rafmacalaba/datause-encoder-data huggingface dataset:
||Simple Addition|Added 2+1=3|
|---|---|---|
||Spatial Vocabulary|Correctly identified the word describing a picture of a<br>ball under a box|
||Backward Digit Span|Could repeat 2 digits in reverse order|
|**Executive**|Forward Digit Span|Could repeat 3 digits in same order|
|**Functioning**|Head Toes Knees<br>Shoulder Task|Correctly followed instructions of a confusing task|
|**Social**<br>**Emotional**|Empathy Perspective<br>Taking|Could identify emotion crying girl was feeling|
|**English**|English Letter|IdentifiedEnglish letter A|
|**Letters**|Identification|Identified English letter Z|

# **_Teach ECE Score Calculation_**

As with the AIM-ECD DA, we followed the standardized guidance for calculating Teach ECE **behavior** , **element** , and **area** scores (World Bank, 2021). Behaviors are the individual items that comprise the Teach ECE assessment. When observing a class, the observer scores each of the 28 **behaviors** during two segments of an observation. Behaviors are coded in an ordinal scale as “Low,” “Medium,” and “High” (1, 3, or 5). To facilitate reporting and interpretation, more emphasis is placed on the coding of the ten **element** scores. Based on the behavior scores within each element, the observer scores each element on a scale of 1-5, with 1 indicating low quality and 5 indicating high quality. It is important to note that, unlike AIM-ECD DA, element scores are _not_ simply the average of behavior scores. While element scores are based on behavior scores, observers have flexibility of assigning slightly higher or lower element scores based on their judgment. Finally, we calculate the **area** scores and an overall Teach ECE score for each segment. The area score is calculated by taking the average of the elements that comprise that area and the Teach ECE Overall score is the average of the Classroom Culture, Guided Learning, and Socioemotional Support scores. Because of the structure of the assessment, there are essentially two sets of scores for each classroom observed—one from the first segment and one from the second segment. Segments are therefore the basic unit of analysis when analyzing Teach ECE scores. As such, when reporting percentages in relation to benchmarks, the actual interpretation is “percentage of segments” rather than “percentage of classes.” This paper primarily focuses on Area and Element scores as well as the overall Teach ECE score. In addition, it presents the results of selected exemplar items (i.e., scored behaviors) as shown in **Table 5** .

_Table 5. Exemplar behaviors from Teach ECE_

|**Area**|**Element**|**Exemplar Behavior**|
|---|---|---|
|**Time on Task**|Time on Task - Student|0.2 The teacher leads a learning activity and<br>most children are on task|
|**Classroom**<br>**Culture**|Supportive Learning<br>Environment|1.1 The teacher treats all children respectfully|

11
power shortages, leading to a huge escalation in sector costs. The situation was later exacerbated by delays in the commissioning of Bujagali hydro-power project, and the volume of short-term leased capacity has risen to 170 mega-watts further inflating costs. Diesel prices in East Africa have been traditionally higher than many other countries in southern and West Africa; in the case of Uganda, the prices are higher still, reflecting both the high costs of importing oil and the not insignificant transportation costs ($0.08 per tonne-km) for transporting oil along East African corridors.

**Table 11. Diesel prices in Uganda are amongst the highest in Africa**

||Cost of power<br>production|**Die**|**sel prices ($ pe **|**r liter)**|
|---|---|---|---|---|
||<br>($ per kWh)|2004|2006|2008|
|Ethiopia|8.4|42|62|89|
|Ghana|12|43|84|90|
|Kenya|14|76|98|114|
|Nigeria|15|45|66|113|
|Rwanda|17|99|108|137|
|Tanzania|14|87|99|130|
|**Uganda**|**24**|**88**|**101 **|**122**|
|_Source:_ GTZ|2009.||||

_The average tariff at $0.16 fails to recover the high costs of power production._ Uganda’s power tariffs had remained fairly steady at around or just below $0.10 per kWh in the early 2000s. Tariffs were increased in 2006 to generate more revenue for the cash starved power sector following the drought and increased reliance on emergency power generation, reaching $0.18 per kWh in the late 2000s. Despite these increases, domestic tariffs only covered about two-thirds of the costs of power production in 2010 (World Bank 2010a).

