rafmacalaba/datause-encoder-data huggingface dataset:
is the risk-free rate, denominated in the same currency as bond _i_ . Appendix A provides the sources for these risk-free rates (total return series on UK and US bonds). For completeness, we also compute excess returns vis-à-vis UK or US bills, which is useful when comparing the risk-return properties of external sovereign bonds with those of other asset classes (Section 6).

Further methodological details are discussed in Appendixes B2 and B3, including a discussion on how we deal with historical bond features such as sinking funds.

# 4.3. Main results on bond returns

Table 3 summarizes our main results. The table shows average yearly sovereign bond returns for our global portfolio of foreign-currency bonds over 200 years. Henceforth, we do not show returns for the period 1974 to 1994 due to a lack of representative bond pricing data in the 1970s and 1980s.<sup>39</sup> This means that the time series for the global portfolio ends in 1973 and starts again in 1995. Appendix B5 shows a number of robustness checks, such as on selection (survivorship) bias and the inflation series used.

Table 3: Returns on a global portfolio of external sovereign bonds, 1815-2016

||Re<br>|al Retur<br>|n<br> <br>|Nomi<br>|nal Ret<br>|urn<br> <br>|Above<br>US/UK|<br> <br>Sharpe<br>rti|
|---|---|---|---|---|---|---|---|---|
||Arithm.<br>Mean|Geom.<br>Mean|<br> <br>SD|Arithm.<br>Mean|Geom.<br>Mean|<br> <br>SD|<br>gov.<br>Bonds|<br> <br> <br>ao|
|**Full sample**, 1815-2016, yearly|6.85|5.78|15.03|<br>7.99|7.13|13.69|<br>4.29|0.32|
|... without world wars|6.99|5.99|14.50|7.69|6.87|13.21|<br>3.94|0.30|
|**By era**, yearly|||||||||
|1815-1869|7.10|5.66|17.28|<br>6.90|5.87|14.84|<br>2.96|0.23|
|1870-1914|6.28|5.97|8.01|6.18|5.91|7.45|<br>3.70|0.48|
|1915-1945|6.01|4.29|19.91|<br>7.12|6.00|16.19|<br>2.33|0.16|
|1946-1973|5.85|4.91|14.52|<br>10.15|9.19|15.12|<br>8.07|0.52|
|1995-2016|9.89|8.96|14.29|<br>12.40|11.41|14.97|<br>6.26|0.33|
|Monthly returns, full sample|0.54|0.47|3.74|0.65|0.58|3.66|<br>0.35|9.33|

_Note:_ Table 3 shows average ex-post investor returns in our total sample of 91 countries and 200 years, as well as for different subsamples. The country composition is changing over time (see Appendix B1). All returns are yearly averages, except for the last line, which shows a monthly frequency. Returns and standard deviations shown are based on a global portfolio that includes all outstanding foreign-currency sovereign bonds at each point in time. Excess returns and Sharpe ratios are computed using US/UK bonds as benchmark.

In the full sample from 1815 to 2016, foreign-currency bonds show an annual ex-post real return of 6.85%, including spells of turmoil due to defaults, wars, and revolutions. The average is slightly higher (7%)

> 39 As we explain in Section 3.1. and in Appendix B1, the 1970s and 1980s were a period dominated by syndicated bank lending to sovereigns. Barely any sovereign bonds were issued abroad. As a result, the number of countries with actively traded bonds dropped to fewer than 10 in the 1980s, making the global portfolio unrepresentative.

