rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 1. Average Annual Cost per Pupil<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>Public general Public vocational Private general Private vocational<br>Household out of pocket cost Public cost<br>000 Rupiah<br><!-- End of picture text --> _Note: household cost is calculated from IFLS 3, while public cost is calculated by Ghozali (2006)._

# _Vocational school expansion plan_

In 2006, the Ministry of National Education began expanding vocational schools. According to their strategic plan (Ministry of National Education, 2006), the main reason for this policy is to increase the size of the labor force that is ready-to-work, especially among those who do not continue to tertiary education. In addition, the Ministry argues that because the unemployment rate of vocational graduates is lower than general graduates, increasing the share of vocational graduates in the mix would result in a lower overall unemployment rate.

The policy’s target is to achieve a 50:50 vocational to general student ratio by 2010, and a 70:30 ratio by 2015. As Figure 2 shows below, the ratio was 24:76 in 2007. In order to achieve this target, the ministry has instituted a moratorium on building new general schools. Instead, the government will construct new vocational schools and convert some general schools into vocational schools.

# _Enrollment trends_

Enrollment in vocational high school has been steadily declining. As shown in Figure 2, the number of vocational students has declined from about 1.6 million in 1999 to about 1.2 million in 2006. Over the same period, the proportion of high school students in vocational schools declined from 27% to just 20%, as more students choose general education over 6
# **Table 2. The relationship between classifications, assessor characteristics and protocols**

|(1|)|(2|)|(3|)|(|4)|
|---|---|---|---|---|---|---|---|
|coef.|dy/dx|coef.|dy/dx|coef.|dy/dx|coef.|dy/dx|
|Familiar with India<br>0.371 ***|0.091 ***|||||0.131 ***|0.032 ***|
|(0.015)|(0.004)|||||(0.017)|(0.004)|
|Knowledgeable on urban issues||-0.296 ***|-0.073 ***|||-0.193 ***|-0.047 ***|
|||(0.018)|(0.004)|||(0.018)|(0.004)|
|Structured protocol||||0.397 ***|0.097 ***|0.297 ***|0.072 ***|
|||||(0.014)|(0.003)|(0.017)|(0.004)|
|Observations<br>68,978||68,978||68,978||68,978||
|Log pseudolikelihood<br>-47030||-47129||-46904||-46857||
|Pseudo R2<br>0.004||0.0019||0.0067||0.0077||

_Note_ : Results are from a Logit model with the dependent variable being whether a place is judged to be rural (0) or urban (1) by an assessor, with one observation per assessor and place, and with standard errors clustered at the place. Coef. stands for estimated coefficient, dy/dx is the marginal effect, the numbers in the parentheses are standard errors clustered at the place level, and statistical significance is reported by *** if p < 0.01, ** if p < 0.05, and * if p < 0.1.

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34 The same effect Is achieved by the leasing-out of ejido parcels, a practice which appears to be at least as widespread as sharecropping. In Mummort's study, only 3 percont of households admitted to renting out their and but, according to key Informants In the ejido, no iess than 30 percent of ejidatarlos were actually **engaged** in loashg-out. In this ejido, renting-out Is percelved as a last resort because the anmual rent received amounts to less than the expected valu of the maize crop that wiN be cultivated on the rented land (Mwuert,1987:532).

71. It is mainly the poor who engage In renting-out because they cannot afford to sharecrop: In other words, the off-farm work on which they depend for survival generates the Income to cover family subsistence but Is not sufficient to cover the Input purchase entailed by sharecropping. While sharecrop arrangements take place primarily between different households In the same ejido, rental arrangements cover the leasing-out of ejido land to private farmers as well as transactions between ojldatarlos.

