rafmacalaba/data-use-mentions-extended huggingface dataset:
been introduced for the electricity sector as a carbon pricing policy. Other carbon pricing instruments, such as carbon tax, could be another policy instrument to achieve its NDC. However, the government’s interest in enacting a carbon tax might be influenced by several factors, including its overall costs to the economy. This study assesses the general equilibrium effects of a carbon tax policy with several alternatives for carbon tax revenue recycling. It also compares the economic impacts of the carbon tax with those when the NDC will be met through an administrative/regulatory mandate on a national CO2 emission standard. We use a computable general equilibrium model of China calibrated with the most recent data (2017 Social Accounting Matrix) to simulate the carbon tax and emission standard policies.

The study finds that Chinese GDP in 2030 would be about 1% lower than that in the baseline if the country constraints its national CO2 emissions at its NDC target without specifying any policy measures. Emitters reduce their GHG emissions the way they find most economical, such as cutting their production, switching to cleaner fuels and technologies, deploying energy-efficient processes and devices, etc. If a carbon tax is introduced, it will cause lower economic costs no matter how the carbon tax revenue is recycled into the economy. Among the nine alternative design architectures considered for the carbon tax in terms of its revenue recycling, the economic costs would be lowest when the tax revenue is recycled to non-energy/electricity sectors, in proportion to the export intensiveness of their outputs, to cut their corporate income taxes. Recycling tax revenues to cut capital taxes would be the next better scheme. Recycling the carbon tax revenues to subsidize solar and wind electricity would be instrumental in promoting these clean technologies; however, doing so is the most expensive carbon tax design scheme considered in the study.

Several policy implications can be drawn from the analysis. First, the economic impacts of a carbon tax are sensitive to its design architecture, particularly the use of carbon tax revenues. There are many alternatives to recycling tax revenues
rural patterns shows larger gaps but still high usage rates for particular functions even in rural areas. Rural residents are less likely than urban residents to make or receive calls on a daily basis (78 percent versus 88 percent). They are also less likely to use text messages on a daily basis (57 percent versus 71 percent) and to use mobile money each day (20 percent versus 28 percent).

The typical mobile money user does not use it every day. Half of all adults (68 percent of all mobile money users) use mobile money less than once per day. However, a substantial majority in all categories uses mobile money at least occasionally: 73 percent of all adults, including 70 percent of women, and 70 percent of rural residents.

# **3 Literature on Mobile Money**

The proliferation of mobile money in Kenya has generated research attempting to explain the roots of the phenomenon and to understand its effects. Kimenyi and Ndung'u (2009) attribute the rapid growth in mobile money in Kenya to four factors: a conducive legal and tax environment, private-public policy dialogue, strategic and prudent macroeconomic policies, and a guarantee of the existence of a contestable market discouraging dominance by initial entrants. Comninos et al. (2008) argue that the initial success of Kenya‘s mobile money transfer industry can be attributed to the high demand for remittances generated by rural/urban migration, while its rapid scaling is due to the mobile providers‘ growth strategy.

Jack and Suri (2011) report results of a 2009 survey of Kenyan households that use M-PESA. They find that M-PESA reached nearly 40 percent of the Kenyan adult population after only two years of operation. While M-PESA was initially adopted mostly by wealthier households, adoption by less wealthy households was also increasing. Jack and Suri also find an increase in the use of M-PESA by the unbanked population. However, their findings suggest that not owning a mobile phone is a major constraint to the adoption of M-PESA. They also find that
significant impact of both in-kind and cash on the intake of vitamin C. When looking at iron and zinc, two key nutrients for brain development, we find large and statistically significant impacts for the in-kind modality, but not for cash. For zinc, the difference between the two treatment types is statistically significant at conventional levels. Similarly the in-kind transfer has a significantly larger impact than the cash transfer on the intake of calcium and retinol. In order to provide an overall estimate of the program effect on nutritional intakes, we study the impact on the first component of the Principal Component Analysis (PCA) of the six nutrients. The in-kind modality shows a larger impact than the cash one with the difference between the two treatment types being non-trivial in terms of size (about 0.10 s.d.) and marginally significant (p-value = 0.09). The results for the overall sample and the sample of those with at least one ENLACE are remarkably similar and this is consistent with the fact that the program had no statistically significant impact on the probability of taking the exam. In order to rule out the possibility that the results are driven either by children with extremely low intakes or those with very high intakes, we study the treatment effects on the probability of having intakes equal or above the RDAs. Results presented in Appendix Table A6 are in line with those discussed above.

