rafmacalaba/datause-encoder-data huggingface dataset:
were applied, mainly on consumer goods. In general, using the constructed value method to estimate the normal value rather than using the price in the exporting country or in third countries allows for greater discretion. In fact, in 57.9% of cases, this was the method applied. Nonetheless, in many cases, the constructed value was estimated on the basis of international prices quoted in specialized publications, especially for inputs and commodities. This approach usually leads to lower prices than the domestic ones. The last column in the Table below shows that Brazil systematically applies an antidumping duty lower than the relative dumping margin, i.e. a rate hardly enough to remedy the injury, as recommended in the Antidumping Agreement; on average, the ratio between the antidumping duty and the dumping margin is of 61.3%.

Table 7 Accepted Antidumping Cases: Preliminary Duty, Normal Value Method and ADD/DM Ratio

|Category of use|Preliminary<br>duty|(%)|Domestic<br>price|(%)|3rd<br>country<br>price|(%)|Constructed<br>value|(%)|na (%)|ADD/DM<br>(%)|
|---|---|---|---|---|---|---|---|---|---|---|
|**1. Durable goods**|**4**|**80.0**|**0**|**0.0**|**3**|**60.0**|**2**|**40.0**|**0.0**|**55.8**|
|**2. Soft goods**|**15**|**25.0**|**0**|**0.0**|**3**|**15.0**|**17**|**85.0**|**0.0**|**48.1**|
|**3. Raw materials**|**34**|**44.9**|**28**|**37.8**|**6**|**8.1**|**38**|**51.4**|**2.2**|**65.4**|
|3.1 Rubber|3|42.9|0|0.0|0|0.0|7|100.0|0.0|66.7|
|3.2 Pharmaceuticals|1|18.7|2|33.1|2|33.3|2|33.3|0.0|44.4|
|3.3 Metals|16|72.7|2|9.1|1|4.5|19|86.4|0.0|74.7|
|3.4 Plastics|4|50.0|6|75.0|0|0.0|2|25.0|0.0|65.8|
|3.5 Non-metal<br>minerals|0|0.0|2|50.0|0|0.0|2|50.0|0.0|24.8|
|3.6 Paper|0|0.0|1|100|0|0.0|0|0.0|0.0|na|
|3.7 Chemicals|5|25.0|9|45.0|3|15.0|6|30.0|10.0|64.1|
|3.8 Textiles|5|83.3|6|100.0|0|0.0|0|0.0|0.0|64.8|
|**4. Spares and**<br>**components**|**1**|**12.5**|**1**|**12.5**|**2**|**25.0**|**5**|**32.5**|**0.0**|**85.3**|
|**Total**|**55**|**41.4**|**29**|**27.1**|**14**|**13.1**|**62**|**57.9**|**1.9**|**61.3**|

Source: Customs, Compendium of Laws on Foreign Trade. Author's calculations.

26
_Sources_ : Original estimations based on migration data from Özden et al. 2011 and UN DESA 2020; and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure traces the destination concentration of emigrants for low-income (panel a) and middle-income (panel b) countries as they have developed. The horizontal axis represents the increase in incomes and the vertical axis represents the increase in migrant concentration, measured by Herfinhdahl-Hirschman index, relative to the baseline year. The baseline year, for each country, is defined as the year in which per capita GDP in constant 2017 US dollars in purchasing power parity terms (2017 PPP$) was at its lowest level since 1960. Each lightly colored lines represents the trajectory for a particular country. The solid lines show the locally linear fit between increases in incomes and changes in destination concentration compared to the baseline year. Low-income countries were among the bottom third circa 1960 in terms of per capita GDP (2017 PPP$), and middle-income countries were among the middle third. Estimates exclude six outliers in terms of changes in emigration rates: three low-income countries/economies (Bosnia and Herzegovina, Grenada, and West Bank and Gaza) and three middleincome countries (Antigua and Barbuda, Dominica, and Saint Kitts and Nevis). CI = confidence interval.

On the other hand, the response is somewhat muted in middle-income countries. The concentration of migrants across destinations increases only slightly (figure 5, panel b). They increase emigration to the same high-income destinations as they grow, concentrating on corridors that offer higher returns to migration.

These patterns are consistent with global integration connecting low-income countries with many other highreturn destinations. But for middle-income countries that are already well-connected in the global economy, further development does not lead to increased migration or newer destinations, but continued or increased emigration to the same high-return destinations.

