rafmacalaba/datause-encoder-data huggingface dataset:
Table A3: Effects of Peers on Financial Investment on Childrens Education (Robustness): Alternative Definition of Peer Group: _±_ 5 Years Age Band

||**Rural (R)**|**Urban (U)**|**R–U**|**Rural (R)**|**Urban (U)**|**R–U**|
|---|---|---|---|---|---|---|
|Father Edu|0.049***|0.109***|-0.061***|0.050***|0.112***|-0.062***|
||(0.006)|(0.010)|(0.011)|(0.006)|(0.010)|(0.011)|
|Peer Edu|0.024|0.158***|-0.134***|0.023|0.153***|-0.130**|
||(0.023)|(0.046)|(0.051)|(0.022)|(0.046)|(0.051)|
|Number of School Age Children||||-0.021<br>(0.026)|-0.082***<br>(0.038)||
|Constant|0.082|-0.982**||-0.021|-0.979**||
||(0.211)|(0.445)||(0.204)|(0.445)||
|Includes Ability|No|No|No|Yes|Yes|Yes|
|Observations|9463|8311|17774|9463|8311|17774|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). The dependent variable is educational expenditure adjusting for inflation. Inflation rates are calculated using price of rice separately for rural and urban areas. A peer is someone who was born in the respondents district within _±_ 5 years of respondents birth. Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square. Ability controls include cognitive ability index and its square.

3
the link between child ability and school enrollment and school continuation decisions is critical for developing policy prescriptions to improve educational outcomes.

We make four main contributions to the literature on explaining household school investment decisions. First, we employ direct measures of a child’s ability for all children of primary school age (5 to 15), regardless of whether or not they are currently enrolled in school. We use the Raven’s Colored Progressive Matrices (CPM) and the Weschler Intelligence Scales (WISC) Digit Span as measures of a child’s cognitive ability. Second, our paper is unique in explicitly incorporating direct measures of the ability of each child’s siblings (both absolute and relative measures) and to show how sibling ability ‘rivalry’ exerts a strong impact on the parents’ decision of which child to send to school. Third, the survey instrument asks parents to provide their perceptions about the likely chances of future economic success for each of their children. We show that a similar pattern of sibling rivalry is observed using either these parent perceptions or the externally validated cognitive ability tests measuring child ability. Fourth, we address potential concerns about schooling influencing measures of child ability by exploiting the panel data structure and focusing on the relationship between the enrollment decision in the survey’s second year and the ability measures observed in the survey’s first year for the subset of young children who were not enrolled and not yet of typical school age in the first year.

We explore both the extensive margin of school enrollment during the 2007-2008 school year and grade progression measures, as well as the intensive margin of school related expenses. We find that a child with a one standard deviation higher ability test score has a 16 percent higher likelihood of being currently enrolled in school, while a child with a higher ability sibling is 16 percent less likely to be currently enrolled and having two higher ability siblings lowers a child’s probability of enrollment by 30 percent. Household fixed effects regressions show that 3
Jules Gazeaud, Nausheen Khan, Eric Mvukiyehe, and Olivier Sterck<sup>∗</sup> _JEL Classification:_ J16, L25, L26, O12 _Keywords:_ Cash Transfers, Financial Training, Gender Roles, Employment