23
Table 3. Di¤erence-in-di¤erence estimates of the program impact on treatment practices at the health facility.

|Dependent variable|Equipment usage|Waiting time|
|---|---|---|
|Speci...cation|(1)|(2)|
|Program impact<br>|0.08**<br>|-14.0*<br>|
|(Treatment*2005)<br>2005|(0.03)<br>-0.07***<br>(0.02)|(7.7)<br>-9.6*<br>(5.3)|
|Mean control group 2005|0.41|133|
|Facility ...xed e¤ects|Yes|Yes|
|Observations<br>|5280|5148|
|R<sup>2</sup>|0.15|0.12|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Speci...cation: (1) Dummy variable indicated whether the sta¤ used any equipment during examination when the citizen visited the health facility; (2) Waiting time is calculated as the di¤erence between the time the citizen left the facility and the time the citizen arrived at the facility minus the examination time.

c. Robust standard errors in parenthesis. Disturbance terms are clustered within catchment areas.

45
and -13 in the early 1990s. As such, by the early 1990s, not only the number of crisis episodes falls but also the degree of each crisis declines. However, this trend reverses in the late 1990s. In addition to the increase in the number of crisis episodes, the degree of crisis increases mainly due to the depth of crises rather than their length.

**Table 7: Crisis Identification on a Periodical Basis in Q1 1980-Q4 2002**

||C<br>|risis Quarte<br>|rs (Periods<br>Total|)<br>Crisis||Crisis Ep<br>|isodes 1/<br>||
|---|---|---|---|---|---|---|---|---|
||No. of<br>quarters|Ratio in<br>samples|<br>degree<br>2/|<br>average<br>3/|No. of<br>episodes|No. of<br>quarters|Total<br>degree 2/|Episode<br>average 3/|
|1980-2002|827|9.2%|-2,121|-2.6|59|203|-1,130|-19|
|1980-1990|477|11.4%|-1,212|-2.5|36|127|-626|-18|
|1991-2002|350|7.4|-909|-2.6|23|76|-504|-21|
|1981-1985|284|15.1%|-713|-2.5|19|83|-420|-22|
|1986-1990|162|8.3%|-421|-2.6|14|33|-157|-11|
|1991-1995|118|6.0%|-308|-2.6|7|14|-92|-13|
|1996-2000|176|8.8%|-456|-2.6|12|50|-338|-28|
|2000-2002|56|7.2%|-145|-2.6|4|12|-73|-18|

_1/ Period classification is based on the beginning of crisis episodes_ _2/ Total degree is the sum of standardizes scores exceeding the threshold_ _3/ Crisis (episode) average is calculated as total degree divided by the number of quarters (episode) Source: Author_ <!-- Start of picture text --> Fig. 6: Number of different crisis types in crisis episodes Fig. 7: Average crisis degree in each episode<br>(cumulative)<br>90 60<br>80<br>50<br>70<br>60 40<br>50<br>30<br>40<br>30 20<br>20<br>10<br>10<br>0 0<br>80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02<br>Source: Author Source: Author<br><!-- End of picture text -->

# **3. Crisis Transmission Mechanism**

The crisis identification exercise suggests that the relationship between crisis types becomes closer in these days than the past. Existing crisis studies mainly deal with a few types of crises such as currency crises and banking crises. Growth rates are mainly used as a measurement of the degree of crises rather than as a separate type of crises. However, growth rates are not only be affected by other types of crises but also affect other crisis types in turn. Literature review regarding the relationships between crisis types provides rich ideas, though some relationships are rarely found. In theory, there are 42 relationships between seven different types of crises if bidirectional causalities are taken into account. However, only 12 relationships are explained in existing studies.

