rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.8. E-commerce development and various categories of household expenditure per capita by region (Extended Model 4)**

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Total household expenditure|0.226***|0.155***|0.692***|0.170**|0.238*|0.538***|
||(0.064)|(0.056)|(0.105)|(0.069)|(0.125)|(0.157)|
|Observations|10,701|5,072|5,629|4,253|3,428|3,163|
|Adjusted R square|0.344|0.324|0.378|0.351|0.320|0.338|
|Cosmetics and beauty|0.353***|0.280***|0.914***|0.396**|0.258*|1.004***|
||(0.113)|(0.106)|(0.251)|(0.150)|(0.150)|(0.328)|
|Observations|10,597|5,023|5,574|4,210|3,399|2,988|
|Adjusted R square|0.343|0.368|0.322|0.332|0.361|0.353|
|Food|0.283***|0.229***|0.737***|0.238***|0.269**|0.479**|
||(0.063)|(0.056)|(0.121)|(0.070)|(0.117)|(0.217)|
|Observations|10,550|4,993|5,557|4,174|3,396|2,980|
|Adjusted R square|0.347|0.343|0.365|0.366|0.310|0.390|
|Of which: Food at home|0.304***|0.266***|0.682***|0.240***|0.309**|0.425*|
||(0.067)|(0.058)|(0.164)|(0.084)|(0.115)|(0.224)|
|Observations|10,547|4,991|5,556|4,172|3,395|2,980|
|Adjusted R square|0.390|0.400|0.390|0.339|0.380|0.476|
|Of which: Dining out|0.424***|0.267*|1.258***|0.210|0.589**|1.137**|
||(0.148)|(0.147)|(0.273)|(0.161)|(0.245)|(0.446)|
|Observations|10,707|5,075|5,632|4,257|3,430|3,020|
|Adjusted R square|0.331|0.322|0.361|0.324|0.329|0.353|
|Clothes|0.192**|0.125|0.634***|0.145|0.232*|0.433*|
||(0.094)|(0.091)|(0.186)|(0.131)|(0.129)|(0.251)|
|Observations|10,437|4,922|5,515|4,121|3,342|2,974|
|Adjusted R square|0.326|0.342|0.319|0.327|0.350|0.295|
|Utilities|0.171***|0.138***|0.321**|0.169***|0.122**|0.432|
||(0.041)|(0.036)|(0.153)|(0.048)|(0.057)|(0.281)|
|Observations|10,493|4,945|5,548|4,178|3,363|2,952|
|Adjusted R square|0.308|0.295|0.329|0.313|0.300|0.337|
|Communications|0.177***|0.132***|0.525***|0.119**|0.210***|0.396**|
||(0.044)|(0.038)|(0.086)|(0.052)|(0.066)|(0.178)|
|Observations|10,383|4,902|5,481|4,110|3,344|2,929|
|Adjusted R square|0.337|0.354|0.334|0.308|0.387|0.316|
|Local transport|0.332***|0.272***|0.601***|0.217*|0.407***|0.215|
||(0.081)|(0.077)|(0.160)|(0.111)|(0.090)|(0.289)|
|Observations|10,327|4,865|5,462|4,086|3,323|2,918|
|Adjusted R square|0.306|0.318|0.298|0.290|0.321|0.321|
|Travel|0.650***|0.465***|1.423***|0.496*|0.626***|1.743***|
||(0.187)|(0.169)|(0.273)|(0.272)|(0.156)|(0.515)|

·· 43
Salehyan, I. (2008) The Externalities of Civil Strife: Refugees as a Source of International Conflict. _American Journal of Political Science_ 52(4): 787-801.

Saleyhan, I. and Gleditsch, K. (2006) Refugees and the spread of civil war. _International Organisation_ 60: 335-366.

Scudder, T. and Colson, E. (1982) From welfare to development: a conceptual framework for the analysis of dislocated people. In Hansen, A. and Oliver-Smith, A. (eds.), _Involuntary Migration and Resettlement: The Problems and Responses of Dislocated People_ . Westview, Boulder: 267288.

Sen, A. (1981) _Poverty and Famines: an Essay on Entitlement and Deprivation_ . Clarendon, Oxford.

