rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1: Experimental Design <!-- Start of picture text --> Individual Training<br>(T1)<br>Subsidized Rubber<br>Seedlings<br>Couples' Training<br>Initial Assignment<br>(T2)<br>Control<br><!-- End of picture text --> The training curriculum consisted of three main blocks, pictured in Appendix Figure 1. The first (“gender reflection”), administered to both the individual and couples’ group, prompted couples to reflect on division of labor, asset ownership and sharing of decision-making and income within the household. The material for this part of the training was adapted from the Gender Balance Tree portion of Oxfam’s Gender Action Learning System (Oxfam 2014).

The second block (“knowledge”) consisted of a standard agricultural extension training related to rubber cultivation, where farmers were taught how to choose the right plot, prepare the land, space trees at planting, apply inputs, weed and intercrop.

The last block (“action plan”) consisted of the creation of an action plan, where farmers had to write down the activities to do to take care of their rubber tree in the first two years, when they would do them, who would manage the task, what resources would be necessary, and where the money would come from. Moreover, before starting to fill out the action plan, farmers had to reflect on how to split decision-making on rubber and income earned from rubber with their spouses. In the couples’ training group, attendance was mandatory for both the lead farmer and his spouse or cohabitating partner (henceforth ‘spouse’) throughout the 3-day training. In the individual training group, the farmer attended the training by himself: attendance of the spouse was only permitted for the gender reflection portion.

# **2.2 Data**

We collected data on study participants through in-person interviews and self-administered assessments throughout the training. Specifically, the data used for this study consists of three primary sources: (i) a household baseline survey administered approximately a month before the training, (ii) data provided by the farmers themselves during the training, which we collected and transcribed and (iii) an endline survey administered approximately two years after the training.

Source (ii) includes farmers’ responses to questions on decision- and income-sharing, the content of the filled-out action plans, and a quiz capturing gender attitudes and agricultural knowledge. This quiz was administered to farmers during the training after the gender sensitization but before the action plan creation 6
**<u>Table 12: Productivity of social insurance contributions (2008)</u>**

|**Country**|**Revenue (% of GDP)**<br>**(1)**|**Total social security rate (%)**<br>**(2)**|**Implicit productivity:**<br>**(1) divided by (2)**|
|---|---|---|---|
|Estonia|12.6|33.0|0.38|
|Slovenia|14.3|38.2|0,37|
|Czech<br>Republic|16.2|45.0|0.36|
|Poland|11.4|37.7|0.30|
|Hungary|13.9|50.5|0.28|
|Lithuania|9.3|34.0|0.27|
|Latvia|8.8|33.1|0.27|
|Slovakia|12.1|48.6|0.25|
|Bulgaria|8.1|34.4|0.24|
|Romania|10.3|52.0|0.20|

_Note:_ The total social contribution rate is the sum of employer and employee contribution rates to public insurances for old-age pensions, sickness, work injury and unemployment.

93. Another crude proxy for the effectiveness of tax collection is the VAT revenue ratio. It is defined as the ratio between the actual value added tax (VAT) revenue collected and the revenue that would theoretically be raised if VAT was applied at the standard rate to all final consumption. The gap between the potential and the actual VAT revenue does, however, not only reflect tax but is also affected by tax exemptions, reduced rates, and other special regimes. Figure 4 shows that among the NM-EU countries for which we have this information, Slovenia has the highest VAT Revenue Ratio, followed by the Czech Republic, Slovakia, Hungary, and Poland.

