rafmacalaba/datause-encoder-data huggingface dataset:
circumstances that challenge the husband’s traditional roles within the household such as inability to provide financially for his family. This is consistent with findings noted earlier that suggest low negative sanctions for households that challenge men’s breadwinner norm, as compared to women’s caregiver norm.

It is worth noting that across all circumstances, respondents’ own views are less conservative that than their reference group’s views, although the extent of the divergence is not consistently large across all situations. For example, while 51 percent of respondents approve of paid worker in the hotel/restaurant sectors, they estimate that only 28 percent of others in their reference group would approve.

**Table 9. Bivariate regression coefficients – Beliefs about whether women should work under different contexts predicting women’s work for pay outside the home**

|||**C lt**||**Paid work**<br>||
|---|---|---|---|---|---|
|**Theme**|**Circumstances/**<br>**contexts**|**ore eemens**<br>**measuring**<br>**social norms**|**Full**<br>**sample**|**Female**<br>**sample**<br>**(self)**|**Male**<br>**sample**<br>**(spouse)**|
|A woman’s|Children <5 looked|Respondent|0.08***|0.14***|0.01|
|characteristics<br>|after by non-family|approval|(0.03)|(0.03)|(0.03)|
|and roles||Community|0.12**|0.14***|0.09|
|||approval|(0.04)|(0.04)|(0.05)|
||Unable to keep up|Respondent|0.09***|0.11***|0.07**|
||with household tasks|approval|(0.02)|(0.02)|(0.03)|
|||Community|0.07***|0.11***|0.02|
|||approval|(0.02)|(0.02)|(0.04)|
|A woman’s job|Occasionally travel to|Respondent|0.10***|0.09***|0.10***|
|characteristics|work outside the<br>|approval|(0.02)|(0.02)|(0.03)|
||village/city|Community|0.05*|0.08***|0.01|
|||approval|(0.03)|(0.02)|(0.04)|
||Job physically|Respondent|0.07***|0.07***|0.08***|
||demanding|approval|(0.02)|(0.02)|(0.03)|
|||Community|0.06*|0.06**|0.05|
|||approval|(0.03)|(0.03)|(0.04)|
||Works in a|Respondent|0.09***|0.10***|0.08**|
||restaurant/hotel|approval|(0.02)|(0.03)|(0.03)|
|||Community|0.05|0.08**|0.02|
|||approval|(0.04)|(0.03)|(0.05)|

_Notes_ : (1) Positive correlation means that approvals are associated with higher likelihood of women working for pay outside the house. (2) Respondent and community approval variables are binary variables coded as 1 if the respondent approves of the behavior. Outcome variable is coded as 1 if the female respondent answered yes to working outside the house for pay. In the case of male respondent, he was asked whether his wife works outside for pay and the variable was coded accordingly. (3) No controls and no regional fixed effects are included. (4) SEs clustered at the ward level. (5) Robust standard errors in parentheses.

(6) *** p<0.01, ** p<0.05, * p<0.1.

27
- 14-

Table 3: Shares of Trade within MERCOSUR, 1988-1993

|||Percenta<br>Exports|ge of Trade|with MERCO|SUR in<br>imports||
|---|---|---|---|---|---|---|
|Country|1988|1992|1993|1988|1992|1993|
|Argentina|9.6|19.1|28.1|21.6|23.4|25.1|
|Brazil|4.7|7.9|13.9|7.3|11.8|12.7|
|Paraguay|30.4|41.7|39.6|43.0|32.7|37.5|
|Uruguay|24.3|36.2|41.2|42.5|43.9|44.9|

Source: Based on data from Comtrade, U.N. Statistical Office
**'0**

Tabl S. CDP Growthb Imports, **and** Effect of Policy Reform (cont d) <!-- Start of picture text --> No.<br>Depeadmat variable: U Ce.taot I TOT nRo D 02 F-stat of obs.<br>(i) gm: 1 a SO rformersr -0.59 0.17 0.06 -002 1.17 0.46 12.7 a<br>0 * C0 Deroforerob (-1.00) (5.04) (1.79) (-1.09) (1.31)<br>(II) -O." 0.17 0.05 1.87 0.43 16.3 72<br>(-.07) (5.52) (1.01) (1.10)<br><!-- End of picture text --> Ne": t-.tatistice Ore *Ithin bracheta.

