rafmacalaba/data-use-mentions-extended huggingface dataset:
for all projects.<sup>16</sup> 7. Diagnostic work should give rise to pre-project base values of performance indicators for outcome assessment.

8. Outcome performance indicators for projects should not only permit project performance to be tracked, but should be chosen to permit hypotheses to be evaluated.

9. The use of taxpayer surveys should be an integral part of both diagnostic work and performance appraisal if not precluded by cost considerations.

10. Standard guidelines for quantitative input, process and output indicators should be laid down and mandated.

> 1<sup>6</sup> A set of performance indicators is being developed by the Tax Policy and Administration Thematic Group. A preliminary version has already been field tested. Similar work is also being done at the Inter-American Development Bank, the IMF and the OECD.

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benefits for consumers and investors.

The literature reviewed in this section adds to this evidence by showing that the functioning of product markets also has direct implications on labor market outcomes, such as employment rates, wages, and job quality. In both in the empirical and theoretical literature, these aggregate economic outcomes are consistently associated with higher employment levels in the long run. There is considerably less empirical evidence on the effects of product market deregulation on wages. However, most of the papers that emphasize this dimension conclude that there is (at least) an increase in real wage levels, i.e., an increase in wages after taking the purchasing power of consumers into account. In some cases, the literature even explicitly finds increased nominal wages.

While more evidence from LMICs would be desirable, findings from high-income countries consistently show that deregulatory product market reforms can entail short-term costs. These occur in the form of increased unemployment and lower consumption in the short run. Also, as reforms initiate a transition toward a new equilibrium, some incumbent firms may cease operation. This necessarily hurts the associated entrepreneurs and employees and may help explain some of the political resistance against product market deregulation. Thus, for a successful policy implementation, it may be helpful to develop a better understanding of which types of workers and 26
food security, agricultural land use, relationship to the household head, indigenous language, occupation, economic activity, and accumulated education. The survey also includes indicators for states, municipalities, and counties.

One of the key features of the survey is its size and the fact that it includes a measure of income at the household level.<sup>23</sup> The inclusion of an income measure allows for a design-based validation of the different methods presented above. The next section describes how the survey is modified to create a census data set and how samples are then drawn from the created census data with the goal of obtaining small area estimates of poverty at the municipal level.

# **4.1 Creating a census and survey**

Because the goal of this exercise is to test how well the different methods perform under a real world scenario, the Intra Censal Survey is modified to mimic a Census in order to allow for a design-based simulation. The first step consists of randomly removing 90 percent of households that reported an income of 0. This is done to ensure that some households with an income of 0 are included in the population, but not as many as in the original data to make it more realistic.<sup>24</sup> In the second step, all municipalities with less than 500 households are removed, thus observations by municipality range from 501 to 23,513.<sup>25</sup> The final “Census” consists of 3.9 million households and 1,865 municipalities.

To draw survey samples, primary sampling units (PSU) need to be created.<sup>26</sup> Within each municipality, the original data’s PSUs are sorted<sup>27</sup> and joined until each created PSU has close to 300 households. Under the proposed approach, original PSUs are never split, just joined to others. Additionally, all original PSUs that were larger than 300 households are designated as a
class sizes may have been slightly larger, however, because training institutions were entitled to recruit privately funded students if maximum class size was not reached. Though this happened occasionally, the significance was marginal, and most courses maintained small class sizes.

The second random allocation, i.e., the newsletter treatment, was implemented after beneficiaries were assigned to a vocational-training treatment group. The allocation of weekly newsletters was implemented at the class level, rather than at an individual level, to prevent spillover effects. 101 classes were randomly assigned to the treatment-newsletter group and forty classes to the control group. Thus, the final sample size was not known a priori and was determined after assigning classes to newsletter treatment and control groups.

The number of letters trainees received in the information-treatment group varied according to the length of training. Individuals assigned to the newsletter-treatment group received from 0 to 4 newsletters, with an average of 2.25. Across vocational trades, the number of letters varied from an average of 3.24 (cooking) to 1.66 (services). Because the total number of letters provided to participants varied depending on differences in the training, and because people who chose longer trainings may had unobservable characteristics that were correlated with training completion, the evaluation framework focused only on the comparison of the treatment and control information groups without considering the intensive margin of the treatment itself.

