rafmacalaba/datause-encoder-data huggingface dataset:
prior thought. More specifically they observe that people stopped paying attention to global climate change when they realized that there is no easy solution for it. Instead they note that many people judged as serious only those problems for which they thought action could be taken.

Krosnick, Jon, Allyson Holbrook and Penny Visser 2000 The Impact of the Fall 1997 Debate about Global Warming on American Public Opinion. Public Understanding of Science 9: 239260.

Krosnik and co-authors examine the effect of the Clinton campaign to put global climate change into the spotlight during the fall of 1997 in order to build support for Kyoto. The authors examine (1) What were Americans' beliefs and attitudes about global warming before the debate? (2) Did the debate catch the public's attention? and (3) Did the debate change people's beliefs and attitudes about global warming? They observed that a majority of the American general public endorsed the views advocated by Clinton before the media campaign began. They conclude that “the debate did attract people's attention and strengthened the public's beliefs and attitudes,” but “produced almost no changes in public opinion when the nation's population is lumped together.” They do note differences between Republican and Democratic support for the President’s position on Kyoto (observations which are repeated in work by McCright and Dunlap as well as others in this bibliography).

Nisbet, Matthew and Teresa Myers 2007. The Polls – Trends: Twenty Years of Public Opinion about Global Warming. Public Opinion Quarterly Vol. 71(3): 444–470.

Nisbet and Myers conduct a systematic review of trends in public opinion regarding global warming. They summarize public opinion across several key dimensions including (a) public awareness of the issue of global warming; (b) public understanding of the causes of global warming and the specifics of the policy debate; (c) public perceptions of the certainty of the science and the level of agreement among experts; (d) public concern about the impacts of global warming; (e) public support for policy action in light of potential economic costs; and (f) public support for the Kyoto climate treaty.

O'Connor Robert , Richard J. Bord , Brent Yarnal and Nancy Wiefek 2002. Who Wants to Reduce Greenhouse Gas Emissions? Social Science Quarterly 83(1):1 – 17.

The authors use a heuristic model approach to compare cognitive, economic, and partisan explanations for who supports reductions in climate emissions. Based on survey data from a mail survey of 623 residents in Pennsylvania, the authors find that people who can both accurately identify causes of climate change and who expect negative future climate scenarios are the most likely to support both government and voluntary actions. “Economic circumstances and anxieties are not important predictors, but the belief that environmental protection efforts do 52
microfinance to its partner organizations, which are mostly NGOs. MRDO is one such nonpartisan NGO funded primarily by PPAF, that mobilizes women using a community based approach. RCons, a survey firm, helped MRDO implement the awareness campaign and collected the baseline and follow-up data. ECI, a non-partisan local training firm, collaborated in the design of the campaign. ECI had prior experience with the development of visual aids and pamphlets related to electoral participation and the balloting process. It was also actively engaged in the training of local election officers nationwide prior to the elections.

# **2.1 Context**

The campaign was carried out in the districts of Sukkur and Khairpur in the southern province of Sindh. The districts were selected because of sharp electoral competition between the two main political parties, the Pakistan People‘s Party Parliamentarians (PPPP), which has a secular-left leaning platform and the Pakistan Muslim League Functional (PMLF), which was allied with the military regime.

The initial sample included 12 villages, 6 villages from each district, and 24 polling stations from two constituencies.<sup>11</sup> All candidates from both parties in these two constituencies were male. Villages were chosen to ensure variation in expected political competition at the polling station level, but given the context of the 2008 elections, 3 villages (3 polling stations) had to be dropped because the safety of the canvassing teams could not be guaranteed. The polling stations in these 3 villages were relatively more contested than those in our final sample of 9 villages and 21 polling stations.

Indeed, the 2008 national elections were held in an environment that was politically charged. After seven years of military rule, culminating in widespread opposition, the government declared emergency rule. The sitting judges of the Supreme Court were dismissed and there were fears that the incumbent government would engage in massive rigging. Scheduled initially for January 8<sup>th</sup> , 2008, the elections were postponed to February 18<sup>th</sup> , 2008 because Benazir Bhutto,

> 11 Twelve polling stations were from the National Assembly constituency NA-199 (Sukkur) and the remaining 12 from NA-215 (Khairpur). NA-199 had a total of 242 polling stations while NA-215 had 270 polling stations. The same 12 polling stations belonged to Provincial Assembly constituencies PS-4 (Sukkur) and PS-32 (Khairpur), respectively. PS-4 had 125 polling stations in total and PS-32 had 114 (see http://www.ecp.gov.pk/ for more information).