**Figure 4: Effectiveness of value added taxes as measured by the VAT Revenue Ratio**<sup>*****</sup> **2005**<sup>******</sup>

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>MEX ITA GRC PRT POL GBR HUN BEL FRA CAN SVK TUR DEU SWE ESP AUS NOR OECD CZE AUT FIN NLD DNK ISL IRL SVN KOR JPN CHE LUX NZL<br><!-- End of picture text -->

* The VAT Revenue Ratio (VRR) is defined as the ratio between the actual value added tax (VAT) revenue collected and the revenue that would theoretically be raised if VAT was applied at the standard rate to all final consumption. This ratio gives an indication of the efficiency of the VAT regime in a country compared to a standard norm. The calculation for Canada is for federal VAT only and the OECD aggregate is an unweighted average of data for the countries shown. **  2007 for Slovenia. _Source: OECD (2009)._

94. Tax compliance costs vary considerably among the NM-EU countries. According to the World Bank ́s Doing Business Report, the time spent by a model firm on tax compliance is lowest in Estonia, which also has a good ranking worldwide. The reasons are that – as mentioned above – the tax system is relatively simple, the overall tax burden is relatively low and tax collection is centralized and eased by electronic tax declarations. By contrast, the Czech Republic has the highest compliance costs among the NM-EU countries and also ranks poorly worldwide (Table 13).

39
WPS4221 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 14</mark> Disarming Fears of Diversity: Ethnic Heterogeneity and State Militarization, 1988–2002<sup>∗</sup> INDRA DE SOYSA Norwegian University of Science and Technology (NTNU) Dept. of Sociology and Political Science 7491, Trondheim, Norway +47-73551141 (ph), +47-73591564 (fax), indra.de.soysa@svt.ntnu.no and Centre for the Study of Civil War International Peace Research Institute Oslo (PRIO) ERIC NEUMAYER London School of Economics and Political Science (LSE) Department of Geography and Environment Houghton Street, London WC2A 2AE, UK +44-2079557598 (ph), +44-2079557412 (fax), e.neumayer@lse.ac.uk and Centre for the Study of Civil War, PRIO World Bank Policy Research Working Paper 4221, May 2007 _The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post-Conflict Transitions Project can be found at_ _<u>http://econ.worldbank.org/programs/conflict</u>_ <u>).</u> _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 these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._

> ∗ Equal authorship. Corresponding author: Eric Neumayer. An earlier version was presented at the 46th Annual

International Studies Association Conference in Honolulu, Hawaii, March 2-5, 2005. We would like to thank Jim Fearon, Norman Loayza, Nils Petter Gleditsch, Halvard Buhaug, and Erich Weede for many helpful comments. All errors are our fault. Eric Neumayer acknowledges financial assistance from the Leverhulme Trust.
**Table 3 Revealed comparative advantage based on GVC incomes by** **<u>product, major EU countries, 1995 and 2008.</u>**

||chem|icals|elect<br>machi|rical<br>nery|foo<br>prod|d<br>ucts|non-<br>mach<br>and m|elec.<br>inery<br>etal|non-du|rables|trans<br>equip|port<br>ment|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1995|2008|1995|2008|1995|2008|1995|2008|1995|2008|1995|2008|
|Germany|1.10|0.80|0.87|0.95|0.72|0.67|1.37|1.43|0.76|0.65|1.26|1.54|
|France|1.08|1.08|0.80|0.72|0.99|1.04|0.86|0.93|0.85|0.77|1.38|1.30|
|United Kingdom|1.30|1.30|0.98|0.81|0.83|0.78|0.99|0.95|0.96|0.90|0.95|1.07|
|Italy|0.92|0.70|0.65|0.65|0.72|0.75|1.32|1.59|1.95|1.82|0.67|0.76|
|Spain|0.96|0.89|0.52|0.54|1.17|1.17|0.58|0.85|1.43|1.07|1.16|1.24|
|Netherlands|1.23|1.40|0.75|0.70|1.31|1.13|0.86|0.96|0.85|0.81|0.68|0.74|
|Belgium|1.30|1.30|0.67|0.69|0.96|0.95|0.95|1.04|0.99|0.87|1.16|1.05|
|Sweden|0.88|0.85|1.18|1.16|0.76|0.65|1.19|1.49|0.61|0.61|1.29|1.26|
|Austria|1.03|0.74|0.90|0.97|0.91|0.76|1.24|1.61|1.22|0.94|0.68|1.01|
|Greece|0.87|0.99|0.31|0.41|1.82|1.62|0.21|0.63|1.82|1.47|0.30|0.40|
|Denmark|0.99|1.42|0.70|0.90|1.43|1.09|1.03|1.20|1.02|0.75|0.47|0.52|
|Finland|0.74|0.70|1.26|1.56|0.97|0.77|1.22|1.50|0.75|0.63|0.62|0.75|
|Ireland|1.27|1.69|1.21|1.37|1.47|1.05|0.44|0.45|0.46|0.47|0.33|0.45|
|Portugal|0.81|0.76|0.50|0.64|1.04|1.06|0.53|0.72|2.69|2.22|0.54|0.71|
|Poland|0.92|0.84|0.51|0.60|1.42|1.25|0.73|0.92|1.33|1.09|0.72|1.03|
|Czech Republic|0.88|0.61|0.60|0.97|1.13|0.81|1.27|1.25|1.16|0.90|0.84|1.51|
|Romania|0.87|0.76|0.49|0.45|1.55|1.35|0.75|0.76|1.55|1.48|0.54|1.06|
|Hungary|1.20|1.10|0.62|1.28|1.47|0.94|0.64|0.90|1.09|0.60|0.68|1.18|
|Slovak Republic|1.23|0.60|0.62|1.18|1.09|0.66|0.88|1.24|1.26|0.92|0.79|1.39|