# **5. Data, Balancing Tests, and Take-Up Assessment**

The empirical framework used in this study is based on individual-level survey data, including a baseline, collected in Fall 2013 and two follow-up surveys administered six and eighteen months after training. The timeline of the intervention and data collection is 14
trends (World Bank, 2009). Another exogenous variable in the model baseline is the growth of energy prices, which are calibrated with projections made by the Energy Information Administration of the U.S. Department of Energy<sup>5</sup> .

For the purpose of the study we define two scenarios. The first scenario considers the implementation of biofuel use targets consistent with what countries already have announced (hereafter “AT” scenario). Please see Column 3 in Table 1 for shares of biofuels under the AT scenario in 2020. The second scenario generally considers a doubling of the announced targets

> 3 The base year for calibrating the model is 2004, the year for which the social accounting matrices, the main database for the model, were constructed. Although our base year is 2004, the model is calibrated to replicate major historical statistics until year 2009.

> 4 Impacts of removal of subsidies and import duties have been analyzed in detailed in a separate paper.

> 5 A module that can represent both conventional and un-conventional oil and gas reserves and production would be ideal; however, the model used here does not have that capacity. Hence, we used energy price forecasts from other sources instead of generating them endogenously in the baseline.

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.0|-1.8|
|Tianjin|-1.0|-1.4|-2.2|-2.1|
|Xinjiang|-0.2|-2.7|-2.4|-2.4|
|Xizang|-0.1|1.7|1.5|1.8|
|Yunnan|-0.4|-2.1|-1.8|-1.7|
|Zhejiang|-0.6|-0.6|-1.4|-1.4|
|China|-3.5|1.5|-3.6|-3.6|

# **5. Conclusions**

In this paper, we analyze the potential impacts on the provincial economies of meeting China’s emission reduction pledges made under the Paris Agreement using a national emission trading scheme. For the analysis, we developed a multi-regional, multi-sectoral, recursive dynamic CGE model and calibrated it with the data from the latest (2012) provincial-level social accounting matrices. We consider two cases for allocation of emission allowances: grandfathering and auctioning. Grandfathering allocation is carried out under two approaches: emission-based and output-based. The revenue collected through allowance auctioning is recycled to the local economy through two schemes: public consumption/investment and lumpsum transfer to households. The national emission trading is assumed to start in 2020 and national caps on emissions will be tightened gradually over the years and fixed at the level in 2030 to meet the emission intensity targets under the Paris Agreement. The study also considers an alternative baseline, which incorporates detailed technological information by using the 55
# **_(b) Access Barriers_**

# **Inequities Linked to Geographical and Insurance Regime Issues**

The latest ENDS survey of 2010<sup>46</sup> showed that 72% of adults who had suffered from health problems during the previous 30 days had been treated in a health center by either a doctor or a nurse. A problem of access (distance, transport costs, or copayments) was the reason given by 7% of those who did not attend a health center.

These figures might lead to the conclusion that there are no serious problems of access to health services that might require attention and solutions. However, on the basis of the various interviews conducted during the Study, the Study Team arrived at a different conclusion. The Team concluded that the quantity, variety and quality of HIV/AIDS care services in Colombia: Is higher in urban than in rural areas; Is higher in the contributory than in the subsidized insurance regime; Varies in the associated regime to the extent that—in places such as Bogotá, the capital city—care in the associated regime can be far better than care provided by either the contributory or subsidized regimes.

The graph provided in Figure 5 below aims to reflect the discrepancies in quality of service available for people with HIV/AIDS, depending on the insurance scheme they happen to belong to and the place where they happen to live (rural or urban setting).

Paradoxically, it so happens that persons from urban areas who are not registered in any insurance scheme—and are therefore treated at ESEs—receive the best care of all. This seems to be due to a variety of reasons. First, ESEs are generally better equipped than most IPSs. Second, ESEs can provide key support HIV/AIDS services—such as nutrition, social care, pharmacy, and diagnostics—in the same building, which benefits patients notably by saving them time and expenses that would otherwise be incurred by travelling to multiple locations.

Persons from
the relevant preference can be obtained from subjective well-being data or from dedicated preference elicitation surveys. We succinctly present a procedure to select the weights from these data sources.<sup>3</sup> Importantly, this procedure accounts for the fact that the weights of multidimensional poverty measures are not marginal weights of substitution. This fact implies that seemingly natural procedures are not fit for purpose.<sup>4</sup> The second is to switch the unit of analysis from the household level to the individual level as soon as some variables are collected at individual level. We discuss the advantages of this switch as well as its associated misinterpretation risk.

