rafmacalaba/datause-encoder-data huggingface dataset:
China's growing import demand gives these economies scope for expanding their agroprocessing, electronics, and other manufacturing industries such as machinery and equipment (Table 2b). Demand from China will be compounded by increased import demand from China's closest trading partners - Japan, NIEs, the EU and the US, which themselves will benefit from China's growth and WTO accession. And, given China's agricultural reforms, there is scope for expansion of agricultural exports to China such as oilseeds and sugar as well as basic raw materials such as timber and energy products.

**Table 2b. Impact of China's WTO accession estimated with dynamic GTAP** _(cumulative volume changes relative to baseline for the period 2001-2010, US$ millions)_

||Indones|ia|Vietnam||Malays|ia|Philippine|s|Thaila|nd|
|---|---|---|---|---|---|---|---|---|---|---|
||Output<br>Ex|ports<br>O|utput<br>Ex|ports<br>|Output<br>E|xportsO|utput<br>Exp|orts<br>|Output<br>E|xports|
|Foodgrains|-14|5|21|11|19|0|31|14|<br>-11|-6|
|Feedgrains|147|2|0|-1|4|1|22|_5_|1|2|
|Vegetables and fruits|-19|-1|<br>0|1|<br>-11|5|-9|10|<br>-9|0|
|Oilseeds|-3|0|0|0|<br>0|0|0|0|<br>-1|0|
|Sugar|-9|1|2|1|-7|-4|10|8|-9|-5|
|Plantfibers|-18|0|0|0|-3|0|-4|2|<br>53|0|
|Livestock|-23|0|1|1|<br>64|47|-4|1|<br>0|0|
|Beveragesandtobacco|-1|1|-6|-7|0|-2|2|5|-5|-4|
|Otherfood|-116|-78|32|28|-74|-51|-2|10|<br>_-55_|-45|
|Wood products|478|373|_5_|4|418|315|16|17|36|59|
|Extractive|68|123|5|14|<br>47|-1|4|11|7|-4|
|Textiles|-660|-470|-61|-13|-204|198|-297|-85|-972|-301|
|Apparel|-1337|-1164|-990|-1002|-1015|-1017|-768|-779|<br>-1094|-1090|
|Lightrmfcs|234|236|91|101|-3|<br>-3|26|25|<br>12|16|
|Processing industry|_15_|123|94|89|<br>381|394|-26|17|987|1126|
|Autos|-68|-68|-2|-1|<br>-11|11|-9|-3|<br>-178|-83|
|Electronics|257|247|2|6|<br>75|75|33|37|<br>211|228|
|Othermnfcs|67|65|21|20|<br>133|138|19|25|213|280|
|Total (GDP)_(%)_|-0.2||-0.4||-0.1||-0.2||-0.2||
|Welfare(EV)|-152||-36||-14||-141||-148||
|Rateofretum_(%)_|-0.07||-0.22||-0.0|2|-0.04||-0.0|4|
|Termsoftrade(%)|0.02||-0.10||0.01||-0.14||-0.0|4|
|Foreignownership(%)|-1.0||-2.0||-0.1||-0.8||-0.|2|
|_Source:Authors'simula_|_tionswith GTA_|_P-Dyn.._|||||||||

Wages of unskilled labor may come under pressure since the sectors that may contract are more intensive in unskilled labor use, compared to those sectors that are set to expand. This implies that urban unemployment, inequality and vulnerability may worsen in the short run. The developing East Asian economies will need to invest in training of unskilled workers and other labor policies in order to facilitate the transition from textiles and clothing production to high end manufacturing.

The high intensity of trade between individual East Asian developing countries and China in 2001 (Ng and Yeats, 2002) suggest that the planned reductions in the protection of China's markets offer some good opportunities for exporters in developing countries. At the same time, there is the threat of increased competition in third country markets from increased exports from China. The garment and textile sectors pose particular challenges.

