rafmacalaba/data-use-mentions-extended huggingface dataset:
the first stage of NSP in 2003 - 2006.<sup>8</sup> This evaluation enabled randomization of NSP across 500 villages spanning 10 rural districts that were sufficiently large to allow for a control group in addition to villages mobilized by the program; contained no villages that were previously mobilized by NSP; and possessed security conditions that would safeguard the well-being of enumerators involved in the administration of household surveys, per human subjects protocols.

Although not a random sample of districts in Afghanistan, these 10 districts are representative of the country’s geographic, ethnic, and economic diversity and cover the western, central highlands, northern, north-eastern, and eastern regions of the country. Using the 2007–08 National Risk and Vulnerability Assessment (NRVA), it is possible to identify any differences between households sampled for the study and a randomly-selected stratified sample of the population of rural Afghanistan. Although there is no significant difference in the age of respondents or income, evaluation villages are more likely to be engaged in production activities related to agriculture, have slightly worse access to medical services and better access to electricity.<sup>9</sup> The magnitude of these differences, however, is quite small.

Importantly for this study, security conditions in the 10 districts are generally representative of those across Afghanistan, with the exception of the south. As shown in Figure 1, the rate of security incidents between January 2006 (a year and a half before the start of the study) and February 2010 (two years and a half after the start of study) are similar for the area around the evaluation villages and for all of Afghanistan excluding the south. Among the 10 districts, two districts in the eastern province of Nangarhar have significantly higher levels of violence and thus provide a basis for inference over the effects of NSP on the reduction of violence in already insecure regions. The other eight districts represent ‘marginal’ areas which may be at risk of increased violence. Collectively, the sample thus provides for the
-11-

# CHAPTER II

# THE RECONCILIATION OF INDUSTRIAL CENSUS DATA WITH THE NATIONAL ACCOUNTS

If comparisons using the "industry of origin" approach are to have their full usefulness in growth accounts or are to be crosschecked with ICP results, it is essential to scrutinise the consistency of the information in censuses of manufacturing with estimates of manufacturing output in the national accounts. This is best done before embarking on very detailed commodity comparisons.

# Coverage of Industrial Censuses and Their Relation to National Accounts

In the case of Brazil, the national accounts for manufacturing are based directly on the manufacturing census, and tnere are no serious problems of reconciliation. In the U.S.A., the link is not direct as the national accounts are not derived from the industrial census results; we were able to make a rough reconciliation with the help of information supplied by the US Dept. of Commerce, but the census unfortunately does not contain as wide a range of information on inputs as is the case in Brazil and Mexico. In Mexico the national accounts make extensive allowance for informta economic activity; as a consequence the national accounts valuation of manufacturing value added is 38 per cent above that in the census. The country notes in this chapter explain these discrepancies in detail by industry branch.

# Definitions of Value Added in Industrial Censuses and National Accounts

The most readily collectable information on manufacturing output refers to physical product at producer prices. This kind of information is av7iafable in fairly comprehensive form in most censuses of production and can often be monitored successfully in intercensal years. This measure is usually called gross output, and refers to aggregate shipments by manufacturing establishments plus net changes in manufacturers' inventories.

However, this measure contains a good deal of duplication, and comparisons between countries on this basis can be misleading. In two countries producing a similar value added, the one with the most specialised plants will have a higher gross output because there will be more interplant shipments for intermediate processing. In order to eliminate this type of duplication and other differences
those already described for deficit rainfall, dependent on consecutive rainy days, aggregate rainfall over a period of between two and four consecutive days, or a piecewise linear function of rainfall in each phase. For example Hess et al. (2005) report on a rainy days product for Mahabubnagar, Andhra Pradesh for which was 10mm of daily rainfall and a claim was triggered if the rainy day index was four or more. The most recent products seem to be mainly based on aggregate rainfall over a period of two, three, or four consecutive days.

# <u>Low temperature or frost indices</u>

Temperature can have a significant impact on yields (Lobell et al. 2011) and Indian insurers have experimented with indices based on weather station temperature readings, particularly for the Rabi (winter) crop.

Northern parts of India are particularly exposed to the risk of low temperatures or frost which can cause severe crop loss in a short space of time for crops like potato, chick-pea and mustard. Indices have been designed to offer some protection against adversely low temperatures, typically defined as a function of the minimum temperature in the cover period (for example, see Table 11).