The third source of potential improvements relates to the way in which the indicator is used. Merely reporting that monetary poor individuals tend to cumulate non-monetary deprivations gives the impression that the exercise has no added value. Multidimensional

> 3To be sure, it is possible to use fixed weights even when their value is selected from information on preferences.

> 4Multidimensional poverty measures will make incorrect well-being comparisons when the values for their weights are selected to correspond to marginal rates of substitution.

4
In this paper, I examine the asset gains experienced by households with a family member who migrates internationally and domestically. I focus on the state of Kerala, in southwest India. Kerala has for several decades seen its residents relocating for work both overseas and within India, primarily to neighboring states. For Keralites working abroad, the most common destination is the countries of the Arabian Gulf, where they hold temporary contract-based jobs.<sup>6</sup> Using panel data on migrant and non-migrant families from the 1998 and 2003 waves of the Kerala Migration Survey (KMS), I examine the assets accumulated—a measure of medium to long-term economic welfare—by Keralite households over a five-year period. The KMS is the only panel data set of Indian households currently available that can be examined for changes over time in the outcomes of households with members who migrated overseas or within India.

A key challenge in identifying the welfare consequences of migration is the non-random selection of migrant households. This can result in a biased estimation of the relationship between migration decisions and household-level outcomes. No easy solutions to this challenge exist. My strategy in this paper for mitigating concerns about endogeneity bias is to use panel data in conjunction with an instrumental variables approach. The identification strategy exploits two key facts during the period observed: First, Muslim households were far more likely to send a family member overseas and much less likely to send a member to work within India. Second, as migration for employment from Kerala was predominantly undertaken by males during this period, the likelihood of migration increases with the number of prime-age males in a household.

The results of the analysis show that the assets accumulated by households over the five-year interval in terms of housing investments and purchases of durable goods were positively associated with the migration of a family member for work. The asset gains are also statistically similar between households whose members migrated overseas and those whose members moved within India for

> 6 The 1998 and 2003 KMS data reported that 95 and 90 percent, respectively,
sri lanka country risk profile 15 between a speedy recovery and a long-term poverty trap [6, 7, 8]. These costs and resources all impact households’ ability to maintain consumption after a shock, or their _consumption losses_ .

Over the long term average, and inclusive of all districts for which flooding data are available, nearly 6, 000 Sri Lankans face transient consumption poverty due to precipitation flooding every year. This is equivalent to 0.5% of national poverty incidence, with significant variation across districts. In some districts, the number of individuals pushed into subsistence represents a significant fraction of the HIES incidence of subsistence. This result is most notable in greater Colombo, reaching 3.5% of the chronic poverty incidence there. These results suggest that precipitation flooding events may be major drivers of extreme poverty in certain districts of Sri Lanka. In these areas, disaster risk management strategies can be a highly efficient tool to decrease poverty and subsistence incidence, either by reducing asset losses, or by enhancing households’ abilities to recover from disasters.<sup>3</sup>

# Socioeconomic characteristics and disaster recovery dynamics

Asset losses (and the foregoing income poverty analysis) describe individual households’ status in the instant after a hazard occurred, and provide useful insights into how governments and other first responders should target humanitarian relief. However, another important question is whether disaster-affected households become mired in poverty or achieve a speedy recovery. In contrast to asset losses, income and consumption losses can inform policy makers on how to manage the duration of disaster impacts.

Expanding our analysis of the 2017-like floods, Figure 4 on the following page maps the expected time to recover 90% of the assets destroyed when a 50-year precipitation strikes each district. The indicates that coastal districts in the northern flood map half of the country take the longest to recover. When disasters strike communities in these districts, many of which are national minorities, recovery is expected to proceed

> 3 Consumption losses are net of household savings, which can be
_Services PTAs_ value added, part of which is embodied in manufacturing exports. We find that individual PTA provisions that relate to investment flows and the movement of people are associated with higher foreign services value added from PTA partners, which is consistent with these effects to manifest in, and contribute to, value chain trade. Together, these results shed light on how services PTAs affect the supply side and configuration of value chain trade, which has thus far not been studied with recent data.

Our finding of large and significant trade effects from services PTAs stand against the long-held view that agreements in the realm of services often do not entail much if any actual liberalisation when compared to actually applied services trade policies (Miroudot and Pertel, 2015; Borchert et al., 2011). The results show that depth matters. The granular information contained in the World Bank’s Deep Trade Agreements 2.0 database enables us to uncover these effects, provided that information at the level of specific policy areas and individual provisions is combined with the best bilateral data on services trade and value added that is currently available.

