rafmacalaba/datause-encoder-data huggingface dataset:
The main objective of this paper is to assess the educational effects of the conflict in Timor Leste. First, we analyze the short-term consequences of the last wave of violence in 1999 on school attendance and on grade deficit rates observed in 2001 among children who were of primary school age in 1999. Second, we examine the medium term consequences of the conflict on school attainment of the same cohort observed again in 2007. Third, we investigate the long term consequences of the most intense early years of the conflict between Timor Leste and Indonesia on school attainment outcomes of exposed individuals observed in 2007. Finally, we assess the average effect of the conflict on primary education outcomes in the longer term by looking at the average effect of exposure to the conflict as a whole. We focus on primary schooling because most individuals in Timor Leste (65 percent) have at most only primary school education (TLSS 2007b). The empirical study is based on two cross-sectional household surveys: the Timor Leste Living Standard Measurement Surveys (TLSS), conducted in 2001 and 2007, jointly by the National Statistics Directorate in Timor Leste and the World Bank. They are both nationally representative household surveys, and include a broad range of individual and household level indicators.

The TLSS 2001 surveyed 1800 households from 100 Sucos (villages) (covering nearly 1% of the population). The survey was conducted between August and November 2001. Interestingly, this survey includes very detailed information on the exposure of individuals and households to the wave of violence in 1999. Respondents were asked whether they were displaced and whether their house was destroyed due to the violent events that followed the withdrawal of Indonesian forces in 1999. We make use of this valuable information to get insights on the characteristics of individuals and households affected by the violent events in 1999.

The TLSS 2007 covered a sample of 4,477 households from all the 498 Sucos that form Timor Leste. The TLSS 2007 was undertaken over a period of 12 months between December 2007 and January 2008. The survey was in fact launched in March 2006 but had to be suspended due to the outbreak of internal violence in the country (mostly in Dili). The survey was resumed in January 2007 and conducted over one year.<sup>6</sup> The TLSS 2007 contains the usual information included in a comprehensive household survey. But contrary to the TLSS 2001, the 2007 household survey does not contain direct information on violence exposure. In order to identify individuals and households affected by the conflict, we make use of data on events and violations contained in the Human Rights Violations Database (HRVD). This dataset is part of the CAVR Timor-Leste Data Publication, developed jointly by the Human Rights Data Analysis Group (HRDAG) and the

> 6  All households interviewed in 2006 (351 households) were revisited and re-interviewed in 2007. Those not found at the time of the new interview (34 households) were replaced with new households. For more information see <u>http://dne.mof.gov.tl/TLSLS/AboutTLSLS/index.htm.</u>

12
# **_3.3. Levels and Impacts of Conflict_**

Comparing the two data sources hints at significant underreporting of conflict in PODES (see Table 6). Of the 25 villages, PODES reported only four as having experienced conflict in the previous year: one out of 13 in East Java (7.7%), and three out of 12 in NTT (25%). While we would not have expected all of the villages to have registered as having conflict in the PODES – because not all of the conflicts we tracked took place in the year 2002 – this does suggest that PODES is not capturing all conflicts.

**Table 6: Comparison of Data Sources - Local Conflict Mapping**

|District<br>(%_desa/kelurahan_<br>reporting conflict<br>PODES)|Overall #<br>_desa/_<br>_kelurahan_<br>(_kecamatan_)|_Desa_<br>_(kecamatan)_<br>studied<br>in field<br>research|# (%) selected<br>_desa_<br>Reporting<br>Conflict<br>(PODES)|# (%) selected<br>_desa_<br>with Conflict<br>(based on Field<br>Assessment)|
|---|---|---|---|---|
|**East Java (3.5%)**|||||
|Ponorogo (1.7%)|303 (21)|6 (2)|1(16.7%)|6 (100%)|
|Pamekasan(3.2%)|189 (13)|7 (2)|0 (0%)|6 (85.7%)|
|**NTT (11.6%)**|||||
|Sikka (14.3%)|140 (8)|6 (2)|2(33.3%)|3 (50%)|
|Manggarai(12.0%)|375 (16)|6 (2)|1(16.7%)|6 (100%)|
|Sources:<br> PODES20|03 andKDP& Co|mmunity Conflict|NegotiationStudy||

The case of Pamekasan, a district on the island on Madura, just off the Javanese coast near Surabaya, provides a stark example. None of the seven villages studied in Madura were reported in PODES as having experienced conflict in the past year. Yet, in six of the seven villages, the World Bank researchers tracked conflicts that had taken place in 2002,<sup>9</sup> including mass sickle battles between communities and the burning of alleged _dukun santet_ (witch doctors). Similarly, in Ponorogo, close to Central Java, PODES reported only one village as

> 9 In the other village, the conflict followed had taken place previous to 2002 and the researchers determined that no significant conflict had taken place in 2002.