7
However, it is probably more realistic to assume that the literacy score does not capture _all_ the labor market relevant skills acquired in school; therefore, S contains those productivity enhancing skills acquired in school and are not captured in the literacy score (see also Tyler 2004). The estimated coefficient δ1 will then represent a mixture of returns to those skills acquired in school and not captured by the literacy score and the signaling value of schooling.

Ishikawa and Ryan (2002), using the 1992 American National Adult Literacy Survey, found that for the most part it is the substance of learning in school which predominantly affects wages. On the other hand, Gintis (1971) and Bowles _et al._ (2001), after surveying the literature over several decades, find that what is predominantly rewarded in the labor market is the (unobservable) non-cognitive component of schooling, rather than basic skills.

# **3. Data**

The International Adult Literacy Survey (IALS) was carried out in 20 countries in the mid- to late 1990s, a project undertaken by the governments of the countries and three intergovernmental organizations.<sup>‡‡</sup> It is a carefully designed, innovative survey of adult populations, and goes beyond just measuring literacy capabilities to assessing how these capabilities are applied to everyday activities. The IALS was followed by an extensive quality review (see Murray _et al_ . 1998) which, after comparing the distribution of the characteristics of the actual and weighted samples, concluded that the actual and weighted samples were comparable to the overall populations of the IALS countries. The questionnaire also included questions about labor market status, earnings, education as well as demographic characteristics.

> ‡‡ OECD, European Union and UNESCO.

10
organised control programmes exist, may be considerably more complete. Preston (1976) considered respiratory TB, accidents and violence as relatively robust categories in all populations, whereas he suggested that "other infectious and parasitic diseases" are likely to be under-recorded in areas where registration is unreliable because of a tendency to assign such deaths to terminal conditions such as pneumonia, or tc symptoms such as fever. Deaths from cardiovascular diseases may likewise be assigned to "unknown cause" or "senility".

Vital registration data on cause-specific mortality for Mauritius, Egypt, Sri Lanka and Thailand are reviewed and discussed in Section 2.4. Data are also included for Guatemala, which is one of the higher mortality countries of Latin America.

Apart from national vital registration data, there have been a few studies of cause-specific adult mortality based on sub-national registration data. Most of these again suffer from low coverage and a high proportion of deaths from ill-defined causes. Botha and Bradshaw (1985) analysed registration data for the black population of South Africa. They considered that there was considerable under-reporting of deaths, because the overall mortality rate was lower than expected. Moreover, 20% of deaths were classified as "ill-defined". Vital registration data for Lagos, Nigeria for the year 1977 were analysed by Ayeni (1980). Registration was estimated to be 60% complete, but of the reported deaths 17% were assigned to ill-defined conditions. Finally, Fargues and Nassour (1987) report an analysis of the 55,000 deaths of all ages registered at the Hygiene Office of Bamako, Mali, from 1974 to 1985. No information appears to be given concerning the completeness of registration.

Other data on adult mortality come from specially conducted surveys and from hospital records. Because of the relatively low mortality rate in the adult age-range, either very large samples or very long periods of follow-up are needed to produce a meaningful number of adult deaths for analysis. For this reason, very few published studies give useful data on causes of adult mortality. Methods ef diagnosis and classification and the additional errors associated with these are discussed in Section 4.2.

Muiler and van Ginneken (1989) report the results of a longitudinal population-based study in Machakos, Kenya, Approximately 4000 households were visited regularly, and 340 deaths were recorded between 1975 and 1978. Data are presented for all ages combined, so that it is not possible to separate the adult deaths from those of childhood and old-age. I A longitudinal study based on an occupational cohort was conducted in Senegal (Borges da Silva, 1987). During a nine year study of the health of a factory workforce, 164 adult male deaths were recorded. Factory employees in developing countries may often be iegarded as fairly typical of the general urban population, although the socio-economic characteristics of those employed in the formal economic sector often differ from the large segment of the population engaged in informal economic activities.

