rafmacalaba/datause-encoder-data huggingface dataset:
security measures are statistically insignificant for both height-for-age and weight-for-age. The few exceptions are the coefficient on household dietary diversity score (HDDS), which is significant for height-for-age at the conditional mean and at the 25<sup>th</sup> and 50<sup>th</sup> quantiles, and 1-SSEXR and per capita expenditure, which are significant at the 25<sup>th</sup> quantile.

**Table 5: Height-for-age Z-score (Pakistan)**

|||**Model 1**<br>|**- Without**<br>|**Controls**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Unco|nditionalqu<br>|antile regre<br>|ssion<br>|
|||10th|25th|50th|75th|
|Per capita kcal|0.040|0.040|0.068|0.001|0.072|
||(0.043)|(0.042)|(0.049)|(0.054)|(0.071)|
|1-SSR|0.072|0.024|0.082|0.109*|0.124*|
||(0.050)|(0.057)|(0.061)|(0.057)|(0.072)|
|1 - SSEXR|0.065|0.089|0.111*|0.043|0.051|
||(0.050)|(0.058)|(0.061)|(0.055)|(0.067)|
|HDDS|0.115**|-0.004|0.125**|0.120**|0.092|
||(0.053)|(0.058)|(0.060)|(0.055)|(0.070)|
|Per capita expenditure|0.051|0.021|0.109*|0.060|0.056|
||(0.048)|(0.050)|(0.057)|(0.057)|(0.074)|
|Food share|-0.024|-0.053|-0.080|-0.052|-0.058|
||(0.047)|(0.050)|(0.059)|(0.056)|(0.069)|

**Model 2 - With Controls**

|VARIABLES|OLS|Unco<br>|nditionalq <br>|uantile regr<br>|ession<br>|
|---|---|---|---|---|---|
|||10th|25th|50th|75th|
|Per capita kcal|0.023|0.032|0.050|-0.007|0.050|
||(0.049)|(0.042)|(0.058)|(0.060)|(0.079)|
|1-SSR|0.056|-0.011|0.072|0.047|0.143*|
||(0.056)|(0.067)|(0.077)|(0.065)|(0.082)|
|1 - SSEXR|0.043|0.090|0.110|-0.017|0.022|
||(0.059)|(0.070)|(0.079)|(0.066)|(0.079)|
|HDDS|0.068|-0.003|0.080|0.049|0.003|
||(0.072)|(0.072)|(0.080)|(0.067)|(0.090)|
|Per capita expenditure|0.043|0.004|0.116|0.041|0.050|
||(0.061)|(0.062)|(0.074)|(0.067)|(0.095)|
|Food share|-0.040|-0.073|-0.101|-0.046|-0.066|
||(0.053)|(0.056)|(0.071)|(0.062)|(0.079)|

Similarly, the coefficients on all food security measures remain insignificant when control variables are introduced in the model in both height-for-age and weight-for-age regressions. This
<!-- Start of picture text --> Figure 1: Evolution of GDP in the Russia Federation, 1990-2013<br>(2005 constant dollars, millions)<br>1,200,000<br>1,000,000<br>800,000<br>600,000<br>400,000<br>200,000<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> _Source:_ WDI. _Notes:_ The vertical dotted gray lines show the occurrence of the 1998 and the 2009 crises.

**Figure 2: Income inequality in OECD countries, 2011**

_(Gini index, disposable income)_ <!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Slovenia Norway Iceland Denmark Czech Republic Slovak Republic Belgium Finland Hungary Sweden Luxembourg Austria Netherlands Switzerland Germany Ireland Poland France Korea Canada Italy Estonia New Zealand Australia Greece Japan Portugal Spain United Kingdom Israel United States Russian Federation Turkey Mexico Chile<br><!-- End of picture text --> _Source:_ OECD Stats. _Notes:_ Data refers to 2011 for most countries; or next year available (2009 or 2010). In particular, the Gini index for the Russian Federation refers to 2010.