17
# **Table 2. Variable definitions**

|Variables|Definition|
|---|---|
|F_c<br>F_cj|The average of foreign ownership shares of all sample firms in the country.<br>The average foreign ownership share of the country-industry-year cell minus that of the<br>country cell.|
|Female-Manager|Equals to 1 if the top manager is a female, and 0 otherwise.|
|Female entrepreneur<br>Small3<br>Large3|Equals to 1 if both the top manager and any of the owner are female, and 0 otherwise.<br>The firm’s number of permanent employees three years ago was less than 20.<br>The firm’s number of permanent employees three years ago was more than 100.|
|age6_10<br>age10plus|The firm’s number of employees three years ago was 20-100 (more than 100).<br>The firm’s age is between 6 and 10 years (or 10 or more years).|
|Capital/Labor ratio|Equals to K/L, and K is replacement costs of land and machine (million dollar), L is<br>number of labors.|
|Skilled workers’ share|The ratio of skilled workers / (skilled workers + unskilled workers)|
|Corruption|City-industry-level average of the firm’s answer on whether corruption constitutes an<br>obstacle, ranking from 0 to 1.|
|Formal Finance<br>Trade Credit|City-industry share of firms having a line of credit or loan from a financial institution.<br>City-industry average of the proportion of total annual sales of goods and services that<br>are paid for after delivery.|
|Inf. Competition|<br>City-industry share of firms who say that they directly compete with informal firms.|
|Outage|City-industry share of firms that experienced a power outage in the survey year.|
|Web|City-industry share of firms that answer that they use websites to conduct business.|
|Cap. Agglomeration|The share of firms whose number of employees exceeding 50, as a proxy of capacity<br>agglomeration.|
|Lngdppc<br>Education|Log of GDP per capita, in constant USD, from World Development Indicators.<br>5th pillar: Higher education and training, from World Economic Forum (WEF).|
|Urbanization<br>Barrier: time required to start a<br>business for females (in days)|Urban population (% of total), from World Development Indicators.<br>Time required to start a business, female (days), from Doing Business for “time required<br>to start a business for female entrepreneurs”.|
|Barrier: financial institution<br>account, female (% for age<br>15+)|Financial institution account, female (% age 15+), from Global Findex of World Bank.|
|Empowerment indicator:<br>Fem. labor participation rate|Labor<br>force<br>participation<br>rate,<br>female<br>(%<br>of<br>female<br>population<br>above ages 15), from World Development Indicators.|
|Empowerment indicator:<br>Fem. Primary completion rate|Primary completion rate, female (% of relevant age group), from World Development<br>Indicators.|

24
to address this concern. First, we characterize the attrited sample by running a regression where the dependent variable equals one if the household did not respond to the second wave on all the covariates before the rollout. survey characterizing migrants program’s As shown in Table B.1 in Appendix B, five of the 22 covariates appear to be correlated at a statistically signficant level, including having a partner in Venezuela, years of education before migration, gender, age, and length of residence in Colombia. Athough the estimated coefficients are small, they suggest that attrited individuals were more vulnerable and less rooted in Colombia.

Secondly, in Table B.2, we estimate PEP’s effects on the fertility rates of individuals who were no longer in the sample by the second wave. Although we do not have data for these respondents in the second wave, we have their responses in the first wave. In line with our main results, we find that when they were interviewed in 2020, PEP reduced the probability of their having children zero years of age.

Finally, we examine whether attrition rates in the second survey wave are correlated with our outcomes of interest during the first survey wave. As illustrated in Table B.3, they are not. This that those individuals not in the second wave were neither more nor implies less likely to have a child less than one year old, one year old, or two years old before they dropped out of the survey.