- s. Exeludimg Guyna. Tgoplavis Zemblo, **and** Zimbebme from the group of forty trado loan recipiiete, but Including the monrociplent reforwe Belivia ad Hliti.

b. Excludlg Bolivia ad Hmitt from th, group of fortyelght aonrecipiate but including the four countries listed in footnote a. c. 1994 **Is** the **reforace** year for noenreciplanta.
-7-

Table 1. Ratios of Military Spending to GDP for Various Country Groups and Time Periods 1/

||Period Wei|ehted Averages. in|Percent 2/|
|---|---|---|---|
||<br>Full Period<br>(1972-90)|<br>Period I<br>(1972-85)|<br>Period II<br>(1986-90)|
|Full Sample|5.10|5.19|4.84|
|Industrial Countries|3.90|3.97|3.70|
|Developing Countries|5.20|5.54|4.26|
|Regional Groupings:||||
|Asia|5.70|6.35|3.88|
|Eastern Europe|12.40|11.75|14.22|
|Middle East|10.00|10.36|9.06|
|North Africa|7.20|8.12|4.60|
|Sub-Saharan Africa|3.20|3.12|3.42|
|Western Hemisphere|2.10|2.16|1.94|

Source: Hewitt (1992).

I/ See Appendix IB for lists of countries included in each grouping for Hewitt's sample. Z/ The weights are each country's share in the group GDP measured in U.S. dollars using official exchange rates.

Table 1 presents these averages for nine country groupings: the full sample of 124 countries; a group of 22 industrial countries; and 102 developing countries subdivided into six regional groups--Asia, Eastern Europe, Middle East, North Africa, sub-Saharan Africa and Western Hemisphere. This table also regroups these regional data into two time
- 45 -

Table 7: Cost Reductions from New Information: Urban C6te d'lvoire

|Initial<br>Information|Model 1|Model 2|New Informa<br>Model 3|tion<br>Model 4|Model 5|Perfect<br>Information|
|---|---|---|---|---|---|---|
|T=10,000,000<br>**1.**None (untargeted)<br>a =1|6,200,000<br>(-621)|<br>6,300,000<br>(-63%)|<br>6,200,000<br>(-62%)|<br>7,000,000<br>(-70%)|<br>7,050,000<br>(-70.5%)|<br>7,100,000<br>(-71%)|
|_a_= 2|7,400,000<br>(-74%)|7,600,000<br>(-76%)|7,100,000<br>(-71%)|8,200,000<br>(-82%)|8,300,000<br>(-83%)|8,900,000<br>(-89%)|
|a=3|8,100,000<br>(-81%)|<br>8,400,000<br>(-84%)|<br>7,900,000<br>(-79%)|<br>8,700,000<br>(-87%)|<br>8,800,000<br>(-88%)|9,400,000<br>(-94%)|
|2.HdefII|||||||
|a =1|_|700,000<br>(-7%)|<br>100,000<br>(-1%)|<br>2,600,000<br>(-26%)|<br>2,800,000<br>(-28%)|<br>3,700,000<br>(-37%)|
|a=2|_|1,700,000<br>(-17%)|<br>400,000<br>(-4%)|<br>2,500,000<br>(-25%)|<br>3,000,000<br>(-30%)|<br>6,700,000<br>(-67%)|
|a =3|_|2,300,000<br>(-23%)|800,000<br>(-8%)|1,800,000<br>(-18%)|2,500,000<br>(-25%)|6,900,000<br>(-69%)|
|T= 20,000,000<br>1. None (untargeted)|||||||
|<br>a=1|11,400,000<br>(-57%)|12,000,000<br>(-60%)|11,800,000<br>(-59%)|13,800,000<br>(-69%)|14,100,000<br>(-70.5%)|14,400,000<br> <br>(-72%)|
|a=2|13,200,000<br>(-66%)|14,400,000<br>(-72%)|13,800,000<br>(-69%)|15,400,000<br>(-77%)|15,400,000<br>(-77%)|17,600,000<br>(-88%)|
|a=3|14,500,000<br>(-72.5%)|15,500,000<br> <br>(-77.5%)|15,100,000<br> <br>(-75.5%)|16,700,000<br> <br>(-83.5%)|16,600,000<br> <br>(-83%)|18,700,000<br>(-93.5%)|
|2. Model**1**|||||||
|a=1|_|1,800,000<br>(-9%)|-800,000<br>(+4%)|5,500,000<br>(-27.5%)|5,900,000<br> <br>(-29.5%)|8,800,000<br> <br>(-44%)|
|a=2|_|1,800,000<br>(-9%)|-1,500,000<br>(+7.5%)|4,400,000<br>(-22%)|6,700,000<br>(-33.5%)|14,200,000<br> <br>(-71%)|
|a =3|_|1,400,000<br>(-7%)|-1,700,000<br>(+8.5%)|4,200,000<br>(-21%)|7,400,000<br>(-37%)|16,300,000<br>(-81.5%)|

