rafmacalaba/datause-encoder-data huggingface dataset:
including the discourses observed on public Facebook pages, and (c) studying the effectiveness of COVID vaccination campaigns.

# **Bibliography**

Alatas, V., Chandrasekhar, A., Mobius, M., Olken, B. and C. Paladines (2019), “When celebrities speak: A nationwide Twitter experiment promoting vaccination in Indonesia”, _NBER Working Paper 25589_ Eurofound (2021), “Living, working and COVID-19 (Update April 2021): Mental health and trust decline across EU as pandemic enters another year”, _April 2021 Factsheet_ Fridman A, Gershon R, and A. Gneezy (2021), “COVID-19 and vaccine hesitancy: A longitudinal study.” _PLOS ONE_ 16(4): e0250123. https://doi.org/10.1371/journal.pone.0250123 Germani, F. and N. Biller-Andorno (2021), “The anti-vaccination infodemic on social media: A behavioral analysis.” _PLOS ONE_ 16(3):e0247642. https://doi.org/10.1371/journal.pone.0247642 Jamison A, Broniatowski D., Smith M., et al. (2020), “Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter.” _American Journal of Public Health_ . 110(S3): S331S339. doi:10.2105/AJPH.2020.305940 Kata, A. (2010), “A postmodern Pandora's box: Anti-vaccination misinformation on the Internet.” _Vaccine._ 28(7); 1709-1716 Meadows C, Tang L. and W. Liu (2019), “Twitter message types, health beliefs, and vaccine attitudes during the 2015 measles outbreak in California.” _Am J Infect Control_ . 47(11):1314-1318. doi: 10.1016/j.ajic.2019.05.007. Epub 2019 Jun 29. PMID: 31266661.

Puri, N., Coomes, E., Haghbayan, H. and K. Gunaratne (2020), “Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases.” _Human Vaccines & Immunotherapeutics,_ 16:11, 2586-2593, DOI: <u>10.1080/21645515.2020.1780846</u> Schmidt A., Zollo F., Scala A., Betsch C. and W. Quattrociocchi (2018), “Polarization of the vaccination debate on Facebook.” _Vaccine_ . 36(25):3606-3612 Troiano G. and A. Nardi (2021), “Vaccine hesitancy in the era of COVID-19 **.** ” _Public Health_ . Volume 194, 245-251.

18
44 21. Adult literacy is available for all 61 countries for which GNP and labor force composition statistics could be obtained for two or more periods. Average years of education completed by adults (age 20-44) may be for some purposes a better measure of labor market skills. But even after considerable effort, an underlying census or survey tabulations needed to estimate years of education was located for only about half of the sample of countries reporting labor force statistics at two points in time (Appendix Table A-1). Estimates of years of schooling are calculated by the author for all national populations tabulated by age and sex an completed education as reported in various UN Demographic Yearbooks.

22. Data are not generally available by sector on the literacy or education of workers. Where years of education for all adults and literacy were estimated, both measures were similarly related to the balance of firm and family employment. Adult literacy is also occasionally related to the sectoral composition of employment holding constant log income per adult. But the direction of the association with education often changes between the cross section and time series. The share of employment in agriculture is lower for more literate countries in the cross-section but higher over time (and by definition the converse holds for the share of nonagricultural employment). The share of manufacturing employment is not related to literacy, while the share of services is lower for more literate populations while the share of employment in commerce is smaller for women in more literate societies in the cross section.

23. A.0. Krueger, "The Political Economy of Rent Seeking Society," American Economic Review, 64:3 (June, 1974) 29-303.
- 15 -

international trade); the Barro-Lee (1993) proxy for the incidence of wars; 1/ the ratio of military spending to GDP; and a dummy variable that catches any otherwise unspecified country-specific effects. In accordance with the Solow-Swan model we assume that the conditioning variables are exogenous with respect to output growth; in particular, the ratio of military expenditures to GDP is assumed to be unaffected by the rate of output growth. 2/ Equation (1) specifies the per capita output growth rate, Zi,t-Zi,t1j, where z represents the natural logarithm of output per capita: where ln indicates a natural logarithm; the indices i and t represent the country and time period, respectively; n is the average population growth rate; g is the technological growth rate, 6 is the rate of depreciation of 1/ The Barro-Lee proxy variable for the incidence of wars is defined for each country as the number of war-years as a fraction of total years in the period 1960-85. See Barro and Lee (1993).

