rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Tables**

**Table 1. Scale of Employment Fund Programs and AGEI Sub‐group**

|All EF programs|2010|2011|2012|
|---|---|---|---|
|Total T&E providers|21|32|35|
|Total Events|598|645|711|
|Total trained|11750|12869|14255|
|AGEI only||||
|T&E providers working with AGEI|11|13|13|
|Total Events|110|218|246|
|Total trained|808|1664|1936|

**Table 2. Summary of Sample of Events for Baseline Surveys**

||2010|2011|2012|
|---|---|---|---|
|Total # events conducted by EF in Jan‐Apr|110|142|143|
|# events randomly sampled|N/A|182|112|
|# events included in baseline survey|65|69|85|
|# districts covered|30|34|29|
|# T&E providers covered|18|26|28|

Notes: More events were sampled than conducted in Jan‐Apr 2011 because some events that were scheduled for Jan‐Apr were delayed and did not start on time.

**Table 3. Summary of Sample of Individuals for 2010, 2011 and 2012 Baseline Surveys**

||**2010**|**2011**|**2012**|**Pooled**|
|---|---|---|---|---|
|_Treatment group_|||||
|Treatment‐ Male|478|447|458|1383|
|Treatment‐ Female|706|790|586|2082|
|Treatment‐ all|1184|1237|1044|3465|
|
of being without adult supervision while their parents are at work.

The last row of panel A shows that the average propensity for violence for T and C groups is 0.038, with a standard deviation of 0.029, ranging from 0.001 to 0.215. This average propensity for violence is 14 times the mean probability that a given student will be vulnerable to violence in Chicago (Chandler et al., 2011). Even when both estimations are not completely comparable because we use fewer violence determinants than Chandler et al. (2011), this difference sheds light on the tremendous propensity for violence among the children in this study.

Panel B shows academic scores and absenteeism for the first quarter of the 2016 school year. We imputed average course grades for missing observations and present the same models using only subsamples with nonmissing information in Table A3 in the Appendix. In a grade scale of 0–10, requiring a minimum grade of 5 to pass each course, enrolled students have between 6.5 and 6.7 points, similar to average grades at national level. Additionally, 25% of enrolled children have a misbehavior report before the intervention. The mean absenteeism rate during the first quarter of the academic year—that is, before the intervention—was 4.8% (1.69 out of 35 days).

Panel C summarizes club characteristics: mean club size was 13 students and community tutors ran approximately 31% of these clubs. The average take-up, defined as the share of sessions attended by each student out of the total number, was 57%. Finally, the mean fraction of treated students by course was 42%.

## **2.6 Compliance**

In terms of compliance, attendance records indicate that no children in C group received treatment. Additionally, school coordinators were instructed to make sure that only children in T group attended the courses. However, since we did not implement an additional compliance check, and considering that treated students attended
largely depends on temporal and spatial diversification of rainfall to smooth weather-induced volatility in incomes, especially since only 30% of land used for agriculture is irrigated (Gadgil et al. 2002; Rao et al. 2000).

Several papers show that household incomes in India are sensitive to rainfall variation. Parchure (2002) estimates that around 90% of variation in Indian crop-production levels is due to rainfall volatility. Using macrodata from 1951 to 2003, Gadgil and Gadgil (2006) find that despite substantial decreases in the contribution of agriculture to Indian GDP, severe droughts have resulted in decreases between 2% and 5% of GDP throughout the period. A World Bank (2006) study of adaptation strategies to droughts in Andhra Pradesh finds that, based on simulations of a crop model, severe droughts (one in 30 years) are likely to decrease rice yields from 29% to 62%, depending on the district. Yield losses of rain-fed crops also appear high, with different crops being particularly vulnerable in different districts. Rosenzweig and Binswanger (1993) also present evidence that weather shocks significantly affect agricultural profits, based on ICRISAT panel data from the 1980s.

