rafmacalaba/datause-encoder-data huggingface dataset:
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- Jerome, E. M., Hamre, B. K., & Pianta, R. C. (2009). Teacher–child relationships from kindergarten to sixth grade: Early childhood predictors of teacher‐perceived conflict and closeness. _Social Development_ , _18_ (4), 915-945.

<mark>Johnson, T. C., Stoner, G., & Green, S. K. (1996). Demonstrating the experimenting society model with classwide behavior management interventions.</mark> _<mark>School Psychology Review</mark>_ <mark>,</mark> _<mark>25</mark>_ <mark>(2), 199-214.</mark>

- <mark>Jones, S. M., Bouffard, S. M., & Weissbourd, R. (2013). Educators' social and emotional skills vital to learning.</mark> _<mark>Phi Delta Kappan</mark>_ <mark>,</mark> _<mark>94</mark>_ <mark>(8), 62-65.</mark>

- <mark>Jones, S. M., & Bouffard, S. M. (2012). Social and emotional learning in schools: From programs to strategies. Social Policy Report.</mark> _<mark>Society for Research in Child Development, 26</mark>_ <mark>(4).</mark>

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- Kember, D. & Leung, D. Y. P. (2005) The influence of active learning experiences on the development of graduate capabilities, Studies in Higher Education, 30:2, 155-170

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52
A key question is how should the lobbying function be formulated in the case of a customs union. We know that if this choice is made arbitrarily, we can obtain a:iy ranking of FTA and CU we like. Therefore, we opt for as neutral a choice as possible.<sup>4</sup> In the case of a customs union, we write the lobbying function for good 3 as where an asterisk is used to distinguish country B's variables and superscript c is used to indicate union-wide variables under a customs union. Accordingly, **t'** is the common external tariff and IC - 13 + 13. We assume that the regional institution is equally responsive to lobbying by agents of the two members. Who invests into lobbying is not important. What matters is how much.

Under an FTA, lobbying is done at the national level and tariffs in the two member countries may be different. We write For ease of comparison, we assume that the lobbying functions under FTA are identical in the two countries.

To maintain neutrality, we assume that functions h3( ) and g3 (') are related with each other according to 4We draw heavily on Rodrik and Panagariya (1993) who, in turn, draw on Rodrik (1986).

l16
household surveys.<sup>23</sup> External validity is also less of a concern because censuses are designed to enumerate the entire population—although this is not always achieved in practice<sup>24</sup> —whereas household surveys often have incomplete coverage by design, as well as being more likely to exclude minority groups due to considerations of security, remoteness and cost (Carr 2013). Finally, because of the number of observations in census data, samples drawn from censuses include large samples of minority groups. Hence, an intersectionality study that uses census data is likely to have greater statistical power compared to one that uses a stratified survey sample.

# **_3.2. Methodology_**

In analyzing how gender and ethnicity play a role on educational outcomes, both together and separately, we use the following reduced form model: where EDUi is an education outcome for individual i; Gi and ETHi are individual i’s gender and ethnicity, respectively; (Gi ∗ETHi) is an interaction term; and Xi is a vector of control variables for individual i, which includes a dummy for rural residence, respondent’s age and age-squared, and a set of dummies for age cohort.<sup>25</sup> The error terms εi are clustered at the household level.

Using the “reference” and “minority” framework described above, the main explanatory variables of interest are Gi and ETHi, which are equal to one for females and for individuals in the minority group, respectively (zero otherwise). Therefore, if β1 < 0 in Equation (1), women fare worse than men in their educational outcomes, whereas β2 < 0 indicates that a minority

> 23 A household survey specifically designed for recovering the mechanisms of exclusion may provide a more detailed explanation for what we find in this paper. This may be an approach for a future paper, but keeping in mind that it may have external validity costs and may replace some limitations of using census data with the complications of household surveys.

> 24 As noted by Buettner and Garland (2008, cited in Carr 2013), it has been shown that some censuses exclude minorities, marginalized groups, non-citizens and specific categories of prisoners or rebels because of political reasons. According to the United Nations Statistical Division, however, census data collection in developing countries has improved since 2000 due to technological innovations in mapping, enumeration and data capture (Carr 2013).

> 25 Age cohort is a set of dummy variables for ages 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, and 70+. These were included as additional age controls in order to account for potential impacts of education policy shifts on different age groups.