2/ There are three differences between the growth equation specified in this paper and the one used in our 1993 paper. First, in this paper we do not include the ratio of government fixed investment to GDP as an explanatory variable, since we found it to be statistically insignificant in our previous study. Second, and more important, we now include as a regressor the ratio of military expenditures to GDP. Finally, to isolate the effect of military expenditures on the allocation of productive resources, we control for the incidence of wars on economic growth by including the above-mentioned Barro-Lee proxy.
Table A4: Characteristics of Schools in 2009 that joined Mais Educação in 2010, by treatment status

||Schools in<br>Panel A|: Fifth-grade sc|hools|Schools in<br>Panel|B: Ninth-grade sch|ools|
|---|---|---|---|---|---|---|
|Variable|_Mais_<br>_Educação_<br>(2010–2011)|Nonparticipa<br>ting schools|Difference<br>(1)−(2)|_Mais_<br>_Educação_<br>(2010–2011)|Nonparticipating<br>schools|Difference<br>(4)−(5)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|∆ Portuguese grades|11.0|7.7|3.3***|11.5|8.4|3.0***|
|∆ Mathematics grades|13.2|10.6|2.6***|3.7|1.7|2.0***|
|IDEB Index|4.3|4.6|-0.3***|3.5|3.9|-0.4***|
|∆ IDEB|0.5|0.4|0.1***|0.3|0.2|0.1***|
|Municipal population|959,776|716,126|243,650***|1,110,275|732,763|377,512***|
|Students' enrollments|701|543|158***|989|752|237***|
|IDHM|0.7|0.7|0.0***|0.7|0.7|0.0***|
|Urban|1.0|0.8|0.2***|1.0|0.8|0.2***|
|Basic sanitation|1.0|1.0|0.0***|1.0|1.0|0.0***|
|Library|0.8|0.6|0.1***|0.9|0.8|0.1***|
|Sports court|0.6|0.5|0.1***|0.8|0.7|0.1***|
|Number of classrooms|11.8|10.1|1.6***|13.9|11.8|2.1***|
|Number of computers|1.0|0.9|0.1***|1.0|1.0|0.0***|
|Science lab|0.1|0.1|0.1***|0.3|0.3|0.1***|
|Computer lab|0.6|0.5|0.1***|0.8|0.8|0.1***|
|Teachers room|0.9|0.8|0.1***|1.0|0.9|0.1***|
|DVD|1.0|0.9|0.0***|1.0|1.0|0.0***|
|Overhead projector|0.7|0.6|0.1***|0.9|0.8|0.1***|
|Number of employees|53.8|42.7|11.1***|70.9|55.1|15.8***|
|School meals|1.0|1.0|-0.0|1.0|1.0|0.0|
|Number of classrooms used|11.7|10.2|1.5***|13.5|11.8|1.7***|
|Morning lessons|0.6|0.6|-0.0***|0.6|0.6|-0.0***|
|Child care|0.0|0.0|-0.18***|0.0|0.0|-0.0***|
|Kindergarten|0.4|0.4|-0.0|0.15|0.2|-0.0***|
|State schools|0.3|0.2|0.0*|0.60|0.6|0.0***|
|High school|0.1|0.1|0.0***|0.5|0.4|0.0***|
|Share classes in school with full-time day (7 or more hours)|0.0|0.0|0.0|0.01|0.0|0.0|
|Total duration of school day (hours)|4.4|4.4|-0.0|4.46|4.4|0.0*|
|N|2,098|23,276||1,976|18,037||

