rafmacalaba/datause-encoder-data huggingface dataset:
DISCRIMINATION AGAINST SEXUAL MINORITIES IN EDUCATION AND HOUSING: EVIDENCE FROM TWO FIELD EXPERIMENTS IN SERBIA

## **Measurement**

Since the data were collected through telephone conversations in natural conditions, the qualitative data were transformed into quantitative outcomes. To ensure the robustness of the quantitative data, evaluation transcripts were done by three independent raters in accordance with the usual standards of evaluations of observations in natural conditions.<sup>xxv</sup> The raters did not take part in the study, do not identify as LGBTI, and did not have a position on LGBTI rights that could skew their assessment.

To evaluate the outcomes, a five‐level scale was used:

1. Accepted without hesitation or additional conditions.

2. Accepted, but with hesitation or postponement.

3. Unclear outcome (final decision not clear).

4. Rejected, but with hesitation.

5. Rejected without hesitation.

# **2.2 Results**

## **Same‐sex couples face discrimination when renting apartments**

Almost one in five (18 percent) same‐sex couples were refused rental of an apartment by the landlord; none of the heterosexual couples were. Same‐sex couples also faced significantly more hesitation (8 percent) when accepted compared to their heterosexual counterparts (1 percent; figure 4).<sup>xxvi</sup> <!-- Start of picture text --> Figure 4: Acceptance and refusal of same‐sex and heterosexual couples when seeking rental<br>apartments<br><!-- End of picture text --> <!-- Start of picture text --> SAME‐SEX COUPLES HETEROSEXUAL COUPLES<br>1%<br>11%<br>7%<br>8%<br>74%<br>99%<br>Accepted without hesitation Accepted without hesitation<br>Accepted with hesitation Accepted with hesitation<br>Refused with hesitation Refused with hesitation<br>Refused without hesitation Refused without hesitation<br><!-- End of picture text --> <mark>Database: all observations, 160 observations per treatment and control groups, 320 observations in total.</mark> 11
By some estimates, all power plants in Pakistan were running below capacity in 2014 because of fuel shortages, with oil and gas shortages alone leading to 5,000 MW of idled capacity (World Bank 2015).

Pakistan started importing liquefied natural gas (LNG) in 2015. The increasing supply of imported LNG is expected to relieve fuel shortages and cut demand for furnace oil for power generation.

Inefficiencies in electricity generation, transmission, and distribution are widespread. Because generators are shielded from market competition and costs are passed through to consumers, incentives to improve efficiency remain weak, especially for publicly owned power plants. By one estimate, on average pubic power plants use 20 percent more fuel than private plants to generate the same amount of electricity (Zhang 2018). In addition, almost a fifth of the electricity produced in Pakistan is lost in transmission and distribution, as a result of technical and nontechnical problems (such as pilferage). Another important cause of power shortage is transmission constraints, which hinder evacuation of electric power. Transmission constraints are estimated to have accounted for 29 percent of the electricity shortfall in fiscal year 2015 (NEPRA 2016).

# **III. Data and Descriptive Analysis**

Data for the analysis come from two sources. The first is the Pakistan Social and Living Standards Measurement (PSLM) Survey for 2007/08 and 2010. Both rounds included detailed questions on households’ social and economic characteristics; income; expenditure; energy use patterns, such as monthly electricity consumption; and grid connection, education, health, water supply, and sanitation status.

The Pakistan Bureau of Statistics implemented the surveys in all urban and rural areas of the country’s four provinces and Islamabad, excluding restricted military areas. The first round, conducted in July 2007–June 2008, covered 15,512 households. The follow‐up round, conducted in January–July 2010, covered about half the households surveyed in the first round. The sample size was smaller in the follow‐up round, because only households that could be interviewed in the same quarter of the year they were interviewed the first time were included.