13
Following the riots, legislation was passed to curb the power of le.nders and protect farmers' land. In the early 1900s, the first public sector credit societies were established, the primary agricultural cooperative credit societies (PACs). Shortly thereafter, the state land mortgage banks were founded, which later became the land development banks (LDBs). In 1935, formal recognition of the importance of agricultu<,al lending was recognized with the establishment of the Reserve Bank of India, with a separate agricultural credit department.

After independence, the All-India Rural Investment Survey found that less than nine percent of farmers' cash borrowing in 1951-52 was from the formal sector (see Table 1). The share of the cooperative sector was only three percent. Moneylenders still provided about 83 percent of cash loans. Broader access to institutional credit became the guiding principle. In 1969, the large commercial banks were nationalized, and the Reserve Bank of India established minimum guidelines on the share of agricultural lending and the number of rural bank branches. The regional rural banks were instituted in 1975 to cater to poor households with limited access to commercial banks. And the National Bank for Agriculture and Rural Development (NABARD) was created for the refinancing of agricultural credit.

Table 1 shows how successful the supply leading approach had heen by the beginning of the 1970s in displacing moneylenders from the rural credit system. By 1971, the institutional sources provided nearly 32 percent of all cash credit. Debt from moneylenders had decreased to 36 percent of the borrowings, and other sources such as 6
**Table 9: Payments Schedule for Institution Building**

|**Type of Appropriations, in € mn, current prices**|**2007**|**2008**|**2009**|
|---|---|---|---|
|CommitmentAppropriations2007<br>11|36%|25%|20%|
|CommitmentAppropriations2008<br>8||36%|25%|
|CommitmentAppropriations2009<br>4|||36%|
|_Total Calculated Payment Appropriations:_|**4**|**5**|**5**|

# _Administration_

Commitments for _Administration_ come to a total of €346 mn (in 2004 constant prices) for both Bulgaria and Romania, in the period 2007-2009. The administrative expenditures cover only accession-related expenditures such as increased interpretation and translation needs. As in the case of the ten NMS, payment appropriations for Administration are not incorporated in the total payments from the EU to Bulgaria as no administrative costs are specifically allocated to the country.

# _Compensation_

According to the Accession Treaty some €258 mn in current prices will be allocated for Bulgaria in the form of temporary budgetary compensations. Budgetary compensations are earmarked to avoid worsening of Bulgaria’s fiscal stance due to the application of the own resources rules in full from the first year of accession. Their aim is to support the implementation of the Schengen agreement and serve as a cash flow facility for the national budget. The exact amount for each of the years 2007-2009 has been determined at the negotiations and will be paid out totally in the year of commitment. For 2007, the appropriations for payments are € 129 mn in current prices, which presents 0.5% of GDP in that year. The size of the temporary budgetary compensations decreases in the next two years to 0.2% of GDP in 2009.

# **_Pre-Accession Aid_**

Pre-accession aid is the second pillar comprising the inflow of funds from the EU to Bulgaria. EU pre-accession aid consists of three main vehicles - ISPA, PHARE, and SAPARD – each described in Box 2. The three pre-accession funds aim to finance actions that would prepare future members (including Bulgaria) for catching up with EU standards and legislation. They also serve as learning instruments in preparation for the use of EU Structural and Cohesion Funds.

**Box 2: Pre-accession Financial Instruments**

Enormous investment is required if the candidate countries are to adapt their standards, especially their industrial and environmental norms, so as to comply with Community legislation when they join the Union. The pre-accession aid involves three main components: (i) PHARE (originally called Poland and Hungary Assistance for the Reconstruction of the Economy) is a pre-accession fund. Its main aim is to finance institution building in all aspects of accession, including the management of the structural finds, telecommunications regulations, customs, health and safety, among others. Phare projects’ goals are to adapt the candidate countries' administrative and legal systems and to develop their infrastructure.

