rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table A.4. RDD regressions of individual’s health and children’s education using VHLSSs 2004 to 2008</u>**

||||De<br>|pendent variabl<br>|es<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Having<br>health<br>insurance<br>(yes=1,<br>no=0)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|The annual<br>number of<br>inpatient<br>healthcare<br>visits|The number<br>of completed<br>schooling<br>years<br>(children<br>aged 6-17)|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program|0.1991***|-0.2849***|-0.2718***|-0.0130|0.0840|0.0230|0.6334**|
||(0.0651)|(0.0877)|(0.0845)|(0.0174)|(0.2655)|(0.0386)|(0.2507)|
|(Poverty rate – 50)|-0.0046|0.0132|0.0109|0.0022|-0.0492*|-0.0089**|-0.0098|
||(0.0064)|(0.0119)|(0.0114)|(0.0018)|(0.0276)|(0.0039)|(0.0227)|
|Program * (Poverty rate – 50)|-0.0060|-0.0094|-0.0071|-0.0022|0.0452|0.0096**|0.0161|
||(0.0070)|(0.0123)|(0.0118)|(0.0020)|(0.0298)|(0.0043)|(0.0270)|
|Age|-0.0012*|-0.0063*|-0.0066**|0.0003|1.2068***|0.1701***|0.3738***|
||(0.0007)|(0.0032)|(0.0032)|(0.0007)|(0.0535)|(0.0116)|(0.0469)|
|Age squared|0.0000**|0.0003***|0.0002***|0.0000***|-0.0226***|-0.0084***|-0.0183***|
||(0.0000)|(0.0001)|(0.0001)|(0.0000)|(0.0024)|(0.0005)|(0.0020)|
|Male|0.0219***|-0.1499***|-0.1384***|-0.0114*|0.1320**|0.0498***|0.1180***|
||(0.0044)|(0.0213)|(0.0197)|(0.0060)|(0.0569)|(0.0102)|(0.0347)|
|Ethnic minorities (yes=1, Kinh=0)|-0.0423|-0.3841***|-0.3644***|-0.0196*|-1.1113***|-0.0862***|0.9549***|
||(0.0312)|(0.0715)|(0.0704)|(0.0103)|(0.1298)|(0.0185)|(0.1138)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2386***|0.9632***|0.8723***|0.0907***|-5.5569***|0.0739|-1.6620***|
||(0.0331)|(0.0936)|(0.0917)|(0.0158)|(0.3042)|(0.0708)|(0.2945)|
|Observations|62,232|62,232|62,232|62,232|19,133|19,133|19,133|
|R-squared|0.115|0.053|0.047|0.014|0.581|0.114|0.142|

Note: This table reports district fixed-effect RDD regressions of individual’s health and children’s education using individual-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

54
<!-- Start of picture text --> DDS Per Capita Consumption<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>Food Share in Total Expenditure<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>RIF-OLS<br>.8<br>.6<br>.6<br>.4<br>.4<br>.2<br>.2<br>0 0<br>0<br>-.2<br>-.4<br>-.6<br><!-- End of picture text --> 70
88

**Table 41. Comparative costs of substitute fuels, 1987**

|Fuel|Overall Cost<br>(1987 US dollars per barrel-<br>gasoline energy equivalent)|
|---|---|
|Crude Oil (assumed price)|$18|
|Conventional Gasoline|$27|
|Compressed Natural Gas|$20-46|
|Very Heavy Oil Products|$21-34|
|Methanol (from gas)|$30-67|
|Synthetic Gasoline (from gas)|$43-61|
|Diesel (from gas)|$69|
|Methanol (from coal)|$63-109|
|Methanol (from biomass)|$64-126<br>|
|Ethanol (from biomass)|$66-101|

Source: [IEA 1990]

# **Factors Influencing** Large Scale Use **of Alternative Fuels**

The introduction of alternative fuels requires changes in distribution, marketing and enduse systems. Irrespective of the economics, inadequate supply of fuel or unreliable distribution systems could adversely affect consumer acceptance of alternative transportation fuels. Experience with the use of ethanol in Brazil and CNG in New Zealand and elsewhere suggests that the main factors influencing large-scale introduction of CNG and alcohol fuels are price competitiveness, availability and cost of feedstock (e.g., sugarcane for ethanol, or natural gas for CNG), fuel safety and quality standards, reliable system of distribution, and technical quality of vehicles (driveability, durability, safety). The Brazil experience with ethanol and the New Zealand experience with CNG clearly show that it is possible to develop a large market for alternative fuels within a reasonable time frame if the financial incentives are favorable and efforts are made to overcome uncertainty on part of industry and consumers [Sathaye, Atkinson, and Meyers 1989]. In both instances, substantial subsidies had to be offered to private motorists to persuade them to convert to alternative fuels [Moreno and Bailey 1989].
DISCRIMINATION AGAINST SEXUAL MINORITIES IN EDUCATION AND HOUSING: EVIDENCE FROM TWO FIELD EXPERIMENTS IN SERBIA