Note: 1. All cost reductions are rounded to the nearest 100,000 CFA Francs (nearest 50,000 In one case).

2. Figures In parentheses give the S decrease in cost of attaining a given poverty level due to new information (relative to the old information set) and are expressed In terms of negative numbers.
<!-- Start of picture text --> Figure 2: Changes Over Time in Governance Indicators 2002-2005<br>Voice and Accountability<br>3<br>2<br>CHL<br>1<br>GHAHR V<br>LSO<br>Y UG<br>NER ID N KEN 0<br>SLE THA<br>-3 -2 -1 V EN 0 1 2 3<br>1998<br>NPL<br>-1<br>CIV<br>ZW E<br>BLR<br>-2<br>-3<br>Rule of Law<br>3<br>2<br>KIR 1<br>0 TTO<br>-3 -2 R W AY U GGEODZA -1 A RG 0 1 2 1998 3<br>TJK ER I<br>LBR -1<br>V EN<br>CIV<br>ZW E<br>-2<br>-3<br>2006<br>2006<br><!-- End of picture text --> 26
108 welfare (living standards) depends on a number of factors, including earnings in the informal urban sector, rural incomes, etc. In this chapter we examine the structure of extended families in the Dakar area and analyze the relationship between wages and family size.

# 8.1 Size of Extended Famlies

The extended family can be defined as the urban and rural group of persons who partly or entirely depend on the income of one wage earner for subsistence. (Pfeffermann considered only the urban group.) The extended family can be broken down into the nuclear family, comprising the wage earners' wives and children, and the non-nuclear farmily, comprising other relatives.

An analysis of the size of the extended family alters the image of per capita incomes of individual workers considerably: Both nuclear and non-nuclear families are remarkably large in Senegal -the former as a resul' of the absence of birth control and the prevalence of polygamy, and the latter because of traditional family solidarity.

# _o.lI Nuclear Families_

In examining the number of wives of male workers in our study, we found that the extent of polygamy has not declined during the 23 years since Pfeffermann completed his study; in fact, it may have slightly risen (see Table 8.1).

The typical family in our 1986 sample is very large-possibly larger than the typical family in the 1963 sample of 188 workers from 13 industrial firms. As can be seen in Table 8.2, the average number of children for monogamous workers was 3.08 in 1963 and 5.08 in 1986. For polygamous workers, the average number was nearly twice as large in both years: 5.93 in 1963 and 9.88 in 1986. The total average number of children per worker in Pfeffermann's 1963 sample is 3.41 and in our 1986 sample it is 5.87. Moreover, it appears that fewer Senegalese workers are now childless. Or, conversely, whereas 75 percent of the 1963 sample of workers had one or more children, the figure rose to 91.4 percent in the 1986 sample.