72. Although It Is kIpossible to quantify the extent of renting-out, there Is a broad concensus that the practice Is so widespread as to open up an enormous gap between the formal distributlon of land between tenure sectors (as reflected In the census data) and the real control that ejidatarlos and private farmers exercise over resources (Reyes Osorlo etal.,1974:451; Zaragoza & Maclas,1980:171; Warman,1977). One source esthuates that the llegal renting of ojIdo parcels affects ufrom 50 to 90 percent of all ejido land In several Irrlgation districts In Sonora and Sinaloa, and from 35 to 50 percent In the BajIo, In Mlchoactn and Jalisco" (Yates,1981a:181). The North American Congress for Latin America reported that, "In Sonora, about 70 percent of the ejidos are rented, especially In the irrlgated areas of NavaJoa and the Valle del Yaqul, while esthiates for... Sinaloa range between 40 and 80 percent" (NACLA,1976:18).
# **2.5.2 Measures to minimize bias and measurement error**

The literature on survey design has identified three types of potential bias and measurement errors based on whether they originate from non-responses, the enumerator, or the respondent (Collins, 2003). In what follows, we briefly describe the steps taken in designing and implementing the FAT survey to minimize these errors. Appendix A.5 provides a more detailed description of the measures implemented to minimize potential bias.

**Non-response bias.** To maximize response rates and minimize potential biases associated with non-response (Gary, 2007), we followed best practice procedures. First, we partnered with national statistical offices and industry associations to use the most comprehensive and updated sampling frame available. Second, we hired data collection companies or agencies which were supported by endorsement letters from local institutions and which had demonstrable experience in nationally representative firm-level surveys. Third, we followed a standard protocol in which each firm was contacted several times to schedule an interview. Fourth, we mostly used face-to-face or phone interviews, which usually have higher response rates than web-based interviews.<sup>9</sup>

**Enumerator bias and error counts.** The survey, training, and data collection processes were designed to minimize enumerator biases and data collection errors. First, we used closed-ended questions to make coding the answers a mechanical task, thereby eliminating the need for the enumerator to interpret the answers or exercise subjective judgement when coding them. Second, the same standardized training was implemented in each country in the local language, with enumerators, supervisors, and managers leading the data implementation. Third, we conducted a pre-test pilot of the questionnaire in each country using establishments not included in the sample. Fourth, to attain greater quality control during the data collection process, enumerators recorded the answers via _Computer-Assisted Personal Interviews_ (CAPI) or _Computer-Assisted Telephone Interviewing_ (CATI) software, and we regularly monitored the data collection process using standard algorithms to analyze the consistency of the data.<sup>10</sup>

> 9These procedures are in line with suggestions of good practice for implementation by (Bloom et al., 2016). We use online surveys only for Georgia and Croatia. In Georgia, we partnered with the National Statistical Office, which resulted in exceptionally high response rate. Face-to-face interviews were not possible during the pandemic. See Table A.16 for the mode and date of data collection in each country.

> 10Randomized survey experiments with household surveys have demonstrated that a large number of errors observed in _Pen-and-Paper Personal Interview_ (PAPI) data can be avoided with CAPI or CATI (Caeyers, Chalmers and De Weerdt, 2012). For Georgia and Croatia, we used Computer Assisted Web Interviewing (CAWI).

12
- OECD. 2009. “Growing Prosperity. Agriculture, Economic Renewal, and Development.” Draft Outcome Document from the Experts Meeting Innovating Out of Poverty. OECD, Paris, April 6–7.

- OECD-FAO. 2012. “Agricultural Outlook 2012-2012.” Annex B, Table B.1. Paris and Rome.

- Petesch, P. and I. Rodríguez Caillava. 2012. "Voices of Young Villagers in Sub-Saharan Africa." Background paper based on Muñoz Boudet, A. M., P. Petesch, and C. Turk with A. Thumala. _"On Norms and Agency: Conversations on Gender with Men and Women in 20 Countries.”_ Washington, DC: World Bank (forthcoming).

- Proctor, F.J. and V. Lucchesi. 2012. “Small-scale farming and youth in an era of rapid rural change.” IIED/HIVOS, London/The Hague.

- Renkow, M. and D. Byerlee. 2010. “The Impact of CGIAR Research: A Review of Recent Evidence”. _Food Policy_ .

- Rosenzweig, M. 2010. "Microeconomic Approaches to Development: Schooling, Learning, and Growth." _Journal of Economic Perspectives, American Economic Association_ Vol. 24(3): 81-96, Summer.