# **Renting Experiment**

## **Independent (treatment) and dependent variables**

Sexual orientation was chosen as the treatment variable (independent variable) for the experiment because compared to other parts of the LGBTI community, lesbian women and gay men are the most visible in Serbia. As such, open expression of sexual orientation was not expected to make a landlord suspicious of the genuineness of the rental inquiry. The dependent variable is the readiness of the landlord to rent the apartment to the couple.

## **Experimental design**

The experiment had two treatment groups, a lesbian and a gay couple, and two corresponding control groups of heterosexual couples. Landlords were randomly assigned to either a gay or lesbian couple and a corresponding heterosexual couple. Each landlord was contacted twice – by the same‐sex couple (treatment group) and by a corresponding heterosexual couple (control group). When the lesbian couple contacted the landlord, the female member of the heterosexual couple would be the one contacting the same landlord as a control for any gender bias. The same applies to the gay couple, where the male member of the heterosexual couple contacted the landlord.

## **Data collection method**

Communication with the landlords was conducted by telephone. Members of the Ipsos research team who have vast experience in conducting ‘mystery shopper’ surveys conducted the interviews. The ‘mystery shoppers’ attended an additional training prior to participating in the experiment.

Data collection was conducted in the period from 10 to 22 February 2017.

## **Scenario of the telephone conversation**

The telephone conversation scenario was as follows:

- At the beginning of the conversation, to ensure naturalness and convey a serious interest in renting the apartment, the mystery shopper asks for information that was referred to in the advertisement (e.g. Internet connection, cable television, parking space, how many floors in the building, payment details, etc.).

- Thereafter, the mystery shopper states that the terms are acceptable and says that there will be two tenants in the apartment. In the control group, the two people are a heterosexual couple. In the treatment group, the two people are either two men or two women in a relationship and living together. The mystery shopper then asks when they could come to see the apartment.

- If the answer is positive, the mystery shopper says that he/she will decide with his/her partner and call back to schedule the time to inspect the apartment. If the answer is negative, the mystery shopper kindly asks about the reason for rejection, and without further discussion, he/she thanks the landlord and concludes the conversation.

21
However, this section opts for a more specific focus on the federal government budget balance for two noteworthy reasons. Firstly, in Argentina ́s context, the trajectory of the general government fiscal accounts is predominantly influenced by the federal government ́s accounts. With the exception of a brief period in the late-1990s, sub-national administrations (i.e., provinces) have generally maintained balanced or slightly deficit-ridden public accounts ( **Figure 18** ). Secondly, in accordance with Musgrave's seminal work in 1959, the stabilizing function is typically entrusted to the central government.

**Figure 18. National and Subnational fiscal balances (1961-2022)**

<!-- Start of picture text --> 4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>-14<br>-16<br>Federal Government Provinces<br>Percent of GDP<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> _Source:_ Ministry of Economy.

Conforming to the literature, we categorize taxes into four distinct groups, each exhibiting different elasticities to the economic cycle. Notably, considering that the federal government retains roughly 42 percent of the receipts for various tax categories in Argentina, this study simplifies its analysis by relying on the resulting share of resources officially recorded as central government revenue, an estimation based on data published by the Ministry of Economy ( **Figure 19** ).<sup>2021</sup> These categories are as follows: i) Personal Income Tax (PIT), averaging 2.3 percent of GDP over the past decade, with nearly 1.1 percent of GDP allocated to the Federal Government; ii) Corporate Tax (CIT), constituting an average of 2.9 percent of GDP in the last 10 years, of which 1.3 percent of GDP remained in the Federal Government; iii) Social Security Contributions (SSC), representing approximately 6.2 percent of GDP on average during the last decade; iv) Indirect taxes, aligned with developed countries, emerge as the most significant source of taxation. The portion retained by the Federal Government averaged 8.8 percent of GDP in the last decade, with VAT serving as the primary tax source (3.5 percent of GDP).