_Sources: Prova Brasil,_ School Census, and INEP. _Note:_ Table reports the average characteristics in 2009 for the schools that joined _Mais Educação_ in 2010 and remained until 2011 (columns (1) and (4)) and for nonparticipating schools (columns (2) and (5)). Columns (3) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
|**Table 3: Heterogeneity in Control**|**Group New App**|**licant counterfactuals**|**of the number**<br>Coun|**of employees condit**<br>terfactual Outcome|**ional on o**|**perating a busines**|**s**|
|---|---|---|---|---|---|---|---|
||Sample Size|Realized Outcome<br>(Mean number<br>of employees)|Mean (s.d.) Tr<br>Nu<br>if in al|immed mean (s.d.)<br>mber of employees<br>ternative treatment|Median<br>)|Treatment Effect<br>according to<br>counterfactual|Experimental<br>Treatment Effect<br>and 95% interval.|
|All Control Group New Applicants|515|6.9|23.2 (31.4)|18.3 (12.5)|15|16.3|2.2 [1.1, 3.3]|
|Females|86|5.8|17.8 (13.9)|16.9 (11.5)|15|12.0|3.0 [‐0.2, 6.3]|
|Males|429|7.1|24.3 (33.8)|18.6 (12.7)|15|17.2|2.4 [1.3, 3.5]|
|University education|345|6.7|23.1 (33.1)|18.0 (12.0)|15|16.4|2.5 [1.1, 3.8]|
|No‐university|170|7.4|23.5 (27.7)|19.0 (13.5)|15|16.1|2.8 [0.9, 4.6]|
|Digitspan recall >=7|279|7.1|24.1 (35.6)|19.0 (13.3)|15|17.0|3.2 [1.7, 4.8]|
|Digitspan recall <=6|176|7.1|24.5 (29.4)|18.3 (12.4)|15|17.4|2.1 [0.5, 3.7]|
|Abuja and Lagos|107|6.5|25.4 (30.5)|19.5 (13.1)|15|18.9|1.2 [‐1.3, 3.8]|
|Other Cities<br>Notes:Trimmed mean drops count|408<br>erfactuals abov|7.1<br>e the 95th percentile|22.6(31.6)|18.0(12.4)|15|15.5|2.7[1.5,3.9]|

22
# 3. Methodology and Data

## 3.1. Data

The UNHS survey is used by the GoU to monitor the well-being of the population. The Uganda Bureau of Statistics (UBOS) has conducted the survey every three to four years since the 1990s, providing a series of comparable data stretching more than 20 years. The last UNHS survey was conducted between July 2016 and June 2017, collecting data from around 15,000 households in all 112 existing districts of Uganda at the time. The UNHS 2016/17 is representative at the national, rural-urban, regional and sub-regional level, and gathers information on the socio-economic characteristics of the household (including income and consumption) and its members (including educational attainment, health status and use of services, etc.). Thus, it is possible to allocate all fiscal interventions for each household in the survey, as explained in detail in the next sections.

## 3.2. Summary of CEQ Methodology<sup>14</sup>

The CEQ Assessment takes specific fiscal policy elements, programs, expenditures, or revenue collections and allocates them to individuals and households appearing in the UNHS 2016/17. Once the allocations are made, analysis is done to calculate different measures of poverty and impoverishment, inequality and progressiveness, and the amount of redistribution accomplished on measures of income that exclude (“pre-fiscal”) and include (“post-fiscal”) these fiscal policy elements. Figure 5 summarizes the construction of these income concepts.

In the case of Uganda, we start with the construction of Disposable Income, which we assume is equal to the total consumption expenditure – including the implied value of any auto-production/autoconsumption and the imputed value of rent for owner occupied housing. We then work “backwards” from Disposable Income to calculate Net Market Income, Market Income plus pensions, and Market Income, as follows:

- Net Market Income: direct transfers are subtracted from Disposable Income, in this specific case, SAGE, NUSAF, UWEP and YLP transfers.

- Market Income plus pensions: direct taxes are added to Net Market Income, namely imputed Personal Income Tax.

- Market Income: pension income is subtracted from Market Income plus Pensions.

We work “forwards” from Disposable Income to calculate Consumable Income, and Final Income, as follows:

- Consumable Income: water and agricultural subsidies are added to Disposable Income, and indirect taxes are subtracted, namely VAT, the petroleum duty and other excise taxes.

- Final Income: in-kind health and education benefits are added to Consumable Income.

> 14 For a more detailed description of the methodology, please see Lustig (2017).

