rafmacalaba/datause-encoder-data huggingface dataset:
**Figures 13(a)-(d):  Sub-Saharan Africa in the New Multi-Polar Growth World**

<!-- Start of picture text --> (b) Share of World Merchandise Exports<br>(a) Annual GDP Growth (%)<br>8 (%)<br>5.0%<br>Sub-Saharan<br>Africa 4.5%<br>6<br>World<br>4.0%<br>3.5%<br>4<br>3.0%<br>2 2.5%<br>2.0%<br>0<br>1.5%<br>1.0%<br>-2<br>-4<br>(c) Share of Merchandise Exports That Go to (d) FDI (net inflows) as a Share of GDP<br>Developing Countries in East Asia & Pacific (%) (%)<br>6 5<br>4.5<br>5<br>4<br>3.5<br>4<br>3<br>3 2.5<br>2<br>2<br>1.5<br>1 1<br>0.5<br>0<br>0<br>1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009<br><!-- End of picture text --> Source: World Development Indicators Technology is also part of the story. From both theory and practice, we expect developing countries to lag the high income countries in technology, since the latter are at the technological frontier in most products that they produce. That said, one would expect the more dynamic middle-income countries to begin to move up the technological ladder as they move up the per capita income ladder. In addition, there are sectors in middle income countries that are reaching (or have already reached) the technological frontier – for example, Brazil‘s deepwater oil drilling capacity. As a result, one would expect these countries to begin to invest more in research and development and begin to produce more homegrown technological innovation.

22
**_Figure 7._** _Scatter plot of organic carbon and clay/silt (%) by self-reported soil quality (left), and box plots of organic carbon by self-reported soil quality (right)._

studies, such as Desbiez et al. (2004) and Mtambanengwe and Mapfumo (2005), but with those studies finding a greater divergence in organic carbon content between categories.<sup>_7_</sup> To better illustrate the distribution of carbon levels across self-reported soil quality categories, Figure 7 presents a scatter plot relating organic carbon, clay and silt content, and self-reported quality category (left) and box plots of carbon levels disaggregated by self-reported quality category (right). The scatter plot reveals that the soils reported as _poor_ are not concentrated in areas with low carbon levels, but rather seemingly randomly distributed. This suggests that the local assessment on _overall_ soil quality may not be a robust method for mapping soil quality and making decisions on potential interventions like fertilizer recommendations to improve land productivity.

Disaggregating the self-reported indicators by respondent, geographic, and plot characteristics reveals slightly more explanation. Splitting the data into two age categories above and below 40 years (excluding the 68 observations in which the plot manager was not the respondent) shows that the younger respondents were able to differentiate between _poor_ and _good_ soils (p < 0.05), and between _fair_ and _poor_ soils (p < 0.01), but not between _good_ and _fair_ soils, where we define successful differentiation as a relative measure (higher carbon levels in reportedly better soils). There is no significant difference in total organic carbon levels across the three self-reported soil quality categories for the respondent age group of greater than 40 years. One might expect farmer age to be inversely correlated with education and literacy,

> 7 Both Desbiez et al. (2004) and Mtambanengwe and Mapfumo (2005) used a binary classification of plots, rather than ‘good’, ‘fair’, and ‘poor’.

16
20 however, were able to get a seat on the board with an ownership share below 1 0%25. A 26 _controlling_ stake is defined here as above 18% (and up to 20%) of a company's capital

**Table** **_II:_ Rank of IPFs by number of companies with > 10% in capital (in % of total number of companies in portfolio) at December 31, 1992 (June 30, 1994)**

|HCC<br>|IB<br> <br>|ICS<br>|I YSE I PPF<br>|I<br>KB<br>|I<br>CA|CP|ZB|AB|
|---|---|---|---|---|---|---|---|---|
|86 (84)|67 (51)|| 53 (47)|T<br>42 (n/a) I n/a (34)|| 33 (n/a)|l 31 (32)|30 (37)|20 (20)|17 (20)|

Remark: for fund names and fund classification see Table 3.

Source: Fund interviews.