The remainder of this paper is organized as follows: section 2 provides a brief overview of the literature, section 3 describes the data we exploit in analysis, section 4 sets out our methodological approach, sections 5 and 6 present the results for services trade and the sourcing of value-added, section 7 concludes.

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error. For example, there is a wide range of macro- and micro-nutrients contained in nearly all individual food items captured in a typical HCES and it is difficult to assign the caloric and nutritional content of each food item consumed (e.g., USDA 2019); it is also difficult to identify the nutritional content of many processed foods and food consumed outside the household that are becoming important increasingly to modern diets (e.g., Deaton and Subramanian 1996); and many of the dietary needs of individuals are unobservable and based on individual choices and activities (e.g., Institute of Medicine of the National Academies 2006). These issues compound other traditional sources of measurement error that affect the measurement of expenditure in HCES’s, such as recall biases, impacts of different questionnaire formats, and a wide variety of other concerns (e.g., Beegle et al. 2012; etc.). The variance on estimates of the quantity and quality of food consumption are therefore large and are potentially increasing over time as households consume more processed foods and meals outside the household (e.g., Tandon and Landes 2011; Tandon and Landes 2014).

Given these difficulties and the need to obtain estimates of food access in real time and in data-poor environments, practitioners and researchers have increasingly relied on metrics that are relatively easy to implement while also approximating the degree of food access in its many dimensions (e.g., Swindale and Billinsky 2006; WFP 2009; Maxwell and Caldwell 2008; etc.). Two common approaches include (1) measuring dietary diversity and the frequency with which individuals and households consume certain food groups, such as the Food Consumption Score and the Household Dietary Diversity Score (e.g., Swindale and Billinsky 2006; WFP 2009); and (2) measuring food coping strategies often associated with consuming too little or consuming a poor quality diet, such as the Coping Strategies Index and the Reduced Coping Strategies Index (e.g
and protocol allowed multiple people to contribute if the primary respondent did not have responses for questions about other household members (for instance, regarding incomes or work arrangements).

The design closely followed protocols applied to Living Standards Monitoring Surveys (LSMS) type surveys. The national sample was stratified by region and by urban areas. These data were crossvalidated with data on “settlements” provided by the National Statistical Agency (NSO), which yielded a nearly exact match at the province level. All data collection efforts (i-iv above) were carried out within the framework of the mahallas randomly selected in the first stage.<sup>4</sup> The NSO was consulted in the design of the study, including the questionnaires used and regarding the sample design. Support for review and design was provided by a panel formed of local think tanks and government representatives. Survey data collection was conducted by a private firm under the direct supervision of World Bank staff.

> 4 Additional information on the sample design, the survey instruments, and related technical details is available at:

5
# **Figure 2. Theoretical Foundation of TIMES model (Reference Energy System)**

<!-- Start of picture text --> Resources Transformation Utilization<br>Coal<br>Gasoline Cooking<br>Crude Oil Oil refinery Diesel Heating Residential<br>commercial<br>Air Cond.<br>Fuel Oil<br>Gas and<br>Natural Gas<br>Processing Kerosene Elec. motor industrial<br>sectors<br>Hydro Electronics<br>LPG<br>Nuclear Power Jet fuel Lighting<br>Plants<br>Others Transport- Transport<br>Renewables ation sector<br>Gas<br>Electricity<br><!-- End of picture text --> The structure of TIMES is defined by variables and equations determined from the data input provided by the user. This information collectively defines each TIMES regional model database, and therefore the resulting mathematical representation of a Reference Energy System for the region. The database comprises both qualitative and quantitative data. The model requires a large set of input data. The major data items and their sources are summarized in Table 1.

The study considered three scenarios for energy system optimization: a Baseline, a Green scenario, and a Super Green scenario. The scenarios, consistent with those used in macroeconomic analysis were defined as shown in Table 2.

6
household was deemed eligible if there was a woman present who was either pregnant or had a child younger than two years old. Baseline data collection occurred in August-October 2013, prior to the start of the intervention. In order to better measure effects of how the cash was used, and because effects of cash have been shown to fade very quickly after the cash is no longer being distributed (Baird, McIntosh, and Ozler, 2016), we conducted a midline and endline survey. One-fourth of counties were randomly assigned to be surveyed at midline in August-September 2014, with the remaining three-fourths surveyed at endline in November-December 2014.

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