> 20 More than 80 percent of taxation in Argentina is collected by the central government, as part of the fiscal federalism arrangement. Part of the centrally collected taxes is automatically channeled to the revenue sharing scheme ( _Regimen de Coparticipacion_ ), which shares the proceeds between the central government and the provinces. Currently almost 60% of the eligible tax collection is transferred to provinces, while 40% remains at the central government.

> 21 It is worth mentioning the role informality might play in different phases of the cycle. In developing economies, where the informal sector is more prominent and higher growth rates are more common, Heintz (2006) suggests that informality may decline, posing an additional effect on tax receipts.
Table 15: Medium-term impacts of _Mais Educação_ on educational outcomes for schools that chose culture and arts field

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.432|–4.559**|–2.563|
||(0.991)|(1.865)|(1.651)|
|_R_ <sup>2</sup>|0.703|0.809|0.819|
|Observations|1,688|1,688|1,688|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|–0.00425|–3.045|–0.194|
||(1.030)|(2.174)|(2.025)|
|_R_ <sup>2</sup>|0.745|0.779|0.784|
|Observations|1,340|1,340|1,340|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined culture and arts field in 2008 and remained in the activity until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil_ , and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
Policy Research Working Paper 10005

# **Abstract**

Air pollution is one of the leading causes of death worldwide, especially affecting poorer people who tend to be more exposed and vulnerable. This study contributes (i) updated global exposure estimates for the World Health Organizations’s 2021 revised fine particulate matter (PM2.5) thresholds, and (ii) estimates of the number of poor people exposed to unsafe PM2.5 concentrations. It shows that 7.28 billion people, or 94 percent of the world population, are directly exposed to unsafe average annual PM2.5 concentrations. Low- and middle-income countries account for 80 percent of people exposed to unsafe PM2.5 levels. Moreover, 716 million poor people (living on less than $1.90 per day) live in areas with unsafe air pollution. Around half of them are located in just three countries: India, Nigeria, and the Democratic Republic of Congo. Air pollution levels are particularly high in lower-middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. The findings are based on high-resolution air pollution and population maps with global coverage, as well as subnational poverty estimates based on harmonized household surveys.

This paper is a product of the Sustainable Development Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at jrentschler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
It is not necessary for every individual match to be close, but it is important for the distributions of covariates for the treated and the comparators to be similar, and this is what is meant by balance. More formally, in order to verify balance we need to check whether _p_ (ˆ _X_ | _T_ = )1 = _p_ (ˆ _X_ | _T_ = 0) (4)

where _p_ ˆ gives the empirical (rather than population) density of the data.

In line with common practice, the algorithm we use is to estimate a propensity score, match treated with non-treated units, and check for balance; if balance is not achieved, we revise the assignment model, and repeat this process until (hopefully) balance is achieved. In checking for balance, we divide the data into strata, based on the estimated propensity score, and then use a series of t-tests or chi-square tests to check that, within each stratum, the values of each covariate (age, literacy rates, and so on) are on average the same. This allows one to “check the adequacy of the statistical model” of assignment (Imbens 2004, p.18).

# _Impact Models_

There is another way to deal with the problem of endogenous program placement. It may be possible to assume that SFD programs have been assigned randomly, _conditional on some observable variables_ , _X_ , that might include measures such as village size, or literacy rates, or the structure of the local economy. This would constitute partial randomization, and is a maintained assumption rather than a testable proposition.

If we are willing to accept the conditional exogeneity of program placement, it may be possible to estimate the impact of a treatment such as an SFD program using a parametric model with controls. Suppose that we may assume where the error terms are normally distributed with zero means and constant variances, and the superscripts refer to the treatment group (T) and the comparison group (C). These two equations are often estimated together in the form of a switching regression using the pooled data from both the treatment and comparison samples, giving _Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 10 of 36_
during 1986-90). This means that, on average for 1991-93, 54 percent of the contribution to the growth of world trade volume was generated by the LMICs, excluding FSU, despite their share of less than 20 percent in total world trade in 1991. East Asia's and Latin America's contributions were 29 percent and 17 percent, respectively. Despite the projected OECD recovery, the OECD share of contribution is not expected to reach the high levels observed in earlier business cycle recovery phases, reflecting the projected high pace of LMIC trade integration. In fact, OECD's contribution to incremental world trade growth will be limited to just over 60 percent, and LMICs will continue to claim about a quarter of the growth in world import demand (of which more than half will accrue to East Asia). The increased role of LMICs in trade creation is likely to stay.<sup>8</sup> Chart 8. Contribution to World Import Growth <!-- Start of picture text --> (period averages)<br>Percent contribution<br>100<br>80 3<br>60<br>40 - ... . . . .l<br>20<br>1986-90 1991-1993 1994-1996 1997-2004<br>(forecast) (forecast)<br>High-income OECD<br>|~ 11Other High-income Countries<br>LMIC<br>Source: Intemational Economics Department, World Bank, August 1995.<br><!-- End of picture text -->