14
# **Table 3(b): Probits for alternative measures of subjective poverty in Guatemala**

||(1)<br>Probit for p<br>oneself on<br>|lacing<br>step 1|(2)<br>Probit for placin<br>at or below vig<br>|g oneself<br>nette 1|
|---|---|---|---|---|
||Coeff.|s.e.|Coeff.|s.e.|
|_Household Characteristics_|||||
|log annual per capita consumption|-0.507***|0.097|-0.426***|0.124|
|log household size|-0.374***|0.105|-0.384***|0.099|
|female headed household|-0.463|0.332|-0.498*|0.284|
|_Respondent Characteristics_|||||
|respondent is head of household|0.294|0.308|0.521*|0.290|
|respondent is female|0.270|0.363|0.567*|0.318|
|age of respondent|-0.004|0.003|-0.002|0.004|
|year of education of respondent|-0.147***|0.023|-0.074**|0.035|
|respondent is employed|0.113|0.176|0.108|0.163|
|single / never married|-0.508**|0.237|-0.684***|0.254|
|union|-0.744***|0.261|-0.845***|0.265|
|separated/divorced|0.511|0.433|0.059|0.402|
|widowed|-0.199|0.312|-0.476|0.321|
|Municipality (reference: Cuilco)|||||
|San Gaspar Ixchil|-0.204|0.167|-0.118|0.127|
|Santa Ana Huista|-0.783***|0.181|-0.602***|0.118|
|Jacaltenango|0.169|0.171|0.144|0.223|
|_Migration characteristics_|||||
|any migrant LA (excluding<br>respondent)|0.212|0.212|0.386*|0.207|
|any migrant US (excluding respondent)|-0.238|0.229|-0.089|0.203|
|migrated internally|-0.026|0.187|-0.210|0.195|
|migrated in Latin America|0.466***|0.160|0.398***|0.148|
|migrated to USA|-0.256|0.238|0.078|0.197|
|Pseudo R<sup>2</sup>|0.182||0.130||

_Note:_ *** p<0.01, ** p<0.05, * p<0.1
# **Table A3.3. Stochastic Frontier, Extended Model**

Dependent variable: real GDP (ln)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|
|_Frontier_||||||
|Employment ratio|0.368***|0.406***|0.449***|0.458***|0.456***|
||(27.95)|(22.01)|(29.41)|(27.86)|(27.68)|
|Capital stock ratio|<br>0.660***|<br>0.626***|<br>0.595***|<br>0.574***|<br>0.575***|
||(58.12)|(37.84)|(42.70)|(39.62)|(39.62)|
|Time trend|Yes|Yes|Yes|Yes|Yes|
|_Inefficiency model_||||||
|**Pillar 1**||-0.168***|-0.569***|-0.472***|-0.455***|
|||(-2.46)|(-4.98)|(-4.23)|(-4.14)|
|**Pillar 2 and 3**<sup>**a**</sup>||<br>-0.211***|<br>-0.249***|<br>-0.302***|<br>-0.293***|
|||(-5.19)|(-3.84)|(-4.38)|(-4.25)|
|**Pillar 4**||||||
|Change in terms of trade||0.175***|0.238***|0.247***|0.252***|
|||(4.32)|(4.03)|(3.86)|(3.89)|
|Output gap|||<br>-0.031***|<br>-0.022***|<br>-0.019***|
||||(-3.36)|(-2.54)|(-2.39)|
|**Pillar 5 and 6**|-0.386***|-0.267***|-0.056|-0.186*|-0.198**|
||(-5.63)|(-4.29)|(-0.64)|(-1.61)|(-1.70)|
|**Pillar 7**||||||
|Regulatory quality|-0.171***|-0.102**|-0.076|-0.204***|-0.202***|
||(-3.13)|(-1.78)|(-1.00)|(-2.45)|(-2.42)|
|Regulatory trade barriers<sup>b</sup>|-0.098***|||0.160***|0.162***|
||(-3.85)|||(3.90)|(3.93)|
|Burden of custom procedure|||0.244***|||
|<br>(WEF)||||||
||||(3.91)|||
|Mean tariff rate<sup>c</sup>||0.172***||||
|||(5.52)||||
|Tax rate||<br>0.962***|0.942***|0.773***|0.725***|
|||(6.00)|(3.19)|(2.50)|(2.35)|
|**Pillar 8**||||||
|Women||||0.083***|0.084***|
|||||(3.09)|(3.12)|
|CBR||0.405***||<br>0.957***|<br>0.993***|
|||(2.81)||(3.87)|(3.91)|
|**Pillar 9**||||||
|Private credit||0.239***|0.805***|0.889***||
|||(3.76)|(2.05)|(2.30)||
|Private credit growth||0.0704||||
|||(0.48)||||
|Private credit (t-1)|||-0.469|-0.521*|0.378***|
||||(-1.17)|(-1.31)|(3.87)|
|**Pillar 12**|||0.0413|0.0745*|0.0571|
||||(0.84)|(1.50)|(1.16)|
|Emerging market dummy|No|Yes|<br>Yes|<br>Yes|<br>Yes|
|_Variance of inefficiency_||||||
|<br>Change in terms of trade||-8.976***|-7.382***|-11.05***|-10.53***|
|||(-4.90)|(-4.98)|(-4.83)|(-4.77)|
|Constant|Yes|Yes|Yes|Yes|Yes|
|Obs|1186|612|576|494|498|
|Wald Test|58764.4|26532.9|34827.3|29684.2|29555.2|
|Wald Test p-value|0|0|0|0|0|
|LogLik|-59.86|105.6|104.8|121.0|121.2|