Overall, the evidence provided in this section suggests that children, who had been exposed to the PAL program for at least 18 months, did not exhibit better health status compared to those who were not exposed. However, they do display better nutritional intakes, especially if they receive the transfer in-kind.

# **5.2 Student**

We use two distinct data sources to study the impact of PAL on child labor. First, we use information from the evaluation follow-up survey in order to study the attendance and labor outcomes of children age 12 and 13 who were enrolled in primary education. These children would have been eligible
were before the earthquake.

# _(d) Directly affected areas_

Table 4 presents descriptive statistics for households living in directly affected areas. The food consumption score is calculated based on the number of different food groups consumed by the household, to represent diversity, and the number of times a week they are consumed. Notably, we observe that the average food consumption score is 55.79, with a standard deviation of 19.75. A majority of households is above the limit food consumption thresholds (the limit consumption threshold being 42 and the critical threshold being 26).

Table 4 also shows that, in June, 44% of the households in directly affected areas had received assistance and that 32% had received food assistance. 37% of the respondents’ houses 14
associated with a job loss rate of only 1.0 percent. Although the modeled and surveymeasured estimates are not as close as they were for Jordan, our prediction model does a reasonable job of matching the aggregate survey-measured job losses of 7.8 percent for that wave.

_Table 5-6 Georgia: Job Loss Projections PFPS Jobs as a Percent of PreCOVID Levels_

||As of Wave 1|Cumulative<br>Wave 2|
|---|---|---|
|Modeled Job Losses: Percent of PFPS<br>jobs lost (relative to Dec 2019) as<br>projected|19.0|5.9|
|_Of which_: Due to Permanent closures|0.0|1.0|
|Survey-measured job losses: Percent of<br>PFPS Jobs Lost(relative to Dec 2019)|18.1|7.8|

Source: Authors’ calculations.

Notes: The post-COVID survey is not directly comparable to the projected jobs lost as it does not contain enough variables to separate PFPS workers.

It appears from the CFUWBES data that Georgian formal private firms were better able to remain in

|_Table 5-5 Georgia: Selected Potential Jobs at Risk Influ_<br>_CFUWBES Waves Rounds 1 and 2_|_ence Variables,_<br>Wave 1|_Means of_<br>Wave 2|
|---|---|---|
|Temporarily closed due to COVID (weeks of closure )*|5.7|2.3|
|Infection risk (score) (0-300)|162.4|162.4|
|Percentage of firm workers working remotely<br>(percentage)|3.8|2.9|
|Sales change (percent change year-on-year, same<br>month in 2019)|-53.8|-28.2|
|Change in export share of sales (percentage difference<br>from 2019 ES)|-0.7|
the fact that their reference group is at the top of the income distribution and thus are unable to see all income spread in their country. In all the diagrams showed to the respondent to illustrate the different income distributions (see section 3 on the ISSP question used), the thicker part of the distribution is at the half bottom of the income distribution. This might imply that individuals with a richer reference group will tend to choose diagrams with more people in the middle, i.e., less inequality; while the opposite is true for the others. This argument is also consistent with the negative correlation between income and perceptions of inequality. Income in the sample is defined in five income brackets that are country dependent. The income coefficients show a 28
ECA CA CE EE RUS SCC TUR WBK<br><!-- End of picture text --> Sources: European Commission; Eurostat; Penn World Tables; UN Population Prospects; World Bank, World Development Indicators database.