> ∗Jules Gazeaud: J-PAL MENA, The American University in Cairo. E-mail: jgazeaud@povertyactionlab.org; Nausheen Khan: World Bank. E-mail: nkhan8@worldbank.org; Eric Mvukiyehe: Duke University. E-mail: eric.mvukiyehe@duke.edu; Olivier Sterck: ODID, University of Oxford. E-mail: olivier.sterck@qeh.ox.ac.uk. We are grateful to Arthur Alik-Lagrange, Diego Angel-Urdinola, Rabah Arezki, Mahdi Barouni, Simone Bertoli, Anush Bezhanyan, Theophile Bougna, Bruno Crepon, Carlo Del Ninno, Laura Derksen, Alison Fahey, Ugo Gentilini, Xavier Gin ́e, Alvaro Gonzalez, Afef Haddad, Mary HallwardDriemeier, Jesko Hentschel, Marcus Holmlund, Jason Kerwin, Elena Lanchovichina, Daniel Lederman, Arianna Legovini, Florian Leon, John Loeser, Fareeba Mahmood, David McKenzie, Lili Mottaghi, Khalid Ahmed Ali Moheyddeen, Yuko Okamura, Laura Ralston, Aminur Rahman, Imran Rasul, Emma Riley, Lea Rouanet, Tony Verheijen, Jan von der Goltz, Damien de Walque, Nahla Zeitoun, and numerous seminar participants for their inputs and support at various stages of the research. We thank Sarah Elven, Samih Ferrah, Joe St Clair, and Varada Shrotri for wonderful research assistance. During survey implementation, we were privileged to work with BJKA Consulting, including Samy Kallel (Director General) and an excellent team of enumerators. We are particularly indebted to Samir Ben Zineb for fieldwork coordination. Finally, we express our deepest gratitude to all households that participated to our surveys. This research would have not been possible without their collaboration. We gratefully acknowledge financial support from the World Bank through the Jobs MultiDonors Trust Fund, the Umbrella Facility for Gender Equality (UFGE), the MNA Gender Innovation Lab (MNAGIL), and the i2i Multi-Donors Trust Fund. This impact evaluation study received Institutional Review Board (IRB) clearance from _IRB Solutions_ , under protocol #2020/11/17 and was registered on the AEA RCT Registry under registration number AEARCTR-0007136. Computational reproducibility was verified by DIME Analytics. All errors and opinions expressed in the paper remain ours, the authors.
# **Multiple Hypothesis Testing—Q-Values**

Table A17: Impact on primary outcomes—with false discovery rate adjusted q-values

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||SR|H Behav|ior||IGA||
||Beta|pval|qval|Beta|pval|qval|
|T1 (safe spaces)|0.204|0.008|0.051|0.051|0.579|0.661|
|T1 + Leaders|0.136|0.146|0.247|0.023|0.836|0.805|
|T2 (safe spaces + IGA)|0.050|0.555|0.661|0.180|0.046|0.126|
|T2 + Leaders|-0.016|0.889|0.805|0.240|0.007|0.050|
|T3 (safe spaces + husbands’ clubs)|0.197|0.041|0.117|0.293|0.000|0.017|
|T3 + Leaders|0.141|0.049|0.127|0.271|0.022|0.082|
|T4 (Control Spillover)|0.078|0.245|0.354|0.074|0.293|0.393|

_Note:_ The coefficients and the p-values in this table (beta and pval) are obtained from the regressions in table 3 and they denote the impact of our treatments. The q-values (qval) are False Discovery Rate adjusted p-values and are estimated using the procedure described in Anderson 2008.

Table A18: Impact on mechanism outcomes—with false discovery rate adjusted q-values

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Know|ledge in|SRH|Gen|der attit|udes|As|piration|s||SES||
||Beta|pval|qval|Beta|pval|qval|Beta|pval|qval|Beta|pval|qval|
|T1 (safe spaces)|0.308|0.012|0.060|0.209|0.022|0.082|-0.183|0.174|0.293|-0.023|0.842|0.805|
|T1 + Leaders|0.208|0.071|0.160|0.084|0.429|0.567|-0.161|0.182|0.300|0.246|0.031|0.101|
|T2 (safe spaces + IGA)|0.312|0.009|0.055|0.162|0.034|0.105|0.128|0.236|0.354|0.248|0.006|0.050|
|T2 + Leaders|0.183|0.119|0.217|0.070|0.494|0.610|-0.037|0.709|0.768|0.302|0.003|0.047|
|T3 (safe spaces + husbands’ clubs)|0.348|0.004|0.047|0.218|0.005|0.048|0.011|0.907|0.805|0.191|0.039|0.115|
|T3 + Leaders|0.225|0.127|0.224|0.203|0.004|0.047|-0.006|0.972|0.805|0.459|0.000|0.002|
|T4 (Control Spillover)|0.008|0.943|0.805|0.042|0.553|0.661|-0.029|0.744|0.786|0.120|0.205|0.332|

