rafmacalaba/datause-encoder-data huggingface dataset:
regularly points out that the average cost per account among private sector pension institutions (life insurance companies and multi-employer funds) ranges between 300 and 1,000 DKK per account, but also notes that the latter institutions incur large marketing and selling costs, which ATP is able to avoid.

**Table 9: Internal Administrative & Investment Expenses per Account, 2001-2006**

|DKK per account|2001|2002|2003|2004|2005|2006|
|---|---|---|---|---|---|---|
|Administrative|25|27|32|32|30|33|
|Investment|11|17|19|16|19|22|
|Total|36|44|51|48|49|55|

Source: ATP ATP is subject to stress test and accounting rules similar to those applied to other pension institutions in Denmark. It carries out regularly the stress testing computations required by the Danish FSA. For a while in the first few years of the millennium, ATP found itself in the 'yellow light' scenario of the DFSA but its financial position has improved considerably since then and its reserves now exceed by a big margin the losses that could be caused by either the 'yellow light' or 'red light' scenarios.

# **9. Concluding Remarks**

ATP has a long history, having been created in 1964. It has an enviable record of high operating efficiency and investment returns. It has been a leader among Danish pension institutions in adopting innovative investment policies, diversifying internationally, increasing the interest rate sensitivity of its assets and, more recently, expanding the use of long-dated interest-rate swaps to hedge its pension liabilities. It has used an effective system of risk sharing with both active and passive workers, offering minimum guaranteed benefits and annual bonuses that depend on both investment returns and longevity trends.

However, ATP suffers from several weaknesses and shortcomings. It has a cumbersome governance structure that reflects the corporate traditions of the Danish labor market (high union membership, central role of collective labor agreements, and significant influence of social partners) but discourages timely increases in contributions and timely changes in other basic features of its services. In fact, one of the most important weaknesses is the very low level of contributions. The social partners have agreed to a further increase in contribution amounts in 2009 and to maintain their level close to 1 percent of average earnings. Nevertheless, it is ironic that a highly efficient institution is used for providing a meager improvement in pensions. In addition, the 4.5 percent interest rate guarantee was kept in place for a much longer period than in private sector pension institutions (life insurance companies or multi-employer pension funds). The structure of its pension benefits used to be based on an 'idiosyncratic' principle of 'equal pensions for equal contributions, irrespective of age'. This was changed in 2002 but its deferred group annuities continue to involve a risk-sharing arrangement among active and retired workers. This is coming under strain because of the financial pressures of increasing longevity and demands for greater individual choice.

16
# **Table 8 Labor market status effects on perception of household‟s financial difficulties in European regions, 2004-2009. Population aged 15-74 Ordered probit coefficients**

||**_South_**|**_East_**|**_West-1 _**|**_West-2 _**|**_North_**|
|---|---|---|---|---|---|
|**_Employment status_**<br>**_(vs. formal employees)_**||||||
|**_Formal self-employment_**|-0.518<sup>***</sup>|-0.640<sup>***</sup>|-0.308<sup>***</sup>|-0.318<sup>***</sup>|-0.093|
|**_Informal self-employment_**|-0.130<sup>***</sup><br>|-0.320<sup>***</sup><br>|-0.018|-0.067|0.090<sup>**</sup>|
|**_Family workers_**|-0.385<sup>***</sup>|-0.633<sup>***</sup>|0.016|0.230|0.237|
|**_Informal dependent employees_**|0.169<sup>***</sup>|0.170<sup>***</sup>|0.127<sup>***</sup>|0.332<sup>***</sup>|0.195<sup>***</sup>|
|**_Unemployed willing to work_**|0.723<sup>***</sup>|0.872<sup>***</sup>|0.908<sup>***</sup>|0.911<sup>***</sup>|0.906<sup>***</sup>|
|**_Other (inactive)_**|0.089<sup>***</sup>|0.264<sup>***</sup>|0.254<sup>***</sup>|0.171<sup>***</sup>|0.493<sup>***</sup>|
|**Other controls**|Gender, fam<br>and time f<br>origin, ru|ily status, chil<br>ixed effects, p<br>ral residence,<br>disabilitys|dren, age, age<br>arental backgr<br>economic acti<br>tatus,househo|squared, educa<br>ound, ethnic/i<br>vity of last em<br>ld size(log)|tion, country<br>mmigration<br>ployment,|
|**Pseudo R-sq.**|0.1160|0.1435|0.0692|0.0913|0.1157|
|**N obs.**|24889|46357|20288|21393|19578|