**Table 12: Rank of IPFs by number of companies with > 18% in capital (in % of total number of companies in portfolio) at December 31, 1992 (June 30, 1994)**

|HCC|<br>IB<br>||PPF|| YSE|CS<br>|| CA|KB|CP|AB|| ZB|
|---|---|---|---|---|---|---|---|---|---|
|. 65(56)|32(25)|n/a(14)|11 (n/a)|9(11)|8(8)|7(n/a)|5(5)|4(5)|0 (0)|
|Remark<br>Source:|: for fund n<br>Fund inter|ames and<br>views.|fund classi|fication se|e Table 3|.||||

Table 1 I and Table 12 show a great diversity in funds' portfolio concentration. Harvard's portfolio is by far the most concentrated, since the fund owns a significant stake in 86% of its companies and a controlling stake in 65% of its companies. What is surprising is the fund sponsored by Ceska Sporitelna, the Czech Savings bank, which is by far the biggest fund in the first wave. This fund is often blamed for having invested in a diversified portfolio with over 500 companies and being unable to exert any influence over them. On the one hand, the criticism is justified to the extent that Ceska Sporitelna could have achieved portfolio diversification by investing in only half the number of enterprises. On the other hand - due to the upper limit of 20% per enterprise - Sporitelna was forced to diversify in a large number of enterprises since it received so many points. Still, Sporitelna is not performing so badly with respect to its potential influence on portfolio enterprises

> 25 See chapter on 'the relationship between banks and investment funds' below.

26 The level of 18% (instead of 20%) had been chosen since - due to the process of the bidding rounds - funds (even bidding for 20%) received in the end stakes that were slightly below this number.
Japan’s area of special expertise, remained virtually absolute until the early 1980s, by which time Japan was already the world’s leading supplier. As late as 1985, when Japan exported well over four million cars, it imported a grand total of just over 50,000, more than four-fifths of them luxury cars from Germany ( _Jidôsha Nenkan Handobukku_ 2002-2003: 408, 430-31).

In the early 1970s the Japanese government rebuffed a determined effort by American assemblers to invest in Japanese firms. After long negotiations with the U.S., the Japanese allowed the American firms to buy small, non-controlling shares in the weakest Japanese assemblers, the main effect of which was to facilitate exports by these weaker players to their new American partners. Foreign direct investment in the Japanese auto industry began to increase significantly only after 1999, when the Ministry of Economy, Trade and Industry (METI, before 2001, MITI) signalled its approval of Renault's effective takeover of struggling Nissan.

Japan’s tax system complemented trade policy in protecting the industry and encouraging specialization. Taxes increased with engine size, discouraging consumption and imports of large foreign cars, and encouraging economies of scale in the production of cars with 1.5-2.0 liter engines.

Direct promotion of the industry was also important, particularly from the early 1950s through the mid-1960s. Government-affiliated banks provided generous funding, first to assemblers and later to parts suppliers, and provided a signal to private banks that the industry was a safe bet, since it was a high priority to the government (Odano and Islam 1994). In the 1950s and 1960s, the auto complex (motor vehicles, steel, and especially roads) accounted for virtually all of the World Bank’s loans to Japan not dedicated to electricity generation (World Bank web page, projects database entries for Japan). While it is true that efforts by the government in the 1950s and early 1960s to attain economies of scale by encouraging mergers of existing assemblers and discouraging new entry met stiff resistance from second-tier 14
|**Variable**|**Description**|**Year**|**Source**|
|---|---|---|---|
|**NTBi (RG**<br>**Weighted)**|Non-tariff barriers in economy i are calculated as the<br>difference between the overall trade restrictiveness<br>index (OTRI) and the trade restrictiveness index<br>(TRI) for each tariff line. It is weighted by reference<br>group weights and converted to logarithm of (1 +<br>NTB).|2001/2004|Kee et al. (2006)|
|**Std. Dev.**<br>**Irreg. Paym.i**|Standard deviation for the answer to the question on<br>irregular payments in economy i.|2004|Global<br>Competitiveness<br>Report(2005)|
|**Tariffi**<br>**(RG**<br>**Weighted)**|The tariff rate of economy i is measured as the<br>effective applied MFN rate, which is defined as<br>(specific applied MFN tariff/Unit Value) + ad<br>valorem applied MNF tariff. It is weighted by<br>reference group weights and converted to logarithm<br>of(1 + tariff).|2002-<br>2004|MAcMAP (2007)|
|**Tariff**<br>**Dispersioni**|Standard deviation of effective applied MFN tariffs in<br>HS4 digitproductgroups in economyi.|2002-<br>2004|MAcMAP (2007)|
||Difference between the maximum and minimum||Logistics|
|**Time Spreadi**|number of days for clearance needed for imports or<br>exports in economyi.|2006|Perception Index<br>(2007)|