_Note:_ The coefficients and the p-values in this table (beta and pval) are obtained from the regressions in table 4 and they denote the impact of our treatments. The q-values (qval) are False Discovery Rate adjusted p-values and are estimated using the procedure described in Anderson 2008.
matters, meaning that the 1,790 judgments available on Manupatra represent about 29% of this overall number.<sup>43</sup> The ratio for 2008 is higher than in past years. In 1990, for example, the Court disposed of 4,348 regular hearing matters (20,890 admission), but only 769 judgments were listed on Manupatra, or 17.7%.<sup>44</sup> Still, a larger per cent of the regular hearing disposed of cases are actually captured in the judgments listed on Manupatra than these numbers imply. Many judgments are often a set of related petitions that are combined into one case for hearing and judgment. Therefore, four related petitions may be heard together resulting in the disposal of four cases, but only one judgment. It is difficult to determine how much this discrepancy accounts for the gap between reported judgments and the number of disposals each year. Regardless, the number of cases that do not result in a reported judgment that can be tracked on Manupatra or other databases is significant.

The chart below shows the reporting rate in 2008 of the High Courts that were examined in this study based on searches of Manupatra. As in the Supreme Court, reporting rates have gotten better across High Courts in recent years, meaning that during most of the years searched the reporting rate was actually less than for 2008. Unfortunately, information was not available demarcating how many cases disposed of in each high court were regular hearing matters and how many were admission matters. Still, looking at this ratio - the total number of matters disposed of divided by reported judgments – does seem to show that fewer cases are reported in the High Courts than at the Supreme Court. There is also marked variation in reporting amongst High Courts (the Gujarat High Court has a reporting rate of almost three times the Bihar High Court based on these numbers).

|2008|Disposed of cases|Reported cases on<br>Manupatra|% of total disposed<br>cases (admission<br>and regular)<br>reported|
|---|---|---|---|
|Kerala|84,213|459|.55%|
|Gujarat|66,382|915|1.4%|
|Bihar|91,230|444|.49%|
|West Bengal|74,228|616|.83%|

To sum up, in both the Supreme Court and the High Courts, reported decisions only represent a fraction of cases actually decided in any given year. Many of these unreported cases undoubtedly concerned contract teachers. In some instances in this study, no record of the High Court case that produced a relevant Supreme Court appeal and judgment could be found. In other instances, High Court cases indicated they had been heard earlier by the Supreme Court, but those Supreme Court judgments could not

> 43 The Judgment Information System (http://www.judis.nic.in/ ) lists 2695 or about 43%, but Judis only gives better numbers in the last couple years, presumably because it has begun putting up unreported judgments as well.

> 44 JUDIS lists significantly fewer cases (405)

12
<!-- Start of picture text --> 0,40%<br>0,35%<br>0,30%<br>0,25%<br>0,20%<br>0,15%<br>0,10%<br>0,05%<br>0,00%<br>2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br>-0,05%<br>irap_bas irap_ires irap_vat<br><!-- End of picture text --> Figure 3: GDP growth from 2018 to 2030, measured as percentage change with respect to the baseline scenario, in all considered simulations.

22
Table 6. Infant and diarrheal associated mortality, yearly absolute and relative percentage change

|YEAR|IMR|DIARRHEAL<br>ASSOCIATED<br>|ABS<br>CHA<br>|OLUTE<br>NGE<br>|PERCE<br>CHA<br>|NTAGE<br>NGE<br>|
|---|---|---|---|---|---|---|
|||MORTALlTY|IMR|DAM|IMR|DAM|
|1965|113.15|59.60||_<br>_<br>_<br>_|||
|1966|127.05|70.03|13.9|10.07|12.28|16.79|
|1967|116.09|64.60|-10.96|-5.43|-8.63|-7.75|
|1968|131.27|67.42|15.18|2.82|13.08|4.37|
|1969|114.60|70.20|-16.67|2.78|-12.70|4.12|
|1970|116.28|56.88|1.68|-13.32|1.47|-18.97|
|1971|103.27|41.83|-13.01|-15.05|-11.19|-26.46|
|1972|115.97|53.24|12.70|11.31|12.30|27.04|
|1973|97.96|48.21|-18.01|-4.93|-15.53|-9.28|
|1974|101.26|53.10|3.30|4.89|3.37|10.14|
|1975|89.17|43.13|-12.09|-9.97|-11.94|-18.78|
|1976|87.49|46.35|-1.68|3.22|-1.88|7.47|
|1977|85.29|43.33|-2.20|-3.02|-2.51|-6.52|
|1978|73.46|38.11|-11.83|-5.22|-13.87|-12.05|
|1979|76.43|40.12|2.97|2.01|4.04|5.27|
|1980|76.02|39.20|-0.41|-0.92|-0.54|-2.29|
|1981|70.29|36.02|-5.73|-3.18|-7.54|-8.11|
|1982|70.49|33.17|0.20|2.85|0.28|-7.91|
|1983|64.64|29.32|-5.85|-3.85|-8.30|-11.61|
|1984|62.10|25.96|-2.54|-3.36|-3.93|-11.46|
|1985|49.28|18.19|-12.82|-7.77|-20.64|-29.93|
|1986|47.14|16.00|-2.14|-2.19|-4.34|-12.04|
|1987|49.43|16.08|2.29|0.08|4.86|0.01|
|1988|42.83|12.32|-6.60|-3.76|-13.35|-23.38|