3
43 Tabte 13 (continued)

||Stationary<br>p|<br>opulati|on||Stationary<br>po|pulati|on|
|---|---|---|---|---|---|---|---|
||||Year||||Year|
|Country or economy|<br> <br>Millions 1|As X of<br>965 pop|when<br>NRR=1|Country or economy|A<br> <br>MilLions 1|s X of<br>985 pop|<br>when<br>NRRr1|
|Czechoslovakia|19|120|2030|Yugoslavia|27|118|2030|
|Dernmark|4|77|2030|Other Europe|0.4|142|2020|
|Finland|4|91|2030|U.S.S.R.|398|143|2020|
|France|58|105|2030|||||
|GermanDen. Rep.|15|93|2030|Oceania|40|165|2030|
|Germany, Fed. Rep. of|40|66|2030|.ustralia|20|130|2030|
|Greece|10|102|2030|Fiji|1|193|2010|
|Hungary|10|92|2030|French Polynesia|0.5|284|2015|
|Iceland|0.3|<br>131|2030|Guam|0.3|221|2020|
|Ireland|5|144|2020|Kirabati|0.2|245|2025|
|Italy|4|81|2030|New Caledonia|0.3|198|2010|
|Luxembourg|0.3|<br>73|2030|New Zealard|4|123|2030|
|talta|0.3|<br>96|2030|Pacific Islands|0.4|270|2015|
|Netherlands|13|88|2030|PapuaNew Guinea|10|298|2025|
|Norway|4|94|2030|Solomon Islands|1|529|2035|
|Polard|48|130|2020|Tonga|0.2|245|2025|
|Portugal|11|104|2030|Vanuatu|1|441|2035|
|Romania|28|123|2030|WesternSamoa|0.4|240|2015|
|Spain|41|106|2030|Other Micronesia|0.1|230|2020|
|Sweden|7|87|2030|Other Polynesia|0.2|224|2010|
|Switzerland|5|79|2030|||||
|United Kingdom|56|9a|2030|World total|10837|224|2045|

Note: Populations of more than 1 million have been rounded to the nearest miltion.

(a) For a listing of countries in the "other" category, see Table 12.
below in sections A through C. Section<sup>D describes policies that were</sup> specific to the Colombian cut flowers<sup>industry.</sup>

# _A. The undoing of a bad policy_

In 1967, the Colombian government took<sup>steps to undo the negative</sup> effects of a three-decade old anti-trade<sup>policy. Like other Latin American</sup> countries, Colombia had followed an<sup>Import-Substitution-Industrialization</sup> strategy. The strategy was implemented<sup>by a reotrictive import policy, which</sup> resulted in a consistently overvalued<sup>peso. Both the import policy and the</sup> overvalued peso discouraged exporters.<sup>The restrictive trade policy made it</sup> more profitable to produce for the home<sup>market than to export, whereas the</sup> overvalued peso hurt exporters by making<sup>Colombian products more expensive</sup> abroad.

The government first took steps to provide<sup>a business climate that did</sup> not discriminate against exporters. First,<sup>it reduced the profitability of</sup> producing for the home market. Beginning<sup>in 1967 and through the end of 1973,</sup> the government relaxed import restrictions."2 Second, and most importantly, the government was permitted to devalue the peso periodically to prevent it from becoming overvalued. This effectively established a crawling peg. The peso-dollar real exchange rate index is<sup>listed in column 4 in table 4. It</sup> shows that from 1967 to 1970, the government used the crawling peg to successfully devalue the peso. The real exchange rate was then maintained reasonably constant up until 1972. The significance of this action cannot be overstated. This was the first time<sup>since 1957-58 that the exchange rate had</sup> depreciated in two consecutive years<sup>(Hutcheson and Schydlowsky 1982, 126).</sup> This was the most important of the changes<sup>made by the government to eliminate</sup> the discrimination against exports.

Although the government lowered import barriers,<sup>import protection</sup> continued to remain (and still does in<sup>1990) at high le7els. To undo the</sup> negative effects of this policy on<sup>exports, the government also implemented</sup> several export promotion measures.<sup>First, the government replaced an existing</sup> 14
deviation change.