Note: CA = Central Asia; CE = Central Europe; ECA = Europe and Central Asia; EE = Eastern Europe; EMDEs = emerging market and developing economies; RUS = Russian Federation; SCC = South Caucasus; TUR = Türkiye; WBK = Western Balkans. GDP-weighted averages (using average real U.S. dollar GDP at average 2010-19 prices and market exchange rates). Period averages.

A. Estimates based on production function approach. Sample includes 53 EMDEs, of which 9 are from ECA (Türkiye, 2 in Central Asia, 4 in Central Europe, 1 in South Caucasus, and 1 in Western Balkans). The Russian Federation and Ukraine are excluded.

B. Bars show averages. Orange whiskers show min-max ranges. Sample includes 13 ECA economies, including Türkiye, the Russian Federation, and Ukraine.

C. Figure shows share of population age 15 and older by gender that is economically active. Unweighted averages. D. Bars show averages. Median marker and whiskers show median and min-max ranges of EMDE regions. Working-age population refers to population aged 15-64 year. Sample includes 22 ECA economies.

E.F. Aggregates calculated as simple averages of country-level data as calculated in World Bank (2020e).
|_0.07_|2009q4|72.54|_0.23_|
|No. with just primary edn.|457.51|_0.00_|2010 q1|-39.42|_0.62_|
|No. with just second. Edn.|774.53|_0.00_|2010 q2|-220.46|_0.01_|
|No. with upr. Vocatl. Edn.|1244.85|_0.00_|2010 q3|-45.04|_0.57_|
|No. with university edn.|2230.56|_0.00_|2010q4|-133.99|_0.09_|
|No. withpost-graduate edn.|4085.72|_0.00_|Constant|9421.57|_0.00_|

Source: Data from Thailand Socio-Economic Surveys of 2007-2010. Data are pooled. Sample size: 176,141 households. Adjusted R<sup>2</sup> = 0.29. Quarterly fixed effects are relative to 2007:Q1.

**Table 2.  Measuring the Impact of the 2008-09 Recession on Real Expenditure, Thailand**

|Change in real expenditure per capita||
|---|---|
|compared to 2007:Q1|Number of|
|2007<br>2008<br>2009<br>2010|households|
|_baht/month_||

Losers and Winners in Recession: Thailand 2008-09 16
We extend this literature in several ways. First, we assess the correlation of mortality to national income and how that has changed over time. Second, we update the analysis of mortality changes in the era of HIV/AIDS. Third, we go beyond “extreme mortality” events and assess how adult mortality changes as a result of conflicts more generally. And last, by using the socioeconomic characteristics of the female respondent as a proxy for the socioeconomic background of her siblings, we analyze how these trends and variations in adult mortality differ with socioeconomic status, including an analysis of the variation over time in the gradient.

# **2) Data**

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere). The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region’s population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population—even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as “representative” of the rest of the world, or even the rest of the developing world.

DHS data are attractive for two main reasons. First, these are surveys that have been carried out systematically using similar protocols and questionnaires.<sup>1</sup> No other collection of nationally representative datasets this large
Growth is Good for the Poor. _Journal of economic growth_ , 7(3), 195-225.

- Dollar, D., Kleineberg, T., & Kraay, A. 2015. Growth, inequality and social welfare: cross-country evidence. _Economic Policy_ , 30(82), 335-377.

- Eastwood, R., and M. Lipton. 2011. “Demographic Transition in Sub-Saharan Africa: How Big Will the Economic Dividend Be?” _Population Studies_ 65(1): 9–35.

- Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" _American Economic Review_ , 105(10), 3150-3182

- Higgins, Matthew. 1998. “Demography, National Savings, and International Capital Flows.” _International Economic Review_ 39: 343–69.

- Higgins, M., and J. G. Williamson. 1997. “Age Structure Dynamics in Asia and Dependence on Foreign Capital.” _Population and Development Review_ 23 (2): 261–93.

- IMF (International Monetary Fund). 2004. _World Economic Outlook: The Global Demographic Transition._ Washington, DC: International Monetary Fund.

- ______. 2015. _World Economic Outlook: Uneven Growth–Short- and Long-term Factors._ Washington, DC: International Monetary Fund.