# <u>High temperature indices</u>

Complex temperature-based indices have been designed to offer some protection to farmers against adversely high temperatures, particularly for wheat crop. For example, AICI and the Indian Agricultural Research Institute (IARI) designed a phase-based high temperature index for wheat crop in Rajasthan for the Rabi 2007 season, where the claim payment to farmers in respect of each phase was a function of the mean temperature for that phase (Table 9). As another example, ICICI Lombard sold a product for wheat crop in 2010 for which claim payments depended on excess daily temperature, rather than excess average fortnightly temperature (Table 10). However, while it is 7
and GPS measurements, finding that the age of the household head has a significant and positive relationship with measurement bias.

The quality of data collected through farmer self-reporting is also significantly degraded by the natural inclination of respondents to round off numbers. Distributional analysis of GPS and self-reported areas of the 2010/2011 Malawi Integrated Household Survey shows clear evidence of heaping at whole numbers and common fractions, such as 0.5 acres (Figure 1). Carletto et al. (2013 and 2015) and Desiere and D ’ Haese (2015) find rounding to be a significant factor in the discrepancy between GPS area and farmer self-reported estimates. Plot characteristics, such as boundary delineation and the existence of property rights, are known to significantly influence the measurement bias (Carletto et al. 2013, 2015). Plot slope or crop-type may also play a role in the ability of a farmer to estimate plot size. De Groote and Traoré (2005) assessed the accuracy of a method in which farmer self-report is elicited during a visit to the plot, 6
Rather surprisingly, in the immediate aftermath of the reform in Indonesia, we do find a positive and significant impact of democratic reform on the amount of revenue generated from own sources by the district governments. This result is contrary to the inverse relationship between own sources of revenue and electoral reforms suggested by the literature cited in Section 2. The statistically significant increase in the fiscal surplus also appears to contradict the prevailing notion in the decentralization literature that fiscal deficits at the district level are likely to increase based on the expectation that they will be absorbed by the central government.

# _Java and Bali vs. Other Regions_

The analysis so far has focused on the average impact of _Pilkada_ on the level of fiscal expenditures and revenues by districts. The wide diversity of Indonesia in geography, culture, ethnicity and religion warrants further investigation on the extent to which there is heterogeneity in the impacts of _Pilkada_ . For this purpose, equation (1) is estimated separately for the group of districts that had direct elections in the Java and Bali region and the group districts that had direct election in the other regions (Sumatra, Kalimantan, Sulawesi, Nusa Tenggara, Maluku and Papua). Of the 174 districts in total for which we have expenditure and revenue data in 2006, about 53 percent (or 92 districts) are not in the Java and Bali islands and about 61 percent of these (or 56 districts) have directly elected heads. Each of these two groups of districts is compared against the set of districts that did not have direct elections as of 2007 (irrespective of the region that comparison districts belong to).<sup>21</sup> A closer investigation of whether the impact of _Pilkada_ varies by region can shed light on an important question: In the geographic areas that benefitted the most, at least in terms of revenue, from the fiscal decentralization that started in 2001, is it the case that having _elected_ local leaders led to greater impacts on expenditure (compared to having district heads who were not elected)
be infected by malicious software or by the attacker constantly sending packets to AMS server ports to jam needed communications for normal AV functions such as routing, warning, and spatial sensing.

_<u>Interrupting communications (active attack):</u>_ A cyber attacker interferes with communications between the control components of an AMS. By issuing a data request that causes the control systems to obstruct the normal processing sequence of the systems to respond to the request, this interference undermines the stability of routine communications and therefore operations of AMS (Kisner et al., 2010; Kisner, 2009). Such disruptions, once detected, will prompt the AMS operator to reduce or even cease provisions of mobility service in order to recover or switch to backup communication networks.

_<u>Generating incorrect output values or commands (active attack):</u>_ This type of attacks generates erroneous output values or commands and sends them to devices in AMS that enable interaction between cyber and physical components. Consequently, an AMS operator can be misled to take AV routing and dispatching decisions that are not the best for the current operational status, or even disrupt AMS operations and traffic on the roads.

_<u>Collecting AMS operational information (passive attack):</u>_ This type of attack attempts to gain access to AMS operation data through eavesdropping AMS servers. Although not disrupting normal functioning of AMS, the information collected can be used to estimate the operational parameters and states of AMS such as distribution, speed, and fueling of AVs, and to help forge more targeted active cyber attacks in the future.

# **4 Enhancing the cyber resilience of AMS**

Given the variety of plausible cyber attacks, making AMS resilient to cyber attacks is critically important. The premise of doing so is to understand what cyber resilience means for AMS. Answering this question will build on the understanding of cyber resilience and resilience in general, as is in subsection 4.1. In subsection 4.
results for many countries. Taken together, the available literature from low- and middleincome countries suggests that vaccine acceptance is higher in East and South Asia and Latin America and lower in the Middle East and Eastern and Central Asia. Results from Africa were mixed, as were results for different subpopulations. Some studies indicated women were more hesitant than men, younger respondents were more hesitant than older respondents, and those with less education were more hesitant than persons with more education. The predominant reason for hesitancy was concern over side effects from the vaccine. Given the limited availability of randomly selected samples that are representative at the national level, an important contribution of this analysis will be to validate these findings.