_Note t_ statistics in parentheses. * p<0.2, ** p<0.1, *** p<0.05.

> **a** Weighted average of energy use pc (ln) and number of individuals using internet (ln), with equal weights **b** Fraser 2016

> **c** Fraser 2016

35
**Table 10: Pro-poor numerical analysis**

||**Pro-poor measure**|**Mali**|**Urban**|**Rural**|
|---|---|---|---|---|
|a|Growth|**+**|**-**|**+**|
|m 1|Ravaillion & Chen(2003)-g|PPG|PRR|PRG|
|Si|Kakwani & Son(2003)-g|PRG|PRR|PPG|
||Kakwani & Pernia(2001)|PRG|PPG|PPG|
|b|Growth|**+**|**+**|**+**|
|1|R&C(2003)|PRG|PRG|PPG|
|Sim|K&S(2003)|PPG|NSPPG|PPG|
||K&P(2001)|PPG|NSPPG|PPG|
|c|Growth|**+**|**+**|**+**|
|m 1|R&C(2003)|PRG|PRG|PRG|
|Si|K&S(2003)|NSPPG|NSPPG|PPG|
||K&P(2001)|NSPPG|NSPPG|PPG|
|a|Growth|**+**|**+**|**+**|
|2|R&C(2003)|PPG|PRG|PRG|
|Sim|K&S(2003)|PPG|NSPPG|PPG|
||K&P(2001)|PPG|PPG|PPG|
|b|Growth|**+**|**-**|**+**|
|m 2|R&C(2003)|PRG|PRR|PRG|
|Si|K&S(2003)|NSPPG|PRR|NSPPG|
||K&P(2001)|NSPPG|PPR|NSPPG|
|c|Growth|**+**|**+**|**+**|
|2|R&C(2003)|PRG|PRG|PRG|
|Sim|K&S(2003)|NSPPG|NSPPG|PPG|
||K&P(2001)|NSPPG|NSPPG|PPG|
|d|Growth|**+**|**+**|**+**|
|m 2|R&C(2003)|PRG|PRG|PRG|
|Si|K&S(2003)|NSPPG|NSPPG|PPG|
||K&P(2001)|NSPPG|NSPPG|PPG|