5
monsoons since birth, cohort 2 are those with four completed monsoons, and so on. Cohort 5, the youngest, has experienced only one monsoon.

Now, let ∆jkc be the excess monsoon rainfall (as defined in equation 2) experienced by children of cohort _c_ in their _kth_ year of life in DHS cluster _j_ (Appendix A lays out the details of cohort construction and the associated rainfall variables). Our regression for the height-for-age z-score of child _i_ in cluster _j_ and survey round _t_ is with the parameters δ, φr, and μt having the same interpretation as in equation (4). In keeping with our weight-for-age results, we focus on shocks experienced in the first three years of life. This implies twelve β−kc parameters to be estimated: five for monsoon rainfall shocks in the first year of life, relevant to cohorts 1 through 5; four for shocks in the second year of life (cohorts 1-4); and three for shocks in the third year of life (cohorts 1-3).

Identification of multiple past-monsoon rainfall effects for a given cohort is facilitated by the lack of correlation in cluster-level rainfall deviations across calendar years. We have no particular priors on the sign or magnitude of the β−kc , however. Even if we find large disease environment or income effects on weight-for-age, there is still the possibility for growth recovery during these formative early years of life resulting in no discernible impact on height-for-age. Conversely, a positive (or negative) estimate of β−kc without a corresponding positive (negative) estimate of β in equation (4) would be inconsistent with the framework laid out in Section 2. For instance, it would be surprising if past monsoon rainfall had no effect at all on short-run nutritional status (weight-for-age), yet a substantial effect on longer run status (height-for-age).

# **5. Results**

# _Weight-for-age results_

Table 3 reports the OLS estimates for weight-for-age, based on the sample discussed in Section 3 and the specification developed in Section 4. The first column includes the current and past rainfall shocks as explanatory variables along with the normal monsoon 11
tions. In columns 3 and 4, we examine Gallup’s Diversity Index, which was designed to measure attitudes towards people from different racial, ethnic, or cultural groups. Consistent with Table 2, we find positive but insignificant effects using the eventregions-only specification, which flip to negative and still statistically insignificant when adding regions without an event. Columns 5 and 6 examine responses to the question “are you satisfied or dissatisfied with the city or area where you live?”, a companion question to our main measure on immigrants. Again neither specification is statistically significant.

# **5.2 Heterogeneity by Employment Policies and Camps**

The section above argues that there is little effect on attitudes towards immigrants in the periods immediately following a large wave of refugees on average across affected regions in LMICs with available data. This averages across significant variations in hosting policies, which we hypothesize could affect social cohesion and other outcomes for host communities.

We next decompose the average effects by variations in the policy index and the presence of camps. Instead of looking at changes within regions, this exercise involves comparing effects across different hosting situations. Policies are not randomly assigned and could be endogenous to attitudes in the host region; the estimates can be interpreted as causal under the assumption that attitudes would have evolved similarly if policies had been the same.<sup>31</sup> In order to gauge the similarities across regions, Table 5 looks at a set of observable characterics compiled from many of the sources listed in Section 4, including GWP, UNHCR, DWRAP, and AidData.<sup>32</sup> Regions may still differ on a number of unobservables, but the observables provide a starting point to assess

> 31In the DWRAP dataset, out of 113 events, we identified 30 cases where there were changes in employment policy towards refugees in the event window. For events with a single change, 9 of them registered a "positive" change and 5 of them a "negative" one. There are 16 events (15 from Turkey and 1 from Rwanda) that experience both a positive and a negative change in their event window. Our main results are robust to dropping these events.

> 32For comparison, Table A11 lists the same descriptive statistics for all regions in LMICs in 2018 in countries with at least 5,000 refugees.

26
And God said, 'Let there be light' and there was light, but the Electricity Board said He would have to wait until Thursday to be connected. Spike Milligan

# 1. Introduction

There is growing evidence, both micro- and macroeconomic, that better electricity infrastructure significantly boosts economic growth and improves a range of development outcomes.<sup>4</sup> Energy is necessary for the operation of productive capital in the industrial sector. Low levels of infrastructure development and poor quality of services can drive up firms’ direct and indirect costs and bias their technological choices away from energy-intensive ones which, in turn, increase the overall costs relative to competitors in other regions.<sup>5</sup> Enterprises typically face more barriers in developing countries where firms have difficulties getting connected to the grid from public utilities.<sup>6</sup> When firms do get connected, the sanctioned load is often lower than their demand and they face frequent scheduled and unscheduled power cuts. In addition, fluctuations in voltage and frequency of power supplied causes machine damage, material losses, and variations in product quality. As a result, production volumes, manufacturing costs and output quality are all adversely affected; firms invest less or in less efficient technologies and have lower productivity growth.

