rafmacalaba/datause-encoder-data huggingface dataset:
# 5. Mathematics Achievement and Tracking

Estimation of our models is complicated by the plausible value estimates of mathematics achievement as well as the presence of intra-cluster correlation and other issues related to the complex survey design. The estimation methodology used in this paper adheres strictly to the methodology recommended by the OECD (2005) which provides unbiased estimators and standard errors assuming the other standard, requisite assumptions for each model are met.

Table 5 presents three estimates of the linear regression model of (2). The first includes only the _some tracking_ variable; the estimate of this coefficient is equivalent to the difference in the mean achievement of students in schools with tracking versus those in schools without tracking. As can be seen, this estimate is positive, implying students at tracking schools have a higher level of achievement, but it is not statistically significant implying the difference could simply stem from sampling variation.

The second estimation includes only the school variables, and the third includes all other variables. Only with the inclusion of student and family characteristics does the positive difference between tracking students and non-tracking students become statistically significant.

Being female is negatively associated with mathematics achievement which is a typical result for many countries. Additionally, being located in an urban or metropolitan stratum instead of a rural stratum is positively associated with achievement, even when differences in student and background characteristics are controlled for. Having a mother’s education less than upper secondary is negatively associated with achievement. Having a mother with more than an upper secondary education is also negatively associated with achievement; however, Table 4 reveals that this represents a small proportion of the sample. Finally owning books positively associates with achievement as well.

11
- [16] Heckman, J. (1979). "Sample selection bias as a speci...cation error.", Econometrica, 47(1), 153-161.

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- [27] Verhoogen, E. (2008). "Trade, quality upgrading and wage inequality in the Mexican manufacturing sector." Quarterly Journal of Economics, 123, 489-530.

18
**TABLE 6. Impact of Nationalization on Network Access Controlling for First-order Autoregressive Disturbances**

|Variables|Pooled FGLS|Pooled OLS|Within<br>Estimator|Within<br>Estimator D-K|
|---|---|---|---|---|
|Private provision|−0.003|−0.019|−0.007|−0.024|
||(0.019)|(0.037)|(0.048)|(0.038)|
|Renationalized provision|0.163***|0.159***|0.168***|0.168***|
||(0.026)|(0.048)|(0.066)|(0.059)|
|Education|0.024***|0.023***|0.019**|0.032**|
||(0.003)|(0.008)|(0.008)|(0.011)|
|(log) Household per capita income|0.159***|0.157***|0.164***|0.137***|
||(0.013)|(0.035)|(0.035)|(0.037)|
|(log) Accumulated precipitation|0.017***|0.029|0.023|0.042*|
||(0.007)|(0.018)|(0.016)|(0.021)|
|Observations|432|432|414|432|
|AR(1)|0.329|0.270|0.209|0.06|
|Sampleperiod||1986–|2009||

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ All columns report estimates using panel data estimators controlling for first-order autoregressive (AR(1)) disturbances. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include individual effects, year effects, and time trends by city. Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

*, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.

31
TABLE OF CONTENTS

|EXECUTIV|EMW|ARY<br>..............<br>I|
|---|---|---|
|INTDROO|CTIO|N<br>.<br>**6**|
|rARTONE:|ANA|LYSIS OF COCOA PRODUCER<br>INVESTMENT<br>RESPONSE<br>TO PRICING POLICY...<br>1.1|
|A.|**SOM**<br>ANAL|**E**ECONOMI**C**ONCEPTS<br>CENTRAL TO THE<br>YSIS OF PRODUCER<br>RESPONSE|
||*|Opportunity Cost .1.1|
||*|<br>Consumption Discount Rate.<br>1.1|
||*|Marginal Efficiency of Capital.<br>1.2|
||*|Risk Premium.<br>1.2|
||*|Economic Rent .1.3|
|B.|A RE|VEW OF THE CURENT STATUS OF GHANAIAN AGRICLTURE .........<br>.<br>1.4|
|||Land .1.8|
||*|'Labor<br>......................................<br>1.13|
||*|Purchased Inputs.<br>1.14|
||*|Farm Operatlons.<br>1.15|
|C.|qNA|LYSISOF PRODUCER<br>INVESTMENT.<br>.<br>..<br>.<br>1.23|
||*|Methodology for Dynamic Analysis of|
|||Response to Pricing Policy.<br>1.23|
||*|Tree Crop Investments.<br>1.25|
||*|Land Allocation.<br>1.26|
||*|ABrief Technical Note .1.30|
||*|Model Validation Using the Annual|
|||Decompositon Model .1.33|
||*|Policy Simulations .1.38|
||*|Scenario 1--Supply Response under Flxed|
|||<br>Resource Supplies .1.39|
||*|<br>Scen:rlo 2--Effects<br>of Population Growth<br>|
|||on Supply Responses .1.47|
||*|Scenario 3---Effects<br>of Growth In Per Capita<br>|
|||Income on Cocoa Supply .1.54|
||*|Scenario 4--Effects<br>of a Policy of|
|||Food Self-Sufficiency<br>on Cocoa Supply .1.68|
||*|Al Dii f h|
|||nnua ecomposton or te1988-97|
|||Decade of Selected Experiments.<br>1.72|
||*|Policy Implications of the Simulation Experiments .1.80|
**Table 4.13 Average Monthly Wages, 2000 (1000 VND)**