Skeldon, R. (2002) Migration and poverty. _Asia Pacific Population Journal_ 17(4): 67-82. Smith, D., Vivekananda, J. (2007) _A Climate of Conflict: The Links Between Climate Change, Peace and War_ . International Alert, London.

Sorensen, N., van Hear, N., and Engberg-Pedersen, P. (2003b) Migration, development and conflict: state-of-the-art overview. In N. van Hear and N. Sorensen (eds.), _The MigrationDevelopment Nexus_ . United Nations, International Organisation for Migration, Geneva.

Sorensen, N., van Hear, N., and Engberg-Pedersen, P. (2003b) The migration-development nexus: evidence and policy options. In N. van Hear and N. Sorensen (eds.), _The MigrationDevelopment Nexus_ . United Nations, International Organisation for Migration, Geneva.

Stark, O. (1991) _The Migration of Labor_ . Basil Blackwell, Cambridge, Massachussetts, Oxford. Stern, N. (2007) _The Economics of Climate Change_ . Cambridge University Press, Cambridge.

Stern, N. (2008) _Key Elements of a global deal on climate change_ . London School of Economics and Political Science: http://www.lse.ac.uk/collections/granthamInstitute/publications.htm last accessed December 13 2008.

Swain, A. (1993) Conflicts over water: the Ganges water dispute. _Security Dialogue_ 24 (4): 429439.

Swain, A. (1996) Environmental migration and conflict dynamics: focus on developing regions. _Third World Quarterly_ 17(5): 959-973.

Tacoli, C. (2007) Migration and adaptation to climate change. _Sustainable Development Opinion._ International Institute for Environment and Development.

60
a policy affects intergenerational mobility within society is hardly estimated, although links between the two concepts have been explicitly pointed out (see Bourguignon, Ferreira and Menendez 2007 and references there). In addition, the depiction of policymaking so far has been very simplistic, assuming for instance that taxation is modified to simplify tax rules—that is, a flat rate is imposed to every type—or a similar increase in education spending for all children across groups. In other words, the notion of fiscally feasible—affordability—has dominated the analysis of more realistic, complex and multidimensional policies. As for the HOI, no application to policymaking has been attempted so far, with efforts mainly directed to estimating and comparing a set of comparable objectives across countries. The rest of this paper remedies this gap by developing a methodology that links HOI with public spending.

# **3. Fiscal Issues and Equality of Opportunities: Methodology**

Methodologically, the paper carries out three separate exercises (Appendix 2). First, it estimates the HOI for access to education in Liberia for children aged 6-15. HOI is estimated in the World Bank ―tradition‖ described above and recently applied to Latin America by Molinas et al. (2010). The set of circumstances relevant for Liberia and the selected opportunity are defined and justified in section 4, below.

Second, the paper expands the traditional benefit incidence analysis into an ―Opportunity Benefit Impact Analysis‖ (Opp-BIA), that is, an incidence analysis of public educational spending along the distribution of opportunities, and compares it with the ―traditional‖ BIA based on income distribution. Table 2 describes the steps for the analysis, which basically consists of conducting 10
research slightly less than half put low value on research for their work, while the rest (one quarter of all respondents) think research is valuable, and are clearly are not getting enough of it.

|||Perceived value of WB researc<br>Low|h for the staff member’s work<br>High|
|---|---|---|---|
|||**“Happily uninformed:”**<br>N=117 (22.54%)|**“Frustrated uninformed:”**<br>N=123 (23.70%)|
|Staff member’s<br>personal|Low|Mean familiarity=3.35<br>Mean value=2.72|Mean familiarity=4.00<br>Mean value=6.35|
|familiarity with<br>WB research|High|**“Independently well-informed:”**<br>N=62 (11.95%)|**“Functionally well-informed:”**<br>N=217 (41.81%)|
|||Mean familiarity=7.26<br>Meanvalue=2.90|Mean familiarity=7.58<br>Meanvalue=7.57|

The ―frustrated uninformed‖ can be thought of as a target group for extra effort at research dissemination. So it is of interest to see where they are found. Tables 14 and 15 give the VPU and sectoral distributions of the four types of staff. WBI has (by far) the highest share of the ―frustrated uninformed‖ staff, with 65% in this category, the rest being ―functionally wellinformed‖—both valuing research for their work and familiar with it, who comprise 42% of respondents.<sup>26</sup> The MNA region has the lowest, as 13%. Breaking it down by sectors, it is transport staff that have the highest share of ―frustrated uninformed‖ group, at 35%, with URB and SDV close behind. EDU has the lowest, at 9%. The share of ―functionally well-informed‖ varies from 35% in SDN to 63% in PRM, while amongst sectors it varies from 14% in energy and mining to 83% in the poverty sector.