- Schultz, T.P. 1988. “Education Investments and Returns.” In _Handbook of Development Economics_ , eds. Chenery and Srinivasan (1988). Elsevier Science Publishers, Amsterdam.

- Selod, H. 2012. “Formalizing rural land rights in West Africa: Results from an impact evaluation in Benin.” Paper delivered at the World Bank _Conference on Land and Poverty_ 2012, April 23-26 2012. Washington, DC.

- Sutton, John and Nebil Kellow. 2010. _An enterprise map of Ethiopia._ International Growth Centre, London, UK.

- Svensson, J., and D. Yanagizawa. 2009. "Getting Prices Right: The Impact of the Market Information Service in Uganda." _Journal of the European Economic Association_ 7(2-3): 435-445.

- Tchale, H. 2012. “Pilot redistributive land reform in Malawi - Innovations and Emerging good practices.” Working Paper for the “Land Administration and Reform in SSA” study. World Bank, Washington, DC.

- Timmer, P. and S. Akkus. 2008. “The Structural Transformation as a Pathway out of Poverty: Analytics, Empirics and Politics.” Centre for Global Development, Working Paper 150. Washington, DC.

Torkelsson, A. 2012. “ Sex Disaggregated Data on Agriculture, Water and Food Security Lessons from the Kenya”. World Water Week Seminar on Global Practice in Promoting Gender Equality in the Water Sector, Stockholm, August 30, 2012.

United Republic of Kenya. 2010. “The Constitution of Kenya 2010.” National Council for Law Reporting.

- UWEZO Tanzania. 2011. “Are Our Children Learning?” Annual Learning Assessment Report. http://www.twaweza.org/uploads/files/ALA_UWEZO.pdf.

- Welch, F. 1970. “Education in production.” _Journal of Political Economy_ 78: 35–59.

- World Bank. Undated. “Sri Lanka Community Development & Livelihood Improvement Gemidiriya Project Analysis of Youth Inclusion and Participation in Gemidiriya.” World Bank, Washington, DC.

40
- Despite Latvia’s excellent stock of human capital, skilled workers and professionals do not seem to be involved in skill-intensive activities. As the chart below suggests, Latvia is weakest cc in terms of skilled workers engaged in either manufacturing or services.

**Figure 5 Human Resources Utilization, 2000-2001**

<!-- Start of picture text --> 10,0<br>9,0<br>8,0<br>7,0<br>6,0<br>5,0<br>4,0<br>3,0<br>2,0<br>1,0<br>0,0<br>Employment in medium-high and high-tech manufacturing (% of total workforce) in 2002 or the closest year<br>Employment in high-tech services in 2001 (% of total workforce)<br>Denmark Filand Ireland EU BulgariaCzech Rep. Estonia HungaryLithuania Latvia Poland Slovenia TurkeyRomania Slovakia<br><!-- End of picture text --> _Source_ : Eurostat, Trendchart 2003, available at <u>www.trendchart.org</u>

- Exports produced by technology-driven industries account for a growing share of exports in nearly all NMS’s, with the highest shares (and largest increases) in Hungary (more than 47% of all manufacturing industry exports to the EU in 1999), Slovakia (30%), Estonia (24%) and the Czech Republic (21%) (Eurostat COMEXT Database).  By comparison, technology-driven industries account for a negligible share of Latvian exports.

> Mexico have generally been stagnant.  Yevgeny Kuznetsov, “Mexico -- Towards a Second Generation NAFTA Agenda: Implications for National Innovation and Enterprise Upgrading Systems,” World Bank processed, February 2003.  Mexico faces many of the same challenges as Latvia.  Both are in danger of becoming low wage production sites for much larger, more productive, and wealthier neighbors.  And both are struggling to find ways to harness sizeable R&D establishments as a resource for wealth creation.  To date, both have made unsatisfactory progress in this regard.