Furthermore, economic integration could be accompanied by policy integration, which can further lower barriers to migration. The findings suggest that connections to new high-income destinations constitute an important part of the migration response from low-income countries as they develop. Greater migration to these new destinations could potentially be facilitated by loosened restrictiveness in immigration policy, at least for some workers (such as high-skilled ones), as low-income countries develop and integrate economically with the broader world.

14
# Table A.3: **Effects of Inpres Schools on Intergenerational Educational Mobility: Dependent Variable: Children’s Primary School Completion (Full Table)**

## **Dependent Variable: Primary Completion**

||**Linear**|**Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Father’s Edu|0.017***|0.028***|0.037***|0.060***|
||(0.003)|(0.003)|(0.004)|(0.005)|
|Born 1968-72_×_Inpres|0.204***|0.216***|0.195***|0.197***|
||(0.041)|(0.044)|(0.042)|(0.047)|
|Father’s Edu_×_Born 1968-72|-0.010***|-0.019***|-0.022***|-0.037***|
||(0.001)|(0.002)|(0.003)|(0.002)|
|Father’s Edu_×_Inpres|0.043***<br>(0.010)|0.044***<br>(0.013)|0.083***<br>(0.018)|0.076***<br>(0.021)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.022***|-0.014**|-0.038***|-0.017*|
||(0.006)|(0.006)|(0.011)|(0.010)|
|Father’s Edu Sq|||-0.002***<br>(0.000)|-0.003***<br>(0.000)|
|Father’s Edu Sq_×_Born 1968-72|||0.001***|0.002***|
||||(0.000)|(0.000)|
|Father’s Edu Sq_×_Inpres|||-0.004***<br>(0.001)|-0.004***<br>(0.001)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.002***<br>(0.001)|0.001*<br>(0.001)|
|R2|0.107|0.172|0.117|0.188|
|Observations|1199814|848350|1199814|848350|

Notes: Robust standard errors are in parentheses, clustered at the district of birth. (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1) Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

55
17 would be the result of administrative decisions geared towards centralizing foreign trade activities.11 An important, indeed crucial, objective of the economic program was to broaden the UP's political base of support very quickly. The UP leaders thought that it was necessary to obtain the support of the middle classes and, more specifically, to draw a large number of voters that had, until then, supported the Clristian Democrats into the UP in order to move towards a socialist system. This political objective plays a significant role in explaining the heavily populist content of the short run program and ;hould be kept in mind when evaluating the short term achievements of the Allende government.

To sum up, the short run macroeconomic program of the UP was aimed at provoking a rapid economic recovery after years of semistagnation and at generating a significant improvement in the living conditions of the poorer groups. The main tool for achieving this was a fast growth in government expenditures geared especially at increasing the real incomes of the lower classes. The intellectual underpinnings of this plan were basically given by the structuralist model that attributes a great role to rigidities and bottlenecks and looks with great contempt at the financial and monetary sectors. A key element of the plan was the belief that, if channeled to the appropriate groups and if accompanied by the right kind of administrative controls, fiscal deficits would not be inflationary.

11See Plan Anual 1971 (Odeplan, 1971).
# **Figure 4: Diet diversity of mothers and children**

a) Diet diversity of mothers <!-- Start of picture text --> Nuts and seeds 1.6<br>3.3<br>Other fruits 5.1<br>5.4<br>Eggs 5.5<br>2.2<br>Other vitamin A-rich vegetables 5.7<br>10.2<br>Flesh foods 7.1<br>1.6<br>Vitamin A-rich dark green leafy 20.0<br>31.7<br>Dairy 20.1<br>13.2<br>Beans and peas 60.3<br>60.4<br>Other vegetables 61.0<br>63.3<br>All starchy staple foods 93.1<br>97.5<br>0 20 40 60 80 100<br>b) Diet diversity of children<br>Poultry, fish, meat 2.9<br>1.2<br>Eggs 6.3<br>10.4<br>Vitamin A rich fruits and vegeta 8.4<br>31.2<br>Dairy products 16.0<br>10.9<br>Legumes and nuts 34.9<br>51.4<br>Other fruits and vegetables 39.5<br>62.0<br>Grains, roots and tubers 84.4<br>96.3<br>0 20 40 60 80 100<br>2019 2020<br><!-- End of picture text --> _Notes_ : Sample weights applied.