16
**1. Introduction**

Pollution control is highly decentralized in Brazil. state governments are e~xpected to ensure that local water and air quality meet federal minimum ambient standards. This decentralization aims at greater autonomy in the implementation of federal guidelines. States can then select and design instruments, within boundaries defined by the Constitution, to achieve federal objectives. Most states rely on regulations such as licenses or standards to achieve their objectives. Fines on polluters are used commonly by the states but are generally not directly related to the level of pollution. In practice, the success of current policies has been limited as in most of Brazil, pollution is increasing. I Differences in decentralized implementation of federal requirements across states may reflect differences in administrative or financial resources available to monitor polluters. Weak implamentation may result from weak effective commitment to the pollution issue by the state or federal governments. States may have multiple objectives and their role as an agent of the federal government may be only one of those objectives. Other goals may include fiscal revenue targets or local jobs creation for. instance. Strong enforcement leads to lower output and hence reduces tax revenue, at least in the short run, by reducing the tax base and cutting jobs. Davis and Lester (1989) and Taylor (1990) provide surveys, respectively of the political science and the economic literature, analyzing evidence for the U.S. on the importance of interstate competition for investment or tax revenue based on the degree of enforcement of pollution laws. They show competition is not widespread but that when it occurs, its impact on resource allocation and mobilization is such that it cannot be ignored.

since the Brazilian experience of the eighties provides ample evidence of tax competition among states to attract jobs and investment across states, the interstate regulatory competition issue in Brazil deserves particular attention. In fact, as in many other decentralized economies, many in Brazilian states administrations would be willing to argue that they face a tradeoff between revenue and environmental quality. The underlying reasoning is as follows. The better a state's enforcement of federal requirements, the less polluted the state is but the lower is state net revenue. Net revenue is lower because output is cut, at least in the short run as capital has to be diverted from production to pollution control. 2 But net revenue is also lower because monitoring is costly and currently only yields trivial penalty returns. This sort of reasoning may explain why the degree of enforcement of pollution laws is often

**1. Foran** t erviewofissuesraisedbypollutioncontrolpoliciesinBazil, seestache(1990).

2. Since thenmodel focunseon the sitort rmnany output effect of technological improvementsbrought about by investment in pollution control is suted out.
<mark>the choice of the method by which maize production and yield (anchored in GPS-based plot area measurement) are computed.</mark> <mark>While farmer-reported production-based plot-level maize yield regressions consistently lend support to the inverse scale-productivity relationship, in magnitudes that are similar to previously published findings on Uganda and other African settings, the comparable regressions estimated with maize yields based on sub-plot crop cutting, full-plot crop cutting, and remote sensing point towards constant returns to scale (CRS). In view of the competing hypotheses for the IR, the regressions control for objective measures of soil fertility, maize genetic heterogeneity, and edge effects at the plot-level; a rich set of plot, household and plot manager attributes; as well as household and parcel fixed effects in select specifications. The existence of the IR anchored in the use of farmer-reported plot-level maize production is also shown throughout the yield distribution, while CRS is documented throughout the distribution of maize yields based on crop cutting and remote sensing variants.</mark> <mark>Our core finding is driven by persistent over-estimation of farmer-reported maize production and yield vis-à-vis their crop cutting-based counterparts, particularly in the lower half of the plot area distribution. The analysis (1) points to the rounding of maize production as a key factor in farmer over-estimation, (2) suggests that farmers do not consider the degree of crop damage when reporting production, and (3) provides evidence for multiple harvests (and the accompanying heterogeneity in harvested crop conditions) contributing to over-estimation. While some may argue that farmer over-estimation is driven by the inability of farmers to distinguish production at the plot level, rather than farm level, our results also serve to refute this claim.</mark> <mark>Though the findings contribute to a larger (and renewed) body of literature questioning the inverse scale-productivity relationship based on omitted explanatory variables or alternative formulations of the agricultural productivity measures, the analysis, together with Desiere and Jolliffe (2017), is among the first documenting how errors in self-reported survey data on production mediate the existence of the IR. The consistency in the findings across our study and that of Desiere and Jolliffe (2017), which has a focus on Ethiopia, is noteworthy.</mark> <mark>Taking into account the similarities in heaping in self-reported maize production information across our study and the Eastern region sub-sample of the UNPS for the same agricultural season, our findings emphasize the need for sustained focus on the improvement of crop production and yield measurement in the context of household and farm surveys that solicit farmer-reported production information and that capture farming at similar scales as well as pervasive use of nonstandard measurement units, and heterogeneity in harvested crop conditions that could be reported across and within households.</mark> 25
experienced a crisis and not being subject to blanket guarantees) experienced an increase in the Lerner index by 0.04.