A closer look at the ―happily uninformed‖ does not suggest that they have simply switched their favors to external (non-WB) sources of research. They give a relatively low rating to academia as a source—a mean score of 5.53, as compared to 6.87 for all other staff, and the difference is significant at the 0.1% level. They also rate consultants lower than average, at a mean of 6.07 as compared to 6.54 for the rest, and the difference is significant at the 5% level. At the same time they have an above average desire to increase their use of the Bank’s research; their mean difference between expected future usage and past usage (both on the 10-point scale) is 0.80 versus 0.54 for the rest of the staff and 0.43 for the ―functionally well-informed.‖ (The

> 26 This number depends (of course) on the precise cut off point used in defining ―high‖ and ―low.‖ For example, if instead one raises the lower bound for defining the ―high-value‖ group by one step of the 10 point ladder then the % of the sample are deemed to be ―functionally well-informed‖ falls from 42% to 38%.

18
years of schooling corresponding to points C and D.<sup>8</sup>

**Figure 3.3: Illustration of the implications for** **_LAYS_ of changing the assumption about when learning starts**

<!-- Start of picture text --> A: Learning starts at Grade “‐6” B: Learning starts at Grade “‐3”<br>A A<br>D D<br>C C<br>B B<br>Years of schooling/Grade (Years of learning) Years of schooling/Grade (Years of learning)<br>Country A Country B Country A Country B<br>800 800<br>700 700<br>600 600<br>500 500<br>400 400<br>Test score Test score<br>300 300<br>200 200<br>100 100<br>0 0<br>-6 (0) -5 (1) -4 (2) -3 (3) -2 (4) -1 (5) 0 (6) 1 (7) 2 (8) 3 (9) 4 (10) 5 (11) 6 (12) 7 (13) 8 (14) 9 (15) 10 (16) 11 (17) 12 (18) -3 (0) -2 (1) -1 (2) 0 (3) 1 (4) 2 (5) 3 (6) 4 (7) 5 (8) 6 (9) 7 (10) 8 (11) 9 (12) 10 (13) 11 (14) 12 (15)<br><!-- End of picture text --> The change in assumptions leads to the following modifications to the relevant formulas. First, average learning per year (the slope of the average learning profiles) is now defined over the full number of years of learning, rather than just years of schooling. This means that Equation 3 is modified to: where Y�� is the number of years of learning prior to Grade 0. Note that there is one additional assumption embedded in this framework: namely, that learning starts at the same point in different countries (that is, Y�� does not vary across countries: Y�� �Y�).9

> 8 The vertical ratio would be equal to the horizontal ratio if we were to use the “years of _learning_ ” scale for the latter.  But since our ultimate objective is to derive a transformation for years of _schooling_ , we carry out the modifications as described here.

> 9 Because the learning assessment is typically done in a given grade across all countries—as with TIMSS in most countries— Y� is also fixed, so the ratio of L� to L� will generally be the same regardless of when learning begins. We say this is “typically” true because, in some cases, assessments are administered in different grades.  For example, the 2015 TIMSS was administered to Grade 9 students in Botswana and South Africa, whereas it was administered to Grade 8 students elsewhere.

13
<!-- Start of picture text -->
* Percent of governing body that belongs to: current students @<br>Almost all or all faculty participated in professional training last year<br>* Percent of faculty working in the industry<br>Industry has internship agreements with HEI @<br>Communicate with local firms about their needs more than once per year @<br>Industry has agreements to train faculty @<br>Loans by banks are used by some students e<br>Collect student satisfaction data more than once per year @<br>Student feedback is very important to update program @<br>Government standards are very important to update program e<br>* Years since last update to curriculum e<br>Test required for graduation @<br>Analyze student performance to solve problems more than once per year @<br>Maintenance of largest lab: every year<br>Program offers at least one online class<br>-1 -.05 (e) 05 J 15<br>Percentage points<br>Infraestructure @ Training and curriculum © Costs<br>@ Link with productive sector Faculty @ Other Practices<br><!-- End of picture text -->