These results tend to confirm the graphical pro-poor analysis we have presented where no clear pattern can be extracted from this. The only simulation producing pro-poor growth with all indices is simulation 2a. For the rural zone, the simulation 1b is the only one generating pro-poor growth for the three indices and simulation 1a and 2a are the only one with at least one index to be pro-poor growth for the urban area. The Ravaillion and Chen (2003) index seem to generate pro-rich growth in most scenarios. Kakwani and Pernia (2001) generate 10 occurrences of pro-poor growth which Kakwani and Son (2002) eight cases of pro-poor growth.

# **5 Conclusions and final remarks**

In this paper, we construct a CGE model and a microsimulation model to analyze the macroeconomic as well as distributional impact of the expansion of jatropha based biodiesel 26
facilities that were already implementing the system, and because P4P was managed at the district level, the GoR required that all facilities within those new districts be in the first phase (treatment) of the rollout. This led the evaluation team to switch the assignment of treatment and comparison for eight districts from four blocks, as well as add one block to the sample. In the end, the study’s nine blocks include 16 districts in total, of which 9 belonged to the treatment group and 7 to the comparison group. Facilities that had P4P prior to the study were excluded from the evaluation (Vermeersch et al., 2010). Only facilities offering HIV/AIDS services were included in the HIV/AIDS P4P model. The 24 facilities that offered HIV services in these districts were enrolled in the study, with 10 facilities in the treatment and 14 facilities in the comparison group.

Facilities in the treatment group started receiving the HIV/AIDS related P4P in January 2007, while the facilities in the comparison group continued with traditional input-based financing until July 2008. Exposure to the HIV P4P model varied between 12 to 15 months in treatment facilities.

Since a primary objective of the evaluation was to isolate the P4P incentive effect separately from the effect of an increase in financial resources, it was necessary to hold the level of resources constant across treatment and comparison facilities. To accomplish this, comparison facilities’ traditional input-based budgets were increased by the average amount of P4P payments to treatment facilities on a quarterly basis during the experiment. Therefore, the differences in outcomes between the two groups at follow-up must be attributed to the difference in incentive structures and not to a difference in available financial resources.

# _3.2 Data_

We conducted a baseline survey of the facilities from August until November 2006 and a follow-up survey from April until July 2008.<sup>5</sup> We also designed a household level survey that

> 5 The study team submitted the research protocol to the Rwanda National Ethics Committee, which approved the research design, methodology and methods for informed consent. Because of the sensitive nature of the sample and survey, the data collection was managed by the National University of Rwanda School of Public Health with guidance from the National AIDS Control Commission (CNLS). The follow-up surveys were also reviewed by the National Institute of Public Health in Mexico Institutional Review Board (IPF Code 3627801).

7
scores for countries across government services and government involvement show that West Bank/Gaza has the most decentralized expenditure system among the group followed, in order, by Egypt, Iran, Tunisia and Yemen.

# **3.3. Revenue Assignment**

Table 5 shows revenue assignment characteristics in the region. West Bank/Gaza is the only country with a truly decentralized revenue system. However, municipal revenue autonomy in West Bank and Gaza is not something granted by the laws. Due to the circumstances, municipalities invent their own ways of raising revenues and they often find ways of rationalizing such practices (World Bank, 2006).

Other than West Bank/Gaza, all other countries in the region have largely an adhoc local revenue system. The only exception might be property tax system in Iran. We see a glimpse of autonomy in land-based taxation in Iran.

# **3.4. Intergovernmental Transfer Characteristics**

Table 6 shows that allocation rules for transfers to local governments are also mainly ad-hoc, except in Tunisia and West Bank/Gaza. In both countries, the transfer system is based on a formula. In West Bank and Gaza municipalities have been historically less dependent on central government transfers compared to other countries in the region. Both due to historical reasons and current political and security problems, West Bank and Gaza municipalities rely little on central government transfers in providing services. In fact, this situation has not been affected significantly by the Intifada period, as there is no difference today in the amount of central government 10
the proportion of skilled workers (especially those with diplomas and university qualifications) is significantly smaller compared with the (smaller) 1998 and 2002 samples.

