rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Spillover Effects to Labor Outcomes in Other (Relevant) Industries

||Ge|ographic c|ontrol group|||Industry co|ntrol group||
|---|---|---|---|---|---|---|---|---|
||Income<br>(main job)|Total<br>income|Income<br>(main job)|Total<br>income|Income<br>(main job)|Total<br>income|Income<br>(main job)|Total<br>income|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Panel A: Freshwater Fishery I**|**ndustry**||||||||
|(HaTinh & QuangBinh) _×_ post|-0.082|-0.130|0.047|0.050|-0.057|-0.058|-0.038|-0.035|
||(0.132)|(0.093)|(0.158)|(0.158)|(0.079)|(0.076)|(0.137)|(0.142)|
|(QuangTri & Hue) _×_ post|0.248***|0.205***|0.263**|0.256**|0.205***|0.195***|0.205**|0.194***|
||(0.063)|(0.066)|(0.116)|(0.097)|(0.070)|(0.068)|(0.082)|(0.065)|
|Observations|4,408|4,408|1,422|1,422|7,941|7,941|2,082|2,082|
|**Panel B: Husbandry Industry**|||||||||
|(HaTinh & QuangBinh) _×_ post|-0.087|-0.076|0.036|0.010|-0.030|0.007|0.049|0.037|
||(0.056)|(0.052)|(0.062)|(0.052)|(0.032)|(0.031)|(0.069)|(0.047)|
|(QuangTri & Hue) _×_ post|0.051|0.034|-0.079|-0.109|0.180***|0.080**|-0.090|-0.128*|
||(0.067)|(0.067)|(0.075)|(0.086)|(0.038)|(0.037)|(0.059)|(0.065)|
|Observations|8,980|8,980|2,558|2,558|9,769|9,769|2,404|2,404|
|**Panel C: Restaurant Industry**|||||||||
|(HaTinh & QuangBinh) _×_ post|-0.206|-0.200|-0.140**|-0.113|-0.050|-0.026|-0.158**|-0.127|
||(0.138)|(0.142)|(0.068)|(0.090)|(0.062)|(0.060)|(0.073)|(0.090)|
|(QuangTri & Hue) _×_ post|-0.040|-0.023|-0.006|0.006|-0.035|-0.016|-0.018|-0.004|
||(0.040)|(0.042)|(0.039)|(0.042)|(0.045)|(0.043)|(0.045)|(0.047)|
|Observations|6,225|6,225|1,854|1,854|8,287|8,287|2,184|2,184|
|**Panel D: Lodging Industry**|||||||||
|(HaTinh & QuangBinh) _×_ post|-0.155|-0.151|0.093|0.092|-0.012|-0.026|0.040|0.047|
||(0.238)|(0.237)|(0.084)|(0.087)|(0.124)|(0.120)|(0.062)|(0.060)|
|(QuangTri & Hue) _×_ post|0.002|-0.005|0.038|0.036|-0.089|-0.082|-0.024|-0.017|
||(0.062)|(0.061)|(0.060)|(0.061)|(0.096)|(0.093)|(0.054)|(0.053)|
|Observations|1,331|1,331|428|428|7,766|7,766|2,024|2,024|
|Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Individual FE|No|No|Yes|Yes|No|No|Yes|Yes|
|Stan|dard errors i|n parenthe|ses, *** _p <_|0_._01, ** _p_|_<_0_._05, * _p_|_<_0_._1|||

Note: this table shows the distributional impact of Formosa on the labor income (monthly; ’000 VND; log-transformed) of individuals working in other relevant industries (i.e. those deemed eligible by the government for official Formosa compensation), separately for the upstream (Ha Tinh & Quang Binh) and downstream (Quang Tri & Hue) Formosa-affected provinces. Panel A, B, C, and D report estimates using respective samples of workers in Freshwater Fishery, Husbandry, Restaurant, and Lodging, with the treated individuals working in (Ha Tinh & Quang Binh) and (Quang Tri & Hue). The control group consists of individuals working in the same industry who lived in unaffected provinces (south of Phu Yen). Standard errors are clustered at the district level.
De Haas, R., and I. van Lelyveld (2014). “Multinational Banks and the Global Financial Crisis: Weathering the Perfect Storm?” _Journal of Money, Credit and Banking_ 46 s1: 333–64.