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policies and the other related to internal migration. To our knowledge, it is the first paper to shed light on the unique interaction between school fees and child migration.<sup>2</sup> On the empirical front, we offer an empirical analysis of household survey data from China, which is a most interesting case to analyze the links between migration and access to education. Indeed, due to the _hukou_ (household registration) system, migrant workers in China are only granted limited access, if any, to the subsidized education and other social services available to local city residents. This contributes to many migrants leaving their children behind when migrating to cities for work. As a result, more than one-fourth of all children in China age 0 to 17—amounting to almost 70 million children—are estimated to be left behind by their migrant parents (UNICEF, 2013).<sup>3</sup> The focus on China offers relevant insights into important policy issues as recent studies overwhelmingly point to the detrimental impacts of parental migration on LBCs on educational outcomes including enrollment (Wang, 2014), grade attainment (Meyerhoefer and Chen, 2011), standardized test scores (Zhang et al., 2014; Zhou, Murphy, and Tao, 2014), and health outcomes including overweight and underweight measures (de Brauw and Mu, 2011) and anxiety levels and self-esteem (Bai et al., 2016). In addition to having worse education and health outcomes, Meng and Yamauchi (2015) find that LBCs spend less time studying after school, receive fewer tutoring lessons outside school, and are more likely to be enrolled in

> 2 The literature so far has mostly focused on the impacts of school fees on enrollment. For China, Yi et al. (2015) find that an unconditional financial aid program (fee-reduction program) had small effects on upper secondary school enrollment for Grade 9 students, but no effects for Grade 7 students; Shi (2012) finds educational fee reductions to be matched by increased voluntary household educational spending. For South Africa, Selod and Zenou (2003) provide theoretical evidence that increased school fees prevent the poor from having access to better schools, and Borkum (2012) finds that the elimination of school fees in poor districts had small positive effects on secondary school enrollment. De Brauw and Giles (forthcoming) discuss the effects of reducing barriers to migration on urban employment opportunities and rural enrollment for middle school graduates in China. See also Dang and Rogers (2008) for a review on studies related to households sending their children to private tutoring (classes with extra fees) and Glewwe and Muralidharan (2016) for a recent review of other studies on education in developing countries.

> 3 This number of left-behind children is slightly more than the total population of a country the size of France or the UK. Also note that migrant workers play a major role in the Chinese economy and account for 44 percent of total urban employment (World Bank and DRC, 2014).

3
DSSI initiative, granting temporary payment suspensions to many developing countries, but as for other creditors, these agreements did not reduce the nominal repayment burden. Even prior to 2020, we find that almost none of the distressed debt restructurings with Chinese policy banks and other state-owned entities involves face value debt reduction.<sup>2</sup> The typical deal involves a lengthening of maturities or grace periods. This “kicking the can down the road” is reminiscent of Western commercial banks in the 1980s, which long opposed face value debt reduction and only agreed to deeper debt relief as part of the US-led Brady deal initiative in the early 1990s. China’s current approach to crisis resolution also resembles the blueprint of the Paris Club, mainly in the 1980s and 1990s, when it opposed deep debt relief and rescheduled the same debts again and again, thus contributing to the “lost decades” in many low income countries, featuring low growth and unresolved debt overhang.

# **2. Credit events on Chinese overseas debts, 2000-2021: The database**

We build a comprehensive database of the external debt credit events of developing and emerging market sovereigns. We move beyond the usual focus on sovereign bonds and account for debt restructurings with bilateral creditors and, in particular, with Chinese lenders.

Due to the lack of official data, it is challenging to track credit events involving Chinese loans. Since Chinese creditors are (mostly) state-owned, their restructurings are not monitored by the major global credit rating agencies. China is not a member of the Paris Club or the OECD and does not report on lending and restructuring activities. We are not aware of an international financial institution that provides comprehensive data on defaults or restructurings on Chinese loans. In this void, researchers have stepped in to provide data hand-coded from official announcements, press reports and other sources.

This paper creates a data set on credit events on Chinese loans based on multiple sources created by researchers. The starting point was our own preliminary work in Horn et al. (2021), which we revise, update, and expand in a much more comprehensive data compilation here. Most importantly, we draw on AidData’s newly released Chinese Official Finance Database 2.0 (Custer et al. 2021), as well as on new data sets and academic work. On the latter, the contributions by Acker et al. (2020), Bon and Cheng (2020, 2021), as well as Kratz et al. (2019, 2020) were particularly helpful and substantive.