Note: Revealed comparative advantage calculated as country share in world GVC income for a group of manufactures divided by same ratio for all manufactures. Food manufacturing products (Food: produced in ISIC rev.3 industries 15 & 16), Other non-durable products (Tex: 17 to 20, 36, 37); Chemical products (Chem: 23 to 26), Machinery & metal products (Mach: 27 to 29); Electrical machinery products (Elec: 30 to 33) and Transport equipment (Tra: 34, 35). _Source:_ Author’s calculations based on World Input-Output Database, April 2012.

27
# Leveraging Women’s Views to Influence Gender Norms around Women Working: Evidence from an Online Intervention in Indonesia<sup>_∗_</sup>

Lisa Cameron<sup>_†_</sup> Diana Contreras Suarez<sup>_‡_</sup> Diahhadi Setyonaluri<sup>_§_</sup>

**Keywords** : gender norms, women’s employment, social desirability bias, Indonesia. **JEL Classification Code** : O12, O15, J16.

> _∗_ We thank seminar participants at the Australian Gender Economics Workshop, Asian Development Bank, George Washington University, International Food Policy Research Institute, the International Bank for Reconstruction and Development, World Bank Jakarta Office, NBER Summer Workshop, University of Indonesia, University of Melbourne and Western Economic Association International Meetings for helpful comments that have improved the paper. We thank Saskia Rizqina Maulida for excellent research assistance and gratefully acknowledge funding from JPAL-SEA’s Indonesia Research Fund and from the Australian Government’s Department of Foreign Affairs and Trade through the Australia-World Bank Indonesia Partnership (ABIP). This paper was commissioned by the Social Sustainability and Inclusion (SSI) and Poverty and Equity (POV) Global Practices under the Indonesia Gender Equality and Growth research program (project code P172182) managed by Emcet O. Tas and Ririn S. Purnamasari.

> _†_ Melbourne Institute of Applied Economic and Social Research, University of Melbourne; Email: lisa.cameron@unimelb.edu.au

> _‡_ Melbourne Institute of Applied Economic and Social Research, University of Melbourne; Email: diana.contreras@unimelb.edu.au

> _§_ Institute for Economic and Social Research (LPEM), Faculty of Economics and Business, University of Indonesia; Email: diahhadi.s@ui.ac.id.
teach in a language they are not well trained to use. Differences between the Malaysian sample and the counterfactual sample reveal the average of these two effects.

If the negative impact on the younger cohort of PPSMI-affected students is larger than the negative impact on the older cohort of PPSMI-affected students, then we have clear evidence that using a non-native language of instruction is detrimental to a student’s learning in mathematics and science. It is important to note that we are not able to differentiate between its impact on a student’s ability to learn and a teacher’s ability to teach.