<mark>De Haas, R., Y. Korniyenko, A. Pivovarsky, and T. Tsankova (2015). Taming the herd? Foreign banks, the Vienna Initiative and crisis transmission.</mark> _<mark>Journal of Financial Intermediation</mark>_ <mark>,</mark> _<mark>24</mark>_ <mark>(3), 325-355.</mark> <mark>D'Hulster, K., and I. Ötker-Robe (2015). Ring-fencing cross-border banks: An effective supervisory response?.</mark> _<mark>Journal of Banking Regulation</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>(3), 169-187.</mark> D’Hulster, K. (2012). Cross-border banking supervision: Incentive conflicts in supervisory information sharing between home and host supervisors. _Journal of Banking Regulation_ , 13(4), 300-319.

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Wu, J., M. Chen, B., Jeon, and R. Wang (2017). Does foreign bank penetration affect the risk of domestic banks? Evidence from emerging economies. Journal of Financial Stability, 31, 45-61.

World Bank (2017). Bankers without Borders. Global Financial Development Report 2017/2018. World Bank Group. Washington DC.

28
<!-- Start of picture text --> <<<br>sO<br> 10 (%) Ds.<br>a<br>ae fe<br>He %<br>— ¥<br>I oe<br>0 100 Ss<br>Rall<br>|<br><!-- End of picture text -->

<!-- Start of picture text --> Poverty rate (%) .~<br>mo 10<br>|<br>Bs: a<br>mm 40 50 \\<br>Se a<br>he -<br>fm 90 - 100<br>: os<br>es<br><!-- End of picture text -->
**Table 4: Summary statistics on selected other indicators**

||GDP per<br>capita,|Po|verty||Access to|Access to<br>improved|
|---|---|---|---|---|---|---|
||2011,<br>$PPP/year|Poverty<br>rate (%)|Year for<br>povertyrate|Female<br>literacyrate|improved<br>water(%)|sanitation<br>(%)|
|Benin|1762|53.1|2011|0.184|75.3|17.8|
|Burkina Faso|1470|55.3|2009|0.216|80.0|18.0|
|Burundi|713|77.7|2006|0.846|75.0|47.2|
|Cameroon|2614|29.3|2007|0.648|73.1|44.9|
|Congo, Rep.|5632|28.7|2011|0.729|51.1|27.2|
|Côte d'Ivoire|2547|29.0|2008|0.305|74.8|14.4|
|Congo, Dem.|||||||
|<br>Rep.|617|773|2012|0.629|80.9|21.3|
|Ethiopia|1165|33.5|2010|0.289|49.7|23.0|
|Gabon|17101|8.0|2005|0.799|91.6|41.2|
|Gambia|1532|45.3|2003|0.446|89.5|58.8|
|Ghana|3431|25.3|2005|0.653|84.3|14.0|
|Guinea|1184|35.3|2012|0.122|73.8|18.4|
|Kenya|2623|33.6|2005|0.669|60.8|29.4|
|Lesotho|2297|59.7|2010|0.850|81.0|28.9|
|Liberia|733|68.6|2007|0.270|72.0|15.8|
|Malawi|1079|70.9|2010|0.513|82.9|39.2|
|Mali|1863|49.3|2009|0.246|68.9|22.9|
|Mozambique|952|68.7|2008|0.365|49.3|19.3|
|Namibia|8626|22.6|2009|0.784|88.0|32.7|
|Niger|807|50.3|2011|0.089|55.1|9.8|
|Nigeria|5231|53.5|2009|0.414|64.5|30.2|
|Rwanda|1397|60.3|2010|0.647|73.7|58.2|
|Senegal|2159|38.0|2011|0.404|75.6|45.6|
|Sierra Leone|1415|52.3|2011|0.350|58.5|12.7|
|Swaziland|7620|42.0|2009|0.824|72.3|57.0|
|Tanzania|2207|46.6|2011|0.744|55.3|13.6|
|Togo|1255|54.3|2011|0.480|60.5|11.5|
|Uganda|1649|33.2|2012|0.620|74.2|18.3|
|Zambia|3343|64.4|2010|0.518|62.2|43.0|
|Zimbabwe|1524|n.a.|n.a.|0.801|77.7|37.5|
|Mean<br>ote Poverty rates are|2806<br>for $1.90 per perso|42.7<br>n per day at 20|2012<br>11 PPP; estimate|0.472<br>s from PovcalNet|71.1<br>, accessed 8/18/|29.1<br>2016. Mean|

Note Poverty rates are for $1.90 per person per day at 2011 PPP; estimates from <u>PovcalNet, accessed 8/18/2016. Mean</u> poverty rate is for Sub-Saharan Africa as a whole. GDP, literacy, access to improved water and sanitation are all taken from the World Bank’s <u>World Development Indicators. Literacy rate for 2011 or closest available year to 2011 in 2007-15; more</u> recent year for ties. Water and sanitation for 2011.