_Notes:_ South : ES, PT, IT, GR, CY, and IL; East : CZ, PL, SK, HU, EE, LV, BG, RO, SI, RU, and UA; North: DK, FI, NO, and SE; West -1: UK, IE, NL, and AT; West-2: DE, FR, BE, and CH.

Larger coefficients indicate larger perceived difficulties. *, **, *** indicate that respective coefficient for the given employment status is significantly different from the coefficient for formal employees at 10%, 5%, 1% level, respectively (based on robust standard errors clustered on within-country region). Countries are not weighted by population size. Design weights corrected for variation of sample size across countries in each round are applied (i.e., all respondents are assumed equally likely to be sampled; countries which did not participate in some rounds are, however, under-represented).

_Source:_ Calculation with ESS data.

55
13 during the restricted period when compared with the unrestricted period. From equation (2), this small difference in unit value growth during the restricted years coupled with the large price increase implies much less upgrading during the restricted than during the unrestricted years (table 4). However, a look at the average growth rates of the Tornqvist price and quality indices across countries suggests that the expected price increase in the restricted markets (US and UK) was also significant elsewhere. Likewise, the slowdown in quality growth during the restricted period was not confined to the US.

To check further whether indeed price increases and quality decreases were significant during the restricted years, we regressed the price and quality indices in tables 3 and 4 on time and country dummies with separate dummy variables for the restricted periods for the US (1978-81) and the UK (1979-81). The results were (country and year dummies omitted): (4) p*- 0.089 DUS + 0.080 DUK (1.93) (1.57) (5) Q - -0.12 DUS + 0.01 DUK (-2.43) (0.27) R2- 0.99 DOF - 64 R2 - 0.99 DOF - 64 The results from the price equation indicate a significant price rise for the US but not for the UK although a specification of (4) and (5) in natural logarithms (not reported here) gives a significant acceleration of price increase for the UK but not for the US. The results from the quality index regression also suggest a significant slowdown in quality growth for the US during the VER-period but no change for the UK. 10/ Thus the fixedeffect regressions further support the direct inferences drawn from tables
higher-income countries. To test this, we add interaction terms to our main specification: where _higho_ equals one if the exporting country is a higher-income country in 2010, according to the World Bank’s classification and other variables are defined as in Equation (1). The coefficients _β_<sup>_m_</sup> measure the impact of a type- _m_ NTM measure on imports from lower-income exporting countries, while the coefficients _ζ_<sup>_m_</sup> measure the differential impact on imports from higher-income exporting countries, relative to lower-income exporting countries.<sup>29</sup> The sum of _β_<sup>_m_</sup> and _ζ_<sup>_m_</sup> gives the impact of a type- _m_ measure on imports from higher-income exporting countries.

Column 1 of Table 2 shows the results. SPS measures reallocate trade from lower-income exporting countries to higher-income exporting countries, while TBT measures reallocate trade from higher-income exporting countries to lower-income exporting countries. These reallocation effects wash out when SPS and TBT measures are implemented simultaneously, indicating the importance of carefully classifying NTMs. In contrast, pre-shipment inspections and non-technical measures have a consistent negative effect across all exporting countries. This core result is robust to excluding disproportionately small or large markets. 2).