32
69 Lesotho 1: PFP - ProWjctlons and Actuals <!-- Start of picture text --> 40 ~ ~ ~ ~ .40 ,,p,, - ,, s ,, " *.<br>66 0? 60' 00, so SI 2<br>Year<br>prol.jesl +A9t1UI<br><!-- End of picture text --> Lesotho 3: PFP - Projectlons and Actuals <!-- Start of picture text --> Gm. k,.d..a~<br>6S~~~~~~~~~~~~~~~~~J<br>2;<br>0? * 0S 01 02<br>Y"r<br>prel"Veae +DfjuI<br><!-- End of picture text --> Losotho 5 . PFP ProletIons and Actuals <!-- Start of picture text --> so1 ___ _ _ _ __ _ _ _ __ _ _ _ __ _ _ _ _IPOSX<br>&a xp * owe a+_l $I? SO SO a el Oa<br>7,qS s ,<br>Sourem "s OW. PM*dlww MM. W4<br>"udo:MOR? 0w0 U 5S# S 0<br><!-- End of picture text --> Lesotho 2: PFP - Projections end Actuale <!-- Start of picture text --> o**~~~~~~~~~~~~~~u. AeVOP<br>4 Y<br>Yew<br>_P.e3*m" +a~. *A.mmITOTe4u<br>TOT * Twmn hrded<br><!-- End of picture text --> Lesotho 4 - PFP Actuals <!-- Start of picture text --> Do*ub .de qslOP<br>So 0<br>Y"r<br>PrJ.Ue_ + *<br>Lesotho 6: PFP - Projections and Actuals<br>mw s 9-<br>ISS<br>.. r<br><!-- End of picture text --> Lesotho 6: PFP - Projections and Actuals
# Introduction

By the late 1990s, health systems in most low-income countries provided subsidized care at public clinics funded through general taxation. Although there was wide variation in how often public clinics were used for primary care (ranging from very little in India to almost exclusively in many Latin American countries), for hospitalization and inpatient care, the public sector consistently accounted for 50-80 percent of health care provision across multiple countries, a share that has remained remarkably stable over time (World Health Organization 2020; Grépin 2016). Surprisingly, this wide availability of subsidized public clinics coexisted with high out-of-pocket expenditures. Indeed, at that time out-of-pocket expenses amounted to 50 percent of total health expenditure in low-income countries with households frequently unable to insure themselves against large health shocks and the related loss of income (Gertler and Gruber 2002). Summarizing the situation at the turn of the 21<sup>st</sup> century, Pauly et al. (2006) observed that: “Virtually every developing country with a functioning government uses publicly-funded and managed systems for thirdparty payment for medical care. ...(but) in many developing countries, this system has failed to provide adequate financial protection for its citizens and adequate access to care. The gap shows up in the form of private out-of-pocket spending for services that “universal insurance” cannot or does not supply.” In response, governments in many low- and middle-income Countries (LMICs) have moved towards some form of dedicated health insurance, which they believed would provide them the instruments they needed to solve pressing problems related to the financing and provision of health care. To begin with, insurance premiums would provide a dedicated source of funding for health care, while at the same time, health insurance would reduce out-of-pocket expenditures and provide much-needed financial protection for citizens. Furthermore, governments postulated that insurance products would also improve health outcomes by altering the behavior of patients and providers. By subsidizing the cost of visiting private sector providers, health insurance would expand patient choice and increase visits to participating private sector clinics, where the quality was thought to be higher. By purposefully redesigning how public and private providers were reimbursed, health insurance would also increase the incentives to provide higher quality care, especially in the public sector. The transition in how providers were reimbursed, referred to as the shift from passive to “strategic purchasing” in the health literature (World Health Report 2000; Hanson et al. 2019), would then improve the quality and cost effectiveness of health service delivery (Londoño and Frenk 1997).