41
48. The comparison of govemment services in ICP is made by input, rather than output, of government activities. In the comparison for this category, as in other service sectors, govenument employees educated at the same level are assumed to have the same productivity in the two countries. A comparison of annual average income of government employees in China and U.S. is shown in Table 8.

Table 8: BILATERAL COMPARISON IN THE PUBLIC FINAL CONSUMPTION

|Category|Price<br>(China's Yuan)|Price<br>U.S. $)|Parity|<br>China<br>Weight (%)|U.S.<br>Wei<br>(/)|
|---|---|---|---|---|---|
|Salary of Government<br>||||||
|Employees|2,554.04|21,018.96|0.1215|<br>4.61|11.41|

# 3.4. Gross Capital Formation Comason

49. The difficulty with the matching of quality and identification of sizes, plus the fact that price data on producer durables are generally not published in the U.S., makes the gross capital formation comparison the most intractable among all compansons. Due to the absence of relevant data, the subaggregates for land improvement and plantation and orchard development (13.000); increase in stocks (18.000); and net export of goods and services (19.000) were deleted from the study.

50. In the comparison, a disfinction should be made between the matching of quaity in an economic sense emphasizing the equivalence in some key properties between two goods, and the matching of quality **in** a technical sense requiring the fiul coincidence in specfication. The principle of the matching of quality in an economic sense has to be followed in this study for practical pipose.

51. In some categories, goods with the same specifications are used in both China and the United States, because the imported goods are dominant in China's market. For example, in office macines 22
(1988, sons), 2,091 (2002 daughters), and 3,573 (2002, sons). The analysis of occupational persistence is based on these samples.

For the analysis of educational persistence, we use the sample of children born in 1967 or later. This is done to make sure that the educational attainment was not directly disrupted by Cultural Revolution, which officially lasted from 1966 to 1976. So the adult children in our samples reached the age of 8 after 1975, which means they entered primary school either at the end of the Cultural Revolution, or after the Cultural Revolution.<sup>17</sup> The sample sizes for the education analysis are: 2,057 (1988, daughters), 1,884 (1988, sons), 2,264 (2002, daughters), an 3,616 (2002, sons). The summary statistics of the explanatory variables for the full sample are presented in Appendix I.

# **3. Empirical Results on Educational Mobility**

## **3.1. Descriptive Statistics of Education Mobility**

Table 1 presents the average educational attainment and basic correlations in the data between parental education and children’s education. The average years of schooling in 1988 are 5.8 years for daughters and 6.8 years for sons, which increases to 9 years for both daughters and sons in 2002. The evidence thus clearly indicates that the educational attainment in rural China has improved significantly over the 14 years, and, more strikingly, the initial gender gap in average education completely disappeared by 2002.

Panel B of Table 1 shows the correlations between parents’ average education (average of mother’s and father’s years of schooling) and a child’s years of schooling. The correlation has gone down for daughters, but has increased for sons from 1988 to 2002.

> 17 Sato and Li (2007a, 2007b) took a similar approach.