15
of Altonji, Elder, and Taber (2005) in that it takes into account whether controls absorb residual variation. This is important for the credibility of the exercise, as one should not expect to observe coefficient instability upon adding controls that are unrelated to the outcome variable. Under the assumption that selection on observables has the same direction as selection on unobservables, the test produces lower-bound coefficients close to 0.1 (see the row headed “Oster bound” in table 3), corresponding to mission cells having a 40 percent higher likelihood of aid allocation. Importantly, the Oster bound is always significant at the 99 percent level.<sup>19</sup> The estimated correlation is higher for Catholic missions, but the difference between denominations is significant at the 90 percent level only in column 6, which uses data from two different sources. Furthermore, there is no feasible way to address differential selection of Protestant versus Catholic missionaries, so it is difficult to interpret the difference between the two coefficients. Finally, if the Democratic Republic of the Congo (DRC) is dropped from the sample, the difference between the two disappears in column 3 and halves in column 6, becoming not significant. The DRC covers 7 percent of the sample cells, making it the largest country in the sample, and it has a high concentration of Catholic missions. Hence, the differential in the coefficient size seems to be driven by this heavyweight outlier. To simplify the exposition, the rest of the article reports results from regressions on the mission data from Beach (1903) only.

# **2.5 Robustness Tests**

## **2.5.1 Subsample Analysis**

The first two robustness tests are aimed at restricting estimation of equation (1) to subsamples that are more likely to exhibit common support in the covariate distribution (see table 4). First, the sample is restricted to cells on the coast or along one of the main rivers (C/R column). As discussed above, this subsample is likely to be more homogeneous and would enhance the credibility of the selection on observables strategy, which relies on mission and

> 19The _R_ -squared from the hypothetical regression in which unobserved controls are included is set to 1.3 times the _R_ -squared from the actual regression on Mission _ik_ and observed controls, as suggested by Oster (2019). The procedure is only suitable for models with one treatment, so bounds are not calculated for columns 3 and 6. Confidence intervals are calculated using standard errors from the regressions.

17
<!-- Start of picture text --> Figure 8A: Poverty Rate, Alternative Scenarios<br>45<br>40<br>35<br>30<br>2<br>ce 25<br>Fe<br>g<br>2 20<br>15<br>10<br>5<br>(e)<br>2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030<br>Year<br>Source: International Futures v7.04<br>Scenario<br>ll Base Case<br>i Growth and Shared Prosperity<br>®@ Improved Institutions and Security Extended<br><!-- End of picture text --> <!-- Start of picture text --> Figure 8B: Poverty Headcount, Alternative Scenarios<br>240M<br>220M<br>200M<br>2<br>5<br>§ 180m<br>3<br>=<br>160M<br>140M<br>120M<br>100M<br>2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030<br>Year<br>Source: International Futures v7.04<br>Scenario<br>ll Base Case<br>i Growth and Shared Prosperity<br>@ Improved Institutions and Security Extended<br><!-- End of picture text -->
# BIBLICGRAPHY

Banks. A.S. (1979). "Cross-National Time Series Archive" SUNY Binghampton.

- Barro, Robert J. 1990. "Government Spending in a Simple Model of Endogenous Growth." Journal of Pclitical Economy 98 (October), $2:103-125.

- -----. 1991. "Economic Growth in a Cross Section of Countries." forthcoming, Quarterly Journal of Economics 106.

- --~--, and Holger Wolf. 1989. "Data Appendix for Economic Growth in a Cross Section of Countries." Harvard University.

- Becker, Gary, Kevin Murphy, and Robert Tamura. 1990. *Economic Growth, Human Capital and Population Growth." Journal of Political Economy 98, 12-37.

- De Long, J. Bradford. 1988. "Productivity Growth, Convergence, and Welfare: Comment." American Economic Review 78 (December) p.1138-54.

- Dollar, David. 1990. "“Outward-Oriented Developing Economies Really Do Grow More Rapidly: Evidence ‘rom 95 LDCs, 1976-85" World Bank Manuscript.

- Easterly, William. 1990. "Endogenous Growth in Developing Countries with Government Induced Distortions." World Bank Manuscript.

- Edwards, Sebastian. 1989. “Openness, Outward Orientation, Trade Liberalization, and Economic Performance in Developing Countries." World Bank PPR wP #191.