|**Industries**|**Direct Workers**|**Management**|
|---|---|---|
|Manufacturing|Mean<br>721|1,324|
||Minimum<br>228|500|
||Maximum<br>1,700|4,500|
|Other industry|Mean<br>914|1,490|
||Minimum<br>400|650|
||Maximum<br>2,000|3,000|
|Services|Mean<br>916|1,540|
||Minimum<br>300|518|
||Maximum<br>2,590|5,000|

Source: World Bank Survey, 2001 37. The VLSS data show that wages of workers in the formal private sector are about 50 percent higher than those in the informal sector, but generally lower than wages in state owned enterprises and foreign invested companies.

**Table 4.14 Average Annual Compensation, 1997-1998 (1000 VND)**

|**Sector**|**Total Annual**|
|---|---|
|Government|5,894|
|Party/Mass Organization|5,044|
|SOE|9,258|
|Cooperative|2,688|
|Private Company *|7,970|
|Household|5,402|
|Foreign|9,376|
|Other|3,870|
|**_Total_**|**_6,436_**|

Source: VLSS 1997-98

* The private company category includes sole proprietorships, not limited liability or shareholding companies

_38._ Private sector jobs often come with less non-salary benefits than more highly sought after, but rare, state jobs. Surveyed companies reported spending only seven percent of total labor costs on social insurance and just two percent on training. Firms that rely heavily on short-term labor are of course able to cut down on their required payments, but even some firms with very high numbers of long-term contracted workers claimed very low social insurance payments.

**Table 4.15 2000 Firm Spending on Workers**

||Share(%)|
|---|---|
|Salary in cash|90.2|
|<br>Benefits in other form|0.2|
|Social Insurance|7.0|
|Training|2.4|
|<br>Bonus|0.2|
|**_Total_**|**_100.0_**|

Source: World Bank Survey, 2001 19
# **1. Introduction**

Women can be disproportionately affected by the negative effects of conflict-induced displacement. Access to essential services such as reproductive health care can be disrupted. Displacement can result in higher levels of gender-based violence (GBV) (Annan & Brier, 2010; Callaway & Martin, 2011; Cohen et al., 2013; Vu et al., 2014; Wirtz et al., 2014). However, it can also provide opportunities to challenge gender norms that limit women’s access to opportunities and ability to make decisions.

Following displacement, women might adopt new roles that would not have been possible before (Justino et al., 2012; Meertens & Stoller, 2001; Pirtskhalava, 2015). For example, in the absence of men, displaced Nuer women in South Sudan took on male responsibilities as income providers and assumed roles perceived as male, including negotiating dowries. To date, however, partly due to data limitations, few quantitative studies take account of the changes in gender relations among women and men in situations of displacement.

This paper aims to bridge this knowledge gap by building on the work by Heise and Cislaghi (2020), who propose a definition of gender norms that brings together two streams of theory and practice around gender equality. The first stream is the work on social norms, which emerged from studies in social psychology and evolved with behavioral economics (Bicchieri, 2005; Mackie et al., 2015). The second stream is the study of gender norms advanced by feminist scholars (Badgett & Folbre, 1999; Connell & Pearce, 2014; Connell & Pearse, 2015). The definition is operationalized by measuring behaviors and attitudes (Alesina et al., 2013; Harper et al., 2020). Following previous studies, survey clusters are used as a proxy for reference networks (Blakely, 2000; Kelly et al., 2018; Storey & Kaggwa, 2009; Uthman et al., 2011; Vyas & Heise, 2016).