# _4.2. Estimates of Education/Skill Premiums and Labor Force Composition_

The tables below summarize the changes in skill premiums in each country. In interpreting the estimates in Tables 4-10, the base education level/skill on which other dummy variables are based are as follows: in Tables 4a-10a the base (reference) education level is “less than completed primary”; however, the presented return estimates for education levels higher than primary are given in comparison to primary. Thus, in Indonesia in 1994, the estimate of 1.125 for tertiary over primary is the difference in coefficients for tertiary and primary in the estimated earnings equation. Dividing 1.125 by the difference in years of education between tertiary and primary education (10 years) implies an annualized return of about 11.2%. Similarly, if one wants to derive an estimate of the return to tertiary over senior secondary (instead of primary), this would be the difference between 1.125 and 0.596, that is 0.529; annualized this implies an average return of 13.2% for each year of tertiary education over senior secondary. In Tables 4b10b a single dummy for skill is used with two alternative specifications: senior secondary and above vs. lower secondary and below and tertiary vs. senior secondary and below. Tables A15 to A21 provide more details on returns by education level by country.

In Indonesia, education premiums declined with the exception of primary education. The largest decline in education premiums over time occurred in junior and senior secondary education, with a smaller decline in tertiary education premiums and a small increase in the premium of primary education. Within senior secondary education, most of the decline in premiums is associated with vocational-technical education. To a large extent, the findings are similar for Thailand: Accounting for the effect of industry affiliation in the regressions, we see declining premiums in junior and senior secondary education and a smaller decline in tertiary education, along with a small increase in primary education premiums. As in the case of Indonesia, most of the decline in premiums in secondary education is associated with vocational-technical education. In fact, with only basic controls, premiums are on the rise in general senior Ssecondary and tertiary education. In 11
stronger state institutions in China, and it has no doubt helped that China did not make the mistake of believing that freer markets called for weakening those institutions. Public administrative and decision-making processes were also crucial to assuring that the state was an effective tool for fighting poverty. Evidence-based policy making has played an important role since the late 1970s. China learnt much from the successes and failures of diverse local initiatives; in effect, the center transmitted the policy lessons from one place to another, backed up by credible research on what was happening on the ground.

It is plain that the combination of sound policy making practices with strong state institutions was a key factor in China’s success against poverty. And it is also clear that the two ingredients are complements, not substitutes. Less ideology helps little if state institutions are weak. China’s lesson for Africa on the importance of “searching for truth from facts” in policy making will bear little fruit if Africa’s state institutions remain weak.

But it must not be forgotten that Africa is 48 countries not one. There is no African central government to transmit policy lessons from one place to another. Here the international community, including China, can play an important role.

# **References**

Alden, Chris, 2005, “China in Africa,” _Survival_ 47(3): 147-164. Anderlini, Jamil, 2007, “China 'ties' $5bn aid to Africa,” _Financial Times_ , June 26.

Anshan, Li, 2007, “China and Africa: Policy and Challenges,” _China Security_ 3(3), pp.69-93. Baochang, Gu, Wang Feng, Guo Zhigang and Zhang Erli, 2007, “China’s Local and National Fertility Policies at the End of the Twentieth Century,” _Population and Development Review_ 33(1): 129-147.

Chaudhuri, Shubham and Martin Ravallion, 2007, “Partially Awakened Giants: Uneven Growth in China and India,” in _Dancing with Giants: China, India, and the Global Economy_ , edited by L. Alan Winters and Shahid Yusuf, World Bank.

Chen, Shaohua and Martin Ravallion, 2004, “Household Welfare Impacts of WTO Accession in China,” _World Bank Economic Review_ , 18(1): 29-58.