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Table 3. Instrumental variable (IV GMM) regressions of the agricultural supply response to changes in instrumented domestic food prices changes

|Regression number|1|2|3|
|---|---|---|---|
|Dependent variable|Agricultural|Food|Crop|
|(growth rates)|production|production|production|
|Estimator|IV GMM|IV GMM|IV GMM|
|Change in log of domestic food prices|1.76**|2.48**|2.86***|
||(0.76)|(0.97)|(1.11)|
|N|300|300|300|
|Tests||||
|Kleibergen-Paap rk Wald F statistic|12.20|12.20|12.20|
|Kleibergen-Paap rk LM statistic|0.01|0.01|0.01|
|Stock-Wright LM statistic|0.00|0.01|0.21|
|Regression number|4|5|6|
|Dependent variable|Crop production|Crop area|Cereal|
|(growth rates)|per hectare|harvested|yields|
|Estimator|IV GMM|IV GMM|IV GMM|
|Change in log of domestic food prices|0.95|1.90**|2.41**|
||(0.84)|(0.87)|(1.19)|
|N|300|300|300|
|Tests||||
|Kleibergen-Paap rk Wald F statistic|12.20|12.20|10.82|
|Kleibergen-Paap rk LM statistic|0.01|0.01|0.01|
|Stock-Wright LM statistic|0.02|0.01|0.09|

_Source_ : Author’s estimates from World Bank (2013) poverty data and ILO (2013) food price data.

_Notes_ : See text for definitions of the variables. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. Standard errors clustered at the country level are reported in parentheses. The excluded instrument is the change in international prices (see section III for details). All regressions control for time dummies. The Kleibergen-Paap rk Wald F statistic measures weak instruments, with critical values varying between 5.53 and 16.38, suggesting that the regressions above may suffer from a weak instrument problem. The null hypothesis of the Kleibergen-Paap rk LM statistic is that the equation is underidentified. The null hypothesis of the Stock-Wright LM test has the null hypothesis that the coefficient on the change in real food prices is equal to zero and overidentifying restrictions are valid.

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average, a cotton family would enjoy a higher z-score of 0.64 points. This is equivalent to 30 percent of the average haz z-score for households in subsistence. There are no significant differences in wasting and underweight between cotton and subsistence households. Also, there is no differential effect between females and males, although the magnitudes for males are much larger and marginally significant. Similar results have been found in, for example, von Braun and Kennedy (1994).

These are very interesting results. They indicate that there are no differences in current nutrition among children living in cotton producing or subsistence households. However, there are statistically significant benefits in terms of long-term nutrition among those children living on cotton farms. One interpretation is that whereas doing cotton or subsistence allows children to be currently well fed, through the consumption of maize or sweet potatoes for instance, cash income derived from cotton would allows farmers to purchase milk, fish, or dairy products that have longer term benefits. Another hypothesis argues that the movement from subsistence to agricultural commercialization implies a change in the use of fertilizers and pesticides that helps prevent health hazards and improve the long-term nutritional status on the children.<sup>19</sup> In the case of education, our findings indicate that educational outcomes are similar in households involved in cotton and in household involved in subsistence. This result holds for the whole population (all children between 7 and 18 years old), children in primary school and children in secondary school.

# **4. Conclusions**

In this paper, we have examined the relationship between cotton reforms and poverty in Zambia. Cotton is one of the main cash crops of smallholders in suitable provinces in rural

> 19 It is also possible that the sample year is one of relatively good subsistence year but relatively bad cotton season. There is actually no evidence that this was so in 1998, however.

25
there is a substantial amount of policy information available even though data is missing for one or two policy measures. This means that under the conventional approach useful policy data would be thrown away because a country lacks information on a single policy. In order to avoid this we turn to well-developed methods for making inferences in the presence of non-random missing data.

Our strategy for dealing with the missing policy data is to employ multiple imputation (MI) methods, first introduced by Rubin (1976). In broad terms, what MI does is to predict the missing policy data (i.e. impute it) in a manner that recognizes uncertainty about the values of the missing data, while at the same time maintaining the overall covariance structure of the right hand side variables. The procedure adopts _multiple_ imputations so that causal inferences are not being driven by the randomness inherent in a given imputation. Multiple imputation also provides a framework for adjusting the standard errors in the estimated relationship, to account for the fact that there is additional parameter uncertainty because some of the independent variables are imputed.