Table 1’s estimates from quantity and price regressions suggest that the impact of SPS measures is driven by the cost channel, while the impact of TBT measures is driven by the information channel. Given this, heterogeneity in information benefits and compliance costs across higher- and lower-income exporting countries could explain the reallocation of trade estimated in Table 2. SPS measures will reallocate trade away from lower-income exporting countries if they are relatively harder to comply with, or if they provide relatively less information about the quality of products from these countries. This can be expected for inspection requirements. Conversely, TBT measures will reallocate trade toward lowerincome exporting countries if they are relatively easier to comply with, or if they provide relatively more information about the quality of products. This can be expected for labeling requirements.

In contrast, pre-shipment inspections and non-technical measures may be equally difficult for firms in higher- and lower income exporting countries to comply with. For example, preshipment inspections may require passage through a particular port, which is costly for firms from any exporting country, and non-technical measures may impose similar import quotas on all exporting countries. For such measures, there is no reallocation of trade across

> 29Notice that the marginal effect of the higher-income country indicator is absorbed by the fixed effects.

17
**Table 1. Correlation between Floods Reported after Agatha and Treatment Status of the Municipality** .

|VARIABLES|Affected municipality (CONRED)|
|---|---|
|Z-score|0.026*|
||[0.013]|
|Area|-0.000|
||[0.000]|
|Constant|0.429***|
||[0.055]|

Observations: 333 municipalities. _Notes_ : Results from OLS regression. Standard errors in brackets The Z-score indicates the number of standard deviations away from the rainfall mean (since 1980). Affected municipality is the probability of a municipality reporting a flood to CONRED in the aftermath of Agatha. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ calculations by the authors based on data from Encovi (INE), Conred and Insivumeh.

**Table 2. Summary Statistics at Baseline in 2006**

|Variable|Obs.|T<br>Mean|reatment<br>Median|Std.error|Obs.|<br>Mean|Control<br>Median|Std.error|Significant<br>difference|
|---|---|---|---|---|---|---|---|---|---|
||||**P**<br>|**anel A: To**|**tal**<br>|||||
|Size of Household|9247|5.04|5.00|0.03|4432|4.94|5.00|0.04|*|
|Urban|9247|0.46|0.00|0.01|4432|0.35|0.00|0.01|*|
|Total Consumption|9247|863|606|8.74|4432|812|579|12.62|*|
|Total Income|9215|1013|593|21.69|4416|968|541|31.33||
|Moderate Poverty|9247|0.44|0.00|0.01|4432|0.47|0.00|0.01|*|
|Children 7-15 enrolled|9247|1.07|1.00|0.01|4432|1.04|1.00|0.02||
||||**P**|**anel B: Urb**|**an**|||||
|Size of Household|4237|4.59|4.00|0.03|1567|4.36|4.00|0.06|*|
|Total Consumption|4237|1104|793|15.24|1567|1061|807|25.06||
|Total Income|4228|1351|802|38.38|1565|1484|836|63.08||
|Moderate Poverty|4237|0.30|0.00|0.01|1567|0.31|0.00|0.01||
|Children 7-15 enrolled|4237|0.93|1.00|0.02|1567|0.92|1.00|0.03||
|||||**Panel C: Ru**|**ral**|||||
|Size of Household|5010|5.42|5.00|0.04|2865|5.26|5.00|0.05|*|
|Total Consumption|5010|660|502|9.21|2865|676|489|12.19||
|Total Income|4987|726|455|23.44|2851|685|419|31.00||
|Moderate Poverty|5010|0.56|1.00|0.01|2865|0.56|1.00|0.01||
|Children 7-15 enrolled|5010|1.19|1.00|0.02|2865|1.10|1.00|0.02|*|

_Notes_ : Summary statistics with differences between treatment and control tested for significance. Total Consumption is the monthly expenditure p.c. of a household in Quetzales of 2006. Moderate poverty means that the p.c. expenditure is under the moderate poverty line. Total income is the sum of labor and non-labor incomes per month per capita. * p<0.05 _Source:_ calculations by the authors based on data from Encovi (INE) and Insivumeh.