____________ and ______________, 2007, “Absolute Poverty Measures for the Developing World,” _Proceedings of the National Academy of Sciences_ , 104(43), pp. 16757-62. Christiaensen, Luc and Lionel Demery, 2007, _Down to Earth: Agriculture and Poverty_ 24
Table 6: Additional EO data used in the maize classification pipeline

|**Feature**|**Explanation**|**Data Source**|**Included in**|
|---|---|---|---|
|Elevation|Obtained using GEE’s<br>inbuilt_terrain_algorithm<br>|Shuttle Radar Topography<br>Mission (30-meter|Malawi, Ethiopia|
|Slope|that uses an elevation<br>raster to generate slope<br>and aspect bands|resolution)|Malawi, Ethiopia|
|Aspect (direction of slope)|||Malawi, Ethiopia|
|Average temperature|Mean daily temperature<br>during growing season|aWhere daily observed<br>weather API (0.1-degree|Malawi|
|GDD|Growing degree days*<br>accumulated during<br>growing season|resolution)|Malawi|
|Total precipitation|Total precipitation during<br>growing season||Malawi|

**Notes:** * A growing degree day is one in which the mean temperature is greater than a base value that must be exceeded for crop growth to occur. For maize, this base value is 10 °C.

# **3. Methodology**

We developed a methodological framework that is presented in Figure 3 and that is designed to quantify the ability of a machine learning model to identify pixels as maize or non-maize under scenarios with limited training data quantity, various data collection methods, and type of satellitederived variables used. The overarching approach was to

1. define a common modeling pipeline that trains and evaluates a maize classification model for a given data set,

2. feed the modeling pipeline with each data set in a sequence designed to emulate hypothetical scenarios of field data collection (varying the number of observations, the plot geolocation method, and the minimum plot size), 3. vary the type of satellite data used by the modeling pipeline (optical only, radar only, both optical and radar), and 4. compare evaluation metrics across different scenarios. Figure 3 depicts the overall structure of the study.

14
when availability of schools is not a prominent reason, lack of financial resources and cultural attitudes toward girls’ schooling are the main reasons reported for never attendance. Similarly, financial constraints and opportunity costs are cited for dropping out by 60 and 39 percent of boys and girls, respectively. Avoiding mixing with the opposite gender or marriage was cited by 11 percent of girls for dropping out.

**Table 5: Reasons for never attending and dropping out of school**

||_Never A_<br>|_ttendance_<br>|_Dro_<br>|_p-out_<br>|
|---|---|---|---|---|
||**Males**|**Females**|**Males**|**Females**|
|Choice|0.472|0.560|0.581|0.518|
|Financial constraints|0.319|0.320|0.148|0.233|
|Opportunity cost|0.293|0.144|0.447|0.158|
|School supply|0.021|0.055|0.012|0.028|
|Customs and traditions or<br>marriage|0.025|0.241|||
|Mistreatment by teachers and<br>discipline|||0.024|0.041|
|Health issues|0.194|0.0364|0.025|0.020|
|Achievements|||0.187|0.117|
|No birth certificate|0.012|0.009|||
|Avoid gender mixing or<br>marriage|||0.002|0.115|
|Observations|225|933|478|729|

_Source:_ SYPE 2009. Sample: Those never attended and ages 10-29 years.

# **3. Inequities in Learning Achievements**

In Section 2, we showed that while there has been democratization in access to basic education in Egypt, there remain persistent disparities in access to general secondary and college education. Given that educational trajectories depend to a large extent on tracking into general and vocational secondary schools at completion of basic education, the quality of the education at lower levels of schooling is likely to be a major determinant of educational inequalities in Egypt.<sup>4</sup> We now turn to inequalities in learning achievements, as measured by TIMSS evaluation of pupils’

> 4 The tracking is determined by scores at the preparatory completion exam organized at the level of governorate.

12