In our specific context we know that the missing policy data lie along a continuum ranging from 0 to 2. Where policy data are available, it is typically the case that the policy scores are not independent of other right hand side variables (e.g. countries with higher per capita GDP have higher scores for most policy bundles). What MI procedures do in our exercise is to replace missing policy data with an empirical distribution of policy scores. This distribution lies between 0 and 2, but it is not uniform; it is massed around the most likely values of the policy data, conditional on the other right hand side variables such as GDP per capita, WTO membership or even other policy data when they are present. There is uncertainty about the true value of the missing policy scores, but the uncertainty is conditional on what the rest of the data reveal about the links between a particular policy and the other variables on the right hand side. The MI procedures also ensure that additional uncertainty about the true values of the missing policy data is reflected in the estimated standard errors.

Multiple imputation is a simulation-based statistical technique for handling missing data that consists of three steps. The first step is to generate _M_ imputations (completed data sets); the second step is to conduct the desired analysis on each imputation separately; and the third step is to combine results obtained from the second step for each completed data set into a single multiple-imputation result.<sup>27</sup> We use the well-established Stata MI package to conduct the above procedures.<sup>28</sup> Given The difference between the two means is significantly different from 0 at the 5 percent level of significance. Similar disparities apply for the other policy measures.

> 27For more theoretical details of multiple imputation, see Rubin (1987).

> 28For technical details, refer to Stata (2013).

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Nevertheless, the poverty figures suggest that in general the age-effect outweighs the gender-effect. This is clear also from Table 9 which classifies households by the number of elderly people (over 65) in the household. In Bulgaria and Hungary, households without elderly members have below average poverty rates and those with elderly members have above average poverty incidence. The latter increases with the number of elderly. Poland is again the exception, where age proves to be an irrelevant dimension of poverty. As discussed previously, Poland's pension system needs to be credited with this result.

Table 9: Poverty and the Elderly

|_Number of ElderlyMembers_<br>_(Over Age 65) in the Household_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
||||Headcou|nt (Po, in pe|rcent)||
|0-|21.9|19.5|22.8|28.5|42.1|37.3|
|1|33.9|23.7|24.1|39.1|45.0|47.4|
|2|38.2|27.6|22.3|36.4|39.4|42.4|
|3 or more|...|56.8|24.7|...|...|50.0|
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||Poverty G|ap (in per|cent)j/||||
|0|19.1|13.9|13.3|19.6|25.0|28.8|
|1|22.2|15.2|13.5|23.6|24.3|32.7|
|2|18.5|13.7|13.2|17.5|29.2|32.1|
|3 or more|...|7.4|13.9|...|...|58.4|
|All|19.8|14.1|13.3|20.2|25.0|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _Notes:_ V The poverty gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

40
our cohort age, this similarity is intriguing because at a cost of US$43 per student per year, the savings and tertiary treatments are less than a quarter of the cost of the PACES vouchers (US$190 per student per year).

# **B. Academic Effort, Consumption, and Labor Outcomes**

From students who completed both the baseline and follow-up surveys, we collected other outcome variables which are presented in Table 7. Because the results were relatively similar across grades, we pooled all of the grades, breaking out grade 11 when appropriate. Panel A displays the results for our measures of academic effort. Panel B contains the results for consumption outcomes. And finally, Panel C contains information on labor market activities for students in grades 6-10 and Panel D contains the same estimates for students in grade 11. The columns are defined as in Tables 5 and 6, and all estimates are again made using Equation 2.

In general, the treatments have little effect on our measures of academic effort in Panel A, except for the rate at which students pass. First, the tertiary treatment increases the time spent on homework by a half an hour a week. The other two treatments do not have any affect. Second, the savings treatment causes a 2 percentage point increase in the number of children passing in the last year. The other treatments have a similar effect (1-2 percentage points), but none of them are statistically significant individually. Again, however, when we pool the results, the overall average effect of the treatments is 1.6 percentage points which is statistically significant at the five percent level. Looking at the remaining outcome variables, students’ grades, none of the treatments cause a significant change, and the point estimates are inconsistent.<sup>17</sup> Compared to the pass rates, it may be that grades are a noisier outcome or that teachers consider more than grades when determining who will pass at the end of the year.