25
May 1997|S. Khan<br>33651|
|WPS1768 Is There a Quantity-Quality Tradeoff<br>as Enrollments Increase? Evidence<br>from Tamil Nadu, India|P. Duraisamy<br>Estelle James<br>Julia Lane<br>Jee-Peng Tan|May 1997|S. Khan<br>33651|
|WPS1769 Information, Incentives, and<br>Commitment: An Empirical Anialysis<br>of Contracts between Government<br>and State Enterprises|Mary M. Shirley<br>L. Colin Xu|May 1997|P. Sintim-Aboagye<br>38526|
|WPS1770 Poverty and Social Transfers<br>in Hungary|Christiaan Grootaert|May 1997|G. Ochieng<br>31123|
|WPS1771 Government Employment and Pay:<br>A Global and Regional Perspective|Salvatore Schiavo-Campo<br>Giulio de Tommaso<br>Amitabha Mukherjee|May 1997|A. Panton<br>85433|
|WPS1772What Drives Deforestation in the<br>Brazilian Amazon? Evidence from<br>Satellite and Socioeconomic Data|Alexander S. P.Pfaff|May 1997|A. M. Maranon<br>39074|
be more numerous or longer in treated versus control sections. We see no indication that treated sections have significantly more or shorter placenames; moreover, the placenames are not more likely to contain a space between words (see Table E.27).

> A10 Of all entries in the case data that fall within the chiefdoms the include our sample, only 0.07 percent are missing an entry for village or parish of residence.

> A11 `http://wiki.openstreetmap.org/wiki/2014_West_Africa_Ebola_Response`

> A12 We use optimal string alignment distance, a variant of the Levenshtein distance, which is commonly employed in geo-coding algorithms.

A63
to measure the learning deprivation; and EMIS and Population Census or Household Surveys which are required to measure to schooling deprivation. To combine these two data sources, we use a nested measure, in which all out-of-school children are assumed to be learning deprived. As a consequence, we do not observe the joint distribution of learning and schooling.

In the unidimensional and univariate cases, poverty and deprivation are the same, since the identification of someone as poor depends on a single deprivation, and the choice of standard will automatically divide the population into poor and nonpoor.

But the multivariate context is more complicated, since a nested identification criterion determines learning poverty status. In that case, the first observation is whether a child is in school, and the second is whether he or she is learning by a certain threshold. If the first threshold is not satisfied, a child will be considered in learning poverty, regardless if he or she achieves the learning MPL. But if a child is in school, only if the second identification criterion is also satisfied will the child be considered in learning poverty. So, in the nested approach, first schooling is assessed, and then learning. If either of the identification criteria is not met, a child is considered in learning poverty.

# Aggregation

Aggregation in this exercise will implicitly follow the most straightforward procedure, a headcount rate. That is, it will use the number of children below either deprivation threshold divided by the total number of children in the age category. This ratio, learning poverty (LP), is extremely simple and clear for policy makers to interpret, given the observable nature of school enrollment and the use of an agreed common standard of proficiency defined in the context of the SDGs.

Learning poverty can also be expressed in terms of the equations below.

The aggregation step of this deprivation is the simple ratio of the number of students, _G,_ who satisfy the identification criterion for learning deprivation, divided by the total number of students _S._ The number of
cat bonds are more transparent and simpler to use and hence have been the preferred type in lesser developed countries.

_Advantages of using a cat bond._ There are several advantages of using a cat bond to provide protection against a catastrophic disaster. They are: (1) _Multi-year coverage and price stability._ Insurance and reinsurance contracts are typically issued for one year and are subject to price increases particularly after a large-scale disaster.<sup>11</sup> Cat bonds offer an important element of stability for their users by guaranteeing a predefined price over several years. As of 2008, more than 170 cat bonds had been issued since 1996, and their average maturity has been three years with a few bonds being as long as five or ten years. Longer bonds reduce upfront costs by allowing fees to be amortized over a longer period of time (Michel-Kerjan and Morlaye, 2008).