**Figure 21. Relatively high tariffs for power in Uganda, 2006**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>US cents per KWh<br>Liberia Chad Cape Verde Senegal Burkina Faso Mali Niger Kenya Congo, Rep. Uganda Rwanda Benin Cote d'Ivoire Namibia Madagascar Cameroon Ghana Botswana Mozambique Lesotho Tanzania Zimbabwe South Africa Ethiopia Botswana Nigeria Malawi DRC Zambia<br><!-- End of picture text --> _Source:_ Derived from Shkaratan and Briceño-Garmendia (2008) and World Bank (2010a). **[[Should this be 2010b?]]** _Note:_ DRC = Democratic Republic of Congo.

35
Table A1.7: European Economic Communities, Numbers of tariff lines for which <u>the special safeguard system was operational (EC schedule lines, 8-digit)</u>

|<br>Marketing Year|<br>Volume-<br>based<sup>a</sup>|<br>Price-based|
|---|---|---|
|1995/1996|not invoked|12|
|1996/1997|26|14|
|1997/1998|26|13|
|1998/1999|27|12|
|1999/2000|26|13|
|2000/2001|27|13|
|2001/2002|27|17|
|2002/2003|27|16|
|2003/2004|27|17|
|2004/2005|27|17|
|2005/2006|27|15|
|Memo item: Numb<br>lines for which trig<br>were notified‘up-f|er of tariff<br>ger prices<br>ront’|531|

Source: Tabulated from national notifications of SSG actions, available on the WTO documents website.

Note: a The EEC has notified annually, 1995/96 through 2005/2006, that volume-based action has not been invoked.’ There is no parallel notification for price-based measures.

57
Table 3. Distributions of Sample Firms During 1997-2001

|**_Total n_**|**_umber of ra_**|**_ted (R) an_**|**_d nonrated_**|**_(NR) firms b_**|**_y city and ye_**|**_ar_**|
|---|---|---|---|---|---|---|
|Year|Status|Huaian|Wuxi|Yangzhou|Zhenjiang|Total|
|1997<sup>a</sup>|Unrated|42|33|64|89|228|
|1998<sup>a</sup>|Unrated|46|26|71|81|224|
|1999<sup>a</sup>|Unrated|46|32|76|12|166|
||Rated||||57|57|
|2000|Unrated|54|43|68|13|178|
||Rated||||78|78|
|2001|Unrated|16|1|13|131|161|
||Rated|39|69|59|91|258|
|1997-2001|Unrated|204|135|292|326|957|
||Rated|39|69|59|226|393|
||Total|243|204|351|552|1350|
||<br>**_Distrib_**|<br>**_ution of ra_**<br>|<br>**_ted firms b_**<br>|<br>**_y rating colo_**<br>|<br>**_rs*_**<br>|<br>|
||Green|Blue|Yellow|Red|Black|Rated|
|ZhenJiang|1|34|17|4|1|57|
|(1999)|(1.75)|(59.65)|(29.82)|(7.02)|(1.75)||
|Zhenjiang|2|48|19|8|1|78|
|(2000)|(2.56)|(61.54)|(24.36)|(10.26)|(1.28)||
|Huai’an|2|29|4|3|1|39|
|(2001)|(5.13)|(74.36)|(10.26)|(7.69)|(2.56)||
|Wuxi|15|20|20|8|6|69|
|(2001)|(21.74)|(28.99)|(28.99)|(10.59)|(8.70)||
|Yangzhou|2|52|5|0|0|59|
|(2001)|(3.39)|(88.14)|(8.47)|(0.00)|(0.00)||
|Zhenjiang|2|60|20|7|2|91|
|(2001)|(2.20)|(65.93)|(21.98)|(7.69)|(2.20)||
|All cities|24|243|85|30|11|393|
|(1998-2001)|(6.11)|(61.83)|(21.63)|(7.63)|(2.80)||

* Figures in parentheses represent the percent of firms by rating colors.

a Green Watch began in Zhenjiang in 1999, and in the other three cities in 2001. Thus, no firms were rated in 1997 and 1998, and only some firms in Zhenjiang were rated in 1999 and 2000.
Box 4: Moving to a Market Economy: **The People's Democratic Republic of Laos** Laos began economic reforms in 1986 in an effort to move from a centrally-planned to a market economy. The steps taken included privatization of public enterprises and preparation for civil service reform. Both steps were expected to lead to the announcement of labor redundancies. In preparation for this, attention was focused on the retraining of workers for new jobs. This required an understanding of the skills workers already possessed, the skill needs of the emerging market economy, and the capacity of training institutions to meet these needs. Very limited information was available to address these issues. A decennial census was completed in 1985, but the information it contained on labor force activity and employment had not been fully analyzed as of early 1991. Even if this had been done, however, it would have provided at best only a benchmark for the changes taking place at the close of the decade. More recent information on the balance of skills demand and supply from household or establishment surveys was unavailable.