If the 1963 and 1986 samples of workers are representative, they clearly indicate a substantial increase in family size. Not only has the number of children and wives per family increased,
# Public Hospital Costs and Quality in the Dominican Republic

# by

Maureen A. Lewis, World Bank Margaret B. Sulvetta, The Urban Institute and Gerard M. La Forgia, The Urban Institute*

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Background|3|
||SESPAS Organization and Financing of Hospital Care<br>Survey Site: Aybar Hospital<br>Medical Staff Characteristics and Earnings|3<br>4<br>5|
|III.|Methodology|5|
||Cost and Data Collection Methodology and Approach|6|
|IV.|Quality of Care|9|
||Medical Staff Time Allocation<br>Shortages and Noncompliance|9<br>10|
||Comparison of Norms and Clinical Practices|11|
|VI.|Conclusions and Policy Recommendations|15|
||Conclusions|15|
||Recommendations|17|
||Generalizability of Study Results and Recommendations for Additional<br>Study|18|
|Refere|nces|19|

# References

> * We would like to thank John Holahan and Philip Musgrove for comments on an earlier version of this paper. Support for the study was provided by USAID through the REACH Project, and the Urban Institute. The views expressed here are those of the authors and do not necessarily reflect those of USAID, the Urban Institute, or the World Bank.
|Malawi Nutrition and HIV/AIDS Project<br>Ziauddin Hyder|
|---|
|Malawi Nutrition and HIV/AIDS Project<br>Ziauddin Hyder|
|Strengthen the Social Assessment Capacityin Urban Construction P Jun Zeng|
|Strengthen the Social Assessment Capacityin Urban Construction P Jun Zeng|
|CIS Statistical Committee TrainingProgram<br>Olga Vadimovna Shabalina|
|CIS Statistical Committee TrainingProgram<br>Olga Vadimovna Shabalina|
|CIS Statistical Committee TrainingProgram<br>Olga Vadimovna Shabalina|
|BuildingTrust in Public Institutions and Policies<br>Diego R. Dorado Hernandez|
|BuildingTrust in Public Institutions and Policies<br>Diego R. Dorado Hernandez|
|BuildingTrust in Public Institutions and Policies<br>Diego R. Dorado Hernandez|
|BuildingTrust in Public Institutions and Policies<br>Diego R. Dorado Hernandez|
|NIGER - Shared Growth Credit I<br>Johannes Herderschee|
|NIGER - Shared Growth Credit I<br>Johannes Herderschee|
|NIGER - Shared Growth Credit I<br>Johannes Herderschee|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|UG Science and TechnologySupport<br>Hongyu Yang|
|Lao Eight PovertyReduction Support<br>Genevieve F. Boyreau|
|Karnataka IDF: PFM CapacityBuilding<br>KrishnamurthySankaranarayanan|
|Karnataka IDF: PFM CapacityBuilding<br>KrishnamurthySankaranarayanan|
|Karnataka IDF: PFM CapacityBuilding<br>KrishnamurthySankaranarayanan|
|Karnataka IDF: PFM CapacityBuilding<br>KrishnamurthySankaranarayanan|
|Karnataka IDF: PFM CapacityBuilding<br>KrishnamurthySankaranarayanan|
|Karnataka IDF: PFM CapacityBuilding<br>KrishnamurthySankaranarayanan|
|Comoros: Coastal Resources Co-management for Sustainable LivelihXavier F. P. Vincent|
|Comoros: Coastal Resources Co-management for Sustainable LivelihXavier F. P. Vincent|
|Comoros: Coastal Resources Co-management for Sustainable LivelihXavier F. P. Vincent|
|Comoros: Coastal Resources Co-management for Sustainable LivelihXavier F. P. Vincent|
|Comoros: Coastal Resources Co-management for Sustainable LivelihXavier F. P. Vincent|
|Comoros: Coastal Resources Co-management for Sustainable LivelihXavier F. P. Vincent|
|Comoros: Coastal Resources Co-management for Sustainable LivelihXavier F. P. Vincent|
|Comoros: Coastal Resources Co-management for Sustainable LivelihXavier F. P. Vincent|
|Bangladesh: Safe Migration for Bangladeshi Workers<br>Aneeka Rahman|
|Bangladesh: Safe Migration for Bangladeshi Workers<br>Aneeka Rahman|
|Bangladesh: Safe Migration for Bangladeshi Workers<br>Aneeka Rahman|
**Table 3.5: Earnings of Worker by Employment Size of Enterprises in Manufacturing, Average Earnings in the Public Sector and the Minimum Wage 1992-94**