[FIGURE 1: The effects of government interventions on Lerner indices]

[FIGURE 2: The effects of government interventions on net interest margins] The empirical patterns are striking. Most countries experience reductions in Lerner indices and in net interest margins following interventions. The increase in competition occurs primarily after recapitalizations and liquidity support, and nationalizations also reduce margins. However, the effects are not uniform. While Thailand and Ecuador display declines in Lerner indices and in net interest margins, other countries such as Germany do not post such declines, suggesting the effects of interventions are amplified or mitigated depending on other characteristics, e.g., the conditions in a banking system prior to these measures. We explore these issues in detail in Section 3.7 below.

# _3.2 Identification strategy_

We now turn to difference-in-difference estimations which compare treatment countries, i.e., countries whose banking systems experienced interventions with countries in a control group both before and after the treatment. The control group consists of countries that did not have interventions (i.e., non-crisis countries).

Our estimator considers the time difference of the group differences, i.e., it accounts for omitted variables that affect treated and untreated countries alike. For instance, Basel II may coincide with changes in competition, but as such changes affect all banks similarly, the estimator only attributes the additional changes in competition to the intervention. Our regression setup is as follows: _Cit = α + βIit +_ ρ _Xit + Ai + Bt +_ ε _it_ where the dependent variable _Cit_ denotes competition in country _i_ during year _t_ . The panel structure permits inclusion of dummy variables to eliminate time-varying omitted variables. We include country ( _A_ ) and year dummy variables ( _B_ ) to capture cross-country heterogeneity and year-fixed effects. The country-fixed effects net out any time-invariant unobserved country-specific factors. The year-fixed effects difference away trends that affect treatment and control group countries such as changes in contestability, consumer trends, and changes in technology that could reduce Lerner indices and interest margins over time. We also include dummies for World Bank income categories.<sup>10</sup> They account for time-invariant similarities between countries in terms of economic development. The vector _X_ captures time-varying country-level control variables explained below, and ε _it_ is the error term.

> 10 The income categories are high-income, lower-middle income, upper-middle income, and low-income.

9
**Table 2. CDM Pipeline Inventory of Projects, by Project Type**

||Number of Projects|Annual CERs<br>2008-2012|Annual CERs<br>2008-2020|
|---|---|---|---|
||4,631|2,786,791|7,416,430|
|Project type||Share of Total (%)||
|Afforestation|0.11|0.01|0.18|
|Agriculture|0.02|0.01|0.01|
|Biomass energy|14.29|7.02|6.49|
|Cement|0.63|1.13|0.79|
|CO2 capture|0.06|0.01|0.01|
|Coal bed/mine methane|1.45|4.62|4.71|
|EE households|0.45|0.14|0.14|
|EE industry|3.13|0.78|0.61|
|EE own generation|9.78|9.27|8.68|
|EE service|0.37|0.03|0.03|
|EE supply side|1.32|1.39|2.70|
|Energy distribution|0.19|0.35|0.34|
|Fossil fuel switch|2.33|6.37|6.13|
|Fugitive gas|0.50|1.92|1.66|
|Geothermal|0.32|0.60|0.58|
|HFCs|0.48|17.10|14.83|
|Hydro|26.97|17.23|20.75|
|Landfill gas|5.94|7.58|6.78|
|Methane avoidance|11.34|4.25|3.73|
|N2O|1.45|8.95|8.44|
|PFCs and SF6|0.30|0.46|0.54|
|Reforestation|0.95|0.49|0.81|
|Solar|0.78|0.08|0.10|
|Tidal|0.02|0.04|0.05|
|Transport|0.26|0.18|0.18|
|Wind|16.56|10.00|10.75|

Source: Based on UNEP Risoe CDM/JI Pipeline Analysis and Database, September 01, 2009.