Developing countries continued to grow and import during the last recession, unlike in previous recessions, for at least three reasons. First, buoyed by successful reform and adjustment policies, domestic investment and consumption remained strong. Second, despite the recession in OECD countries, export growth in developing countries actually

**s** Prospects are discussed in detail in the following sections.

11
12 marginal costs. The model also ignores all routine maintenance costs, which generally account for at least half total road maintenance expenditures.il/ Routine maintenance amounts to between $1,000 and $3,000 per km for paved roads and between $500 and $1,500 per km for unpaved roads, depending on the country and volume of traffic. Up to 70 percent of these costs are typically fixed, the remainder being attributable to the passage of vehicles (patching) and the incidence of heavy vehicles (e.g., shoulder maintenance).

<!-- Start of picture text --> __Vefta Profil D _ _<br>Tuvfajn Roswa<br>4 2 a ,1.<br>300 000<br>122 ~~/Q / 3.<br>/ 2 4<br>I ,~~~~~~I Number of Lanes<br>N1i Shoulderan arnll 1.5 m wide, pavemcents ae3.5, 7.0 and 2 x ?.0 m wide and the<br>2-lane road has a S m central reservation.<br>sow= Amedo-C:n 1990. r<br>liS= 2: &,onornis<br><!-- End of picture text --> <!-- Start of picture text --> &,onornis of Scalc in Constructing Inter-Urban Roads<br><!-- End of picture text --> D1 In Arizona, U.S.A., with over 100,000 Ian of road and sw, fixed costs accounted for about 65 per cent of annual rod ma_nac expendkuu in 1989 (Arizona De-partment of Transportation, 1989).
# **Table 5 (continued)  The error-correction model with asymmetric price transmission**

|||India|India|Uganda|Madagascar|Togo<br>|Cameroon|Central<br>African<br>Republic|Ghana|
|---|---|---|---|---|---|---|---|---|---|
|Type of<br>coffee||Other<br>milds|Robusta|Robusta|Robusta|Robusta|Robusta|Robusta|Robusta|
|Reform year||1996|1996|1992|1988|1996|1995|1991|1992|
|price decreases<br>Short-term<br>|Before reforms (_δ1_)|0.05|0.05|-0.31<br>**|0.02||||0.08|
|transmission|After reforms (_δ 3_)|0.47 ***|0.47 ***|0.66 ***|0.15<br>*|0.30 ***|0.03|0.17<br>*|*<br>-0.31|
||Change significant|Yes ***|Yes ***|Yes ***|No||||No|
|price increases|Before reforms (_δ 2_)|0.05|0.00|-0.16|0.01||||-0.10|
||After reforms (_δ4_)|0.42 ***|0.56 ***|0.71 ***|-0.13<br>**|0.11|-0.32 ***|0.02|-0.07|
||Change significant|Yes ***|Yes ***|Yes ***|No||||No|
|Asymmetric price transmission<br>|Before reforms|No|No|No|No||||No|
|significant|After reforms|No|No|No|Yes<sup>-</sup><br>**|No|Yes<sup>-</sup><br>**|No|No|
|Net change in the short-term tran|smission<sup>1)</sup>|||||||||
|||0.04|-0.14|0.10|0.27||||-0.41|
|Speed of adjustment|Before reforms (_θ1_)|-0.08 ***|-0.05<br>**|-0.14 ***|-0.09||||-0.09 ***|
||After reforms (_θ2_)|-0.28 ***|-0.14 ***|-0.26 ***|-0.09 ***|-0.20 ***|-0.09<br>**|-0.11 **|*<br>-0.19 ***|
||Change significant|Yes<br>**|Yes<br>*|No|||||Yes<br>*|
|Target share of world price|Before reforms (_γ1_)|0.57 ***|0.56 ***|0.32 ***|0.22 ***||||0.43 ***|
||After reforms (_γ2_)|0.69 ***|0.85 ***|0.91 ***|0.47 ***|0.63 ***|0.49 ***|0.44 **|*<br>0.49 ***|
||Change significant|Yes ***|Yes ***|Yes ***|Yes ***||||Yes<br>*|
|Adjusted R-squared||0.23|0.14|0.27|0.06|0.18|0.11|0.09|0.09|