The impact of these policy changes on the exposure of aggregate capital flows to common shocks varies across the different columns of Table 9. If we take the estimates in column 6, for example, the increase in financial openness would raise exposure by 1.1 percent of trend GDP. In turn, the increase in exchange rate flexibility by one standard deviation would lower exposure by 1 percent of trend GDP. Table 6 showed that, on average, the exposure attributable to the international financial cycle is around 5 percent of trend GDP, with a standard deviation around 5-6 percent. Thus, these policy changes would change exposure by about one-fifth of its standard deviation. A more radical policy change, such as replacing a pegged exchange rate with a free float – with other things equal – would reduce exposure by four-fifths of its standard deviation.

# **4.6 Trends in globalization**

Following the global crisis of 2007-2008, the fall of international capital flows – especially marked in the case of cross-border bank lending – has raised the question of whether financial globalization is undergoing a reversal (e.g, Forbes (2014)). What should be the proper measure of financial globalization in this context has been subject of debate (Cerutti and Zhou (2017), McCauley et al. (2017)). Our framework allows us to shed light on this issue, as it yields a natural measure of the overall degree of financial globalization, given by the exposure of countries’ cross-border flows to common shocks, as discussed above.

Thus, to assess the trends in globalization we reestimate the factor model over rolling 20-year windows and recalculate at each step the fraction of the standard deviation of flows, relative to trend GDP, attributable to the common factors.<sup>32</sup> Figure A.6 in appendix A shows that the windowspecific estimates of the common factors obtained in this way track fairly closely their full-sample counterparts.

Figure 6 summarizes the trends in exposure to common shocks computed in this manner. The show the over each win- graphs average exposure dow, denoted by its respective end-year, along with 95-percent confidence 32Goldberg and Krogstrup (2018) also use rolling windows to assess changes over time in the correlation of their index of capital flow pressure with a global risk factor, summarized by the VIX.

27
final outcomes by credible (experimental or non-experimental) methods, although it can still be a useful complement to such evaluations, to better understanding measured impacts.

Project monitoring data bases are an important, under-utilized, source of information for understanding how a program works. Too often, however, the project monitoring data and the information system have negligible evaluative content. This is not inevitably the case. For example, Ravallion’s (2000) method of combining spending maps with poverty maps can allow rapid assessments of the targeting performance of a decentralized anti-poverty program. This illustrates how, at modest cost, standard monitoring data can be made more useful for providing information on how the program is working and in a way that provides sufficiently rapid feedback to a project to allow corrections along the way.

The _Proempleo_ experiment in Argentina provides an example of how information external to the evaluation can carry important insights into external validity. _Proempleo_ was a pilot wage subsidy and training program for unemployed workers. The program’s evaluation by Galasso et al. (2004) randomly assigned vouchers for a wage subsidy across (typically poor) people currently in a workfare program and tracked their subsequent success in getting regular work. A randomized control group located the counterfactual. The results did indicate a significant impact of the wage-subsidy voucher on employment. But when cross-checks were made against central administrative data, supplemented by informal interviews with the hiring firms, it was found that there was very low take-up of the wage subsidy by firms. The scheme was highly cost effective; the government saved 5% of its workfare wage bill for an outlay on subsidies that represented only 10% of that saving. However, the cross-checks against other data revealed that _Proempleo_ did not work the way its design had intended. The bulk of the gain in employment for participants was not through higher demand for their labor induced by the wage subsidy. Rather the impact arose from supply side effects; the voucher had credential value to workers – it acted like a “letter of introduction” that few people had (and how it was allocated was a secret locally). This could not be revealed by the evaluation, but required supplementary data. The extra insight obtained about how _Proempleo_ actually worked in the context of its trial setting also carried implications for scaling up, which put emphasis on providing better information for poor workers about how to get a job rather than providing wage subsidies.