<!-- Start of picture text --> Update / review admin data more than once per year @<br>Interview is an admission requirement e@<br>Class planning is very important in faculty evaluation<br>Students and peers’ informal comments are very important in faculty evaluation<br>* Percent of faculty with graduate degree<br>Classroom observation is very important in faculty evaluation<br>Peer evaluation is very important in the faculty evaluation<br>Evaluate faculty more than once per year<br>HEI trains students for job interviews e@<br>Collect data on employment or employers' satisfaction more than once per year @<br>HEI providesjob market information e@<br>Loans by banks are used by some students @<br>Offers remediation classes during the program @<br>Offers remediation classes before starting the program @<br>Teaches persistence in complex tasks @<br>Student feedback is very important to update program @<br>Test required for graduation @<br>Teaches numerical competencies @<br>Analyze student performance to solve problems more than once per year @<br>-10 -5 0 5 10<br>Percentage points<br>Infraestructure @ Training and curriculum @ Costs<br>@ Link with productive sector Faculty @ Other Practices<br><!-- End of picture text -->
For completeness, we check for potential heterogeneous responses with respect to the start year of coal plants, as well as national pollution and income levels. The hypothesis is that countries with lower incomes and higher pollution levels may prioritize economic growth, and thus respond differently than high-income low-pollution countries. However, results reported in Table S3.2, S3.3 and S3.4 still show small and imprecise estimates on the interaction term with economic variables as left-hand sides, indicating negligible impacts of plant operations on income levels regardless of countries’ development stages. Section S2.2 summarizes findings from a set of sensitivity checks. Results remain strongly robust to alternative definitions of up- and downwind areas to construct the sample (i.e. alternative angles and distances).

There are at least three potential reasons for the null results in this section. First, adverse impacts of pollution on health and labor productivity may take many years to materialize. The time ranges considered in our sample may not be long enough to capture a meaningful reduction in income levels. Second, the GDP and HDI data are constructed using national economic values and subnational population as inputs (Kummu et al., 2018). Compared with our detailed pollution trajectory modeling, income data may not be granular enough to capture small-scaled local differences. Third, even if air pollution affects health outcomes and the quality of life, this may not translate into a significant reduction in income levels. On the contrary, high spending on healthcare could even push up measures of national production, such as GDP.

# **5 Conclusion**

Many countries continue to rely on coal power plants for electricity generation, despite their tremendous financial, societal, and environmental costs. This paper documents inequalities in people’s exposure to toxic air pollutant emissions from the world’s almost 4,000 coal-fired power plants. By modeling the wind-dispersion of air pollutants, we generate a high-resolution winddirected global air pollution map for coal plants. We find that, globally, 2.32 billion people are exposed to air pollution originating from coal power plants. Globally, 160 million people face SO2 pollution from coal plants that exceed the World Health Organization (WHO) guideline value of 40 _μ_ g/m<sup>3</sup> – and this does not account for pollution from other sources. Moreover, 247.5 million people are exposed to transboundary pollution – namely, toxic air pollution originating from coal plants outside their country.

We find that, overall, air pollution from coal plants increases with income levels. Richer areas have more coal plants, thus more coal plant-related pollution. However, air pollution from coal plants increases at a diminishing rate at high income levels. This is consistent with the notion that high-income countries transition to more efficient less polluting power sources. Our results 20
# **4.2 Factors and Outcomes**

Factors and behaviors or outcomes are strongly related. As a comprehensive measure of risk and protective factors and outcomes, the mean of these indicators (indicated by a star in Table 4.1) is used for each cluster. Figure 4.1 shows that protective factors are positively correlated with positive behaviors or outcomes: Clusters with more protective factors tend to have better outcomes than clusters with less protective factors. At the same time risk factors are negatively correlated with positive behaviors or outcomes: Clusters exposed to more risk factors tend to have worse outcomes than clusters with fewer risk factors.