|Earnings in<br>Manufacturing:<br>(monthly, in cedis)|Wave 1 (1992)|Wave 2 (1993)|Wave 3 (1994)|
|---|---|---|---|
|Large (100+).|41,420|48,876|56,849|
|Medium (30-100)|31,820|38,392|41,017|
|Small (5-29)|17,943|25,098|26,682|
|Micro (<5)|13,419|13,161|14,264|
|All|25,696|33,272|36,215|
|Average Earnings in<br>the Public Sector|62,687|||
|Minimum Wage|13,617|||

_Source: The data for the manufacturing enterprises are generated by the RPED surveys and are taken from the analysis of F. Teal (1996), Table 3, p. 971._ The average public sector earnings for 1991 are taken from ISSER 1994, Table 8.14, p. 153. They have been blown up by 80 percent in the across-the-board wage increase granted to public employees in July 1992. The figure for the minimum wage is from the same source, adjusted by the cost-of-living index for Accra.

While the figures of average wages in Table 3.5 strongly suggest that the recent wage hike has taken public sector wages to a new plateau-exceeding wages in the private formal sector by a substantial margin, the evidence is not conclusive because we are not able to control for differences in the quality of labor. It is possible to have more conclusive evidence if we confine ourselves to the sub-sector of public enterprises. The RPED surveys include state owned enterprises along with private manufacturing firms. The earnings regressions performed on the sample of all male workers covered by this data set by Francis Teal is reproduced in Table 3.6. Teal pooled the data for all three waves. Price changes between the surveys were controlled for by the use of dummy variables for the three years.

16
75 in a low incone country. Investing in the human capital of women to perform market and nonmarket work **more** productively is a more promising way to increase women's economic capabilities. Formal education. is the most widely documented way to achieve this goal efficiently. With better education of women, household spending is likely to go increasingly for child health, nutrition, and schooling. Basic schooling appears to ba th **most** efficient and equitable investment option, and one that is often now allocated disproportionately to mn. This allocation of human capital requires more study; its justification on "cultural' grounds should be reckoned carefully in terms of economic costs reflected in shorter lifetimes, higher fertility, and lower per capita productivity.

Regional Patterns of Investment in Women and Men The largest investments in human capital are in schooling and health. Differences in education between women and men can be inferred from two sets of data: school attainment as reported by individuals in a census and enrollment rates as reported by schools to UNESCO. Although both types of data have their shortcomings, and probably overstate the human capital received by women in relation to men, **the** patterns across countries in both are similar. Today in the industrially advanced high income countries women receive nearly the same number of years of schooling as do men, and woen often report higher enrollment rates than men until secondary school. Latin America and East Asia are quite similar to the developed countries in this regard. At the other extreme are most of the countries of South and West Asia and North and subSaharan Africa where women receive between half and three-fourths the years of schooling as do oen. This regional pattorn is illuatrated in column 1 of Table 5 using estimates of years of completed schooling from a recent census,
# **<<B>>** _<mark>Descriptive Statistics</mark>_

We follow the school and work status of children in households with at least one child aged 7–14 years at the baseline across the first follow-up survey (1 year later) and the second follow-up (3.5 years after the baseline) until they are 17 years of age, at most. Because we consider the effects of the father’s departure since the baseline, we restrict the sample to households in which both parents are present at the baseline.<sup>4</sup>

**<<C>>** _<mark>Outcomes.</mark>_ We consider two outcomes: school enrollment, which relates to whether the child is enrolled in school at the time of the survey, and work participation, which includes all types of paid and unpaid economic activities and includes looking for work as a main activity.<sup>5</sup> Table 1 shows the proportions of our sample enrolled in school and participating in work by age and gender (table 1). The table indicates that school participation rates are high among children aged 7–11 years, which corresponds to primary school.<sup>6</sup> The first substantial drop in school enrollment is observed at age 12, which is the transition from primary to secondary school. Another point worth noting is that the school enrollment of females is higher than that of males. Engagement in work is approximately twice as high for males as for females and is very low for both before the age of 12 (participation in work is not recorded for individuals under 10 years of age).