Spillover effects also point to the importance of a deeper understanding of how a program operates. Indirect (or “second-round”) impacts on non-participants are common. A workfare 22
sector due to the inflation-erosion of the real value of internal debt and the stock of base money. These are represented by Prb-i and P(7t/l+ 7r)h-i. respectively. (ii) The capital losses (gains) of the private and public sectors due to a devaluation (appreciation) of the real exchange rate that causes an increase (decrease) of the real value of external Colombian debt in terms of Colombian goods. These adjustments are captured by P[r*(1+c) +±]fpr-i and P[r*(l+s) +c]fpbij, respectively. As a consequence, private and public adjusted disposable income (YDp and YDpb, respectively) are: YDp= Y+ONFS _+TR*,-_ P[r*(I+g) +c]fpr.- + Prb-i -P(7r/l+ 7r)h-t - T - TR- OR+ S (2) YDpb = T + TR _+ TRPb_ -P[r*(l +8) +E]Jfpb-i - Prb-. +P(7r/I+ 7r)h-i + OR -S (3) Finally, it should be pointed out that identity (1) indicates that private savings can be derived by subtracting private consumption from private disposable income (i.e. the first parenthesis of (1)), or as the difference between aggregate investment and the sum of public and foreign saving (the last two parenthesis of (1)). The measure of private saving should be identical independently of the way it is calculated, condition that is satisfied by the series presented in Annex 1 and 26. Once the corrections to the saving series have been made, the description of its recent behavior can be properly done. This exercise and the comparison of the traditional and adjusted measures of savings are carried out in the next section.

3. A **Comparison Between the Adjusted and Traditional Measures of Savings** Figure 2 presents the adjusted measure of national savings and its components (i.e. public and private savings) as a percentage of GNP. In addition, it shows a measure of adjusted private savings as a percentage of adjusted private disposable income (YD). The latter is a better measure of the private saving rate, but its behavior is very similar to the rate expressed as a percentage of GNP. Several issues should be highlighted when these series are compared with the traditional data of the National Accounts (Figure 1):

> 6Annex I and 2 present the unadjusted (i.e. the raw variables of the Colombian National Accounts) and adjusted measures of saving, respectively. The data were joined using the procedure suggested by L6pez et.al (1996a) which ensures that the measure of private saving will be the same independently of the way it is calculated.

6
power grid is green, then this purchase decision can decouple consumption gains from greenhouse gas production.

The state of California provides unique zip code level data on vehicle registrations.<sup>1</sup> The data are available from October 2018 and January 2020. In each of these two cross-sections, the data report each zip code’s count of vehicles by fuel type and by model year. In total, the data set includes over 30 million vehicles. We have merged year 2018 Internal Revenue Service zip code data on the household income of tax filers.<sup>2</sup> For roughly 1,900 zip codes in California, we merge in data to identify the 25% highest income zip codes in the state. The zip codes are sorted by the percentage of tax filers with incomes over $200,000. In Table 5, we report the empirical distribution of vehicle fuel types in 2018 and 2020 and we report these tabulations for rich and non-rich zip codes. The first key finding is that even in rich, educated and progressive California, the percentage of electric vehicles in the fleet is very low. The second fact is that the share of electric vehicles is growing over time. At each point in time, richer zip codes have a much larger share of electric vehicles.

The California vehicle registration data also reports the model year distribution of the vehicles by zip code. In the bottom of Table 5, we report the empirical distribution of vehicles by three categories; vehicles built before 2010; vehicles built between 2010 and 2015 and “new” vehicles. The key point that emerges here is the durability of the capital stock. The vehicle stock is old and predominantly gasoline based. Given that cars can live on for decades, even California stakeholders have an incentive to oppose higher gas taxes.

In this section, we have presented new evidence documenting the growth of EVs but the very small market share of this product even for rich people. We have also emphasized the durability of these products. The long lived nature of the capital stock slows down the adjustment process and creates vested interest groups with a stake in maintaining the status quo.

# **Within-Nation Carbon Geography: Evidence from Europe**

> 1 https://data.ca.gov/dataset/vehicle-fuel-type-count-by-zip-code

> 2 https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-zip-code-data-soi

11
see that although the share of expenditure is higher for poorer households as for energy products, the subsidies per capita are more pro-poor, particularly in the Islamic Republic of Iran. In Djibouti, however, subsidies on flour are pro-rich.