Source: Annual Vital Statistics Reports published by Central Agency for Public Mobilization and Statistics, Egypt.

16
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE benchmarked against other regional power pools, namely, the East African Power Pool (EAPP-NB, expanded to include the key Nile Basin trading partners Egypt, Ethiopia, and Sudan) and the SAPP.

As of today, only 90 percent of the region’s effective power demand is being met. The baseline total net demand for power in the CAPP was 10.7 terawatt-hours (TWh) in 2005, making it by far the smallest power market in Sub-Saharan Africa. At the country level, between 7 and 11 percent of total power demand is suppressed. In the Central African Republic, the Republic of Congo, and Gabon, frequent outages suppress as much as 10 percent of demand. In Cameroon, where there are several power generation plants, outages suppress demand by as much as 7 percent.

Power demand in the CAPP area is expected to almost double over the next decade. Taking into account the need to fully satisfy existing demand for power—plus the anticipated expansion in market demands driven by economic growth in commerce and industry, plus the need to provide additional power to support the planned expansion of electrification from 35 percent to 53 percent of households — it is estimated that power demand could reach 20.2 TWh by 2015. This would require the development of 4,400 megawatts (MW), or 2.5 times existing generation capacity (table 3.2). Over half the current capacity—900 MW—needs to be refurbished.<sup>5</sup>

**Table 3.2. Demand and suppressed demand in the CAPP**

||**Total net demand**<br>**in 2005**|**% of suppressed**<br>**demand as a total**<br>**of net demand**|**Market demand**<br>**2015**|**Social demand**<br>**with national**<br>**targets 2015**|**Total net demand**<br>**2015**|
|---|---|---|---|---|---|
|**CAPP**|10.7|**9**|**17.1**|**3.1**|**20.2**|
|WAPP|31.3|42|69.6|24.8|94.3|
|SAPP|258.8|1|383|14|396.9|
|EAPP|100.6|1|144.8|24.2|169|
|Island states|1.1|5|1.6|1.5|3|
|Cameroon|3.4|7|5.2|1.2|6.4|
|Central African Republic|0.1|11|0.1|0.4|0.6|
|Chad|0.1|10|0.1|0.8|1|
|Congo, Rep. of|5.84|11|9.8|0.5|10.3|
|Equatorial Guinea|0.03|10|0.1|0.05|0.1|
|Gabon|1.2|11|1.7|0.1|1.8|

_Source:_ Rosnes and Vennemo 2009.

_Note:_ WAPP = West African Power Pool; EAPP = East African Power Pool; CAPP = Central Africa Power Pool; SAPP = Southern African Power Pool.

Future power demand can be met by either expanding national production or by expanding crossborder power trade within the CAPP. Two alternative scenarios will be considered in this report. The the SAPP, where it already participates in regional trade. While this is a limitation, an additional scenario is considered in which part of the Democratic Republic of Congo’s hydropower generation is sold into the CAPP. 5 These projections are based on economic growth forecasts prior to the onset of the global financial crisis of 2008. On the assumption that the economic crisis could halve anticipated economic growth rates over this region, the estimate of demand for 2015 is 2.25 TWh (or 9 percent), mostly due to decreases in demand in the Republic of Congo (10 percent) and Cameroon (9 percent).