# **Table** **<u>3 Economies included in the dataset.</u>**

|**Group**|**Members**|
|---|---|
|**Importers**|Australia*, Brunei, Canada*, Chile*, China*, Hong Kong China*, Indonesia*, Japan*,<br>Korea*, Malaysia*, Mexico*, New Zealand*, Papua New Guinea, Peru*, Philippines*,<br>Russia*,Singapore*,Chinese Taipei,Thailand*,USA*,Vietnam*.|
|**Exporters**|Australia*, Brunei, Canada*, Chile*, China*, Hong Kong China*, Indonesia*, Japan*,<br>Korea*, Malaysia*, Mexico*, New Zealand*, Papua New Guinea, Peru*, Philippines*,<br>Russia*,Singapore*,Chinese Taipei,Thailand*,USA*,Vietnam*.|

_Note: * indicates economies included in the effective sample for the regression._

# **Table 4: ITI and ETI principal factor weights.**

||**ITI**|**ETI**|
|---|---|---|
|**Percent unbound**|0.05413|NA|
|**Std. dev. tariffs**|0.01701|NA|
|**Std. dev. irreg. pay. **|0.18255|0.26815|
|**Std. dev. time**|0.0498|0.05531|
|**Lack e-readiness**|0.10241|0.14315|
|**Time**|0.22514|0.25988|
|**Documents**|0.04792|0.03612|
|**Agencies**|0.06361|0.14932|
|**Favoritism**|0.11891|0.09455|
|**Irreg. payments**|0.15849|0.1532|
|**Hidden barriers**|0.19511|NA|

_Note: Time, documents, and agencies refer to import time, number of import documents, and number of import agencies for the ITI, and the corresponding export variable for the ETI._ 35
Differences across regions were statistically significant and, for some regions, quite large: those living in Europe and Central Asia are 36.3 percentage points more likely to be hesitant than those living in the reference region of Latin America and the Caribbean, those in the Middle East and North Africa are 23.1 percentage points more likely and those in East Asia and the Pacific, 21.1 percentage points more likely. Differences between Sub-Saharan Africa and Latin America and the Caribbean were not statistically significant, nor were differences by country income group. Over time, the probability of being hesitant to take the COVID-19 vaccine declined but not significantly.