_Excluding households along the Colombian-Venezuelan border_ We also experiment with excluding from the sample individuals along the ColombianVenezuelan border to avoid including Venezuelan residents who only visited Colombia for health care purposes. Thus, we exclude individuals residing in Colombian departments that border Venezuela and we re-estimate our models. Results from this exercise are in Table C.1. We continue to find evidence of fertility declines as captured by a similarly sized reduction in the likelihood of having a child less than one year old or one year old as in Table 2, thereby supporting our main conclusions.

13
<!-- Start of picture text -->
| Sector si Sectoral Elasticity of Substitution Elasticity of Substitution<br>Agriculture, hunting, forestry, and<br>pe 11.23<br>fishing<br>Mining and quarrying -0.72<br>Manufacturing 2.80<br>Electricity,. gas, and water supply -38.82 -2.33<br>Financing, insurance, real estate,<br>and business. services. -0.40<br>Community, social, and personal<br>. 2.88<br>services<br><!-- End of picture text -->

||<br>Sector<br>si|Sectoral ElasticityofSubstitution|ElasticityofSubstitution|
|---|---|---|
|Agriculture, hunting, forestry,and<br>pe<br>fishing|11.23||
|Miningand quarrying|-0.72||
|Manufacturing|2.80|-2.33|
|.<br>Electricity,gas, and watersupply|-38.82||
|Financing, insurance, real estate,<br>.<br>.<br>and business services|-0.40||
|Community, social, and personal<br>.<br>services|2.88||

<!-- Start of picture text --> Share of Syrians with work permit<br>80%<br>7% ———.:_*.S_ —_—_<br>60%<br>50%<br>40%<br>30%<br>20%<br>wr<br>o%<br>Minieg<br>agtcme. tng Migandcuanirgond querryig Menacung Manufecturing Sereeectgard ForenPann rues,rr, gran Community<br>business services<br>2017 2018 TM2019<br><!-- End of picture text --> <!-- Start of picture text --> Syrians with work permit<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0 o_o mm =<br>Agriculture, Mining and Manufacturing Electricity, gas, - Financing, Community,<br>funnest quarrying and water supply eeecal corea os<br>business services<br>2017 2018 TM2019<br><!-- End of picture text -->
Growth, Distribution, and Poverty in Africa: Messages from the 1990s Luc Christiaensen, Lionel Demery, and Stefano Patemostro The World Bank, 1818 H Street NW, Washington, D.C. 20433 March 2002 Acknowledgements: This paper synthesizes and builds on the work of a large team of researchers who contributed to a series of _Poverty Dynamics_ country studies in Africa, coordinated by the Africa Region of the World Bank. It benefits enormously from their careful and competent analysis. The authors are grateful for helpful comments from Alan Gelb and especially acknowledge the responsive and enthusiastic research assistance of Angelica Salvi. The work was supported by a group bilateral donors: Italy, the Netherlands, Switzerland, the U.K., and the U.S.
the benefits received by the poor and vulnerable are not high enough to overcome the impact of direct and indirect taxes. The marginal contribution of the different transfer programs varies, depending on the size and progressivity of the programs. For instance, the state allowance for children has the biggest distributional impact, even though it is not the most progressive, simply because it is the largest program (Figure 12A). When it comes to the combined effect of taxes and social spending, we find that the marginal contribution of social transfers to poverty reduction is reversed by the combination of direct and indirect taxes and contributions (Figure 12B).

<!-- Start of picture text --> Figure 12. Romania. Poverty Headcount Rate, 2016<br>A. Progressivity and Redistributive effect of Taxes B. Marginal contributions to Poverty Reduction<br>and Contributions (from market to consumable income)<br>(from market to consumable income)<br>0.20 0.025 0.05 0.04<br>0.15 0.020 0.04<br>0.10<br>0.015 0.03<br>0.05<br>0.00 0.010 0.02<br>-0.05 0.005 0.01<br>0.00<br>-0.10<br>0.000 0.00<br>-0.15<br>-0.005 -0.01 0.00 0.00<br>-0.20 -0.01<br>-0.01-0.01<br>-0.25 -0.010 -0.02<br>-0.03 -0.02-0.02<br>-0.04 -0.03<br>Kakwani Coefficient Marginal Contribution<br>Source: World Bank estimates based on the Romanian 2016 HBS.<br>Kakwani coefficient Redistributive effect<br>VAT<br>Regressive <---------> Progressive Unequalizing <-------> Equalizing<br>Excises fuel All taxes VAT<br>Contribution to Poverty Reduction<br>All direct taxes All contributions Excises tobacco Excises alcohol Excises energy All indirect taxes<br>Excises fuel<br>(Change in poverty headcount at US$5.50 PPP) All direct taxes All contributions Excises tobacco Excises alcohol Excises energy All indirect taxes<br>All direct taxes and contributions All taxes and contributions pensions<br>Direct transfers excl contributory All direct taxes and contributions<br><!-- End of picture text --> In terms of in-kind transfers for education and health, we must caution that our analysis does not address the quality of such spending. We use government expenditure data on the various forms of education and health services to estimate unit costs of these programs. The analysis thus assumes that the actual benefit received by individuals is equal to the amount spent per student or per patient. Although we differentiate between rural and urban areas for primary and secondary education, a finer disaggregation was not possible. Given that the quality of school infrastructure, teachers, and health clinics and hospitals varies across the country, this is a clear limitation of the analysis.