Figure 4.1: <u>Correlation Between Protective/Risk Factors and Behaviors/Consequences, Argentina 2005</u> <!-- Start of picture text -->
1.00<br>0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>P ro tective Facto rs<br>0.30<br>Ris k Facto rs<br>0.20<br>Linear (P ro tective Facto rs )<br>0.10<br>Linear (Ris k Facto rs )<br>0.00<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80<br>Factors<br>Note : Size of bubble indicates cluster size.<br>Source : Own calculations on ESCVJA (four urban areas)<br>Behaviors<br><!-- End of picture text -->

Income is positively correlated with behaviors or outcomes. The impact on outcomes from higher income is evident.<sup>9</sup> Higher income is associated with higher education, access to information, and exercising citizenship, which is emphasized as a crucial element for youth development in the 2007 World Development Report (World Bank 2007). It is clear that youth with the highest risk level are also those with the lowest composite income proxy (Figure 4.2). Higher income protects against risk factors, such as an insecure neighborhood, and facilitates that youth can attend school, even if there is a cost connected such as with private schools or universities.

> 9 _Income proxy_ is a composite variable, consisting of indicators for the number of rooms and the quality of bath/toilet facilities in the household, whether the young person has a mobile, an email account, or a pc, whether the person uses the internet, and has attended a private school (see also Appendix B).

20
Winchester, N., M.L. Rau, C. Goetz, B. Larue, T. Otsuki, K. Shutes, C. Wieck, H. L. Burnquist, M. J. Pinto de Souzaand R. N. de Faria (2012). The Impact of Regulatory Heterogeneity on Agri-Food Trade. _The World Economy_ . Vol 35 (8): 973-993.

Wolf, H. (2000). (Why) Do Borders Matter For Trade? In G. Hess and E. van Wincoop, (eds.), _International Macroeconomics_ . Cambridge: Cambridge University.

Xiong, B. and Beghin J. (2010). Does European Aflatoxin Regulation Hurt Groundnut Exporters from Africa? _European Review of Agricultural Economics_ . Vol 39 (4): 589-609.

<mark>Xiong, B., a</mark> nd Beghin J. (2013) <mark>. “Stringent Maximum Residue Limits, Protectionism, and Competitiveness: The Cases of the US and Canada”, in John C. Beghin (ed) Frontiers of</mark> <u><mark>Economics and Globalization,</mark></u> <mark>Emerald Press, Bingley, West Yorkshire, UK.</mark>

**Figure 1 – Importers’ Income and Standards**

19
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Mali > Telecom investments > Macro results**

||||Reduction in<br>other public|Te|lecom<br>I|Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|1,480,726.40|0.38|0.44|0.46|0.50|0.46|
|EV|Equivalent variation||0.38|0.43|0.45|0.50|–0.01|
|s|Wage|1.00|0.26|0.77|0.94|1.28|0.97|
|yg|Government income|278,133.30|0.50|2.77|2.77|2.77|2.77|
|g|Total government expenditure|255,957.00|–1.19|1.28|1.28|1.28|1.28|
|ye|Firm income|477,113.00|0.51|0.32|0.26|0.14|0.25|
|It|Total private investment|439,511.00|0.62|0.44|0.38|0.25|0.25|
|GDP|GDP|1,819,413.40|0.44|0.44|0.44|0.44|0.44|
|e|Nominal exchange rate|1.00|0.24|0.21|–0.32|–1.41|0.05|