This paper extends the existing literature in the following four ways. First and most importantly it provides estimates of vaccine hesitancy for 53 developing countries, which are comparable across countries and largely based on household surveys that are more representative of the national population. In total, these 53 countries represent approximately 30 percent of the population of all low- and middle-income countries and 53.3 percent excluding India and China. Second, it investigates heterogeneity in hesitancy across demographic characteristics of the respondent such as urban or rural residency, gender, age and level of education. Better understanding this heterogeneity can inform the design of vaccination rollouts by helping to target specific population groups. Third, the study expands the evidence base on the reasons behind individuals’ reluctance to take the COVID-19 vaccine, even when it is available and free to them. Finally, the analysis enables a comparison between representative and previously published results from less representative sources. Taken together, the paper provides a rich set of results which can aid policy makers in the design of vaccination campaigns in developing countries.

The rest of the paper is structured as follows. Section 2 describes the data and methods, and section 3 provides the results, first for levels and correlates of vaccine hesitancy, followed by changes over time and concluding with reasons reported for being hesitant. Section 4 discusses the results
property must be registered for transfer or for any other type of transaction. The state government assigns a percentage to the market or transaction value that must be charged as stamp duty. The percentage varies for rural and urban areas, and even within urban areas it may vary for corporations, councils, and nagar panchayats. In some cases, stamp duties are also assigned based on the size of the property. In a few states, such as Andhra Pradesh, Goa, and Telangana, stamp duty is charged at a fixed rate that increases along with the property’s transaction or market value. In most states, the registration fee is typically charged at 1 percent of the transaction value. In a few cases it is also charged as a fixed value.

States may also levy an additional surcharge along with stamp duties and registration fees. This surcharge is typically an ad valorem rate decided by the state and may vary from region to region within the state. For instance, an additional charge of 1 percent is levied in Pune as a “local bill collection charge,” along with the standard stamp duty and registration charge.

# Legal provisions for levying recurrent property tax on immovable property

The Indian Constitution under Article 243X, authorizes state governments to devolve the power to levy and collect taxes, duties, fees, and tolls to ULBs. State governments lay down the limits for the rates of these taxes, charges, and fees and the procedure for levying them. The functions to be devolved to the ULBs are laid out in a separate schedule, but no ULB tax list is mentioned in the Constitution of India. Thus, the devolution of the power to levy and collect taxes is done at the discretion of the individual state governments.

Recurrent property taxes in India are typically levied by ULBs in keeping with the procedures laid down by the states in their individual Municipal Acts. The procedures laid down in these acts cover tax bases, tax rate structures, policies for rebates and exemptions, and measures for dealing with defaults and
children are typically in the same village small group (a sub-village administrative unit) and live in close proximity with one another. Given increases in housing wealth accumulated in rural areas since the mid-1980s, the trend toward nuclear families may well signal a wealth effect independent of the traditional value of providing support and care to elder parents.

A more important concern may be the proximity of adult children, and not whether they co-reside. In Figure 2 we summarize the living arrangements by age cohort from a supplemental survey conducted in 2004 with the Research Center for the Rural Economy (RCRE) at China’s Ministry of Agriculture (and which is discussed below), and includes information on children living within the same village as their elders. While co-residence with adult children was common for fewer than 60 percent of respondents among those aged 60 to 70, more than half the elderly living alone or with a spouse in this age range had at least one adult child living in the village.<sup>6</sup> This suggests that while co-residence was well below levels of the 1930s, adult children were still potentially available to provide both instrumental care and financial support. As elder parent age increases, the probability of co-residence with an adult child approaches 100 percent.<sup>7</sup> Decline in co-residence reflects a decline in support for the elderly only if it is associated with increased elderly abandonment. The rural elderly may have sufficient support if they are receiving transfers from local or migrant adult children. Moreover, the adult children of rural elderly who have become infirm may yet respond to traditional values and return to care for their parents. Giles and Mu (2007) explore this possibility conducting separate analyses using two different data sources (the CHNS and the RCRE surveys), and find a significantly lower

> 6Note that co-residence in rural areas of the four RCRE provinces was also somewhat higher than what we observe for rural areas of the CHNS panel.