22
and non-income factors. What is needed is a set of tools for consistently aggregating the empirical micro relationships to throw light on the macroeconomics of human development. This paper aims to help bridge this gap, by developing and implementing a micro-based decomposition method for investigating the proximate determinants of aggregate human development outcomes. The aim is to measure the relative importance of growth in mean incomes versus changes in the distribution of income and “non-income” characteristics of the population, such as maternal schooling. In essence we provide a growth-redistribution decomposition method for HD indicators analogous to the widely used Datt-Ravallion (1992) decomposition for poverty measures. However, in the latter case there is a precise mathematical link between the measure of poverty and the mean and distribution. That is not the case with HD indicators, which makes the decomposition a more difficult task. In investigating income distributional impacts on human development we use non-parametric regression methods for the HD indicators. This allows a high degree of flexibility in representing the underlying nonlinearity in the micro relationship between human development attainments and household incomes, so as to estimate the decomposition. The modeling and decomposition methods developed in this paper are available in a user-friendly STATA program.

As empirical applications, we use our decomposition method to study the proximate determinants of changes over time in schooling attainments and the inter-group disparities in schooling found in two developing countries. In the context of schooling, there are reasons to think that both mean income and its distribution will matter to aggregate outcomes given the likely nonlinearities involved (whereby marginal income effects on HD outcomes are likely to be lower the higher the level of income). However, past research on micro data has also pointed to

> 6 For a survey see Strauss and Thomas (1995).  Recent examples of micro approaches to studying HD outcomes include Bhargava (1999), Filmer and Pritchett (1999), Glewwe and Jacoby (1998).

3
Table C12: Employment shares across locations

|||Agric|ulture|||Ser|vices||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Panel A: DOU**|||||||||
|Urban center|-0.614***|-0.605***|-0.528***|-0.182***|0.465***|0.456***|0.375***|0.131***|
||(0.004)|(0.004)|(0.004)|(0.003)|(0.004)|(0.004)|(0.004)|(0.003)|
|Urban cluster|-0.245***<br>(0.004)|-0.242***<br>(0.004)|-0.207***<br>(0.004)|-0.070***<br>(0.003)|0.198***<br>(0.004)|0.194***<br>(0.004)|0.159***<br>(0.004)|0.055***<br>(0.003)|
|**Panel B: DB**|||||||||
|Core|-0.436***|-0.427***|-0.355***|-0.118***|0.330***|0.323***|0.252***|0.079***|
||(0.004)|(0.004)|(0.004)|(0.003)|(0.006)|(0.006)|(0.006)|(0.004)|
|Suburb|-0.308***|-0.302***|-0.254***|-0.080***|0.161***|0.158***|0.111***|0.024***|
||(0.006)|(0.007)|(0.007)|(0.004)|(0.007)|(0.007)|(0.007)|(0.004)|
|Town|-0.096***|-0.094***|-0.075***|-0.027***|0.061***|0.059***|0.042***|0.012***|
||(0.005)|(0.004)|(0.004)|(0.003)|(0.004)|(0.004)|(0.004)|(0.003)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Education|No|No|Yes|Yes|No|No|Yes|Yes|
|Employment|No|No|No|Yes|No|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.342|0.350|0.389|0.791|0.204|0.209|0.260|0.731|
|Panel B Adj.R<sup>2</sup>|0.221|0.234|0.305|0.782|0.120|0.129|0.209|0.726|
|Nr. of countries|19|19|19|19|19|19|19|19|
|Nr. of hh|372027|372027|372027|372027|372027|372027|372027|372027|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a share of household workers employed in a respective sector. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