15
|Risk Appetite (0-|6.61|6.84|6.56|6.49|1.174|0.318|
|---|---|---|---|---|---|---|
|10)|||||||
|N|259|264|255|260|||

Table 1b: F test of joint significance of treatment arm means (H0: X B1 = X B2 = X B3 = X B4): Retirement Savings No retirement Savings Monthly pension based on

||No retirem<br>|ent Savings<br>|Monthly pens<br> contrib<br>|ion based on<br>utions<br>|||
|---|---|---|---|---|---|---|
||5% higher<br>earnings|10% higher<br>earnings|5% lower<br>earnings|10% lower<br>earnings|||
|**Variable**|**Job B1**|**Job B2**|**Job B3**|**Job B4**|**F-stat**|**P-value**|
|Male|0.53|0.56|0.55|0.54|0.175|0.913|
|Has Children|0.41|0.4|0.38|0.33|1.579|0.193|
|Single/ Unmarried|0.48|0.48|0.48|0.51|0.29|0.833|
|Age|36.2|35.22|35.1|35.82|0.618|0.603|
|Chinese|0.2|0.13|0.15|0.19|1.714|0.162|
|Indian|0.05|0.08|0.06|0.05|0.726|0.537|
|Bumiputera|0.72|0.76|0.77|0.74|0.679|0.565|
|EDU: up to<br>secondary|0.17|0.19|0.18|0.18|0.101|0.959|
|EDU: Post-<br>secondary|0.25|0.29|0.22|0.24|1.357|0.254|
|EDU: Bachelor’s|0.45|0.38|0.44|0.42|1.002|0.391|
|EDU: Professional|0.03|0.05|0.06|0.05|0.468|0.704|
|EDU: Masters+|0.08|0.09|0.11|0.12|0.953|0.414|
|Low Income|0.32|0.32|0.34|0.34|0.297|0.828|
|Med Income|0.32|0.39|0.31|0.31|1.841|0.138|
|High Income|0.36|0.29|0.34|0.34|1.159|0.324|
|In Standard<br>Employment|3.74|3.89|3.64|3.96|0.365|0.778|
|Time Preference<br>Index|0.49|0.52|0.52|0.46|0.669|0.571|
|Risk Appetite (0-<br>10)|6.74|6.52|6.51|6.74|0.913|0.434|
||259|262|267|250|||

Table 1c: F test of joint significance of treatment arm means (H0: X B1 = X B2 = X B3 = X B4): Workplace Injury Insurance

||Insurance to cover|workplace injuries|||
|---|---|---|---|---|
||0.5% lower earnings|1% lower earnings|||
|**Variable**|**Job B1**|**Job B2**|**F-stat**|**P-value**|
|Male|0.52|0.58|2.16|0.142|
|Has Children|0.32|0.44|7.559***|0.006|
|Single/ Unmarried|0.56|0.42|10.516***|0.001|

9
Minimum wages have recurrently been discussed within the development literature.5 The proportion cf workers that are ia principle affected by minimum wages is larger in developing than in developed countries.6 Thus, the potential beneficial and detrimental effects appear to be of a larger magnitude among developing countries. Mainstream development economists have generally argued that minimum wages are one of the principal sources (the other being labor unions) of labor market segmentation, unemployment, underemployment and other labor market distortions typically observed in developing countries.

Starting with Todaro (1969], minimum wages have been seen as generating continuous rural-urban migration in the presence of high open unemployment rates. Minimum wages have also been singled _ou.;_ as one of the important factors explaining the development of the urban informal sector. A minimum wage above the level that would equilibrate the labor market reduces employment in the formal sector, increasing 5Minimum wages received prominent attention in Berry and Sabot's (1978] and Squire's (1981] surveys of labor markets in LDCs.

6In many Latin American countries, approximately one fourth of the urban labor force earns no more than the minimum wage. For example, the estimates for Chile are 25*, and for Costa Rica, Brazil, and Mexico 30% (see Solimano [1988], Pollack (1989], Maia-Saldanha (1989] and VillarealBreach (1988]).