> 7Of course, two very different conclusions are consistent with evidence
By including household fixed effects, we effectively purge from our estimates time-invariant differences between treated and non-treated groups that could confound PEP’s fertility effects. In addition, by flexibly accounting for non-parametric temporal changes in a rich set of pre-migration household characteristics, we address dynamic differences between eligible and ineligible migrants. As such, _β_ 1 measures fertility changes among treated migrant households relative to non-treated migrant households, from before to after PEP’s rollout.<sup>9</sup> Specifically, we gauge the impact of regularization on the probability of having children less than one, one, two, or three years old in 2020 and 2021. Since the amnesty was announced in July 2018 and registration did not open until one month later, changes in fertility behaviors induced by the policy would only be observed during or after 2019. In 2020 and 2021, we should be able to observe changes in the likelihood of having children less than one, one, and two years old. However, we should not be able to observe changes in the likelihood of having children three years old. We will consider the likelihood of such an event to be a falsification test.

# **VI PEP’S FERTILITY IMPACTS**

Table 2 illustrates the results of estimating equation (1) in three panels. Panel A shows results using the data from baseline and 2020 (the first wave of VenRePS). Panel B presents results using the data from baseline and 2021 (the second wave of VenRePS). Finally, panel C shows results stacking the three periods of data: (i) baseline data from before PEP, which relies on recall questions; (ii) the first survey wave (2020); and (iii) the second survey wave (2021). Each column corresponds to a different regression evaluating the effects of PEP eligibility on the probability of having children less than one year old (column 1), one year old (column 2), two years old (column 3), and three
the MENA region as well. For instance, school disciplinary climate had a positive and significant impact in distinct cultural settings using PISA 2003 data in the Republic of Korea, Japan and the United States (Shin and others 2009). Evidence showed that the educational context and pedagogies in these three countries do effectively differ, as competitive-learning preference and mathematics interest were stronger predictors of performance in Korea and Japan while instrumental motivation had more explanatory power in the United States . Using TIMSS data pooled from the participating countries from the same year showed that disciplinary climate, classroom disruptions but also school violence reported by students all have a significant negative impact on math performance.

The benefits of improving disciplinary climate in a MENA school are on average about 50 percent higher than in other regions in terms of students’ performance. Although the PISA sample usually contains a limited number of MENA countries, the positive effect of disciplinary climate on mathematics performance here is stronger than in schools outside the MENA region.<sup>9</sup> Similar results have been found when applying a similar methodology on the TIMSS data<sup>10</sup> (see Table 4). Improvement in the school safety indicator by 3 standard deviations roughly increases math performance by about 25 points. One explanation of the greater importance of disciplinary climate in MENA could be the far more deteriorated climate, which tends to act as a main bottleneck for learning in the region.

The positive association between classroom disciplinary climate and students’ performance stems from the fact that fewer disruptions in the classroom increases the effective learning time and time-on-task. While classroom observations were not conducted for this analysis, it is presumed that a major hindrance to student performance in classrooms with high prevalence of disruption is the loss of instructional time, likely as a result of time spent on discipline management. It is unclear how much time is lost in MENA classrooms that have a high occurrence of classroom disruption. However, international evidence shows

> 9 Jordan, Tunisia Qatar, United Arab Emirates, Algeria,
3 Improving on comparisons based on the headcount only**

Our indices also allow for comparing countries in which life expectancy and poverty evolve in opposite direction, as in the Pakistan-Bangladesh comparison presented in Table 2 or in the comparison between the years 1990 and 2007 in South Africa (Figure 4). However, the choice of _θ_ may not be innocuous in these comparisons. We now focus on these cases. For these cases, we discuss the extent to which _PALEθ_ offers comparisons that are robust, that is, for which the ranking is not affected by the choice of _θ_ . Note that if the ranking proposed by _PALEθ_ is robust to the choice of _θ_ , this implies that _PALE_ 0 and _PALE_ 1 yield the same ranking. Since _PALE_ 0 corresponds to life expectancy, a measure solely based on the headcount provides a

> 24We take 70 as the lifespan deprivation threshold since world life expectancy is 71 years in 2019. This threshold is therefore a reasonable choice given our maximalist perspective: dying before this age means dying below the average expected age of death at the world level.

18
living in Chad, and that the variables currently available in the ProGres UNHCR registration system can be combined with other survey data to predict the aggregate welfare of refugees efficiently.

The cross-survey imputation literature<sup>9</sup> stresses the importance of selecting a few key predictors, and our results from Model 1, which contains only demographic variables, are in line with this

> 8 To check for possible heterogeneity, we split the sample with respect to country of origin. The results were similar except the larger estimate variances (less precision), which might be due to sample size for refugees from the Central African Republic. 9 See, for example, Dang et al. (2017a); Dang and Lanjouw (2018); Dang and Verme (2019). See also Luca et al. (2018) for a related study on variable selection with linear models.

12