11
**Table 29: Piecemeal Sensitivity Analysis for Preferential Liberalization between Tanzania and the EU in Professional Services**

||**Par**|**ameter val**|**ue**|**% Welf**|**are Chang**|**e (EV)**|
|---|---|---|---|---|---|---|
|Parameter|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj)– services sectors|1.5|3|4.5|0.26|0.04|0.03|
|σ(qi, qj)–goods sectors||See below||0.05|0.04|0.04|
|σ(va, bs)|0.625|1.25|1.875|0.03|0.04|0.05|
|σ(D,M)|2|4|6|0.04|0.04|0.04|
|σ(L,K)|0.5|1|1.5|0.04|0.04|0.04|
|σ(A1,...An)|NA|0|0.25|NA|0.04|0.04|
|σ(D,E)|2|4|6|0.04|0.04|0.04|
|εTZA|Central va|lues of all 4 s|ets of ε|0.04|0.04|0.04|
|εEU|parameters|are listed in|table 6B.|0.03|0.04|0.05|
|εAFR|Lower and|upper value|s are 0.5|0.04|0.04|0.04|
|εROW|and 1.5 t|imes central|values.|0.04|0.04|0.04|
|θr|NA|0|1|NA|0.04|0.03|
|θr - CRTS model|NA|0|1|NA|0.01|0.00|
|θm|0.025|0.05|0.075|0.04|0.04|0.04|
|σ(qi, qj)–goods sectors|||||||
|Processedfood|4.8|9.6|14.4||||
|Beverages & tobacco products|1.5|2.3|3.1||||
|Textile &leatherproducts|2.8|3.6|4.4||||
|Manufacture ofbasic &industrialchemicals|1.7|2.5|3.3||||
|Manufacture of fertilizers & pesticides|1.6|2.4|3.2||||
|Petroleum refineries|2.8|3.6|4.4||||
|Glass & cement|2.3|4.6|6.9||||
|Ironsteel&metalproducts|3.7|7.3|11||||
|Mining & quarrying|2.8|3.6|4.4||||
|Grain milling<br>**Key:**|2.4|3.2|4||||

σ(qi, <u>qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services</u> <u>σ(D, M): Elasticity of substitution between domestic and imported varieties</u> <u>σ(L, K): Elasticity of substitution between primary factors of production in value added</u> σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods <u>σ(D, E): Elasticity of transformation (domestic output versus exports)</u> εTZA: Elasticity of national service firm supply with respect to price of output εEU: Elasticity of EU service firm supply with respect to price of output εAFR: Elasticity of AFR service firm supply with respect to price of output εROW: Elasticity of Rest of World service firm supply with respect to price of output θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports <u>Source: Authors’ estimates.</u> 97
in order to protect them from future climate risks. Mainstreaming adaptation into the larger developmental processes will help the city achieve economic growth as well as assist in poverty alleviation.

# References

Annez, P., Bertaud, A., Patel, B., Phatak, V. (2010). Working with the Market: Approach to Reducing Urban Slums in India. Policy Research Working Paper No. 5475. World Bank, Washington, DC. Brouwer, R., Akter, S., Brander, L., Haque, E. (2007). Socioeconomic vulnerability and adaptation to environmental risk: A case study of climate change and flooding in Bangladesh. Risk Analysis, 27(2), 313‐326. Dutta, D., Herath, S., Musiake, K. (2003). A Mathematical Model for Flood Loss Estimation. Journal of Hydrology, 277, 24‐49. GoI (2011), Census 2011 Tables on Mumbai. <u>http://www.censusindia.gov.in/Tables_Published/Basic_Data_Sheet.aspx</u> ‐ ‐ GoI (2011), Slums of India – Census 2011. censusindia.gov.in/2011 <u>Documents/On_Slums 2011Final.ppt</u> GoI (2013). Press Note on Poverty Estimates 2011‐12. Planning Commission, Government of India. <u>http://planningcommission.nic.in/news/pre_pov2307.pdf</u> GoM (2006). Accounts General Report. Government of Maharashtra. ‐ <u>http://agmaha.cag.gov.in/Audit%20Reports/Pr_Aud_Mum/2005 06/civil%20performance/civilper_chap_3.pdf</u> GoM (2007). Economic Survey of Maharashtra 2007‐08. Directorate of Economics and Statistics, Government of Maharashtra. GoM, (2014). Economic Survey of Maharashtra 2014‐15. Directorate of Economics and Statistics, Government of Maharashtra.

Groupe SCE (2012). Existing Land Use Survey. Development Plan for Greater Mumbai 2014‐2034. Groupe SCE India Pvt. Ltd.

