aleman and Nathalie Basto provided excellent research assistance. We gratefully acknowledge the financial support the of Inter-American Development Bank, and thank the Colombian Ministry of Education for kindly sharing their data.

> _¶_ University of Turin (ESOMAS) and Collegio Carlo Alberto. E-mail: `juan.vargas@unito.it` . Π Research Group, World Bank. E-mail: `sandrarozo@worldbank.org` .
# **1. Introduction**

With an estimated 40 percent of the world’s extreme poor living in Sub-Saharan Africa (Ferreira et al., 2016),<sup>1</sup> the vast majority of whom derive their economic livelihood from agricultural activities (Livingston et al., 2001), the productivity of land is central to the global goals of poverty reduction and economic development. More directly demonstrating this point, Irz et al. (2001) estimate that a 10 percent increase in farmland productivity can reduce the number of poor people in Africa by 7 percent. Despite the global importance of land productivity in determining the well-being of the poor, one very basic question of land productivity remains a puzzle: is there, as is commonly accepted, an inverse relationship (IR) between farm size and productivity?

Two empirical regularities between land size and land productivity have puzzled development economists for decades. The first regularity is well-known; that is the farm-size productivity inverse relationship (hereafter referred to as farm-size IR), first noted by Chayanov (1926) in Russia and later rediscovered by Sen (1962) in India. It states that land productivity decreases with farm size. This relationship has been observed in many developing countries and can be considered a stylized fact (Kimhi, 2006; Larson et al., 2014). The farm-size IR has received considerable attention from researchers and policy makers because of its controversial implications for land reform (Collier and Dercon, 2013): if small farms are intrinsically more productive than larger farms, land redistribution would not only shift the distribution of wealth but also increase land productivity generally.

The second empirical regularity has received far less attention. We call it the inverse _plot-size_ productivity relationship (and refer to it as plot-size IR). This relationship, observed in many Sub-Saharan African countries, states that smaller plots are more productive than larger plots, even within a farm household (Ali and Deininger, 2015; Assunção and Braido, 2007; Barrett et al., 2010). Although rarely noted, such a relationship implies that land fragmentation—subdividing already small plots into even smaller units—would heighten productivity. But more and smaller plots lead to increased travel time (in moving from plot to plot), more wasted space along borders, and fewer options for mechanization, which typically functions best on larger plots. For these reasons and others, this finding does not seem credible. A common policy recommendation to improve land productivity is not fragmentation but

> 1 Estimates from PovcalNet ( _iresearch.worldbank.org/PovcalNet_ ), the World Bank data tool for estimating extreme poverty, indicate that most of the poor in the rest of the world also live in rural areas. For example, in China essentially all the extreme poor (living on less than US$1.90 a day) are estimated to be in rural areas; and in India about 80 percent.

2
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35
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# <u>Intoxicating Substances in Historical Context</u>

# _Drug Use_

People have cultivated and ingested naturally occurring intoxicating and hallucinatory substances since the beginning of civilization. The most widely used naturally occurring drugs were opium from the opium poppy ( _papaver somniferum_ ); the flowers, leaves and resin of the cannabis plant ( _cannabis sativa_ ); and the leaves of the coca plant ( _erythroxylum_ ).

There were six main reasons for drug consumption in ancient and modern societies (Inglis 1975). The most significant was pain relief. Ancient Indian and Chinese manuscripts recommended the inhalation or eating of cannabis for a range of diseases such as gout, cholera, tetanus, neuralgia and for pain relief in childbirth. Underscoring the medicinal value of cannabis, the U.S. pharmacopoeia recommended it for the primary treatment of more than 100 illnesses in its publications from 1850 to 1937. Owing to the presence of 46 alkaloids including the analgesics codeine and morphine, opium was also highly valued for medical treatment, beginning with the Persians and Greeks. After Greek traders introduced opium to South Asia, the drug was used in medical practice in India and China, according to records dating from 400 A.D. (Booth 1999; Scott 1969).