35
_Figure 13: Vulnerability threshold and the official and moderate poverty lines, current prices BYN_ <!-- Start of picture text --> 350<br>300<br>250<br>Vulnerability threshold<br>200 Official poverty line<br>Moderate poverty line<br>150<br>100<br>2014q1 2014q2 2014q3 2014q4 2015q1 2015q2 2015q3 2015q4 2016q1 2016q2 2016q3 2016q4 2017q1 2017q2 2017q3 2017q4<br><!-- End of picture text --> Note. Vulnerability threshold is estimated as an average predicted income for the probability of being poor in the interval between 0.09 and 0.11. Official poverty line varies with the individual’s age, the figure represents the average for the HBS quarterly samples.

Source: Authors’ calculations based on quarterly HBS 2014q1‐2017q4 There is some, but not complete overlap between vulnerable and moderately poor groups. We find that 6.7 percent of the population are both vulnerable and moderately poor; these people reside predominantly in rural areas; have low education and typically live in households with children. At the same time, 8 percent of the population are vulnerable but not moderately poor; these are mostly the people with low socio‐economic standing (rural area residents; low‐educated; partially employed or non‐employed) and relatively high incomes (most of them come from the 4<sup>th</sup> ‐7<sup>th</sup> disposable income deciles). Also, 12.6 percent of the population are moderately poor, but not vulnerable; these are mostly employed, working‐age people from large cities, who are not predicted to be vulnerable based on their socio‐economic characteristics, but fall into moderate poverty due to low wages.

# 4. Main Reasons for Well‐Being Deterioration and Coping Strategies Reported by Households

## 4.1. Change in the well‐being of households and reasons for its deterioration

Households are asked on an annual basis about changes in their well‐being over the past year and reasons behind its improvement or worsening. The households’ assessment of their financial well‐being changes is consistent with the recession‐induced increase in the vulnerability discussed in the previous section. Pre‐ recession, in 2014, 22 percent of households reported that their well‐being deteriorated compared to the previous year, but the size of this group increased sharply to 34 percent in 2015 and to 38 percent in 2016. Consistent with these trends, the share of households who report that their well‐being improved vis‐à‐vis the past year fell from 16 percent pre‐crisis to only 8 percent in 2016 (Figure 14).

12
||Table 3b. CorrelationC|oefficient|s Significant a|t the1%L|evel, Chile|||
|---|---|---|---|---|---|---|---|
|||Not<br>inactive|School<br>completion|Older<br>age<br>when<br>started<br>working|Safe<br>sexual<br>behavior|Older age<br>at first<br>pregnancy/<br>parenthood|Participate<br>in<br>activities|
|s|No early school<br>dropout|0.38||||||
|come|Older age when<br>started working|0.13|0.22|||||
|s/Out|Sexual behavior|0.30|0.27|0.29||||
|havior|Older age at first<br>pregnancy/ child|0.36|0.40|0.13|0.41|||
|Be|Participate in<br>extracurricular<br>activities|0.19|0.17|--|0.14|0.19||
||Low parental<br>education level|-0.16|-0.23|-0.14|-0.08|-0.09|-0.10|
|ors|Rural residence|-0.11|-0.11|-0.10|--|--|-0.05|
|k fact|Physical/Verbal abuse<br>inthehome|-0.04|-0.06|-0.05|-0.05|-0.04|--|
|Ris|Substance abuse in the<br>home|-0.05|-0.07|-0.08|-0.10|-0.04|--|
||Socialexclusion|-0.26|-0.23|-0.09|-0.17|-0.31|-0.21|
||Indigenous|--|--|-0.05|--|--|--|
||Good relationship<br>with father|0.10|0.13|0.11|0.13|0.14|0.07|
||Good relationship<br>with mother|0.13|0.16|0.10|0.15|0.15|0.06|
|factors|Spiritual influence in<br>beliefs, opinions and<br>attitudes|--|0.07|0.05|0.16|0.05|0.20|
|ective|Trust in governmental<br>institutions|--|--|0.04|0.09|--|0.04|
|Prot|Trust in community<br>institutions|--|--|0.06|0.09|--|--|
||Optimism towards<br>future work<br>opportunities|0.09|0.05|--|--|0.05|0.05|
||Preparation forwork|--|--|-0.06|-0.09|-0.04|--|

-- not significant at the 1% level

27
# **A4. Supply model**

The supply of physicians and nurses/midwives can be projected to 2030 based on historical data on the increase in physician and nurse/midwives densities (i.e. per 1,000 population) in each country. Yearly health workforce density data since 1990 are available from the WHO Global Health Observatory database. We extract all available data points for physicians and nurses/midwives.