[Figure 6.1 here] Figure 6.1 shows the actual onset of the monsoon over Kerala as announced by the Indian Meteorological Department (IMD) as deviations in days from the normal onset of June 1. It is clear that there is significant volatility. This volatility, however, would be less of a concern if available forecasts of the onset were accurate, since if they were, farmers could use them to adapt their agricultural production decisions accordingly. The IMD issues a single long-range forecast of the onset of the monsoon over Kerala around May 15. Despite recent advances in forecasting techniques, even the onset conditional on the IMD forecast still displays substantial volatility. Figure 6.2 shows the difference in days between the forecast and the actual onset for each year. (For example, in 2005 the IMD forecast that the monsoon would arrive in Kerala on June
(83 percent vs. 34 percent). Although during the period covered in this study almost all households were connected to the electricity grid, as much as 70 percent of the population suffered from extensive shortages of electricity, especially during winter months. Electricity shortages in 2013 were estimated at about 14 percent of annual electricity demand and imposed economic losses estimated at US$200 million per year, or 3 percent of GDP.

The next section introduces the survey and automated data collection methods employed in this study. Section III describes the analytical approaches used, focusing on comparisons between the surveybased reporting and the unbiased automated monitoring system. Section IV describes and the results and provides interpretation of their significance for monitoring initiatives and policy making. Section V concludes.

# II – Data

Data from two sources are used in this study: data gathered from a monthly phone-based panel survey of households, and from an automated system of Smart Survey Boxes (SSBs). The survey data begin in May 2015. However, SSB monitoring began in November 2016. Thus, for the direct comparisons that follow, only survey data covering comparable periods is used.

## II.I – Listening to Tajikistan

The analyses in the following section use novel data collected by the authors in the L2T survey. Fieldwork proceeded in two stages. The first “baseline” stage included an extensive face-to-face interview with household members. Households were selected using a stratified two-stage clustered sample design based on the 2010 national census of Tajikistan. A total of 150 clusters were selected, with a probability of selection proportional to size. From these clusters, 3,000 households were selected to participate in the baseline survey. During the baseline interview, detailed information was gathered on household composition, asset ownership, and migration. A full household roster was collected (including age, gender, etc.), as well as a comprehensive consumption module that allows analysis in terms of monetary welfare groups in the country.

The baseline survey was completed in March 2015. Following completion of the face-to-face
Another, even more fundamental, price point for compensating host countries when implementing comprehensive carbon taxes could be defined as the “development support price”. Some countries may develop plans to implement their own comprehensive carbon taxes independent of knowledge about carbon tax support programs, and in principle could be willing to implement such taxes even without support. However, these countries might not have the capacity to develop and implement a carbon tax; or it may be politically difficult to do it on their own. The donor community might then want to provide the necessary funding to cover the host country’s policy costs in order to overcome such barriers. Such a “development support price” ought to be lower (or at least no higher) than either of the two price points derived here.

We will in section 2 discuss the ICP and OCP in the most basic cases. We will revert to more general discussion of the ICP in section 3 for our main case, where the host country’s NDC is not yet fulfilled. A more general discussion of OCP follows in section 4. Section 5 uses basic data on specific countries to calculate illustrative compensation amounts using our price formulas.

The concepts of “unconditional” and “conditional” NDCs are useful for understanding the issues considered in the paper. While these concepts are not addressed in the Paris Agreement (PA) text, they are used by many PA signatories in their NDC documents submitted to the UNFCCC. The “unconditional NDC” is the target that a given country aims to attain by itself, without outside finance or technical assistance. The “conditional NDC” is assumed to require outside help for its achievement, and it is always as or more ambitious than the unconditional NDC plan. Note that for support to a lower-income host country for carbon tax implementation to be meaningful, the host country’s unconditional NDC must be “real:” the target must at a minimum correspond to the host country’s “business-as-usual” (BAU) emissions rate.