To offset these negative impacts, industrial firms in developing countries are often opting for self-generation even though it is widely considered a second best solution. Of the 25 Sub-Saharan countries reviewed by Foster and Steinbucks (2009), in-house generation accounts for more than 25% of the installed generating capacity in 3 countries, and for more than 10% in 9 others. This of course has a negative impact on their overall investment capacity: In Nigeria, where 40% of electricity consumed is produced through autogeneration, firms spend up to 20–30% of initial investment on measures to enhance the reliability of electricity supply.<sup>7</sup> It also drives up costs: Foster and Steinbucks (2009) estimate that in Africa, own-generated electricity is on average 313% more expensive than that from the grid. In environments with important credit constraints, such investments in mitigating

> 4 See for example Calderón and Servén (2003), and Calderón (2009) for cross-country estimations, and Dinkelman (2009) and Lipscomb, Mobarak and Barham (2009) for microeconomic evidence. 5 Eifert, Gelb and Ramachandran (2008) . 6 World Bank (2005); Bartelsman, Haltiwanger and Scarpetta (2004) . 7 Adenikinju (2005) .
It is possible to identify three distinct phases in the evolution of PISA test score means over the period 2000-2012.<sup>4</sup> As shown in Figure 1, mean scores decreased between 2000 and 2006 for all three subjects assessed in PISA, then increased between 2006 and 2009, and remained roughly stable between 2009 and 2012. Changes in reading scores are larger than those in math and science, with these last two areas showing very similar trends.

_Figure 1: Trends in Learning Outcomes, 2000-12_ <!-- Start of picture text --> 430<br>420<br>410<br>400<br>390<br>380<br>370 Reading<br>Math<br>360<br>Science<br>350<br>2000 2006 2009 2012<br>Source: OECD’s Program for International Student Assessment<br><!-- End of picture text --> Table 1 shows the changes in mean score by year and subject area, including the t-statistic for the test of constant levels of learning outcomes across any possible combination of years.<sup>5</sup> A statistically significant negative change (at the 99 percent level) occurs in reading scores from 2000 to 2006 and a positive one from 2006 to 2009, while the change is not significant between 2009 and 2012. Changes in means in math and science are not statistically significant in any of the values’ proportion varies considerably across years. While a relatively large proportion of observations in 2000 report missing values, the share is almost insignificant in 2006 and even less so in 2009 but then again significant in 2012.Missing values are unlikely to be at random, particularly in the 2000 edition of the survey, as they seem to be related to students from the lowest quintiles of the income distribution. A second concern is that causes behind the missing values are unlikely to be the same across years. To limit potential bias, we exclude from the analysis variables that present more than 20 percent of missing variables in at least one of the four PISA rounds.

4 Note that, due to a change in the scaling methodology of test scores, only means in reading are strictly comparable throughout the period 2000-2012. Scores in math and science are comparable only starting from the 2003 and 2006 editions of the survey, respectively (OECD, 2010).

5 Final test scores in PISA are expressed in five plausible values (PV), rather than a point estimate like a weighted likelihood estimate (WLE), where the mean final score is the simple average of the five plausible values. The use of PVs, however, implies an additional source of variation known as imputation variance. Moreover, the arbitrary choice of items that ensure comparability of tests across PISA editions gives rise to a linking error that has to be taken into account when estimating the standard errors of the difference in means scores (OECD, 2010). The use of a t-statistic or similar analysis that does not take into account the PISA survey design and PV would lead to a serious underestimation of variance and misinterpretation of results (see Annex A for more details).