The seventeenth century brought the commercialization of medical ‘drug’ use, underscored by the launch of _Sydenham’s Laudanum_ , an opium based medication in the UK in the 1680s. Competition among apothecaries and rising demand for selfmedication among the new urban working classes in the nineteenth century spurred the opium based patent medicine market, with products such as _Gowan's Pneumonia Cure_ , _Godfrey’s Cordial_ and _Dr. Moffett's Teethina_ sold without prescription or regulation in grocery stores (Berridge 1981; Hodgson 2001).

After the isolation in 1803 of morphine, the analgesic compound in opium, the German pharmaceutical firm E. Merck and Company began commercial manufacture and morphine-based products such as _Winslow’s Soothing Sirup_ , _Children's Comfort_ , _Dr. Seth Arnold's Cough Killer_ and _One Day Cough Cure_ were launched as a superior form of pain relief. The popularity of morphine was in turn surpassed by diacetylmorphine, which was sold under the brand name Heroin by the German company Bayer. First synthesized from boiling morphine in 1874, it was ten times stronger than morphine and marketed worldwide as a cure for bronchial problems. Indian cultivated cannabis was also commercialized by the burgeoning 3
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21
of transformation at each level. The model assumes product differentiation by region of origin through the Armington assumption. This assumption is embedded in the model through a twolevel nested constant elasticity of substitution (CES) structure. At the first level the aggregate Armington demand is allocated between goods produced domestically and aggregate imports. At the second level, aggregate import is further disaggregated across trading partners. The degree of product differentiation depends upon the magnitude of the substitution elasticity at each level.

The closure rules used in the model include: (a) savings of households are endogenous and are affected by demographic factors through a saving function; (b) government revenues are endogenous but government expenditures are a fixed share of nominal gross domestic product; (c) government balance is fixed through a uniform shift in the household direct tax; (d) investment is savings driven; and (e) current account is exogenous, thus foreign savings is fixed. Changes in foreign trade are balanced through changes in the real exchange rate.

There are three factors that drive the dynamics of the model: (a) exogenous growth in population and labor (based on the population projections of the United Nations); (b) capital accumulation (based on capital stock at previous period, investment in the current period and capital depreciation); (c) factor productivity/efficiency parameters which are spread almost throughout the model.

# **2.2 The GIDD Model**

The second model is the Global Income Distribution Dynamics, or the GIDD model (Bussolo, de Hoyos, and Medvedev, 2008), which utilizes the results of the CGE to simulate the effects on global income distribution and poverty. The GIDD model uses household survey data of 116 countries, which represent about 90 percent of the world population. The GIDD model projects household survey data using three sets of ex-ante macroeconomic information: (a) changes in demographic composition which consist of projection of population by age and by educational attainment; (b) movement of labor between agriculture and non-agriculture; and (c) economic growth. Figure 1 illustrates how the GIDD model incorporates these three changes to adjust the data in the household surveys in the base year to some year in the future.