The differences between male and female PPSMI-affected students, especially among the younger cohort, provide evidence on whether using a non-native language is detrimental to a student’s ability to learn. Since male students tend to have a slower development in language skills (Gawda and Szepietowska, 2013), they are likely to learn less well when using a non-mother tongue than female students do. As both male and female students are taught by the same teachers, the differences between their test scores are likely to be independent of the effect of a teacher’s ability to teach in an unfamiliar language.

The difference between a student’s mathematics and science test scores after the introduction of the PPSMI policy may also provide corroborative evidence on the adverse impact on a student’s learning of using a non-native language. Specifically, because science curriculum involves more reading, comprehension, and writing in the language of instruction, the role that language plays in science is likely stronger than that in mathematics. Thus, we would expect the negative impact on a student’s performance of using a non-native language to be stronger in science than in mathematics.

Finally, the adverse effect of PPSMI on a student’s TIMSS performance is likely to be more severe for a non-ethnic Malay student than for an ethnic Malay student. TIMSS papers are 12
Table 3: **Sector shares of gross exports – World Trade**

||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|
|Agr, Forestry, Fisheries|0.041|0.034|0.029|0.026|0.023|0.021|
|Processed Foods|0.031|0.042|0.038|0.035|0.036|0.034|
|Energy Extraction|0.079|0.053|0.062|0.058|0.086|0.114|
|Minerals nec|0.017|0.014|0.006|0.005|0.010|0.013|
|Beverages and Tobacco Products|0.008|0.007|0.008|0.007|0.007|0.007|
|Textiles|0.027|0.026|0.030|0.028|0.025|0.020|
|Wearing Apparel|0.028|0.027|0.022|0.022|0.019|0.016|
|Leather Products|0.014|0.014|0.012|0.013|0.010|0.009|
|Wood Products|0.016|0.017|0.017|0.017|0.016|0.015|
|Paper Products, Publishing|0.022|0.025|0.021|0.021|0.018|0.016|
|Chemical, Rubber, Plastic Products|0.082|0.097|0.093|0.099|0.111|0.109|
|Mineral Products nec|0.009|0.011|0.016|0.016|0.010|0.010|
|Ferrous Metals|0.022|0.028|0.023|0.020|0.024|0.027|
|Metals nec|0.016|0.018|0.024|0.022|0.024|0.031|
|Metal Products|0.016|0.018|0.018|0.018|0.018|0.019|
|Transport Equipment|0.090|0.090|0.095|0.103|0.107|0.099|
|Machinery and Equipment nec|0.219|0.242|0.257|0.257|0.235|0.214|
|Manufactures nec|0.026|0.023|0.022|0.026|0.017|0.017|
|Electricity, Gas, Water|0.001|0.001|0.004|0.004|0.004|0.005|
|Construction|0.002|0.004|0.006|0.004|0.004|0.006|
|Trade and Transport Services|0.147|0.129|0.111|0.103|0.098|0.096|
|Other Commercial Services|0.080|0.056|0.073|0.082|0.085|0.092|
|Public Services|0.007|0.023|0.015|0.015|0.014|0.012|
|Dwellings|0.000|0.000|0.000|0.000|0.000|0.000|
|Total|1.000|1.000|1.000|1.000|1.000|1.000|

10
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weighted by population at the district level and are obtained by overlaying the gridded CRU TS climate data with population and district-level grid maps.

Data on demographics and socioeconomics at the individual, household and district levels are drawn from three main sources. The first one is the 2008-09 income and expenditure household survey (known as IOF, for its acronym in Portuguese) collected by the National Institute of Statistics of Mozambique. The IOF survey is representative at the province level and, in addition to providing detailed data on expenditures (including self-consumption) and incomes, it contains a wealth of information on sociodemographic characteristics, education, health, children’s anthropometrics, labor market indicators and housing conditions. The second source of data is the 2011 Demographic and Health Survey (DHS), which provides a range of variables in aspects related to population, health and nutrition. Lastly, we used a 10% random sample of the 2007 population census to obtain additional information on human capital indicators.