Panel B shows our findings for consumption and is divided into food and school expenses. First, we report effects on food consumption (first two rows). All the treatments, except the basic treatment in Suba, increase the number of meals children have eaten over the last three days by about 0.17 to 0.24 of a meal. Similarly for the same treatments, the number of

> 17 It is interesting to note the relative similarity of the effects estimated through self-reported and verified grades. Despite the significant overestimates for enrollment and attendance, students’ reports of their grades are remarkably similar to those that we were able to verify.  Self-reported grades are slightly higher, but measured treatment effects are very similar.

- 27 -
11 With respect to poverty, the paper finds that the receipt of international remittances reduces the poverty headcount index by 1.6 percent and the more sensitive poverty gap index by 12.6 percent. This means that international remittances have a greater impact on reducing the depth as opposed to the level of poverty in Guatemala.

Table 4 from the Guatemala paper shows why international remittances reduce the depth of poverty more than the level of poverty. This table ranks all 7276 households in the Guatemala data set into decile groups on the basis of per capita expenditure (excluding remittances). Column (2) of this table shows that households in the lowest decile group receive a huge share – over 60 percent -- of their total household income from international remittances. When households in this bottom decile group – the “poorest of the poor” -- receive international remittances their income status changes dramatically. This in turn has a large effect on any poverty measure – like the poverty gap – which considers both the number and the distance of poor households beneath the poverty line. By contrast, Table 4 shows that households near the poverty line (roughly the fifth decile group) do not receive a very large proportion of their household income from remittances. As a result, the poverty headcount measure does not decline much in Guatemala with the inclusion of international remittances in household income.

The fifth paper focuses more on international migration than remittances, and adopts a different methodological approach to examine the impact of international migration on child health outcomes in rural Mexico (Hildebrandt and McKenzie, 2004). Based on a nationally-representative 1997 demographic survey of 16,500 children in 12,400 households in rural Mexico, the paper compares differences in infant mortality, birth weight and health inputs between international migrant and non-migrant households. About 19 percent of the survey households have an international migrant. Since international migration may be endogenous to health outcomes, the paper uses instruments – focused on historic Mexico-to-US migration patterns – to control for this problem. Using these instruments, and controlling for various factors, the paper finds that children born in international migrant households are 3 percent less likely to die in their first year than children in non-migrant households. Children born in an international migrant household are also estimated to weigh 360 grams – 0.64 of a standard deviation – more than children in non-migrant households. The paper suggests that these positive
FIGURE 6. NEONATAL MDRTALITY TRENDS BY INCOME GROUP IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986) <!-- Start of picture text --> Low income<br>I' /' ;~ >'S " v-' /,./ - ~ > N All income groups<br>o 0 -__.- - High Income<br>I 4<br>el9- .97 -. 29s0 I.<br>Year (1)<br><!-- End of picture text --> NOTE: (1) NNMR 5 - YEAR MOVING AVERAGE
**Figure 8: Entry Salaries by Grade and Year of Entry, Selected Grades, 1987-2015, Expressed as Differences with the Men – Part 1 Average (cont’d)**