**<<C>>** _<mark>Permanent absence of the father.</mark>_ To capture a potentially important disruption to family life and long-term reduction in income, we focus on the departure of the father from the household since the baseline. In particular, we focus on departure that results in the father’s subsequent permanent absence from the household.<sup>7</sup> Divorce and death are the two primary reasons for permanent absences that are identifiable from the data. Because these are relatively rare events, we pool them to improve statistical

9
indices may also help, though only if they are sufficiently accurate that benefactors are then not sufficiently motivated to learn the true needs of recipients.

A simple way to reduce aid misallocation is through subsidizing recipient insurance, assuming that the private sector is reliable enough not to misallocate. Combining payout triggers across public and private programs may also reduce misallocation through a similar mechanism, but here there is a risk that if the incentive to misallocate is strong enough, this may contaminate previously well-functioning insurance systems. Disaster indices may also be less vulnerable to political misallocation, though of course if basis risk is high enough misallocation may worsen.

Finally, insurance subsidies and benefactor insurance may mitigate the problem of delayed disbursements if it is easier for benefactors to coordinate over payment into these products than disaster relief ex-post. Even if free-riding is not reduced, delays over the purchase of insurance products are likely to be less costly than delays in relief disbursement.

Overall, Table 1 makes clear that there are several reasons why involving the private sector in each way may be beneficial for governments and benefactors who suffer commitment problems. Placed alongside this, of course, it should be noted that these kinds of private finance are typically expensive. This can be due to lack of competition, poor insurance regulation, high administrative costs or ambiguity averse pricing from regulated insurance companies. These costs should obviously be weighed against the potential benefits that have been the focus of this paper.

# **References**

**Bailey, Rob** , _Managing Famine Risk: Linking early warning to early action_ , Royal Institute of International Affairs, 2013.

- **Bevan, David and Christopher Adam** , “ Public Capital, Disaster Risk Insurance and Macroeconomic Responses to Topical Cyclone Damage: An Application to Jamaica,” Technical Report, World Bank Policy Research Working Paper 2015.

**Borensztein, Eduardo, Eduardo Cavallo, and Olivier Jeanne** , “The Welfare Gains from Macro-Insurance Against Natural Disasters,” Technical Report, National Bureau of Economic Research 2015.

22
attitudes towards gender equity; instead, we find a marginal improvement (although insignificant) in men’s attitudes (please see column 2 in Table 19b). This is indicative of an overall improvement in women’s empowerment and bargaining power devoid of backlash by men; thereby, decreasing the likelihood of exposure to violence.

Finally, we also find an improvement in women’s social networks, possibly due to the 4P family development sessions (FDS). Some of these sessions had explicit discussions on intrahousehold dynamics, which could have discouraged men from perpetrating violence and women from tolerating violence. Further, development of social capital among women via attending FDS could have also emboldened women to not tolerate violence and seek help in the eventuality of experiencing violence.

Overall, we find evidence in support of all the channels. The reduction in emotional NPDV could be associated with one or more of these mechanisms or their interplay. Admittedly, since we do not have any baseline data on the intermediary outcomes and each of these outcomes could have in turn been affected by the CCT program, we cannot draw any causal inference from these estimates. These results are at best suggestive. Nonetheless, these findings provide important evidence on pathways through which inflow of cash within the household in the hands of the woman could mitigate her risks of experiencing violence.

_Table 19a: Effect of CCT on Intermediate Outcomes (female respondents)_

|(1)|(2)|(3)|(4)|
|---|---|---|---|
|Bargaining Power<br>Index|Empowerment<br>Index|Social networks<br>Index|Well-being Index|
|RD Effect<br>0.206**|0.164*|0.181*|0.071|
|(0.02)|(0.06)|(0.07)|(0.51)|
|FDR q-val<br>0.07|0.10|0.10|0.51|
|Observations<br>564|564|564|564|
|Control mean<br>0.000|0.000|0.032|0.001|
|P-values in parentheses: * p<0.1 ** p<0.05 **|* p<0.01|||

_Note_ : All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all regressions in the table.

26