For most countries, this trade-off creates a dilemma. On the one hand, that subsidies are pro-rich would clearly speak in favor of eliminating subsidies with little consequences on welfare. On the other hand, these subsidized products can be relatively more important for the poor, even if subsidies are in place. The elimination of these subsidies would be felt more by the poor than by the rich with a likely effect on poverty. As we saw, the trade-off does not necessarily apply to all countries; instead, it varies across products, and the size of the trade-off may be different across products and countries.

We should also note the structural relation between the values on the y-axes of the two panels in figure 5. Let _p_ = unit free market price, _s_ = unit subsidies, _q_ = quantities, and _y_ = total income. The y-axis of the panel a is then ( _pq-sq_ )/ _y_ and the y-axis of panel b is _sq_ . Income and quantities being equal, the higher the unit subsidy, the lower the expenditure share. Subsidies and quantities being equal, the higher is income, the lower is the share of expenditure. Because the unit market price and subsidy are set by the government and equal for all, the shape of the lines largely depends on the distribution of incomes in each country. Therefore, knowledge of the household <u>income or expenditure distribution is an essential prerequisite to prepare subsidy reforms.</u> Figure 5: Expenditure Shares of LPG versus Subsidies per Capita <!-- Start of picture text --> .03 a. Expenditure share 150 b. Benefits per capita<br>Djibouti Iran Djibouti Iran<br>Libya Morocco Libya Morocco<br>Tunisia Tunisia<br>.02 100<br>.01 50<br>0 0<br>0 .19 .38 .57 .76 .95 0 .19 .38 .57 .76 .95<br>Percentiles of population Percentiles of population<br>US$-PPP<br>LPG expenditure share (in%)<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

15
guarantee that the clearing house gives to its members) to the CMF for approval.<sup>79</sup> . Clearing houses are also subject to rules of conducts (they shall perform their activities diligently, honestly and impartially). Finally, clearing houses must publish a yearly report on the conditions in which they exercise their functions (this report must include a description of how supervision and monitoring are organized and a description of the tasks performed in carrying out these duties).

# **CHAPTER 5 - COUNTRY CASE: DENMARK**

This chapter is divided into three parts: Chapter 5.1 is an overall introduction to the Danish fixed income market and a description of the Danish Government and corporate fixed income securities market. Chapter 5.2 describes the different authorities involved in the regulation and supervision of the Danish fixed income market. Chapter 5.3 describes the main regulation of the Danish fixed income securities markets.

# **5.1 The Danish fixed income market**

# **5.1.1. Introduction**

The Danish fixed income market is developed around relatively proliferated mortgage bond and government bond sectors. The total outstanding volume of listed Danish Kroner (DKK) - denominated fixed income securities amounted to DKK 2,410 billion by end of 2002, equal to 176 percent of GDP. As a special feature, the outstanding volume of Danish mortgage bonds is more than twice that of government bonds. (See Table 3)

**Table 3:  Market value of Danish listed bonds by end 2002**

|Type of<br>security|Market value<br>(DKKbillion)|Market value<br>(%)|Turnover (DKK<br>billion)|Turnover rate|Number of<br>bond series|
|---|---|---|---|---|---|
|Treasury bonds|631|26.2|2,014|3.2|42|
|Treasury bills|63|2.6|178|2.8|4|
|Mortgage credit<br>bonds|1,426|59.2|3,349|2.3|1,745|
|Special<br>institutions|163|6.8|214|1.3|359|
|Other (incl.<br>corporate<br>bonds)|116|4.8|142|1.2|100|
|Total|2,410|100|5,899|2.4|2,250|

Source: Danish Stock Exchange (www.xcse.dk) and Charlotte Christensen (2002): Denmark – a chapter on the bond market » During the last three decades, fixed income markets has gained an increasingly important role in financial intermediation in Denmark, especially fostered by pension reforms in the late

> 79 See Title IV of CMF General Regulations.

66
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