# *
a low share of spending on social assistance towards low-income groups, and the neutral impact of the personal income tax (ibid). Furthermore, the recent recession and continued period of slow growth in the country and the international sanctions are putting pressure for fiscal consolidation, bringing additional challenges to avoid worsening income distribution.

The purpose of this paper is to introduce applications of RUSMOD—a microsimulation model for fiscal incidence analysis in Russia- to answer questions about the distributive impact of fiscal reforms. RUSMOD combines household survey micro-data and fiscal policy rules to simulate the Russian taxbenefit system: the size and distribution of taxes collected and benefits paid, and the impact of the system on different population groups. Microsimulation models, such as RUSMOD, are habitually used in developed countries, and can be versatile budgetary policy tools. Using this model, we examine the tax-benefit system in the country. The impact of the system is measured across the income distribution, age groups, family types, localities, as well as across time.

RUSMOD can be used to further inform fiscal and social protection policies in Russia. Challenges to make taxes more progressive while raising revenues, to realign spending without increasing poverty and to consolidate and better target benefits are likely to increase in importance. RUSMOD, as part of a larger toolbox, is well-suited to contribute to answer this type of questions. This paper includes three applications of the current version of RUSMOD. First a description of the impact of the Russian tax-benefit system upon poverty and inequality. Second, an assessment of the distributional changes brought about by changes in tax and fiscal policy over the period 2010-2017. Third, a simulation of the impact on poverty and inequality of a hypothetical tax reform scenario. In this case, we investigate whether the existing system creates disincentives for formalization in terms of reducing disposable incomes, increasing poverty and inequality; and whether a hypothetical tax reform may be able to reduce the opportunity costs of formalization for informal workers, improve distributional outcomes

> 1 Gini index equal to Gini coefficient multiplied by 100
<!-- Start of picture text --> Distribution of accessibility indexes<br>Distribution of<br>90 accessibility indexes<br>80 e= allcurr p e ntrmutationsNairobi |<br>2 70<br>2<br>£ 60<br>—]<br>£<br>® 50<br>Qa<br>2 40 |<br>Cc<br>a<br>8 30<br>20<br>10<br>(@) _<br>40 45 50 55 60 65 70 75 80<br>Share of accessibile opportunities within 60 min (%)<br><!-- End of picture text -->
An influential paper by Martin, Thierry, and Thoenig (2008) argues that this conventional wisdom is only partially true, and depends crucially on whether trade is bilateral or multilateral in nature. Bilateral trade increases the opportunity cost associated with the loss of trade between two countries, and makes them therefore less likely to engage in war. In contrast, multilateral trade decreases bilateral dependence to any given country and thus the cost of bilateral conflict, making countries more likely to end up in war. The recent war in Ukraine and Russia’s reliance on China to survive the trade disruption caused by sanctions imposed by the West fit this paradigm.

On the other hand, one might counter that multilateralism had been severely compromised prior to the war, and that it was in fact fractures in the world trading system that ultimately enabled the invasion of Ukraine. For instance, one might wonder if China would have been willing to provide a lifeline to Russia if it its economic relationship with the West had not deteriorated so much in the past four years. The decline of multilateralism may not be evident in the trade data yet, though trade between the US and China has declined following the trade war between these two countries. But trade data may take some time to adjust to reflect changes in attitudes and political climate that contribute to armed conflict.

The period leading to World War II provides a case in point. De Bromhead et al (2019) study interwar trade and show that in the 1930s there was a dramatic shift away from multilateral trade towards trade within empires, or informal spheres of influence. For instance, the British Empire accounted for 30 percent of UK imports in 1929, but 42 percent by 1938. Several observers have argued that this changing nature of trade not only reflected, but potentially exacerbated, the international tensions of that period, an era now known as “pre-belligerency.” The changing nature of globalization in the past five years bears an eerie resemblance to that time.