The situation, however, did not demand an immediate large scale investment in labor market information. In the small country of Laos, other less costly strategies could be adopted initially. The major restructuring of employment would be in urban areas, principally Vientiane. The United National Development Program (UNDP), as a consequence, funded several small scale studies to aid the Government in assessing retraining needs. A census of government workers was planned to gather information on their skills along with a survey of the expected skill needs of a restructured civil service. A small survey of private establishments in the Vientiane area was planned to gather information on employer assessments of skill needs. And finally, a survey of public training institutions was planned to assess the training capacity and performance of these institutions. The judgements formed from this information would guide public spending on training for the restructured economy. The Government, meanwhile, sought further technical assistance to improve its manpower planning capacity.

27
**Table 13: Effect of disclosure by firm characteristic, within firm**

||**All categories**|**Disclosure**<br>**categories only**|**Child labor**|**Contracts**|**Discrimination**|**Forced**<br>**Labor**|**Freedom of**<br>**association**|**Occupational safety**<br>**and health**|**Work time**|
|---|---|---|---|---|---|---|---|---|---|
|POST|‐0.046***|‐0.060**|‐0.094***|0.106***|‐0.006**|‐0.003|‐0.141***|‐0.105***|‐0.081***|
||(0.005)|(0.026)|(0.024)|(0.017)|(0.002)|(0.002)|(0.014)|(0.011)|(0.022)|
|Firm size|0.052*|‐0.473*|0.047|0.035|‐0.009|0.003|‐0.034|0.027|‐0.016|
||(0.028)|(0.272)|(0.036)|(0.102)|(0.016)|(0.002)|(0.089)|(0.059)|(0.127)|
|Firm age|0.009|0.027|0.060*|‐0.068|‐0.017|0.000|‐0.04|0.05|‐0.016|
||(0.028)|(0.272)|(0.035)|(0.102)|(0.015)|(0.000)|(0.089)|(0.059)|(0.127)|
|Baseline|0.046|0.033|‐0.060*|0.003|‐0.014|0.000|0.04|0.043|0.107|
|noncompliance|(0.028)|(0.273)|(0.035)|(0.102)|(0.015)|(0.000)|(0.089)|(0.059)|(0.127)|
|Length of|‐0.018***|‐0.016*|‐0.013|‐0.044***|0.001|0.000|‐0.017***|‐0.028***|‐0.012|
|participation|(0.003)|(0.008)|(0.010)|(0.009)|(0.001)|(0.001)|(0.006)|(0.005)|(0.009)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|209150|3004|7465|20671|48001|12016|24032|77959|17504|
|R‐squared|0.029|0.245|0.256|0.096|0.024|0.035|0.053|0.056|0.071|

Note: Non‐compliance=1. Firm size, firm age, baseline non‐compliance, and length of participation in program are all identified as a dummy variable equal to 1 if the firm is above the median. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance. Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

27
Table A.1: Relevant features of household survey over time (2001-2019)

|Year|Self-emp. <br>Module|Ag.<br>Imputed<br>rent|13/14vo<br>identifer|Formality<br>(contributes<br>to IHSS)|Self-emp.<br>reference<br>period|Census<br>weights|
|---|---|---|---|---|---|---|
|2001||Estim ptile|||3 months|2001|
|2002||Estim ptile|||3 months|2001|
|2003||Estim ptile|||3 months|2001|
|2004|✓|Estim ptile|||3 months|2001|
|2005|✓|Estim ptile||✓|3 months|2001|
|2006|✓|Estim ptile|✓|✓|3 months|2001|
|2007|✓|Estim ptile|✓|✓|3 months|2001|
|2008|✓|Estim ptile|✓|✓|3 months|2001|
|2009|✓|Estim ptile|✓|✓|3 months|2001|
|2010|✓|Estim ptile|✓|✓|3 months|2001|
|2011|✓|Model|✓|✓|3 months|2001|
|2012|✓|Model|✓|✓|3 months|2001|
|2013|✓|Model|✓|✓|3 months|2013*|
|2014||Model|✓|✓|6 months|2013|
|2015||Model|✓|✓|6 months|2013|
|2016||Model|✓|✓|6 months|2013|
|2017||Model|✓|✓|6 months|2013|
|2018||Model|✓|✓|6 months|2013|
|2019||Model|✓|✓|6 months|2013|