_Table 1: Multivariate analysis of vaccine hesitancy in 53 developing countries_

|Dep. Var: Hesitancy|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Male|-0.028*|-0.027*|-0.028|-0.027*|-0.031*|-0.038**|
|Head of HH|(0.013)<br>0.012<br>(0.015)|(0.013)<br>0.009<br>(0.014)|(0.014)<br>0.019<br>(0.013)|(0.013)<br>0.000<br>(0.017)|(0.013)<br>0.011<br>(0.015)|(0.012)<br>0.007<br>(0.013)|
|Education of respondent (ref – Tertiary)|||||||
|No education|0.123***|0.125***|0.116***|0.134***|0.125**|0.143***|
||(0.033)|(0.035)|(0.032)|(0.037)|(0.040)|(0.036)|
|Any primary|0.061**|0.063**|0.060**|0.067**|0.052*|0.070**|
|<br>Any secondary|(0.018)<br>0.064***|(0.019)<br>0.065***|(0.018)<br>0.062***|(0.019)<br>0.065***|(0.020)<br>0.058***|(0.021)<br>0.062***|
||(0.011)|(0.011)|(0.011)|(0.012)|(0.011)|(0.011)|
|Age group (ref. -- 34 and younger)|||||||
|Working age (35-64)|-0.047**<br>(0.014)|-0.048***<br>(0.013)|-0.042**<br>(0.015)|-0.050***<br>(0.013)|-0.045**<br>(0.015)|-0.049***<br>(0.012)|
|Retirement age (65+)|-0.103***|-0.102***|-0.096***|-0.103***|-0.099***|-0.099***|
||(0.023)|(0.024)|(0.023)|(0.022)|(0.024)|(0.023)|
|Rural area|0.016|0.014|0.013|0.010|0.012|0.011|
||(0.019)|(0.018)|(0.018)|(0.015)|(0.015)|(0.011)|
|Region (ref – LAC)|||||||
|EAP|0.211**|0.218*|0.241**|0.127|0.299|0.249|
||(0.076)|(0.086)|(0.084)|(0.076)|(0.155)|(0.137)|
|ECA|0.363***|0.364***|0.347***|0.376***|0.369***|0.345***|
|MNA|(0.075)<br>0.231***|(0.076)<br>0.230***|(0.073)<br>0.262***|(0.076)<br>0.240***|(0.056)<br>0.288***|(0.068)<br>0.393***|
||(0.043)|(0.043)|(0.062)|(0.036)|(0.059)|(0.050)|
|SSA|0.082|0.089|0.078|-0.005|0.072|0.057|
||(0.075)|(0.079)|(0.074)|(0.101)|(0.086)|(0.103)|
|Income group (ref. – LIC)|||||||
|LMIC|-0.073|-0.071|-0.052|-0.090|-0.049|-0.053|
||(0.127)|(0.124)|(0.122)|(0.129)|(0.113)|(0.081)|
|UMIC|-0.052|-0.050|-0.071|-0.096|-0.026|-0.130|
||(0.154)|(0.154)|(0.146)|(0.154)|(0.135)|(0.107)|
|HIC|-0.161|-0.160|-0.183|-0.208|-0.175|-0.280*|
||(0.159)|(0.162)|(0.153)|(0.162)|(0.136)|(0.108)|
|Survey month (ref -- Nov 2020 - Jan|||||||
|<br>2021)|||||||
|March - May 2021|-0.073|-0.062|-0.102|-0.056|-0.113|-0.110|
||(0.095)|(0.108)|(0.104)|(0.101)|(0.112)|(0.095)|
|June - August 2021|-0.018|-0.012|-0.018|-0.028|-0.049|-0.026|
||(0.090)|(0.099)|(0.099)|(0.089)|(0.097)|(0.079)|
|New COVID-19 cases per million, terciles|||||||
|||-0.022||||0.023|
|Cases (middle tercile)||(0.065)||||(0.057)|
|||-0.009||||-0.011|
|Cases (top tercile)||(0.054)||||(0.049)|

11
# **Abstract**

Breaking the inter-generational transmission of poverty requires far-reaching actions in the education sector. Widespread poverty affects both students’ performance and their availability to attend school. Low-quality education leads to low income, which in turn perpetuates poverty. Furthermore, low levels of education affect growth though low labor productivity.

Although Paraíba, Brazil suffers from a history of educational neglect, the state has recently made significant gains in primary enrollment; 93 percent of the children aged 7- 14 are enrolled in school. However, 30 percent of the population aged 15 and older are illiterate and, unfortunately, it is not only the older generations that cannot read and write: 15 percent of children aged 10 to 15 are illiterate. However, substantial achievements in education have not helped the extremely poor segment of population as much as expected. Probit analyses reveal that education attainment is the single most important poverty-reducing factor. All levels of education from primary to tertiary are significant and negatively associated with the probability of being poor.

2
24 Balassa, Bela (1968). 'The Structur of Protecion in Industrial Countries and its Effects on **t** he Exports of' Processed Goods from Developing Countries," in UNCTAD, The Kennedy Roust: Estimatd Effects on Tariff Barriers, _(TDI6IRev._ 1), (New York: United Nations).