# **III. Conclusion**

The
the fraction of the total budget that must be transferred when comparing two of the following list of indicators: monetary poverty ( ), multidimensional poverty (H) and PALE. The fraction transferred corresponds to 6 percent of the budget when moving from monetary poverty to PALE MM HH (baseline design). Again, the table suggests for the baseline design that moving from monetary poverty to multidimensional poverty impacts these transfers more than moving from multidimensional poverty to PALE. Then, the fraction transferred increases to 11 percent of the budget when moving from monetary poverty to PALE (preferred design). The table suggests again that this increase is more driven by the change in normative weight given to mortality than by the different designs for multidimensional poverty.

_Table 6: Fraction of (hypothetical) budget reallocated across states between pairs of well-being indicators_

||**Baseline**|**Preferred**|
|---|---|---|
|<br>|**(%)**|**(%)**|
|HM-VS- PALE<br> <br>|5.6|11.3|
|<br>HM-VS-H<br>|4.7|6.0|
|<br> <br>H-VS- PALE<br>_Source:_Authors’ estimates b|2.2<br>ased on data from NLSS 2019|13.5|

_Note_ : The table considers a hypothetical policy that allocates a budget across Nigerian states proportionally to (minus) the state’s total wellbeing (see Appendix 5 for details). The table presents the fraction of the budget that must be re-allocated across states when changing from one well-being measure to another: monetary poverty rate ( ), multidimensional poverty rate ( ) and poverty-adjusted life-expectancy (PALE). MM HH HH 31
# _5._ **Empirical Approaches**

Two types of comparison may be carried out using international data on privately-owned utility companies. First, comparisons may be drawn between companies operating in the same utility sector but in different countries where alternative regulatory regimes are in force. Second, the effects of changing regulatory systems on systematic risk may be assessed by studying the same number of cases where such shifts have occurred. This section considers these approaches in turn.

# 5.1 **Cross-country comparisons**

Cross-country comparisons of utility beta coefficients are informative if one of the following two circumstances applies:

- two companies in the same utility sector, but operating in different countries, are similar in every respect apart from their regulatory regime;

- a number of companies in the same utility sector operating in different countries differ in many respects, which may or may not include their regulatory regimes.

The first case is a straightforward comparison of regulatory systems, controlling for all other influences. This is the ideal form of international comparison but it is impossible to carry out because such a pair of comparators does not exist-often, even within a country, it is difficult to find two directly comparable companies. Direct comparisons of just two countries without regard for other important differences, such as the existing UK/US studies, are likely to be misleading and reveal little about the influence of regulatory risk on the cost of capital.

The second case is more complex but is more likely to allow the effects of alternative regulatory regimes to be inferred. Provided that a sufficient number of comparator 21
8 forced Ivanic and Martin (2008) to ignore changes in a large number of food prices, both on the consumption and production sides. A good example is the fact that welfare impacts in Nicaragua are estimated without taking changes in the price of coffee (an important export) into account, because coffee was not among the seven staples whose prices are followed in the study.<sup>5</sup> Labor market and substitution effects are also absent from the analysis in Son and Kakwani (2009) – another study closely related to ours. These authors use the first-order approximation to the welfare effect of a price change to derive the elasticity of the Foster, Greer and Thorbecke (1984) class of poverty indices with respect to prices. They calculate these elasticities for various categories of goods in the large urban areas of Brazil, between 1999 and 2006, using two of the three data sets we use in this paper: the Household Budget Survey (POF) and the Consumer Price Index (INPC) microdata.

In this paper, we seek to contribute to this literature in four ways. First, we use a time-series of domestic consumer prices, captured monthly in eleven urban areas covering all five macro-regions of Brazil. We observe changes in the prices of 156 individual food items, which are then grouped into 16 food categories. These categories account on average for 97 percent of food consumption (and 22 percent of household consumption expenditure on all goods) recorded in the Household Budget Survey. This is a more finely disaggregated set of prices than has generally been used in the analysis of the 2007-2008 crisis. Second, we use occupational data that maps individual agricultural workers to the production of specific food groups, to compute estimates of income gains that might accrue to them under two different assumptions about the pass-through from food prices to agricultural wages.