* significant at 10% ** significant at 5%

*** significant at 1%

Yes- means that short-run transmission is significantly higher for price increases than for price decreases 1) Change in the transmission of price decreases net of change in the transmission of price increases, _(δ3 - δ1) - (δ4 - δ 2)_ 26
Table 4: Private school response to girls’ scholarship program

|outcome|||private|school sh|are of tes|t takers|||num|ber of pr|ivate sc|hools|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|sample||girl|s|||bo|ys||||||
||@)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|go)|@))|2)|
|girls’scholarshipprogram|O.17***|=-0.23**|—-0.20**|-0.14|0.23**|-0.08|0.25**|-0.02|-0.06|0.09|-0.07|0.27|
|<br>|<br>(0.01)<br>|<br>(0.04)<br><br>|(0.04)<br>|(0.16)<br>|(0.02)<br>|(0.02)<br>|(0.04)<br>|(0.16)<br>|(0.19)<br>|(0.14)<br>|(0.22)<br>|(0.28)<br>|
|interaction of program with:|[01]|[05]<br>~—|s [02]|~—s8a]|| [03)|= faz]|~—sf.03|}~—s.97] ||E73]|Leo)|E:7u]|—.-44]|
|Region2||0.56***||||0.47***||||-0.25|||
|<br>||(0.02)<br>||||(0.004)<br>||||(0.12)<br>|||
||||||||||||||
|enrollment|||-0.36||||-0.26||||0.06||
||||(0.18)||||(0.15)||||(0.22)||
||||[.29}||||[.24]||||[.82]||
|wealthy||||0.36||||0.29||||-0.45|
|||||(0.19)<br>||||(0.19)<br>||||(0.19)<br>|
|||||[.50]||||[49]||||[.32]|
|N|184|184|184|184|184|184|184|184|184|184|184|184|
|R-squared|0.86|0.90|0.87|0.88|0.83|0.91|0.84|0.85|0.34|1.00|1.00|1.00|
|treatmenteffect||0.32**|-0.15|0.22***||0.40***|=-0.004|0.27***||-0.16|-0.01|-0.18|
|<br>with interaction||(0.18)|(0.14)|(0.14)||(0.24)|<br>(0.07)|(0.17)||(0.24)|(0.17)|(0.21)|
|||[.03}|(40)|.0]||[.01}|[.90)|—L.01]||[54]|[84]|[43]|
|mean outcome|0.38|0.38|0.38|0.38|0.22|0.22|0.22|0.22|12.30|12.30|12.30|— 12.30|
|Outcome is share of students taking Grad|e 12 exam|in private s|chools. Sa|mple is dist|rict panel|, Regions 2-|6, 1998-20|12. Schools|is numbe|r of priva|te secon|dary schools. Low|
|enrollmentandwealthinteractionsaredi|strictchar|acteristicsfr|om1998|households|urvey.Lo|wenrollmen|t=below|nationalm|ediansec|ondary-a|ge(13-18|)enrollmentrate.|
|<br>Wealthy=atorabovenationalmedianho|<br>useholdas|<br>sets,byfirst|<br>principal|<br>component.|<br>Allregre|<br>ssionsinclud|<br>edistrict|<br>andyearfix|<br>edeffects.|“Treatm|<br>enteffect|<br>withinteraction”|
|<br>is sum of program coefficient and coeffici<br>wild-t cluster bootstrap of Cameron et al<br>significantat5%;***significantat1%.|<br>ent on int<br> (2008) wi|<br>eraction term<br>th Webb (20|<br>.<br>Standa<br>13) 6-poi|<br>rd errors in <br>nt distribut|<br> parenthes<br>ion of wei|<br>es, clustere<br>ghts. Signi|<br>d by regio<br>ficance lev|<br>n.<br>p-values <br>els are base|<br> in bracket<br>d on thes|<br>s, calcul<br>e p-values|<br>ated from<br>:<br>* signi|<br> 200 iterations of<br>ficant at 10%; **|