_Note:_ Own elaboration based on yearly questionnaires and micro-data from EPHPM. ”Self-emp. Ag. module” refers to the existence of additional module on income from agricultural activities from self-employment. ”Imputed Rent” refers to the method used for estimating imputed rent: for the period 2011-2019 it is estimated from a model that imputes rent for owners using characteristics from households that rent and declare rental value; for 2001 - 2010 imputed rent is estimated using average shares of imputed rent, by percentile, from 2011 - 2019. ”13/14vo identifier” refers to the existence of question to workers whether they receive 13th and 14th additional salaries. ”Formality” refers to the existence of a question asking for formality status of workers. ”Self-employment reference period” presents the differences in recall period for income from self-employment. Finally, ”Census weights” show the changes in Census weights used for the survey (* pre-census weights for 2013).

41
plies that a standard deviation increase in the tariff rate increases HHI by 1, 794 × 0.05 = 90 points. The coefficient on the interaction between tariff and fuel export share is positive β3<sup>fuel</sup> = 8, 696 (1,907) indicating that the positive association between tariffs and concentration is much stronger in fuel exporting economies: at the median, a 5 percentage point increase in the tariff rate (the standard deviation) and an 18 percentage point increase in fuel export share (the standard deviation) will raise HHI by about 8, 696×0.05×0.18 = 78 points. The interaction term for ores and metals exports is positive, though much smaller, and the interaction term for food exports is negative. The result is clear that tariffs contribute to import market concentration, but especially in fuel exporting economies.

To confirm that the interaction effect is due to greater tariff evasion in fuel exporting economies, we use a proxy for tariff evasion as the dependent variable in the same regression. We use the measure of Fisman and Wei (2004, note 6) based on import underinvoicing: where exports are total exports by all countries w of product i to destination country c (one of our importing countries) in year t reported in COMTRADE, and imports are as reported in our customs data. Results are reported in Column 3 of Table 3. The interaction coefficients follow a similar pattern as in Column 2, where the coefficient on the interaction between tariff and fuel export share is positive β3<sup>fuel</sup> = 0.36 (0.17), indicating greater evasion, but the coefficients on the interaction between tariff and ores and metals export share and food export share is negative.<sup>9</sup> Higher tariffs are associated with higher values of the proxy for tariff evasion in fuel exporting economies, but not in ores and metals or food exporting

> 9The import underinvoicing proxy for tariff evasion has been found to be positively correlated with higher tariffs (Bhagwati, 1964; Fisman and Wei, 2004; Mishra, Subramanian and Topalova, 2008; Javorcik and Narciso, 2017). In our sample, the value of β2 + β3<sup>fuel</sup> = −0.11 (0.18) so a positive association between tariffs and evasion cannot be rejected in fuel exporting economies (i.e., the upper bound of the 95% confidence interval of this term is 0.24).

16
**Table 7.** **<u>Conditional Logit Coefficient Estimates by Gender and Education</u>**

||Male<br>Coefficient (se)<br>(1)|WTP<br>(2)|Female<br>Interaction (se)<br>(3)|WTP<br>(4)|Less than Prim<br>Coefficient (se)<br>(5)|ary<br>WTP<br>(6)|Secondary<br>Interaction (se)<br>(7)|<br>WTP<br>(8)|
|---|---|---|---|---|---|---|---|---|
|Contract - 6 months|0.99***|18.7|-0.093|19.5|0.86***|16.9|0.19*|20.7|
||(0.065)||(0.11)||(0.085)||(0.11)||
|Contract - 1 year|1.43***|27.0|-0.30***|24.5|1.15***|22.5|0.37***|30.0|
||(0.071)||(0.11)||(0.090)||(0.12)||
|Contract - long-term|2.31***|43.6|-0.45**|40.3|1.81***|35.5|0.68***|49.1|
||(0.11)||(0.18)||(0.14)||(0.19)||
|Notice|0.020***|0.38|-0.0019|0.39|0.018***|0.35|0.0028|0.41|
||(0.0010)||(0.0018)||(0.0014)||(0.0018)||
|Hours|-0.020***|-0.38|-0.0093***|-0.64|-0.020***|-0.39|-0.0026|-0.45|
||(0.0015)||(0.0027)||(0.0021)||(0.0027)||
|Leave|0.028***|0.53|-0.0066|0.46|0.028***|0.55|-0.0028|0.50|
||(0.0029)||(0.0049)||(0.0040)||(0.0051)||
|Provident Fund|0.94***|17.7|-0.20*|16.1|0.93***|18.2|-0.032|17.7|
||(0.063)||(0.11)||(0.091)||(0.11)||
|Perc. Change Income|0.053***<br>(0.0021)|1.0|-0.0069**<br>(0.0035)|1.0|0.051***<br>(0.0029)|1.0|-0.00030<br>(0.0037)|1.0|
|Observations|23,568||||23,496||||