Third, we attempt to shed light on the magnitude and effectiveness of the social policy response of the Brazilian government, in terms of the extent to which it helped mitigate the
respectively, of total tax expenditures in year 2016 (or 0.7, 1.5 and 0.7 percent of total fiscal revenues in 2016). Data provided by the Mongolia Ministry of Finance in April 2017.

> 4 Exemptions on hybrid cars are estimated to represent tax expenditures of nearly MNT 160 billion, which represents around 23 percent of total tax expenditures for year 2016 (or 2.7 percent of total fiscal revenues in 2016). Data provided by the Mongolia Ministry of Finance in April 2017.

> 5 Pensions can be interpreted as a transfer or as delayed savings. The former is the usual interpretation for noncontributory pensions, whereas the latter is more common for contributory pensions. In any case, the case of <u>contributory pensions, if under a pay-as-you-go system, may have a transfer rather than delayed saving interpretation,</u>

MONGOLIA Distributional impact of taxes and transfers / May 2018 3
The demand projections would be used to increases the transparency of the prospects to any potential private investor but also to develop the capital and operating program for the commuter service, the financial analysis which would eventually be used by the regulator to estimate the requirements of financial support by the government.

The data available was reasonable although not perfect. The forecasters could rely on a 1994 survey of trips origin and destinations in Mexico city conducted by the Mexican National Institute of Statistics. They also has estimated trip growth rates to allow an update of the matrix. These growth rates (1.2% average annual from 1994-2030) is based on population growth estimates and land use trends. Data on the characteristics (trip time, costs, frequency, reliability...) of bus, commuter rail and metro travel in Mexico collected by a local consulting firm and a couple of traffic counts at key locations in the corridor. The forecaster then conducted a stated preference survey of potential commuter rail users to estimate users preferences for alternative public transportation modes. The demand is based on diversion from other modes of public transportation only (buses and metro). It excludes diversion from cars (to get a lower bound) and ignores induced demand as well. The results are then introduced into a model generating trip volumes for the commuter rail. It includes up to 2030 estimates of annual passenger demand, annual passenger-kms, demand on weekdays and weekends and boarding and Based on: Mercer Management Consulting (1998), "Commuter Rail Demand Study for the Buenavista-Huehuetoca Line", prepared for the Mexican Communications and Transport Secretariat

> **20** Another, less 'textbooks", set of very relevant illustrations is provided by D. Pearmain (2000), "The measurement of users' willingness to pay for improved rail facilities", in Ortuzar (2000), op. Cit.. It includes a brief survey of the UK experience with WTP methods and a couple of examples illustrating some of the implementation difficulties of the stated preference method in
variables based on subjective perceptions of the degree of obstacles (Corruption Obstacle, Skill Labor Obstacle, Land Access Obstacle),<sup>6</sup> we transform the ranking to a dummy variable indicating moderate or more serious obstacles. In total, we have seven Enterprise Survey indicators to measure the business environment. It is worth noting that even though some of the indicators are binary variables at the firm level, when aggregated to the city-industry level, all these ES indicators are continuous.

When constructing _Doing Business_ indicators and Global Competitiveness indicators, we find the most relevant variables for each BE category. Two points are worth noting. First, when more than one indicator measures the same aspect of the BE, we choose only one variable among the candidates according to the number of non-missing observations and the correlation with the corresponding Enterprise Survey indicator. Second, although _Doing Business_ indicators and Global Competitiveness indicators overlap to some extent with Enterprise Survey indicators, not all BE categories in Enterprise Survey indicators can be matched to a

> 6 The original ranking ranges from zero (no obstacle) to 4 (very severe obstacle).

9