2
Policy Research Working Paper 9404

# **Abstract**

Many Sub-Saharan African countries have instituted free primary education policies, and this has led to a significant increase in the primary school enrollment rate. However, many children who are in school are not learning. It is not clear whether free primary education policies have contributed to the decline in the quality of education and whether these learning effects are long-lasting. This paper addresses the latter question and estimates the long-term effects of free primary education on educational achievement in Lesotho where the program was phased-in on a grade-by-grade basis, beginning with grade one in 2000. The timing of the implementation created changes in program coverage across age (and grade) groups over time. A semiparametric difference-in-differences strategy is employed that exploits these variations to identify the long-term effects of the free primary education policy on educational achievement, using university examinations records data for student cohorts with and without free primary education. The results indicate that the effect of free primary education on academic performance is bounded between 2 and 19 percentage points, implying that the program increased enrollment without hurting education quality.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 author may be contacted at rmoshoeshoe@worldbank.org.

_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
Table 5: Cyclical Sensitivity of Fiscal Policy: Subnational Level – By Regions

|Variables|AllStates||North|Northeast||South|Southeast||Center-<br>West||
|---|---|---|---|---|---|---|---|---|---|---|
|**_Revenues:_**|||||||||||
|Total Revenue|0.273|***|0.243|***<br>0.004||0.408|**<br>0.605|***|0.189|*|
||[0.036]||[0.056]|[0.086]||[0.199]|[0.150]||[0.108]||
|Intergovernment Transfers|0.091||0.176|**<br>-0.138||0.285|-0.068||0.038||
||[0.066]||[0.072]|[0.106]||[0.257]|[0.320]||[0.203]||
|Tax Revenue|0.309|***|0.336|***<br>0.195|***|0.392|**<br>0.129||0.250|***|
||[0.030]||[0.074]|[0.067]||[0.199]|[0.103]||[0.058]||
|V.A.T. (ICMS)|0.312|***|0.340|***<br>0.172|**|0.171|0.199||0.185|***|
||[0.040]||[0.074]|[0.081]||[0.199]|[0.133]||[0.071]||
|**_Expenditures:_**|||||||||||
|Total Expenditure|0.305|***|0.467|***<br>0.281|**|0.445|**<br>0.378|**|0.124||
||[0.031]||[0.104]|[0.117]||[0.223]|[0.163]||[0.133]||
|Primary Expenditure|0.703|***|0.464|***<br>0.223|*|0.189|0.330|**|0.098||
||[0.057]||[0.107]|[0.121]||[0.209]|[0.171]||[0.128]||
|Maintenance and capital|0.340|***|0.560|***<br>0.337|**|0.832|**<br>0.155||0.248||
||[0.089]||[0.154]|[0.175]||[0.432]|[0.297]||[0.190]||
|Personnel Expenditure|0.703|***|0.834|***<br>0.367|**|0.488|*<br>0.580|**|0.450|***|
||[0.057]||[0.172]|[0.153]||[0.284]|[0.268]||[0.164]||
|Interest Payments|0.516|***|0.267|0.635|*|1.559|*<br>1.169|*|0.521|*|
|***, **, and * denote significanc|[0.127]<br>e at the 1%, 5%,|and 1|[0.354]<br>0% levels, re|[0.384]<br>spectively.||[0.916]|[0.700]||[0.309]||

<mark>***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Estimation done with FGLS.</mark> 13
28 pulled together but the distinction<sup>between private and public was</sup> retained. Two sets of regressions<sup>were implemented. In the first, the</sup> following equation was estimated for both<sup>the private sector and the public</sup> sector:

# (6) TFP - f(Qit, mit, Di, Dt)

Growth in total factor productivity<sup>is related to the rate of growth in</sup> output (Q), the rate of growth in<sup>import penetration (m) measured as the</sup> 25 share of imports in total domestic absorption , and a set of industry and year dummy variables. Output growth<sup>is intended to capture scale effects</sup> and variations in capacity utilization.<sup>If economies of scale are present</sup> and/or if production capacity is underutilized, output growth is expected to have a positive sign. Import penetration<sup>rates are intended to capture</sup> the effect of exposure to foreign competition<sup>on efficiency. If exposure to</sup> foreign competition forces domestic<sup>firms to maximize production</sup> efficiency, the growth in import penetration<sup>is also expected to be</sup> pesitively related to TFP growth. As with<sup>the regression analysis of price-</sup> cost margins, industry and year dummy<sup>variables are included to control for</sup> industry-specific and time-specific factors.