> 2 China does regularly cancel debts of aid-like Zero-Interest Rate Loans given out by the Ministry of Commerce to poor countries, but the amounts lent and forgiven tend to be very small. See Appendix I for details.

4
would be for practical purposes mostly irrelevant. This is an empirical question, but evidence does suggest the presence of upper bounds on working time for individuals. For example, using data from Ecuador, Newman (2002) shows that when women took advantage of new labor market opportunities in the cut flower industry, their total labor time remained constant, so that men had to provide higher amounts of work in unpaid tasks. The analysis of seasonality in time use in Malawi provided by Wodon and Kathleen Beegle (2006) also suggests that there may be labor scarcity at crucial periods of the year despite underemployment in many other periods. These examples suggest that the concept of time poverty is a potentially important one.

A few more comments may be useful before presenting an empirical illustration. Firstly, when measuring time poverty, we have data at the individual level, while in most cases, when measuring income or consumption poverty, we only have aggregate data at the household level. This means that for time poverty, we can look at intra-household allocations and at the impact of intra-household time inequality on time poverty.

Secondly, there is always a difficulty in traditional poverty measurement in comparing the welfare of households of different sizes and composition, because of differences in needs between individuals, as well as economies of scale in consumption. To some extent, these difficulties persist for the measurement of time poverty, as there may be differences in needs for time poverty measurement, for example if children need more rest and leisure time than adults. By contrast, even though there are clearly economies of scale at the household level in terms of the amount of time required to perform some domestic tasks that benefit all household members at once, this is not problematic for the measurement of time use because we observe the hours of work of each individual.

Thirdly, an alternative definition of time poverty is obtained by defining as time-poor those individuals who have less than a certain amount of time for leisure and rest. This approach is perfectly equivalent to ours because the amount of time available in one day is fixed. If the amount of time available in a day were not bounded, we would need to use the “above the line” approach both for measurement and for assessments of the robustness time poverty comparisons, as done in the case of pollution and CO2 emissions by Paul Makdissi and Wodon (2004).

Fourthly, what is perhaps more arbitrary when analyzing time poverty as compared to consumption poverty is the choice of the time poverty line above which individuals are considered as overworked or time-poor, and thereby lacking enough time for leisure and rest. In 11
that pricing at a lower level would result in a duty, and so had a strong incentive to price at the minimum posted price so that the SSG would not be invoked. Another reason for low official notifications of SSG use appears to be that many members have ignored the requirement to report use of the SSG to the WTO’s Committee on Agriculture within 10 days of implementation (Hallaert 2005). Despite this, Hallaert concludes that the use of the SSG has increased over time as WTO members become more familiar with its provisions.

Another possible reason for limited use of the SSG is that it was frequently not politically attractive because of the weak relationship between its mechanical formulas and policy makers’ goals. While the quantity-based SSG might permit the use of safeguards following a crop failure, policy makers may not have wanted to use this measure under these circumstances because of pressure from consumers concerned about high food prices.

We now turn to our empirical framework which permits a more thorough assessment and comparison of the implications of the proposed P-SSM and Q-SSM measures.

# **Empirical Framework and Scenario Design**

_Modeling Framework:_ For our analysis, we build on a paper by Valenzuela et al. (2007), which uses a stochastic simulation approach to validating Computable General Equilibrium (CGE) models, with a focus on the world wheat market. In this study, we employ a more recent version of the GTAP model that has been specifically tailored to agricultural applications (Keeney and Hertel 2005). Nicknamed “GTAP-AGR”, it incorporates segmented factor markets to mimic short run rigidities in supply response and more detailed information about supply and demand elasticities pertinent to agricultural production and food consumption. We use the Armington import demand specification with econometrically estimated elasticities of substitution between varieties of wheat in the model to allow for differentiation between wheat produced in different countries (Hertel, Hummels et al. 2008). As we will see below, product differentiation by origin plays an important role in the price-based SSM.

11