- Feder, Gershon. i983. "On Exports and Economic Growth." Journal of Development Economics, 12 p.59-74.

- Gastil, RD. 1987. Freedom in the World. Westport, CT: Greenwood Press. Grier, Kevin and Gordon Tullock. 1989. "An Empirical Analysis of Cross-National Economic Growth, 1951-1980." Journal of Monetary Economics 24, p.259-276.

- Crossman, Gene M. and Elhahan Helpman. 1990. ‘Trade, Innovation, and Growth.” American Economic Review 80, (May) p.86-91.

- Kormendi, Roger and Philip Meguire. 1985. “Macroeconomic Determinants of Growth: Cross-Country Evidence." Journal of Monetary Economics 16, p.141-163.

- Kravis, Irving and Robert Lipsey. 1990. "The International Comparison Program: Current Status and Problems.* NBER Working Paper #3304.

- Landau, Daniel. 1983. "Government Expenditure and Economic Growth: A CrossCountry Study.” Southern Economic Journal (January) p. 783-92.

- Leamer, Edward E. 1978. Specification Searches: Ad Hoc Inference from Nonexperimental Data. New York: Wiley.

- erces, 1983, “Let's Take the Con out of Econometrics." amersican Economic Review 73, (March) p.31-43.

29
|P055383|2001|2004|6/28/2004|7 Closed|Albania|
|---|---|---|---|---|---|
|P055383|2001|2005|6/29/2005|9 Closed|Albania|
|P055383|2001|2006|12/23/2005|10 Closed|Albania|
|P055383|2001|2006|4/3/2006|11 Closed|Albania|
|P055383|2001|2007|12/12/2006|12 Closed|Albania|
|P055383|2001|2008|8/17/2007|13 Closed|Albania|
|P055383|2001|2008|2/29/2008|14 Closed|Albania|
|P055383|2001|2008|5/30/2008|15 Closed|Albania|
|P055383|2001|2009|3/19/2009|16 Closed|Albania|
|P055383|2001|2009|6/3/2009|17 Closed|Albania|
|P055383|2001|2010|12/9/2009|18 Closed|Albania|
|P055383|2001|2010|4/23/2010|19 Closed|Albania|
|P055383|2001|2011|12/12/2010|20 Closed|Albania|
|P055383|2001|2011|6/29/2011|21 Closed|Albania|
|P055383|2001|2012|1/2/2012|22 Closed|Albania|
|P055383|2001|2013|7/1/2012|23 Closed|Albania|
|P055383|2001|2013|1/8/2013|24 Closed|Albania|
|P055383|2001|2013|3/31/2013|25 Closed|Albania|
|P055388|1999|1999||1 Closed|Brazil|
|P055388|1999|2000|6/23/2000|4 Closed|Brazil|
|P055388|1999|2001|6/14/2001|6 Closed|Brazil|
|P055388|1999|2019|5/7/2019|7 Closed|Brazil|
|P055432|1999|1999||0 Closed|Bosnia and Herzegovin|
|P055432|1999|2000|6/22/2000|2 Closed|Bosnia and Herzegovin|
|P055432|1999|2001|6/27/2001|4 Closed|Bosnia and Herzegovin|
|P055432|1999|2002|6/17/2002|6 Closed|Bosnia and Herzegovin|
|P055432|1999|2003|12/19/2002|7 Closed|Bosnia and Herzegovin|
|P055434|2003|2003|4/22/2003|1 Closed|Bosnia and Herzegovin|
|P055434|2003|2004|1/15/2004|3 Closed|Bosnia and Herzegovin|
|P055434|2003|2005|5/19/2005|6 Closed|Bosnia and Herzegovin|
|P055434|2003|2006|12/8/2005|7 Closed|Bosnia and Herzegovin|
|P055434|2003|2006|6/19/2006|8 Closed|Bosnia and Herzegovin|
|P055434|2003|2007|11/27/2006|9 Closed|Bosnia and Herzegovin|
|P055434|2003|2007|6/12/2007|10 Closed|Bosnia and Herzegovin|
|P055434|2003|2008|11/19/2007|11 Closed|Bosnia and Herzegovin|
|P055434|2003|2008|5/30/2008|12 Closed|Bosnia and Herzegovin|
|P055434|2003|2009|12/29/2008|13 Closed|Bosnia and Herzegovin|
|P055434|2003|2010|8/6/2009|14 Closed|Bosnia and Herzegovin|
|P055434|2003|2010|1/7/2010|15 Closed|Bosnia and Herzegovin|
|P055434|2003|2020|9/18/2019|16 Closed|Bosnia and Herzegovin|
|P055446|1998|1998||0 Closed|Papua New Guinea|
|P055446|1998|1999||3 Closed|Papua New Guinea|
|P055446|1998|2000|6/30/2000|6 Closed|Papua New Guinea|
|P055446|1998|2001|6/19/2001|8 Closed|Papua New Guinea|
|P055446|1998|2002|6/18/2002|10 Closed|Papua New Guinea|
|P055454|2001|2001|4/19/2001|2 Closed|India|
|P055454|2001|2002|6/19/2002|4 Closed|India|
**Table 3.8: Marginal Propensities to Find a Job**