<!-- Start of picture text --> GE Staff<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>GG Staff<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>-14<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary differences relative to Men Part 1 ($'000)<br>Salary differences relative to Men Part 1 ($'000)<br><!-- End of picture text --> <!-- Start of picture text --> GF Staff<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary differences relative to Men Part 1 ($'000)<br><!-- End of picture text --> <!-- Start of picture text --> GH+ Staff<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>21<br>Salary differences relative to Men Part 1 ($'000)<br><!-- End of picture text -->
# **Table 2: Supplementary regressions with lagged dependent variable**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||GE|RQ|RL|CC|CPI|ECI|
|Cabinet size|-0.076**|-0.035|-0.090***|-0.097***|-0.101*|0.005|
||(0.031)|(0.025)|(0.026)|(0.032)|(0.059)|(0.034)|
|Polity|0.225**|0.219***|0.305**|0.161|-0.099||
||(0.096)|(0.076)|(0.136)|(0.111)|(0.088)||
|Electoral democracy||||||3.937<br>(3.302)|
|Left|-1.696***|-0.156|-1.146**|-1.455**|-0.460|<br>-0.843|
||(0.497)|(0.554)|(0.459)|(0.642)|(0.514)|(0.553)|
|Government fractionalization|<br>-2.177*|<br>0.460|<br>-1.148|<br>-0.322|<br>-0.230|<br>-0.357|
||(1.196)|(0.974)|(1.163)|(1.156)|(1.302)|(0.960)|
|Programmatic parties|<br>-0.047|<br>-0.482|<br>0.305|<br>-0.765|<br>-0.142|<br>0.593|
||(0.617)|(0.776)|(0.740)|(0.907)|(0.988)|(0.870)|
|GDP per capita|<br>3.816**|<br>2.934*|<br>3.545**|<br>2.873*|<br>5.055**|<br>-1.127|
||(1.884)|(1.517)|(1.602)|(1.578)|(2.263)|(0.762)|
|Military|-0.497|0.834|1.016|0.558|-1.905**|1.978**|
||(0.620)|(0.518)|(1.221)|(0.383)|(0.802)|(0.842)|
|Defense minister|-0.270|-0.470|1.246**|0.430|-0.991|0.014|
||(0.506)|(0.357)|(0.562)|(0.589)|(0.655)|(0.491)|
|Years in office|<br>0.055**|<br>0.053***|<br>0.010|<br>-0.035|<br>-0.042*|<br>-0.015|
||(0.026)|(0.017)|(0.023)|(0.024)|(0.023)|(0.018)|
|Years to election|0.037|-0.023|-0.036|-0.019|0.072|-0.002|
||(0.050)|(0.058)|(0.054)|(0.070)|(0.088)|(0.066)|
|Lagged dependent variable|<br>0.520***|<br>0.656***|<br>0.622***|<br>0.634***|<br>0.598***|<br>0.935***|
||(0.046)|(0.046)|(0.050)|(0.079)|(0.051)|(0.020)|
|Country FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|436|436|436|436|441|1,001|
|R-squared (within)|0.483|0.580|0.577|0.552|0.674|<br>0.848|
|Countries|40|40|40|40|40|40|

Notes: Standard errors clustered by country in parentheses. Dependent variables are from the September 2018 update of the Worldwide Governance Indicators (estimates of GE = Government Effectiveness; RQ = Regulatory Quality; RL = Rule of Law; CC = Control of Corruption), various editions of the Corruption Perceptions Index (CPI), and the July 2018 version of the V-Dem dataset (ECI = Executive Corruption Index, reversed). See data appendix for full variable definitions and sources. *** p < 0.01, ** p < 0.05, * p < 0.1.

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<!-- Start of picture text --> predicted to affect output, but some potential indication of a direct channel<br>from higher debt to lower GDP.<br>Further evidence of the direct channel can be found in the case of Estonia,<br>in Figure 16a. The traditional VAR finds a response of GDP to increases<br>in government expenditure that is not significantly different from zero. The<br>regressions with debt dynamics indicate, however, that this may overstate<br>the effects of government expenditure on output in Estonia. In fact, once<br>debt dynamics have been accounted for, GDP declines significantly in the<br>medium term in response to increases in government expenditure. In con-<br>trast, in Figure 16b, a tax increase causes a greater decline in GDP once debt<br>dynamics have been accounted for. This unintuitive result, can be explained<br>by a glance at the right-hand panel of Figure 16c. The increase in taxation<br>decreases the debt-to-GDP ratio only temporarily. Debt then increases in the<br>upcoming years. This is because government expenditure shows a persistent<br>increase following the increase in tax revenues, as can be seen in the upper-<br>left-hand panel of Figure 16a. In Estonia, the fiscal space freed by increases<br>in taxation is more-than-fully used to increase government expenditures in<br>subsequent years. These increases in government expenditures increase debt<br>levels, which have a deleterious effect on GDP.<br>In the remaining three countries, as in the Favero and Giavazzi’s (2007)<br>exploration of the United States, we find little difference between the regres-<br>sions with and without debt dynamics. Only in the case of South Africa do<br>debt dynamics appear to diminish the effects of fiscal policy, due to future<br>policy reversals, but this effect is not statistically significant.<br>29<br><!-- End of picture text -->