8
# FIGURE 17: SHARE OF TRANSPORT AND COMMUNICATION SERVICES IN

# EMPLOYMENT: SOVIET REPUBLICS AND OECD

<!-- Start of picture text --> 12%<br>10%<br>RP; E5S<br>USSR LA<br>USSR<br>AZ AZ * $<br>w~~wB<br>8% U<br>GE<br>T.4U zlZUi AR 1<br>I~~ ~ * ~~~~~~~~~~~~~~~ s**<br>6%; MO * S<br>4 %~~~~~~~~~~~~~~~~~~~~~~~~~<br>4% 1- I -- I lI * - I l l S<br>7 7.5 8 8.5 9 9.5 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
Figure 4. Distribution of African and rest of the world Ginis, 1960-2000 <!-- Start of picture text --> .04<br>Africa<br>.02<br>0<br>0 20 40 60 80<br>yy<br><!-- End of picture text --> Note: Calculated from the WIDER data base; only person- (not household-) based Ginis.

Thus while, because of lack of data, we cannot say much about the direction of change if inequality in Africa, it seems plausible that high inequality predates independence and that Africa does have higher inequality than the rest of the world. The question of course is Why? For, as we have seen, neither its low income level nor its relatively equal distribution of land seem to predispose Africa for such high levels if inequality.

14
# **Table 5. Price Elasticities**

|_Panel A: Peru_||Credit|||Savings||
|---|---|---|---|---|---|---|
||All|Fin li|teracy|All|Fin li|teracy|
|||No|Yes||No|Yes|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Brochures|-2.154***|-1.767*|-2.451***|0.003|0.000|0.007|
||(0.622)|(0.967)|(0.781)|(0.008)|(0.010)|(0.014)|
|Simplified format|-5.537***|-4.878***|-6.169***|0.037***|0.030***|0.048***|
||(0.421)|(0.664)|(0.630)|(0.009)|(0.011)|(0.015)|
|Market designed KFS|-2.286***|-1.897**|-2.558***|0.012|0.004|0.024|
||(0.523)|(0.860)|(0.681)|(0.008)|(0.011)|(0.014)|
|_Panel B: Mexico_|||||||
|||Credit|||Savings||
||All|Fin li|teracy|All|Fin li|teracy|
|||No|Yes||No|Yes|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Brochures|-0.968***|-0.042|-1.563***|0.034***|0.017|0.042***|
||(0.278)|(0.417)|(0.345)|(0.007)|(0.012)|(0.009)|
|Simplified format|-2.875***|-2.835***|-2.744***|0.029***|0.008|0.037***|
||(0.229)|(0.275)|(0.315)|(0.008)|(0.014)|(0.009)|
|Complex Table (5 institutions)|-2.722***|-2.484***|-2.698***|0.057***|0.031**|0.075***|
||(0.157)|(0.275)|(0.269)|(0.009)|(0.013)|(0.013)|
|Simple Table (5 institutions)|-2.337***|-2.383***|-2.090***|0.054***|0.028|0.070***|
||(0.165)|(0.178)|(0.279)|(0.012)|(0.019)|(0.016)|
|Simple Table (10 insitutions)|-2.541***|-2.065***|-2.646***|0.081***|0.063***|0.089***|
||(0.141)|(0.391)|(0.214)|(0.015)|(0.024)|(0.018)|
|_Panel C: Pooled_|||||||
|||Credit|||Savings||
||All|Fin li|teracy|All|Fin li|teracy|
|||No|Yes||No|Yes|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Brochures|-1.038***|-0.759***|-1.196***|0.024***|0.012***|0.031***|
||(0.046)|(0.079)|(0.054)|(0.002)|(0.003)|(0.003)|
|Simplified format|-3.185***|-2.960***|-3.166***|0.029***|0.014|0.037***|
||(0.157)|(0.254)|(0.248)|(0.006)|(0.010)|(0.008)|

Notes: This table reports the price-elasticities of the probability that a specific prouct is chosen first by an individual. These elasticities are estimated based on the following rank-ordered logit specifications: Ordercijk = αj + β1 _Price_ cijk + εcijk. Each elasticity is calculated with correspondent data samples. An observation is a choice c made by each individual participant i in round j and session k. _Price_ ijc is either the total loan cost or the savings yield of the each product. yijc is a variable that takes the value of 1 if the individual choses product first. Standard errors are clustered at the participant level and are reported in parenthesis under coefficient estimates. Levels of significance * p<0.10 ** p<0.05 *** p<0.01.