6
Table C1: Workers’ Adjustment to GDP Decline in Firms’ Destinations - All Coefficients

||Average months<br>|Average real|
|---|---|---|
||worked|wages|
||(1)|(2)|
|GFC frm shock * 2009|-0.0567***<br>|-0.0086***<br>|
||(0.0106)|(0.0031)|
|GFC frm shock * 2010|-0.0517***<br>|-0.0113***<br>|
||(0.0088)|(0.0029)|
|GFC frm shock * 2011|-0.0411***|-0.0114***|
||(0.0082)|(0.0030)|
|GFC frm shock * 2012|-0.0365***|-0.0121***|
||(0.0077)|(0.0030)|
|GFC frm shock * 2013|-0.0315***|-0.0130***|
||(0.0074)|(0.0031)|
|GFC frm shock * 2014|-0.0292***|-0.0132***|
||(0.0072)<br>|(0.0032)<br>|
|GFC frm shock * 2015|-0.0267***|-0.0127***|
||(0.0070)|(0.0032)|
|GFC frm shock * 2016|-0.0242***|-0.0116***|
||(0.0070)|(0.0033)|
|GFC frm shock * 2017|-0.0229***|-0.0109***|
||(0.0069)|(0.0033)|
|Age base (2007)|0.2268***|-0.1239***|
||(0.0108)|(0.0050)|
|Age sq. base (2007)|-0.0034***|0.0014***|
||(0.0002)|(0.0001)|
|Higher education or more|0.6409***|0.4986***|
||(0.0281)|(0.0183)|
|Male|0.5600***|0.2655***|
||(0.0247)|(0.0104)|
|Previous labor force attachment|2.5960***|-2.9123***|
||(0.0676)|(0.0345)|
|Total employment base (log 2007)|0.0304|-0.0010|
||(0.0237)|(0.0067)|
|Importer base (2007)|0.3814***|0.1493***|
||(0.0534)|(0.0227)|
|Firm avg. growth in total employment|-0.1230|-0.0570|
||(0.2042)|(0.1026)|
|Firm avg. growth in total wages|0.0561|-0.0744|
||(0.1962)|(0.0772)|
|Observations|3,046,074|3,030,524|
|R-squared|0.1275|0.2757|
|2-digit industry X Year FE|Yes|Yes|
|State X Year FE|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. FE stands for 45
Figure 11: Contribution of Entry and Allocative Distortion to TFP Gains <!-- Start of picture text --> TFP Gain Relative Full Reform TFP Gain Misallocation Only TFP Gain Entry Barrier Only<br>Kenya<br>Portugal<br>Salvador<br>Kenya<br>Ethiopia<br>Ghana Portugal Kenya<br>BulgariaIndiaRomania PeruColombia Chile<br>Belgium<br>Ghana<br>HungaryMalaysia EthiopiaSalvador<br>LatviaSpain F Italyranceinland BulgariaIndiaRomania Peru ColombiaHungaryLatviaMalaysiaChileSpainBelgium F Italyranceinland EthiopiaGhanaSalvadorBulgariaIndiaMalaysiaRomaniaPeruPortugalColombia Hungary Latvia Chile SpainBelgium F Italranceinlandy<br>5 6 7 8 9 10 12 5 6 7 8 9 10 12 5 6 7 8 9 10 12<br>log GDPpc log GDPpc log GDPpc<br>1.6 1.6 1.6<br>1.5 1.5 1.5<br>1.4 1.4 1.4<br>1.3 1.3 1.3<br>1.2 1.2 1.2<br>Relative to Initial SS Relative to Initial SS Relative to Initial SS<br>1.1 1.1 1.1<br>1.0 1.0 1.0<br>0.9 0.9 0.9<br><!-- End of picture text --> Note: The figure decomposes the total gains in TFP resulting from the withdrawal of both entry barriers and idiosyncratic distortions into those stemming from removing a single distortion while holding the other one fixed. The left-most panel reproduces the overall gains. The middle panel computes the gains from removing idiosyncratic distortions, setting _γi_ = 0 while keeping the corresponding entry barriers _τi_<sup>_E_.The</sup> right-most panel sets the entry barrier to zero, _τi_<sup>_E_</sup> = 0 , keeping the productivity elasticity of distortions _γi_ vanced ones. The noteworthy exception among the latter group is Portugal, where misallocation accounts for a larger share of the gains.

A second implication from Figure 11 is that the total gains are larger than the sum of the individual gains. Proposition 3 established that when both distortions interact, they mitigate each other’s effect on aggregate productivity. It follows, then, that the difference between the total gains and the sum of the partial gains captures the mitigating force of the interaction. To better appreciate the quantitative significance of this mitigation, Figure 12 illustrates the total gains in TFP on the vertical axis against the sum of the partials on the horizontal axis and a 45-degree line.

Figure 12 shows that the mitigation is substantial in countries where id46