# _Rainfall shocks_

Mozambique has a tropical climate that is characterized by two seasons. The wet (rainy) season goes from October to March, and is characterized by heavier rainfall between December and March. Most cyclones are also experienced over the same period, especially in the coastal areas. The dry season extends from April to September, and is particularly pronounced in the south of the country, making it more prone to drought. Due to Mozambique’s location in the tropical zone, temperatures do not fluctuate much within and across seasons.

Rainfall shocks are prevalent across the country. We define them as district-level annual cumulative rainfall events between 1950 and 2014 that were two standard deviations above (for floods) or below (for droughts) the historical annual mean for the corresponding district. The annual historical mean is calculated over the 1901-2014 period for each of the 128 districts comprising the 11 provinces of Mozambique. It is observed that extreme precipitation (whether in deficit or in excess) occurs with relative frequency. There is wide geographical variation across the country; provinces such as Niassa, Tete, Inhambane and Manica record the largest number of rainfall anomalies (Figure 1). Major disasters such as the floods that occurred in 2000, which were caused by Cyclone Eline and Cyclone Judah and affected mostly districts in the Inhambane and Gaza provinces, are well captured by the rainfall shock variable (Figure 2).

8
**Table 4. Uniform Contribution Rates for Partially Funded Defined Benefit System (percentage of wage bill)**

||1995-2100|1995-2050|2051-2100|
|---|---|---|---|
|(1) Current system--<br>baselinea|41.1|32.1|54.7|
|(2) Current system-<br>slow growth|48.7|36.0|61.6|
|(3) Reduced replacement ratio|31.3|24.9|41.0|
|(4) Increased retirement ageb|30.5|26.0|37.3|
|(5) Price indexation|34.9|27.5|46.3|
|(6) Price indexation- slow growth|45.8|34.3|57.5|
|(7) Rapid interest rate reforms|40.5|31.6|54.7|
|(8) Delegated interest rate reform|50.6|35.9|56.0|
|(9) Combined reform|19.8|17.2|23.8|
|(10) Combined reforms with coverage|18.3|14.6|23.1|

- Column 1 gives uniform contribution rate through time that will equate the present value of revenues and expenditures from 1995-2100. Columns 2 and 3 give the uniform contribution rates for 1995-2050 and 2051-2100 if the system is balanced separately for these two period. See Table 3 for other definitions.

**40**
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45
# **IV.** **<mark>Poverty Goals and Profiles with 2011 PPP</mark>**

The rest of the analysis is based on exploring the current, past, and future profiles of our candidate global poverty lines in 2011 PPPs ($1.82 and $1.92) and compares them each with the 2005 $1.25 line.

# **_A. Global Profiles of Poverty in 2011_**

The latest World Bank (2014a) poverty estimates for 2011 suggest that 1.01 billion people, or 14.5 percent of the global population, live below the $1.25 line in 2005 PPPs. As discussed in the previous section, these estimates are using adjusted 2005 PPPs for China, India, and Indonesia to correct for presumed sampling bias in the 2005 ICP and rural-urban price differences in the country. For the analysis in this paper, we estimate fitted distributions of consumption and income for every country and then derive poverty estimates from these distributions. Because we are using fitted Lorenz curves, the estimates reported in this paper differ slightly from World Bank (2014a) but the differences are small. For example, in our replication of the global count based on the $1.25 line and the 2005 PPPs, we get the same estimate of 1.01 billion poor in 2011, and a global poverty rate of 14.5 percent, which is the same as the World Bank’s official 14.5 percent.<sup>29</sup> Our regional profile is nearly identical with World Bank (2014b) as well as the country-by-country comparisons.<sup>30</sup> Despite slight differences with World Bank (2014b), an important advantage of our approach is that the analysis is based on readily available, public data

> 29 Similar World Bank (2014b), we assume both that the world population in 2011 is 6.96 billion and that there is no extreme poverty in rich countries (using the same classification as in in World Bank (2014b)). Throughout this paper we use the same aggregation and ‘line-up’ method as PovcalNet and explained in World Bank (2014b).

> 30 The only region where the estimate from our data differs slightly from the PovcalNet estimates is in the Latin America region. For more details on the data used for our estimation and how it differs from PovcalNet estimates, see Annex 1.

20