11
**Table A4: Country-Specific Sibling Correlation Estimates (1990 Cohort)**

|**Country**|**Sibling**<br>**Corr.**|**S.E.**|**N**|**Country**|**Sibling**<br>**Corr.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.685|0.016|1751|Benin|0.579|0.019|1915|
|Indonesia|0.523|0.014|4540|Burkina Faso|0.506|0.055|241|
|Philippines|0.550|0.014|4301|Burundi|0.500|0.018|2484|
|Vietnam|n.a|n.a|n.a|Cameroon|0.629|0.021|1502|
|||||Chad|0.760|0.018|1058|
|Europe & Central Asia||||Congo, Rep.|0.625|0.043|291|
|Albania|0.389|0.036|1115|Cote d'Ivoire|0.640|0.057|154|
|Armenia|0.492|0.065|428|Ethiopia|0.630|0.016|1935|
|Kyrgyz Republic|0.342|0.094|304|Gabon|0.576|0.056|249|
|Tajikistan|0.424|0.030|1744|Ghana|0.500|0.036|577|
|Turkey|0.433|0.038|742|Guinea|0.559|0.026|949|
|||||Kenya|0.503|0.019|2568|
|Latin America & Caribb|ean|||Lesotho|0.483|0.036|593|
|Bolivia|n.a|n.a|n.a|Liberia|0.522|0.026|909|
|Brazil|n.a|n.a|n.a|Madagascar|n.a|n.a|n.a|
|Colombia|0.546|0.014|3729|Malawi|0.585|0.017|2178|
|Dominican<br>Republic|0.435|0.044|540|Mali|0.573|0.022|1332|
|Guatemala|0.680|0.012|2690|Mozambique|0.681|0.041|279|
|Haiti|0.678|0.013|2144|Namibia|0.516|0.063|279|
|Peru|0.546|0.019|2645|Niger|0.645|0.041|308|
|||||Nigeria|0.741|0.010|5396|
|Middle East & North Af|rica|||Rwanda|0.522|0.023|1362|
|Egypt, Arab Rep.|0.588|0.018|2934|Senegal|0.588|0.040|354|
|Jordan|0.551|0.015|5047|Sierra Leone|0.515|0.026|1139|
|||||South Africa|0.501|0.043|578|
|South Asia||||Tanzania|0.484|0.030|1213|
|Afghanistan|0.591|0.011|4857|Togo|0.519|0.038|410|
|Bangladesh|0.609|0.018|1712|Uganda|0.552|0.021|1710|
|India|0.631|0.003|72192|Zambia|0.620|0.018|2011|
|Nepal|0.588|0.024|1317|Zimbabwe|0.625|0.037|518|
|Pakistan|0.679|0.009|5583|||||

_Notes_ : This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1990s birth cohort sample.

42
would come from pasture. In order to check if this scenario a viable one, we compare the available pasture land with deforested land due to biofuel production in the previous AT and ET scenarios (see Table 2).

**Table 2. Comparison of land deforested due to biofuel expansion with available pasture land**

|Country/Region|Deforested land aspercentage of av|ailablepasture land|
|---|---|---|
||AT|ET|
|**World total**|**0.2**|**0.9**|
|**High-income**|**0.4**|**1.9**|
|Aus-NZ|0.0|0.1|
|Japan|5.8|73.0|
|Canada|6.2|31.0|
|United States|0.1|0.5|
|France|2.9|8.7|
|Germany|2.1|7.5|
|Italy|1.4|4.2|
|Spain|0.5|1.4|
|UK|1.0|2.7|
|Rest of EU & EFTA|1.6|5.1|
|**Middle & Low-income**|**0.1**|**0.7**|
|China|0.1|0.4|
|Indonesia|6.8|32.5|
|Malaysia|6.0|31.6|
|Thailand|170.8|815.8|
|Rest of EAP|0.1|0.9|
|India|5.4|14.5|
|Rest of SA|0.0|0.1|
|Argentina|0.0|0.1|
|Brazil|0.9|4.4|
|Rest of LAC|0.1|0.6|
|Russia|-1.5|-2.5|
|Rest of ECA|0.0|0.1|
|MENA|0.0|0.0|
|South Africa|0.0|0.2|
|Rest of SSA|0.0|0.1|

It is interesting to note that the total land deforested at the global level to meet biofuel targets are 0.2% and 0.9% of the total pasture land available under the AT and ET scenarios. In most countries, the deforested lands are a small fraction of total pasture land. This demonstrates that if proper policies are put in place to encourage farmers to use pasture land 21
22 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity Cheng, Y., Zhao, L., Wan, W., Li, L., Yu, T., & Gu, X. (2016). Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information. Journal of Geographical Sciences, 26(3), 325-338.

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Duque, J. C., Lozano-Gracia, Patino, J. E., and Restrepo, P. 2017. Urban form and productivity: in what shape are Latin American cities? LCR Flagship Report on Cities and Productivity. World Bank (forthcoming).

De Esteban, A. (2009). Área metropolitana. Diccionario Crítico de Ciencias Sociales.

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Feiock, R. C. (2009). Metropolitan governance and institutional collective action. Urban Affairs Review, 44(3), 356-377.

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Caeyers, B. and S. Dercon. 2012. “Political Connections and Social Networks in Targeted Transfer Programs: Evidence from Rural Ethiopia.” _Economic Development and Cultural Change_ , 60 (4): 639-675.