The analysis focuses on Colombia, a country with a long-standing conflict and home to the second largest internally displaced population in the world. It uses three rounds of Demographic and Health Surveys (DHS) data for the 2005-2015 period to examine the extent to which gender norms that limit women’s access to reproductive health, economic opportunities, and mobility, and norms that tolerate violence against women, and endorse patriarchy become less traditional among 2
Figure 2. Macroeconomic impact of potential PTAs on Indonesian economy by 2030 (percent deviations from the baseline)

||**GDP**||**Exports**||**Imports**|
|---|---|---|---|---|---|
|EU‐IDN||EU‐IDN||EU‐IDN||
|FTAAP||FTAAP||FTAAP||
|TPP15||TPP15||TPP15||
|TPP11<br>RCEP||TPP11<br>RCEP|Productivity kick|TPP11<br>RCEP||
|TPP12||TPP12|Standard|TPP12||
||0<br>5<br>10|‐5<br>|0<br>5<br>10<br>15|‐5|0<br>5<br>10<br>15|

**Table 2. Impact of potential PTAs on Indonesian economy by 2030 (percent deviations from the** **<u>baseline).</u>**

||||**Stand**|**ard sim**|**ulations**||**S**|**imulatio**|**ns with**|**product**|**ivity kic**|**k**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||RCEP|FTAAP|TPP11|TPP12|TPP15|EU‐IDN|RCEP|FTAAP|TPP11|TPP12|TPP15|EU‐IDN|
|**GDP**|0.18|0.89|0.07|0.16|0.58|2.13|0.40|1.61|0.07|0.17|1.16|9.14|
|**Income**|0.46|1.45|‐0.09|‐0.24|1.14|2.59|1.13|2.29|‐0.06|‐0.21|1.72|7.82|
|**Exports**|1.59|4.97|‐0.40|‐0.86|2.84|7.00|2.74|6.01|‐0.36|‐0.82|3.56|13.44|
|**Imports**|1.52|4.75|‐0.38|‐0.83|2.73|6.72|2.64|5.77|‐0.34|‐0.78|3.43|13.05|

Indonesia faces relatively high protection in the EU markets.<sup>11</sup> While the estimated trade-weighted average tariff that Indonesia faces in the EU markets is close to zero (0.02%), the NTMs are the highest among all considered PTAs. In particular, the trade-weighted average NTM in trade with the EU equals 15.42% with the highest values in food processing (40.88%), communication and business services (40.23%), construction (32.47%), trade and transport (32.03%), agriculture (31.81%) and textiles (30.82%). Regarding tariffs, the highest ones are observed for wearing apparel (7.4%), textiles (6.58%), food processing (5.67%) and transport equipment (4.13%) – see Figure 3.

> 11 All assumptions for the EU-Indonesia simulation are based on the EU-Vietnam FTA commitments suggested by Petri et. al. (2012).

10
Table B8: Variance contributions for Tanzania, by sub-group

||||Absol|ute (s.d.<br>|units)|||Rela<br>|tive (in|%)||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Strata|Level|σ�<br>�|σ��<br>�|σ�<br>�|σ�<sup>�</sup>|2Σ��|σ��<br>�|σ�<br>�|σ�<sup>�</sup>|2Σ��|σ�<sup>�</sup>|
|Female|0|1.01|0.24|0.42|0.36|0.26|5.53|17.34|12.63|6.76|57.72|
||1|0.98|0.22|0.42|0.36|0.26|5.17|17.99|13.27|6.82|56.76|
|Age|6|0.98|0.28|0.42|0.36|0.23|8.03|18.57|13.54|5.74|54.12|
||9|1.00|0.20|0.42|0.37|0.27|4.06|17.72|13.93|7.32|56.97|
||12|1.01|0.21|0.42|0.34|0.27|4.47|17.48|11.56|7.37|59.12|
||15|1.02|0.25|0.42|0.32|0.27|5.95|16.76|10.04|7.15|60.10|
|Grade level|0|1.03|0.15|0.51|0.26|0.28|2.13|24.89|6.26|7.59|59.13|
||1|1.02|0.15|0.43|0.37|0.23|2.13|17.68|13.51|5.19|61.50|
||3|1.07|0.17|0.43|0.38|0.28|2.41|16.00|12.74|6.88|61.95|
||5|0.77|0.17|0.37|0.29|0.18|4.81|22.93|13.78|5.49|52.98|
|SES tercile|1|0.95|0.17|0.41|0.37|0.25|3.04|18.16|15.04|6.77|56.98|
||2|0.89|0.07|0.38|0.32|0.25|0.56|18.52|13.04|7.72|60.16|
||3|1.03|0.25|0.44|0.36|0.25|6.07|18.34|12.46|5.68|57.44|
|Mother primary|0|0.99|0.24|0.42|0.36|0.24|5.80|17.91|13.09|5.99|57.22|
||1|0.91|0.20|0.38|0.34|0.23|5.03|17.25|13.87|6.34|57.51|
|All|.|1.06|0.25|0.45|0.37|0.28|5.64|17.81|12.05|6.70|57.80|