(2) _Guaranteed expedited payment._ Another key advantage of a parametric cat bond is that the money can flow to the government in just a few weeks. By design, the capital of the bond is commonly invested in risk-free assets, such as U.S. Treasury money market funds, so there is limited credit risk.<sup>12</sup> (3) _An alternative to a government reserve._ A typical financial policy tool for governments is to build up a reserve of money over time to be used in the case of a catastrophe. However, a catastrophe could occur in the very first years so that the fund simply does not have enough money to pay for the losses. If the country does not suffer major losses for a long period, attention fades and the reserves may be transferred to other programs particularly when budgets are tight.<sup>13</sup> It is difficult to have a long-term perspective on these issues for reasons discussed

> 11 The Guy Carpenter Rate-on-Line index shows a 30 percent annual volatility over the past ten years. Premiums also differ markedly among perils
concludes.

# **2 Export survival: What do we know?**

At the product level, the determinants of export survival have been explored by a small but growing literature. Besedes and Prusa (2006) used two panels of U.S. imports, one spanning 1972-88 with tariff-schedule data, the other spanning the 1989-2001 period with 10-digit data (the Feenstra-NBER dataset). In both cases, they found that half of all trade relationships lasted only one year and three quarters lasted three years or less. Once censoring was taken into account, median duration was two years. Most strikingly, this pattern of short duration was robust to aggregation at HS6, even though one would expect interruptions to be smoothed out by aggregation. They also found negative duration dependence, meaning that the hazard rate fell as export spells grew older. This finding, however, has been recently contested by Brenton, Saborowsky and von Uexhull (2010).<sup>1</sup> In terms of survival determinants, Besedes and Prusa (2006) found that industrial-country exports lasted longer, and so did exports of machinery, a finding confirmed by the analysis of Asian trade flows by Obashi (2010).

Besedes and Prusa (2006) explored the determinants of export survival further by testing the implications of a search model proposed by Rauch and Watson (2003) in which importers search for low-cost suppliers and exporters invest optimally in production capacity in the face of moral hazard (risk of non-payment). Such model implies that, in general, smaller initial transactions have a lower life expectancy; however, differentiated goods, where moral hazard is highest, involve both smaller initial transactions _and_ longer life expectancy. The model’s predictions are upheld by Cox regressions on U.S. import data using Rauch’s (1999) index of product differentiation as a regressor. That is, the hazard rate is 23% higher for homogenous products than for differentiated ones, although initial transactions are 40% to 350% larger. In
# **2.2.2 FDI presence**

The data I use include ownership information for 2012, 2015 and 2016. Given the timing of the export data, I focus on ownership in 2012 and 2015. I define a firm as foreign owned if the Global Ultimate Owner (GUO) reported by ORBIS is foreign.<sup>11</sup> If the GUO is reported to be domestic or the GUO nationality is missing, I use information on shareholders of a given firm reported in ORBIS. I define a firm as foreign it has at least one foreign shareholder with more than 10% equity share.<sup>12</sup> I define industry-level FDI presence based on the standard formula used in the literature: where _i_ denotes firm, _s_ 3-digit NACE sector and _t_ year. In addition, _fjt_ is an indicator variable for foreign owned firms and _Yjt_ is the operating revenue. Given the limited availability of the ownership information, I measure Own FDI at two points in time, namely in 2012 and 2015.

To capture vertical spillovers from FDI in the input-sourcing sectors, I rely on the as– sumption that a domestic firm is more likely to supply foreign affiliates and benefit from – vertical spillovers if foreign affiliates account for a larger share of downstream industries’ output. As in Javorcik (2004), I do not include inputs’ sales within the firm’s own sector, as this effect is captured by the Own FDI variable. Thus, this proxy will be capturing a lower bound on the effect of FDI in the input-sourcing industries. I define the downstream FDI measure as

> 11A company’s GUO is the individual or entity at the top of the corporate ownership structure, controlling at least 25.01% of that company.

> 12I exclude capital from ‘tax havens’ when defining foreign ownership. I treat the following countries as ‘tax havens’: Anguilla, Aruba, Bahamas, Barbados, Belize, Bermuda, British Virgin Islands, Cayman Islands