Carlin, W. 2012. “A Progressive Economic Strategy: Innovation, redistribution and labourabsorbing services.” Policy Network Working Paper, London. Retrieved on 4 August 2015 at: <u>http://www.policy-network.net/publications_detail.aspx?ID=4269</u> Carlson, E. 2015. “Ethnic Voting and Accountability in Africa: A Choice Experiment in Uganda.” _World Politics_ , 67 (2): 353-385.

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24
quantity, safety, and service options, but not hours of supply. However, the poor have lower ability to tradeoff income for services (i.e., higher marginal utility of income). Section 4 presents the policy implications of our findings – findings that have serious equity implications for allocating scarce public services and achieving universal water access. We conclude with some suggestions for future uses of conjoint analysis in the design and evaluation of water and sanitation programs.

# **2. Conjoint Surveys as Field Experiments**

Conjoint analysis has long been applied in market research to assist in the design of new products (Cattin et al., 1982). Many environmental economists have turned to conjoint analysis methods because of validity concerns identified in some contingent valuation studies (Adamowicz et al., 1999). Similarly, health economists also have become interested in the use of conjoint analysis as an alternative to contingent valuation (Ryan et al., 2001). The conjoint methodology is based on rigorous utility-theoretic principles (Louviere, Hensher, and Swait, 2000). Using conjoint analysis, goods are described by a series of attributes with varying levels. Using an experimental design, the attribute levels are combined to produce a collection different goods, or WSS alternatives in the case of our study. In the survey, respondents are presented with two or more options for WSS service levels and associated prices. Respondents select the option they prefer. Each respondent answers a set of choice questions (the optimal number, also determined by an experimental design, depends on the number of attributes and levels) to provide the level of variation needed for estimating the marginal utilities of each attribute. By asking respondents to make choices about different attribute combinations, researchers can determine the importance of different attributes and levels, the probability that individuals would select, for example, a particular WSS alternative compared to their current WSS situation, and to estimate willingness to pay for changes in attribute levels or for one WSS alternative versus another (Hensher et al., 2004; Mercer and Snook, 2004).

DeShazo and Fermo (2002) assert that conjoint methods can obtain more information at higher quality and lower costs than contingent valuation methods for service delivery programs. The data can provide planning information for agencies that deliver services, establish appropriate service levels and tariffs, balance service quality and price, and determine appropriate levels of service attributes to offer to customers (Hensher et al., 2004). By assessing the economic and non-economic criteria that consumers 4
________________________________________________________________________________________________________________________________

|Country<br>_____________|Year<br>__________|Variable<br>All<br>firms<br>__________________________|______|20 – 49<br>employ<br>________|<br>ees<br>__________|50 – 24<br>employ<br>___________|9<br>ees<br>_______|250 – 499<br>employ<br>______________|ees<br>______|500 and more<br>employ<br>_______________|ees<br>__|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Colombia|1981|Participation rate 17.4||6.5||23.1||47.1||61.8||
|||Export intensity|13.1||42.3||19.7||9.0||10.4|
|||Number of firms 3,900||1,960||1,594||210||136||
||1991|Participation rate 26.6||14.2||36.9||59.1||71.9||
|||Export intensity|17.8||33.8||22.8||14.5||14.5|
|||Number of firms 4,348||2,379||1,636||198||135||
|Denmark|1999|Participation rate 77.3||68.9||88.1||92.5||97.5||
|||Export intensity|29.3||21.4||33.8||50.5||54.8|
|||Number of firms 3,888||2,277||1,331||161||119||
||2002|Participation rate 77.2||68.9||88.1||90.2||87.3||
|||<br>Export intensity|30.5||22.1||39.6||50.3||52.4|
|||<br>Number of firms 3|<br>,326||1,902||1,161||153||110|
|France|1990|Participation rate 69.4||60.5||78.0||90.4||95.3||
|||Export intensity|17.6||14.5||18.8||24.7||28.6|
|||<br>Number of firms 22,220||12,542||7,749||1,078||851||
||2004|Participation rate 74.8||66.0||84.0||92.2||96.1||
|||Export intensity|23.8||18.3||26.6||35.1||39.8|
|||Number of firms 18,124||10,203||6,184||958||779||
|West Germany|1995|Participation rate 66.0||52.9||72.1||87.6||92.3||
|||Export intensity|22.8||17.7||23.0||29.1||36.6|
|||<br>Number of firms 34,682||15,038||15,263||2,495||1,886||
||2004|Participation rate 69.3||56.0||77.4||90.0||92.0||
|||Export intensity|29.6||22.8||30.4||40.7||47.5|
|||Number of firms 33,668||15,218||14,583||2,322||1,545||

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