S
17 but also over time. Therefore the null of homoscedasticity of the error term had to be tested. The null was rejected for equation (1). Therefore corrected standard errors of the estimated coefficients were used to calculate the t-values given in brackets.<sup>8</sup> The OLS estimators of the coefficients are still consistent, but not most efficient any more.

The overall explanatory power of the equation is rather weak; at least the lagged stock variable is significantly less than zero. The coefficient of the rate of change of the exchange rate indicates strong valuation effects and the market orientation of German FDI is confirmed. The coefficient of the degree of openness turned out to be insignificant. There are various possible reasons for this outcome: First, the degree of openness might in fact be irrelevant for investment decisions (if FDI is import-substituting, for example); second the measurement of the FDI outflow by changes in stocks maybe inappropriate; third, the omission of other explanatory variables may lead to biased estimates, and fourth, the FS-index might be unsuitable to measure the degree of openness.

Thus one way to further investigate this issue is to improve the data base. There is, however, a price to pay: the number of countries for which be:ter data is available, decreases to 18 leaving us with a potentially higher selection bias.<sup>9</sup> Let us start on the last issue raised above. Estimating equation (1) in a subsample for the stock data set and varying the indicator for the degree of openness does not lead to any further insight. The results actually become 8For testing for homoscedasticity and correcting of the standard errors see White (1980).

9These countries are Argentina, Brazil, Colombia, C6te d'Ivoire, Egypt, Hong Kong, India, Indonesia, Korea (South), Malaysia, Mexico, Morocco, Nigeria, Peru, Singapore, Thailand, Tunisia, and the United Arab Emirates.
The following is the set of 64 countries and economies with official reported deaths used in the analysis: Afghanistan; Albania; Algeria; Argentina; Australia; Austria; Bangladesh; Belgium; Brazil; Canada; Chad; Chile; Colombia; Costa Rica; Czechia; Denmark; El Salvador; England and Wales; Eswatini; Finland; France; Germany; Greece; Haiti; Hong Kong SAR, China; Hungary; Iceland; India; Indonesia; Israel; Italy; Jamaica; Japan; Jordan; Malawi; Mexico; Moldova; Nepal; Netherlands; New Zealand; Nigeria; Northern Ireland; Norway; Paraguay; Peru; Philippines; Portugal; Romania; Scotland; Slovak Republic; Slovenia; Somalia; South Africa; Republic of Korea; Spain; Sweden; Switzerland; Taiwan, China; Togo; Turkey; Ukraine; Uruguay; United States; Vietnam.

**Excess all-cause deaths from vital statistics records** , from the following sources:

Short term mortality fluctuations (STMF) harmonized data series from the Human Mortality <u>Database</u>

- For this analysis; the STMF input files are used. These input files cover 38 countries and economies: Australia; Austria; Belgium; Bulgaria; Canada; Switzerland; Chile; Czech Republic; Germany; Denmark; Spain; Estonia; Finland; France; Northern Ireland; Scotland; England and Wales; Greece; Croatia; Hungary; Iceland; Israel; Italy; Republic of Korea; Lithuania; Luxembourg; Latvia; Netherlands; Norway; New Zealand; Poland; Portugal; Russian Federation; Slovak Republic; Slovenia; Sweden; Taiwan, China; United States.

- Russia does not have data for 2020.

- Chile and Germany do not have data for 2015.

Colombia vital statistics tabulations ( _Defunciones no fetales_ ) were compiled by DANE. 2019 and 2020 tabulations are preliminary.

Historic (2015-2019) Mexico vital statistics microdata are those compiled ( _Defunciones registradas, mortalidad general_ ) by INEGI, 2020-21 deaths microdata based on death certificates compiled by RENAPO and accessed via the government of Mexico’s open data <u>platform.</u> 17