- Hallegatte, S. et al. (2010). Flood Risks, Climate Change Impacts and Adaptation Benefits in Mumbai: An Initial Assessment of Socio‐economic Consequences of Present and Climate Change Induced Flood Risks and of Possible Adaptation Options. OECD Environment Working Papers, 27. OECD Publishing. DOI: 10.1787/5km4hv6wb434‐en

Herweijer, C., Ranger, N., Ward, R. (2009). Adaptation to Climate Change: Threats and Opportunities for the Insurance Industry. The Geneva Papers, 34, 360‐380. The International Association for the Study of Insurance Economics, Palgrave. Khandlhela, M., May, J. (2006). Poverty, vulnerability and the impact of flooding in the Limpopo Province, South Africa. Natural Hazards, 39, 275‐287.

MCGM (2006). Fact Finding Committee on Mumbai Floods, Final Report. Volume I, March 2006. Municipal Corporation of Greater Mumbai.

MCGM (2014). Flood Preparedness Guidelines 2014. Municipal Corporation of Greater Mumbai. <u>http://www.mumbaimonsoon.com</u> MCGM (2014a). Greater Mumbai City Development Plan 2005 to 2025. Municipal Corporation of Greater Mumbai. <u>http://www.mcgm.gov.in/irj/portal/anonymous?NavigationTarget=navurl://095e1c7b9486b1423b8 81dce8b106978</u> MCGM (2015). City Map. Municipal Corporation of Greater Mumbai. <u>http://mcgm.gov.in/irj/portal/anonymous?NavigationTarget=navurl://ce7407c74001ac932426502e 58da0827</u> 30
In every IV regression, the instruments satisfy the relevance and validity conditions. Indeed, the Hansen statistic has high p-values, suggesting that the null hypothesis is not rejected and the instrument excludability requirement is satis...ed. The ...rst-stage statistics support instrument relevance and strength: the F-statistics are above 10; the low p-values of the Anderson canonical correlation likelihood ratio test, indicate rejection of the irrelevance hypothesis. The Cragg-Donald weak identi...cation test rejects the weakness of the instruments. However, the C-tests strongly suggest rejection of endogeneity. This indicates that the variable ln(m90) can be treated as exogenous. It follows that the OLS method is the most appropriate estimation technique. Thus, based on the F-Statistic, the OLS 2 model appears to be the superior speci...cation. The growth rate of human capital between 1990 and 2000 exhibits an elasticity between 3.2 and 4.2 percent with respect to skilled migration (Beine et al (2007a) found 4.8 percent) and an elasticity of 12 percent with respect to public subsidies. Appendix 6.3 discusses the robustness of these results.

# 3.4 Results for the subsidy equation

Table 2 gives the OLS and IV estimates for education expenditures per student. In all regressions, we obtain a negative and signi...cant impact of ln(m90) on the subsidy rate. We note that the coe¢ cients of ln(m90) are biased downward in OLS estimations models compared to their magnitudes in IV models. The quality of governance (RLAW ) is an important factor, signi...cantly and positively a¤ecting public education subsidies. Subsidies are signi...cantly higher in middle-income countries; remittances and investments have no e¤ect on education expenditures per student. All regional dummies proved to be signi...cant and positive (compared to the reference region, South Asia).

18
# **2. Data and Methodology**

In order to carry out our analysis, we use daily 10-year benchmark government bond yields (obtained from Thomson Reuters datastream) for a sample of eleven countries; France, Germany, Italy, Belgium, Netherlands, Ireland, Spain, Portugal, Austria, Finland and Greece. The analysis spans a time period ranging from March 1, 2002 (end of dual circulation period in Greece) until May 2, 2014.

<!-- Start of picture text --> EMU<br>France<br>35<br>Germany<br>Italy<br>30<br>Belgium<br>Netherlands<br>25<br>Ireland<br>Spain<br>20 Portugal<br>Austria<br>15 Finland<br>Greece<br>10<br>5<br>Jun.02 May.04 May.06 May.08 Apr.10 Apr.12 Apr.14<br>10Y Benchmark Yield (points)<br><!-- End of picture text --> Figure 1: EMU sovereign bond yields from March 2002 to May 2014 Bond yield convergence in EMU is validated by Fig. 1 for the first 10 years of the eurozone. However, it does not tell much about the exact timing and the exact amount of yield co-movements. For that purpose, we apply the consistent dynamic conditional correlation model (cDCC) of Aielli (2013) to track the timevarying co-movement degree. The methodology is explained in the following sub-section.