||||**on the 2ndqu **|**arter**|
|---|---|---|---|---|
|**Variables**|**2007-2008**||**2008-200**|**9**<br>**2009-2010**|
|**By sex**||||<br>|
|Male|-0.0420<br>|***|-0.0366<br>|***<br>-0.0359 ***<br> <br>|
|<br>|(0.0031)||(0.0031)|<br>(0.0034)|
|**B schoolin level**<sup>**(1)**</sup>|||||
|**y g**<br>Sd|-0.0108|***|-0.0087|**<br>-0.0083 **|
|econary|(0.0036)||(0.0036)|<br>(0.0039)|
|High School|-0.0151<br>|***|-0.0115<br>|***<br>-0.0255 ***<br> <br>|
||(0.0036)||(0.0036)|<br>(0.0039)|
|Professional|-0.0401|***|-0.0351|***<br>-0.0504 ***|
|<br>|(0.0037)||(0.0037)|<br>(0.0039)|
|**By region**<sup>**(2)**</sup>|||||
||-0.0027||0.0095|<br>0.0080|
|Center region|(0.0061)||(0.0064)|<br>(0.0073)|
|Ctl lf i|-0.0151|**|-0.0008|<br>-0.0036|
|enra gu regon|(0.0070)||(0.0078)|<br>(0.0088)|
|h l i|-0.0039||0.0055|<br>0.0118|
|Nort centra regon|(0.0065)||(0.0069)|<br>(0.0081)|
||-0.0051||-0.0050|<br>0.0116|
|North pacific region|(0.0071)||(0.0070)|<br>(0.0087)|
|Central acific reion|-0.0049||0.0030|<br>0.0073|
|p g|(0.0062)||(0.0064)|<br>(0.0075)|
|Sth ifi i|-0.0052||-0.0058|<br>-0.0055|
|ou pacc regon|00091||00090|<br>00104|
||(.)||(.)|<br>(.)|
||-0.0052||-0.0141|**<br>-0.0045|
|Peninsular region|(0.0070)||(0.0064)|<br>(0.0080)|
|Border|0.0021||-0.0066|<br>-0.0002|
|<br>|(0.0063)||(0.0060)|<br>(0.0071)|
|**B ae**<sup>**(3)**</sup>|||||
|**y g**<br>|-0.0401|***|-0.0318|***<br>-0.0443 ***|
|From 26 to 35 years|(0.0033)||(0.0033)|<br>(0.0036)|
||-0.0534|***|-0.0365|***<br>-0.0550 ***|
|From 36 to 45 year|(0.0033)||(0.0034)|<br>(0.0036)|
|Frm 46 t 55 r|-0.0397|***|-0.0328|***<br>-0.0482 ***|
|o o yeas|(0.0037)||(0.0038)|<br>(0.0040)|
||-0.0237|***|-0.0195|***<br>-0.0285 ***|
|From 56 to 65 years|(0.0048)||(0.0049)|<br>(0.0050)|
|**By position in the household**|||||
||-0.0418|***|-0.0425|***<br>-0.0373 ***|
|Household head|(0.0036)||(0.0035)|(0.0039)|
|Pseudo R2|0.1395||0.1302|0.134|
|N|42915||42170|40336|

26