Various econometric approaches can be used to project supply numbers, each with advantages and disadvantages as described in Table A.4. Of the three types of econometric approaches, th growth rate method is the simplest, most straightforward, and requires the least amount of data, but may be less accurate given stronger functional form assumptions. Other methods rely less on the functional form assumption, but require more data points. A moving average or distributed lag model (2) which gives more weight to more recent data requires that data be available for a continuous number of years, and an ARIMA (autoregressive integrated moving average) model (3) requires that a long time series be available (i.e. ideally back to 1980) with very few missing data points. Given the availability of the health worker data, we chose to proceed with the growth rate model, which has previously been employed in health worker projections (Scheffler et al. 2008).

**Table A.4 Econometric modeling approaches for projecting supply**

|**Model**|**Advantages**|**Disadvantages**|
|---|---|---|
|**1. Growth**<br>**rate**| Simple and straightforward<br> Only need 2 data points<br>minimumper country|Potentially less accurate if<br>inappropriate functional form|
|**2. Moving**<br>**average/**<br>**distributed**<br>**lag**| Gives more weight to recent data<br> Relies less on functional form<br>assumptions|Requires that the workforce numbers<br>be populated for at least some number<br>of continuous years|
|**3. ARIMA**| Can account for cyclical<br>fluctuations<br> Relies less on functional form<br>assumptions|Need data for a longer time period<br>(back to 1980) with very few missing<br>observations.|

This growth model assumes that current trends in the growth of physician numbers will continue as they have historically for each country. Using the growth rate approach, we explored two functional forms:

1. Exponential (i.e. log‐linear)

- (Eq 4a) ln( _Physicians per 1000 populationt_ ) = _α_ 0 + _α_ 1* _yeart_ + _εt_

31
**Table 5:** Effect on parenting and teaching behaviors

||Paren|ting|Teach|ing|
|---|---|---|---|---|
||Aware of<br>school<br>assignments<br>(1)|Helps with<br>homework<br>(2)|Days absent<br>in past month<br>(3)|Student-<br>centered<br>instruction<br>(4)|
|_Double grant experim_|_ent_||||
|Treatment (1 year)|0.018|0.021|-0.335|0.031|
||(0.026)|(0.026)|(0.367)|(0.055)|
|WY _p_-value|0.790|0.790|0.790|0.790|
|Treatment (2 year)|0.047|0.043|0.295|0.038|
||(0.034)|(0.033)|(0.299)|(0.059)|
|WY _p_-value|0.550|0.560|0.590|0.590|
|Treatment (3 year)|0.021|0.030|-0.172|0.017|
||(0.033)|(0.033)|(0.433)|(0.064)|
|WY _p_-value|0.930|0.780|0.950|0.950|
|Control mean|0.437|0.414|1.597|0.076|
|Observations|8881|8881|8881|8881|
|_Information experim_|_ent_||||
|Treatment (1 year)|0.045**|0.072***|-0.087|-0.002|
||(0.021)|(0.021)|(0.466)|(0.058)|
|WY _p_-value|0.130|0.010|0.990|0.990|
|Control mean|0.245|0.203|1.534|-0.094|
|Observations|7950|7950|7950|7950|
|_Single grant observat_|_ion_||||
|Treatment (1 year)|-0.038|-0.043|0.694|-0.039|
||(0.036)|(0.031)|(0.610)|(0.072)|
|WY _p_-value|0.560|0.550|0.560|0.580|
|Control mean|0.306|0.270|2.591|-0.013|
|Observations|6288|6288|6288|6288|

Notes: Robust standard errors in parentheses. Westfall and Young (WY) p-values reported. Dependent variables in columns (1) and (2) are binary (where 1=Yes). Dependent variable in columns (3) and (4) are continuous. Data source: Student surveys. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01