Note: for each stratifying variable, indicated in the first column, sub-groups (second column) are mutually exclusive and span the entire dataset; female is a dummy variable (i.e., 0 = male, 1 = female); age and grade levels are grouped (e.g., age level 6 indicates children aged 6-8; age level 15 is children 15 and above; grade level 0 contains never enrolled children; grade level 5 is all those with highest grade 5 and above); for SES tercile, level 1 is the poorest group; mother edu. takes a value of 1 if the mother has attended primary school; variance components are as per equations (3b)-(3c) and the 2 individual effects are aggregated for simplicity (denoted, σa�); absolute and relative contributions are as per earlier tables, reported in standard deviation units and percentages respectively. Source: own calculations.

53
The differences between teacher types largely disappear in elementary-school classrooms, although there are some differences in Instructional Support dimensions (figure A.5). However, the sample above represents only 18 teachers, six for each teacher type. Additionally, instructional support is often the hardest domain to gauge effectively, making judgments regarding teacher types and instructional effectiveness difficult.

**Figure A.6: Differences between Teacher Types in Middle School**

<!-- Start of picture text --> 7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0 New<br>1.0 Candidate<br>0.0 Backbone<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

A look at middle-school teacher experiences paints a different picture. As can be seen in figure A.6, new teachers in middle school have a more difficult time in Emotional Support and Instructional Support, scoring behind candidate and backbone teachers. Candidate teachers perform well in Emotional Support and Classroom Organization, while scoring slightly lower than backbone teachers in Instructional Support.

It appears as if the transition to elementary schools is significantly easier on new teachers than the transition to middle school. While new teachers are generally able to maintain Classroom Organization, they appear less able to effectively provide emotional or instructional support. There are of course many differences between the two contexts: class sizes are generally larger, there is more curricular pressure, and the content is more difficult for students, not to mention the difficulty of emotionally supporting teenagers.

Within middle schools, the outperformance of candidate teachers stands out. Candidate teachers, supported by a more comprehensive training program as well as mentor teachers and administrators, may feel motivated in their professional development and increase their efforts in areas that may reflect favorably on their candidacy. Finally, there may be additional factors separate from those identified that play a more significant but yet unmeasured role. It is possible that the most impactful variable is related to a single subject, or a subset of rural candidate teachers versus urban candidate teachers. Given the number of teachers observed, it is not particularly useful to add additional filters to the data—one or two individual teachers would end up misleading rather than informing.

# **A.3 By training history**

Along with the other information collected as part of the study, we also attempted to obtain data related to teachers’ training history to measure potential effects of training on teaching practices. Flaws in the data collection methodology led to an incomplete sample being collected. We made two adjustments to deal 28
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11
policy. Policies to promote ‘green’ jobs have even been alleged to be “terribly economically counterproductive” (Alvarez et al., 2010).

This paper explores the possible relationship between labor markets and policies to promote sustainable growth – improving well-being with societies taking proper account of resource depletion and environmental impacts. In Section 2, the concept of ‘green’ jobs is discussed. In Section 3, some of the empirical estimates of job creation are reviewed, concentrating on the issue of how labor markets are assumed to function. Most of the literature is focused on developed industrial economies, but there have been attempts to estimate some of the employment consequences for developing countries. However, it is not clear that these make appropriate allowance for the range of ways in which labor markets function, an issue discussed in Section 4. Section 5 draws attention to the heterogeneity of developing countries with respect to the opportunities for job creation in green growth. In Section 6, the question of the skills necessary to support green growth is considered. Finally, some of the possible implications for policy design in developing countries, and for research priorities, are considered.

# **2. The concept:  What are ‘green’ jobs?**

There is no single agreed definition of a ‘green’ job. That makes it hard to compare studies of ‘green’ job creation (GHK, 2009) and has led some researchers to eschew use of the term completely. In a loose sense, though, ‘green’ jobs can be regarded as those associated with environmental objectives and policies.

Some definitions of ‘green’ jobs or related concepts focus on occupations and skills with an identifiable environmental focus, but most focus on employment in industries (or specific projects) the products of which are deemed to be of environmental benefit. Such benefits can be defined more or less broadly – for example, some concentrate on renewable energy, including or excluding biofuels, while others also include environmental services and/or employment related to improving energy efficiency or developing less-carbon-intensive products (e.g. building railways).

3