Boltuck, Richard and Robert E. Litan (eds) (1991). Down in the **_Dumps:_** Amnsrton of the Unfair Trade Laws, (Washingon D.C.: The Brookings Institution).

Conumonweakh Secretariat (1982). Protectionism: Threat to International Order, (London: Commonwealth Secetariat).

Finger, **_3._** Mdicael (ed) (1993). Antddunping: How it Works and Who Gets Hurt, (-Ann Arbor: The University of Michigan Press).

Finger, J. Mfichael and Tracy Murray (1993). "Antidumping and Countervailing Duty Enforcement in the UniteDd States"- in Finger (edl), **Antidumping:** How it Works and Who Gets Emit.

GATT (1993). An Analysis of the Proposed Uruguay Roiund Agreement, with Particular Emphasis on Aspects of Interest to Developing Countries, (MTN.TNC/W/122)(MTN.GNGlW/30), (Geneva: GATT, 29 November).

GATT (1994). Final Act Embodlying the Results of the Uruguay Round of Mtlultilatera T)rade Negotiations, (Marrakesh: GATT, 30 March).

Greenaway, David (1985). "Non-Leather Footwear from Taiwan and Korea," in David Greenaway and Brian Hindley (eds.), What Britain Pays for Vohmntary Export Restraints, Thames Essay No. 43 (London: Trade Policy Research Centre) Helleiner, Gverry and Douglas Welwood (1978). Raw Material Processing in Developing Countries and Reductions in the Canadian Tariff, (Ottawa: Economic Council of Canada).

Laird, Sam and Alexander Yeats (1990). Quantitative Methods for Trade Barrier Analysis, (London: Macmillan Press).

Low, Patrickc (1993). Trading Free: Thbe GATT and U.S. Trade Policy, (New York. The Twentieth Centry Fund Nress).

Messerlin, Patrick (1989). "The EC Antidumping Regultions: A First Economic Appraisal _(1980-85),_ Weltwirschaftliches Archiv Band 1US, (Heft 3).

OECD (1987). Nationad Polices and Agricultural Trade: Study on the European Eotomic Community, (Pads: OECD).

Saxon, Eric and Kym AndersM (1982). "Japanese Agidculnal Protection in a Historical Perspective," Pacfic Economic Papers No. 92, (Canberra: Austraian National University, July).
With lower travel budgets, the decline in average accessibility with fuel price increases is less marked. Once budget thresholds are below 10% of income, there is almost no variation in average accessibility with increasing fuel prices. Losses in accessibility are greatest for lower fuel price increases, after which most citizens are unable to afford any transit at such low travel budget levels.

# 3.3 Income distribution and spatial analysis

The previous section describes how fuel price increases may impact average accessibility levels in both cities and suggests measures to mitigate the loss of accessibility that carbon taxes would trigger. However, this analysis does not assess how specific neighborhoods, communities or socio-economic groups may be more severely impacted than others. Further analysis in subsequent sections investigates how accessibility losses caused by fuel price increases are distributed both spatially and between income groups.

For different fuel price increase levels (20%, 50% and 100%) we calculated the average accessibility level in each hexagon across all travel budget thresholds<sup>14</sup> and the difference between this value and the average accessibility in the baseline scenario (prior to any fuel price increase). The accessibility loss data is then combined with income data to assess the impact on specific socio-economic groups.

The losses in average accessibility for Rio de Janeiro with increasing fuel prices and a maximum travel time of 75 minutes are shown spatially on the left side of Figure 7 below. The middle box plot describes the reductions in accessibility per income decile in absolute terms (% of jobs no longer accessible), while the right-most box plot shows a similar figure with loss of accessible jobs shown in relation to baseline accessibility, i.e., the loss in percent accessible jobs is divided by each income decile’s baseline accessibility (percent of total jobs accessible). The red line indicates the population weighted median, while the upper and lower limits the 25<sup>th</sup> and 75<sup>th</sup> percentiles. The blue dots indicate the hexagons that compose each decile, irrespective of population.