_Source_ : Authors’ analysis based on survey data.

_Note_ : WTP= willingness to pay. Columns (1) and (5) show the baseline coefficients for men and for workers with primary or lower education, while Columns (3) and (7) show interactions for women and for workers with a secondary or higher education, respectively. The WTP for each attribute for women is calculated by summing the coefficients on that attribute in Columns (1) and (3), respectively, and dividing by the sum of the coefficients on income in Columns (1) and (3). A 46
8 conditions determining the latter (e.g. adequate and timely rainfall in the monsoon period) would also be reflected<sup>in the agricultural output for the</sup> entire year.<sup>11</sup> Monthly data on rainfall for four meteorological<sup>sub-divisionsin</sup> Maharashtra were collated from various issues<sup>of the Statistical Abstract of</sup> India and the Agricultural Situation in IndiA.<sup>Rainfall for Maharashtra is</sup> derived as a weighted average of the<sup>rainfall in the four sub-divisions, the</sup> weights being proportional to the average<sup>net sown areas in the sub-divisions</sup> during 1978/79 to 1980/81 (COM, 1984). The<sup>effect of rainfall on monthly EGS</sup> labor attendance, although potentially significant,<sup>need not be straight-</sup> forward. The rainfall effect would presumably<sup>be seasonally differentiated,</sup> depending on whether there is deficit<sup>or excess rainfall, and on the amount of</sup> deficit or excess. We thus use a fairly flexible<sup>approach in introducing the</sup> rainfall variables, by defining the following<sup>"rainfall excess" (RE) and</sup> "rainfali deficit" (RD) variables: where Mj is a dummy variable for month<sup>j, DE is a</sup><sup>_dummy_variable for excess</sup> rainfall (equals one if there is excess<sup>rain, zero otherwise), R and NR ar-</sup> the actual and normal rainfall respectively.<sup>The normal rainfall for any</sup> month is defined as the average rainfall<sup>for that month over the entire 13-</sup> year period 1975/76 to 1988/89.

The wage rate in agriculture is probably<sup>the most important omitted</sup> variable in our model. We considered including<sup>the annual agricultural</sup> wage
|assumptions:<br>excess supply|_φ_|2/12|2/12|4/12|4/12|2/12|2/12|4/12|4/12|
|---|---|---|---|---|---|---|---|---|---|
|min. calorie pppd||2200|2300|2200|2300|2200|2300|2200|2300|
|maximum distance||200|200|200|200|300|300|300|300|
|ln(distance)|_βdis_|0.451|0.489|0.384|0.472|0.480|0.449|0.410|0.443|
|||(4.3)|(5.2)|(3.3)|(4.1)|(8.2)|(8.3)|(6.1)|(5.9)|
|ln(fuel price)|_βfp_|1.332|1.113|0.979|0.957|1.284|1.102|1.083|1.164|
|||(3.4)|(3.1)|(2.4)|(2.5)|(4.0)|(3.6)|(3.1)|(3.5)|
|trend|_βt_|-.0057|-0.0062|-0.0042|-0.0054|-0.0050|-0.0052|-0.0040|-0.0050|
|||(1.8)|(2.2)|(1.3)|(1.8)|(1.9)|(2.1)|(1.5)|(1.9)|
|dbuy x dmonth|_νj, δm, _<br>_τjm, _|yes|yes|yes|yes|yes|yes|yes|yes|
|dsel x dmonth<br>|_μk,λkm_|yes|yes|yes|yes|yes|yes|yes|yes|
|adjusted R<sup>2</sup>||0.3335|0.3652|0.3510|0.3618|0.3886|0.4044|0.3986|0.3868|
|number of<br>observations||766|802|639|649|1090|1131|895|867|