The estimation results of equation<sup>6 for the private sector for</sup> the periods 1976-77 to 1984-85 are<sup>reported in Table 23. The two models in</sup> Table 23 differ only in that Model<sup>2 does not include the industry dummy</sup>

> 25.If export penetration ratio is<sup>also included in the same equation or if</sup> it replaces import penetration, its<sup>coefficient is never significant for</sup> either the private or the public<sup>sectors.</sup>
by introducing a calorific-based approach to tackle the limitations of the existing literature. Instead of traditional weight-based measures such as metric tons or kilograms, we convert annual crop production into calorific content and subsequently aggregate the production and yield of all food commodities into single metrics. This aggregation accounts for heterogenous yield patterns due to changes in production composition, the transition from low-yield to high-yield crops and varieties as well as shifts in country and regional significance. Additionally, this approach provides an easy-to-implement, yet standardized and universal framework for analyzing and comparing yield growth at any level, from individual crops to commodity groups to global aggregates.

Using these calorific-based indices, we apply statistical methods to select the most appropriate model for charting the yield paths of commodities at global, regional, and crop-group levels. Production and calorific content data for 144 major crops covering the 1961-2021 period from the Food and Agriculture Organization (FAO) are included in the analysis. These crops combined account for approximately 98 percent of the world’s agricultural land area. The evidence suggests that the aggregate global yield index has not been subjected to growth deceleration over the past six decades. Thus, slow growth in certain commodities, regions, or countries, documented in the literature has been offset by accelerated growth in others.

The remainder of the paper proceeds as follows. The next section summarizes the literature on yield growth. Section 3 discusses the aggregate yield index, the modeling framework for evaluating yield growth, and the data. Section 4 presents the results. The last section concludes and discusses avenues for further research.

# **2. A Brief Review of Literature**

The literature assessing yield growth performance can be broadly delineated into three principal strands. One strand delves into yield growth from experimental data, serving as a tool for plant scientists to discern performance nuances and facilitate the selection of crop varieties (Crow 1998; Kiær et al. 2009; Reiss and Drinkwater 2018). The second strand focuses on evaluating the statistical distribution of yields, with the explicit objective of assisting farmers to make informed selection of crop varieties, while also aiding the insurance industry in determining premia for potential crop losses. Pioneering contributions to this strand include an early study by Day (1965), subsequently expanded upon by Just and Weninger (1999), Atwood et al. (2003), Norwood et al. (2004), and Sherrick et al. (2004). The third strand, particularly pertinent in the present context and the subject of this section, examines yield growth through the lens of sustainability and food security considerations.

_— 3 —_
the null of no cointegration is rejected by group tests (Gt and Ga) as well panel tests (Pt and Pa) level in favor of the existence of cointegration in the panel.

# **Table B2: Results of Panel Cointegration Tests**

|Panel Statistics|Physical Infrastructure|Social<br>Infrastructure|Financial Infrastructure|
|---|---|---|---|
||Test Values|Test Values|Test Values|
|GT|-1.99|-2.13*|-2.35*|
|Ga|-8.92*|-4.19|-4.64|
|PT|-7.30|-10.32*|-10.58*|
|Pa|-6.40*|-4.56|-5.76|

Notes: * Indicates the rejection of null of no cointegration at 5 percent level.

36
for borrowing. It may be reasonable to assume that this effect will be dominated by the other positive effect on borrowing.

# **4. Empirical Analysis**

In this section we develop and test an empirical framework suitable to analyze two of the hypothesis derived from our theoretical model. The key predictions of the theoretical model are that an increase in aid reduces the tax efforts of the government (proposition 2) and the proportion of public expenditure that is spent on public investment (proposition 1). Using a time series data set for Rwanda, we will test these two hypotheses in the context of an econometric model derived as a reduced form of the theoretical model.

We consider linear regressions of the form: _PUBINV_ / _TOTEXP_ = β 0 + β 1 _ODA_ / _GDP_ + β 2 _EXP_ / _GDP_ + β 3 _LAB_ (24) + β 4 _DUMMY_ (96 + ) + β 5 ( _ODA_ / _GDP_ ) * _DUMMY_ (96 + ) + ε 1 where PUBINV/TOTEXP = Public investment as a percentage of total public expenditure, TAXREV/GDP= Tax revenue as a percentage of GDP, ODA/GDP = Overseas development assistance as a percentage of GDP, EXP/GDP= Exports as a percentage of GDP, LAB= Labor force (number of people who meet the ILO definition of the economically active population), 23