> 14 This was calculated for all travel budgets between 0 and 100% of monthly income (in 2.5% increments) with each travel budget weighted equally.

17
<!-- Start of picture text --> Figure 5: Ratio of salaries after 15 years of experience/minimum<br>training to GDP per capita (2009)<br>Korea<br>Portugal<br>Germany<br>Japan<br>Ireland<br>Spain<br>Denmark<br>Switzerland<br>England<br>Belgium<br>Australia<br>Netherlands<br>Greece<br>Finland<br>Italy<br>Austria<br>Norway<br>France<br>United States<br>Sweden<br>Iceland<br>Poland<br>Estonia<br>Hungary<br>0.0 0.5 1.0 1.5 2.0 2.5<br><!-- End of picture text --> Source: OECD 2011 Notes: 1. Figures refer to annual statutory teachers’ salaries in public institutions after 15years of experience at primary level, in equivalent $US converted using PPPs; 2. Belgium (Fl.) is used as proxy for Belgium Furthermore, designing and implementing effective teacher development for better teaching is instrumental for raising student learning outcomes. In fact, many measures instituted by governments seem not to work in many countries. Pre-service credentials do not predict teaching effectiveness well; conventional in-service professional development in many cases turned out not to be useful in altering teachers’ instructional methods or children’s experiences, just to name a few. Teacher development might well be a policy area requiring perhaps the greatest degree of innovation. Effective teacher professional development arms teachers with state-of-the-art subject content knowledge and adequate skills. Empirical studies have identified the following alternatives as components of effective professional development:

- Method-centered rather than theory-centered training that corresponds to prospective teachers’ future tasks;

- Updated curriculum for professional development, such as synthetic phonics including phonemic awareness, fluency, vocabulary, and comprehension;

- Focus on activities that are more relevant to classroom-based teaching and school-based activities;

- Active pedagogy that integrates real-life knowledge and skills with students’ learning; and

- Coherence between pedagogy for teacher training and taught pedagogy for teaching students (Vegas 2005).

14
migration. These go well beyond the traditional realm of the Ministry of Agriculture. Motivated by persistent low labor productivity in African agriculture, the salient nature of the digital revolution and rising anti-immigration sentiment in the current policy debates, this broader global assessment of the future of work in agriculture zooms in on the role of productivity-enhancing innovation and technology and immigrant agricultural labor.

# **Technology Is Key**

Over the next 30 years, global calorie demand is expected to rise in tandem with the population, which is projected to increase by about 25% to nearly 10 billion people (see Figure 3). Ultimately, farmers will have to grow more food with fewer workers, and this will require the development and adoption of new labor-saving technologies. The induced innovation hypothesis, first advanced by Hicks (1932), posits that changes in relative factor (input) prices drive the development of new technologies. As wages rise, so do the incentives for researchers at private and public institutions to develop labor-saving solutions (Hyami and Ruttan 1971). Others view research and development (R&D) as largely an exogenous, self-perpetuating process: new

**Figure 3.** Projected Global Population and Calorie Demand

<!-- Start of picture text --> 12<br>29<br>11<br>27<br>10 25<br>23<br>9<br>21<br>8<br>19<br>7<br>17<br>6 15<br>Global Population Global Calorie Demand<br>Billions Trillions<br><!-- End of picture text -->

**<u>Source:</u>** FAO (http://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en).

inventions lead to others by lowering the cost of technological development over time (e.g., Arrow 1962; Levin 1988). Both could be at work in practice, with the development of digital technologies for example partly driven by forces exogenous to agriculture, but their adaptation and adoption in agriculture are partly driven by the rising costs of labor.

The classic example of labor-saving technology in fruit and vegetable production is the processing tomato harvester developed by researchers at the University of California, Davis (UC Davis) and commercially released by Blackwelder in the mid-1960s (Coatney 2006). Within five years of its commercial release, virtually 100 percent of processing tomato farms in the United States used the harvester, and most planted a tomato variety engineered (also at UC Davis) to go with it. Integrating mechanical engineering and agronomics was a novel feature of the tomato harvester’s 9