94
**Table 1** . Estimation results from a mixed logit model to predict WTP. The model is estimated using data on all respondents (1,373 respondents), predicting the joint probability of the five choice made by each individual (total 6,865 choices). Note that both models also incorporate observed heterogeneity in the program cost coefficient and control for program misconceptions; see Supporting Information for full estimation results.

|||Model 1<br>Correlated<br>|: Mixed Lo<br>Random P<br>|git with<br>arameters<br>|Model 2:M<br>Progra<br>|odel 1 + C<br>m Misconc<br>|ontrols for<br>eptions<br>|
|---|---|---|---|---|---|---|---|
|Variable||Est.|t-value|p-value|Est.|t-value|p-value|
|Avoided forest<br>|Mean|5.27|7.39|0.00|4.97|6.78|0.00|
|loss, WTP in $ per 1 % point|Std.<br>dev.|8.67|2.95|0.00|8.57|3.03|0.00|
|Reduced species<br>|Mean|3.03|2.07|0.00|3.19|2.18|0.03|
|threats, WTP in $ per 1 % point|Std.<br>dev.|16.30|3.68|0.00|15.19|3.73|0.00|
|LF,S||-3.72|-3.95|0.00|-3.47|-3.63|0.00|
||Mean|-1.06|-4.14|0.00|-1.13|-4.45|0.00|
|Program cost|Std.<br>dev.|-0.46|-4.87|0.00|0.44|5.05|0.00|
|StatusQuo,WTP i|n$|-95.81|-3.75|0.00|-134.96|-4.70|0.00|
|SQ+1||1.08|4.72|0.00|1.01|4.28|0.00|
|Log-likelihood||-6|382.01||-6|372.08||

versus removing the status quo, regardless of the attributes of new conservation program options, and it could also pick up possible non-linearities in preferences.

The estimation results show patterns consistent with both economic theory and common sense. For example, the mean WTP for avoided forest losses and reduced species threats is positive and statistically significant. This shows that additional WTP for increased protection is positive and sensitive to the scope of benefit achieved. The coefficient for program costs is also consistently statistically significant, indicating that the costlier program options, keeping everything else constant, are less likely to be selected, as one would expect.

19
tensive margin impacts of SPS and TBT inclusion in PTAs being stronger for smaller firms. Our paper relates to three literatures. A first extensive literature studies the impacts of preferential trade agreements on trade using structural gravity equations. Some selected key papers are Baier and Bergstrand (2007); Baier et al. (2014); Mattoo et al. (2017); Larch et al. (2018); Baier et al. (2019); Correia et al. (2020); Disdier et al. (2020); Anderson and Yotov (2020). These papers provide evidence of a strong positive impact of PTAs on bilateral trade based on state-of-the-art estimates of gravity equations by PPML. Mattoo et al. (2017) use the same World Bank Deep Trade Agreements database that we exploit and show that deep agreements lead to more trade creation and less trade diversion than shallow (not deep) agreements.<sup>8</sup> We innovate relative to previous studies by being the first to examine the impact of SPS and TBT provisions in PTAs on exports at the firm-product level. To our knowledge, only two papers examine the impact of SPS and TBT liberalization in the context of PTAs between high-income (mostly the European Union) and developing countries and focusing on trade at the product level and find that such liberalization fosters trade, especially when enforceable international regulatory mechanisms are included (Disdier et al., 2014, 2020).

The second literature studies the role of harmonization and mutual recognition of standards for trade. Conceptually, discussions on the contrast between mutual recognition and harmonization of standards for trade are provided in Baldwin (2000) and Costinot (2008). Empirically, harmonization and mutual recognition of standards among high-income countries are shown to increase trade, with a larger effect for mutual recognition by Chen and Mattoo (2008). Disdier et al. (2014) find that the harmonization of TBT standards in PTAs between high-income countries and developing countries has a detrimental effect on their trade when the harmonization is done on the basis of regional standards. A few studies take a micro perspective and examine impacts on firms. Portugal-Perez et al. (2010) show that the harmonization of EU electronics regulations with international standards led to the en-

> 8The paper uses several measures of the depth of PTAs based on the extent to which the different policy areas are covered and are legally enforceable.

6