The table reports transaction cost estimations on the basis of district maize prices from June 1999 to October 2009. Estimation is by OLS (district fixed-effect estimations are available from the author on request). The transaction cost and fuel price are deflated with the Malawi consumer price index. The sample is restricted to observations below a maximum distance. Absolute _t_ -statistics are given in parentheses (.) below the coefficient. We do not report the coefficients and t-values of the constant term and dummy variables. Columns reflect different assumptions with respect to the minimum per person per day calorie intake (either 2200 or 2300 kcal), excess supply over requirements before districts start to export (either two- or four-month district requirements; see also _φ_ in equation (2a)), and maximum distance, the distance beyond which trade is assumed to be unlikely (either 200 or 300 km).

The transaction cost equations are estimated by OLS. Trade pair fixed-effect estimations (not reported) generate a similar output for transport fuel price and trend and thus confirm the OLS results. In the trade pair fixed-effect estimation, the distance variable drops out because of the within-transformation. Because we are keen to quantify the coefficient of distance, since this variable reflects a variety of influences on transaction costs, we maintain the presentation of OLS results.

The estimation results are well behaved. The reported estimation output of all equations indicates that distance, fuel price, and the common trend are statistically significant (or close to significant, in the case of the trend variable) and have the expected sign. The elasticity of transaction costs with respect to fuel price approaches unity in most equations, with values between 0.96 and 1.33. This outcome is reasonable because fuel prices reflect the variable transport cost, which must be passed on in a competitive market and because transport costs constitute a large component of transaction costs. The elasticity of transaction costs with respect 19
# **<u>Appendix S8: Effects of Priming on Feeling</u>**

|Dependent variables:<br>|Feel<br>frightened<br>|Feel<br>tensed<br>|Feel<br>nervous<br>|Feel<br>uncomfortable|
|---|---|---|---|---|
|_Panel A: No covariates_|(1)|(2)|(3)|(4)|
|Vaccine condition & Fear|0.360***<br>(0.028)|0.368***<br>(0.028)|0.398***<br>(0.032)|0.312***<br>(0.030)|
|Observations|2467|2467|2465|2466|
|R-squared|0.119|0.132|0.136|0.095|
|Control mean of dependent variable|0.292|0.242|0.280|0.289|
|Fixed effects by village (80 villages)|X|X|X|X|
|_Panel B:With covariates_|(1)|(2)|(3)|(4)|
|Vaccine condition & Fear|0.336***<br>(0.030)|0.360***<br>(0.033)|0.389***<br>(0.034)|0.302***<br>(0.021)|
|Observations|2202|2202|2200|2201|
|R-squared|0.157|0.183|0.182|0.151|
|Control mean of dependent variable|0.293|0.239|0.278|0.292|
|Covariates|X|X|X|X|
|Fixed effects by village (80 villages)|X|X|X|X|

_Source:_ Authors estimate _Notes:_ These are based on the analysis sample of 2,482 women. Missing observations are due to missing values in dependent variables. Robust standard errors clustered by village (80 villages) are in parentheses. All the dependent variables are dummy variables which take 1 if a respondent answers "very much" or "much" to the question: "How did you feel about the flipchart you were just shown? Feel frightened, feel tensed, feel nervous, feel uncomfortable?" after the flipcharts intervention. Covariates are Clinic condition, Woman's age, Age squared, Muslim, Any education, Marital status = Single, Currently pregnant, Have children, Has paid work, Monthly HH earning per capita (naira), Distance to clinic (km), Transport to clinic (naira), Opportunity costs to clinic (naira), Ever used clinic, Received tetanus vaccine before, Likely to contract tetanus, Number of people who die of tetanus, Very worried about Tetanus, Tetanus is very bad, Very important to be protected from tetanus, Vaccine efficacy, Vaccines protect from disease, Needles are scary, Vaccines have side effects, Vaccines give diseases, Vaccines give HIV, Heart rate (beat/min). Control mean of dependent variable is mean under Vaccine condition. * significant at 10%; ** significant at 5%; *** significant at 1%