# **Mali > Telecom investments > Sectoral results**

||||Reduction in|Te|lecom|||
|---|---|---|---|---|---|---|---|
||||other public||Imort||Income|
|Variable|Sector|Base|<br>expenditure|VAT|p<br>duties|Foreign aid|<br>tax|
||Crop agriculture|379,937.10|0.39|0.33|0.31|0.28|0.26|
||Export agriculture|297,375.30|0.48|0.44|0.41|0.37|0.41|
|Va|Mining and gas|206,350.40|0.32|0.23|0.11|–0.06|0.21|
|Value added or|Industries|167,281.20|0.44|0.22|0.26|0.39|0.19|
|output|Construction|116,213.20|1.88|1.13|1.31|1.59|1.42|
||Private services|516,027.70|0.64|0.45|0.50|0.46|0.46|
||Public services|136,228.50|–1.27|0.71|0.63|0.79|0.78|
||Crop agriculture|1.01|0.08|0.15|0.20|0.19|–0.19|
||Export agriculture|1.02|0.01|0.06|0.00|–0.23|–0.28|
||Mining and gas|1.00|0.34|0.21|0.05|–0.34|0.04|
|pq<br>Market prices|Industries|1.17|0.20|0.44|0.65|–0.83|0.12|
||Construction|1.02|–0.03|0.60|0.36|–0.04|0.12|
||Private services|1.03|–0.32|0.17|–0.02|–0.03|–0.23|
||Public services|1.00|0.11|0.55|0.64|0.47|0.48|
||Crop agriculture|1.00|0.47|0.47|0.44|0.55|0.06|
||Export agriculture|1.00|0.55|0.43|0.20|–0.18|0.19|
|r<br>|Mining and gas|1.00|0.71|0.33|–0.75|–2.09|0.29|
|Rental rate of<br>capital|Industries|1.00|0.70|0.65|0.91|1.59|0.76|
||Construction|1.00|1.75|1.22|1.63|2.37|1.82|
||Private services|1.00|0.25|–0.05|0.33|0.50|0.17|

35
**Table 3a: Status of Hydropower and Economy in the Himalayan Region**

|**Country/State/Region**<br>|**Potential**|**Installed**<br>**maximum **|**Key present and potential future**<br>**economic sectors**<sup>**5**</sup>|
|---|---|---|---|
|**Nepal**<sup>**~~1~~**</sup><br>|43,000|633|Agriculture, tourism, remittance,<br>hydropower|
|**Bhutan**<sup>**~~2~~**</sup><br>|23,760|1,488|Agriculture, forestry, hydropower and<br>related services and industries,tourism|
|**India**<sup>**~~3~~**</sup><br>|148,701|32,782||
|**Arunachal Pradesh**<sup>**~~3~~**</sup><br>|50,328|405|Agriculture, Tourism, mining, forest,<br>hydropower|
|**Sikkim**<sup>**~~3~~**</sup>|4,286|570|Agriculture, mining, forest, hydropower,<br>construction tourism|
|**Uttrakhand**<sup>**~~3~~**</sup>|18,175|3,226|Tourism, horticulture and floriculture, bio<br>technology,and hydropower|
|**Himachal**<sup>**~~3~~**</sup>|18,820|6,193|Agriculture, Horticulture, Tourism,<br>hydropower|
|**Jammu & Kashmir**<sup>**~~3~~**</sup><br>|14,146|2,340|Agriculture, horticulture, Tourism,<br>hydropower|
|**Xizang Province**<sup>**~~4~~**</sup>|110,000|150|Agriculture, Tourism, Construction and<br>hydropower|

1 USAID, 2009; 2 World Bank, 2010 (2010 Bhutan Economic Update); 3 CEA, 2011; 4 Climate Connect, 2010; 5 Compiled from various sources

**Table: 3b  Attributes of Climate Change Impacts on Resources  (Agrawala et al., 2003)**

|Resources/Sector|Rank|Certainty<br>of impact|Timing of<br>impact|Severity of<br>impact|Importance<br>of resource|
|---|---|---|---|---|---|
|Water resources<br>and hydropower|1|High|High|High|High|
|Agriculture|2|Medium-<br>low|Medium-low|Medium|High|
|Human health|3|Low|Medium|Uncertain|High|
|Ecosystems<br>/Biodiversity|4|Low|Uncertain|Uncertain|Medium-<br>high|

In the Himalayan region, almost all of the existing economic assessments are limited to agriculture in India (Dinar, 1998; Kumar and Parikh, 2001) and China (Wang et al., 2009). A notable exception is the work of Cline (2007) which includes a fairly detailed global analysis with results for several countries and regions in the Himalayas, which will be discussed later.

22
# Inflation and Seigniorage in Argentina

by Miguel A. Kiguel and Pablo Andr6s Neumeyer

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Financial Arrangements and Inflation Tax|4|
|III.|Seigniorage and Inflation Tax in Argentina|9|
|IV.|The Demand for Money and the Inflation Tax<br>Laffer Curve|1l|
|V.|Implications and Final Reflections|25|
|Refe|rences|27|
|Tabl|es|31|
|Appe|ndix|35|
|Figure|s|37|