3
<!-- Start of picture text --> A%<br>0%<br>‘<br>4% \‘;<br>\--Baseline scenario<br>-8% \‘ —Scenario where mining sector<br>\, revenues flow only to hhs<br>12% \ .<br>‘\ wee<br>716% Neen<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text -->
-2%om = f _ ‘<br>“A%F Baseline scaling-up scenario<br>ai<br>—Scenario with all domestic<br>“8% inputs & spending of mining<br>-10% receipts<br>-12% 2<br>“14%<br>“16%<br>“18%<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text -->

<!-- Start of picture text --> —Pvt sector spends mining dividends (re-scaled impact)<br>+++ Scenario with only intermediary inputs<br>--Scenario with only capital inputs<br>—Scenario with only labor inputs<br>0%<br>“1% woooes<br>2%-3% ana arenn stn IT ETSTSETI TIT<br>4%<br>5%<br>on 2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> Bit—ScenarioScenario Sector withwith spendsonlyonly capital minngdvdedsintermediary inputsinputs. (scaled impac)<br>a ° celiaid with only labor inputs<br>“2%<br>4%<br>6%<br>7%<br>“10%<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text -->
schooling beyond middle school. In this age group, young rural residents thus almost always have already finished their schooling.<sup>10</sup>

# **The Treatment Variable**

We code a village in a particular year as being covered by the water improvement program when either of the two conditions hold: (1) over 80 percent of village households have a water plant as their water source in the first year; (2) less than 80 percent of village households enjoy a water plant in the first wave, but plant coverage rises by more than 20 percentage points per year since the last wave.<sup>11</sup> Once having access, a village is assumed to always have access in subsequent years. Whether a household has access to water plant is based on a survey question that is answered by the household about its water source, which includes water plants, wells, springs, and rivers. Figure 2 shows the trend of the coverage of plant water in our sample. Starting at the nadir of 20 percent of the sample having plant water in 1989, the ratio rose to 47 percent in 2011.<sup>12</sup>

# **Specification and Empirical Strategy**

Before we proceed to empirical specification, it is useful to be clear about our estimation sample. Since we are interested in the final schooling attainment of rural youth, for each person between 18 and 25, we only keep _one observation per person_ . The observation present the final year for all the years that she/he is in the sample so that we know her or his final schooling attainment. Our sample is thus of the nature of a cross-sectional sample. However, since the final year differs for each individual, each observation could be in different year, and we shall use _t_ to indicate this time dimension. Our final sample consists of 4,729 observations.

> 10 As discussed later, our main results remain intact when restricting the sample to be young people of 19-25, or 2025, or 21-25 years of age.

> 11 For example, if over 40 percent of households in a village report that their water sources switched to plants from 1989 to 1991, then Water Plant is set to 1.

> 12 The ratio decreases slightly in 2004 as compared to 1997, from 30.7 to 29.1 percent, due to a slight change in the survey areas.  Heilongjiang province was initially surveyed as a substitute for Liaoning in 1997, and both provinces were included since 2000. In 2004 CHNS expanded the number of their surveyed villages.

9
**Table 5: Contribution of Each Driver to Food Price Changes, 1997-2004 to 2005-2012 (percentage changes)**

||_Maize_|_Wheat_|_Rice_|_Soybeans_|_Palm oil_|
|---|---|---|---|---|---|
|_Change in nominal price_|_90_|_81_|_99_|_77_|_81_|
|Stock-to-Use ratio (_S/Ut-1_)|22|9|—|-3|5|
|Oil price (_Pt_<sup>_OIL_</sup>)|52|64|61|77|114|
|Exchange rate (_XRt_)|—|10|29|13|—|
|Interest rate (_Rt_)|-4|—|-8|—|—|
|Income (_GDPt_)|-32|—|—|—|—|
|Manufacture prices (_MUVt_)|20|—|—|—|—|
|_SUM_|_58_|_83_|_82_|_87_|_119_|

**_Source:_** Authors’ calculations based on table 2 estimates.

**_Note_ :** The contribution was based on the 1960–2012 model parameter estimates reported in table 2. “—“ implies that the respective parameter estimate was not significantly different from zero. GDP = gross domestic product; MUV = manufacture unit value.

— 24 —