25
Table 8: Growth decomposition by income sources for coffee farming households and households in coffee regions

|||_Growth in income_|_from_|||_Per capita_|
|---|---|---|---|---|---|---|
|_Expenditure_<br>_dil_|_coffee_<br>_other_|_crops_<br>_other_<br>_ilt_|_non-_<br>_agricultural_|_remitt.,_<br>_transfers,_|_Total_<br>_growth_|_expenditure_<br>_level, initial_|
|_ece_||_agrcuure_|_act._|_other_||_year_|
|_Coffeefarmer, 1_|_992-95_||||||
|bottom 30%|35.1|6.6<br>-5.4|11.0|-2.6|44.6|16173|
|middle 40%|30.7|1.8<br>-5.3|7.6|-0.9|33.8|26027|
|top 30%|23.1|-6.3<br>-1.9|23.8|5.2|43.9|40745|
|Total|27.8|-1.2<br>-3.7|16.2|1.7|40.9|27648|
|_Coffeefarmer, 1_|_995-99_||||||
|bottom 30%|-22.0|30.3<br>1.1|-1.7|2.6|10.4|23394|
|middle 40%|-18.8|32.5<br>1.3|-0.2|1.3|16.1|34837|
|top 30%|-7.2|30.0<br>3.1|-7.0|-2.7|16.2|58630|
|Total|-13.6|30.8<br>2.2|-3.9|-0.5|15.0|38953|
|_Rural household_|_s in coffee regions, _|_1992-95_|||||
|bottom 30%||23.8<br>-8.1|9.0|1.7|26.4|13493|
|middle 40%||15.5<br>-3.3|13.9|4.3|30.4|21854|
|top 30%||-8.1<br>-3.7|25.0|0.9|14.1|34417|
|Total||5.4<br>-4.4|18.4|2.1|21.6|23254|
|_Rural household_|_s in coffee regions, _|_1995-99_|||||
|bottom 30%||20.9<br>1.0|4.7|0.6|27.1|17059|
|middle 40%||13.3<br>0.9|5.6|-2.1|17.8|28497|
|top 30%||28.2<br>3.3|4.5|1.3|37.2|39269|
|Total||21.7<br>2.0|4.9|0.0|28.7|28275|
|_Urban househol_|_ds in coffee regions,_|_1992-95_|||||
|bottom 30%||24.6<br>-1.6|15.3|1.4|39.7|17507|
|middle 40%||9.6<br>3.4|35.8|14.0|62.8|29096|
|top 30%||0.8<br>-3.2|7.3|16.3|21.2|60084|
|<br>Total||7.1<br>-1.1|16.4|13.2|35.6|35562|
|_Urban househol_|_ds in coffee regions,_|_1995-99_|||||
|bottom30%||-3.6<br>1.4|29.4|2.6|29.8|24449|
|middle 40%||-3.0<br>-0.9|14.8|-2.9|8.1|47365|
|top 30%||1.8<br>1.6|69.7|-1.3|71.7|72813|
|<br>Total||-0.7<br>0.7|44.9|-1.2|43.8|48209|

Source: Authors’ calculations.

To put the above findings into perspective, income growth decompositions are also reported for non-coffee regions (Appendix D). The sluggish growth performance of rural households in these regions in the early 1990s highlights the importance of the coffee price boom for rural areas in coffee regions. In contrast, urban growth performance is very similar in coffee and non-coffee regions in both periods except for the increase in remittances and transfers in coffee regions during the price boom. The decomposition shows that income growth of rural households in non-coffee regions in the second period was mainly driven by agricultural growth, although income from non-agricultural activities also became somewhat more 25
damaged. Following Emanuel (2011) we use a value of 93 km (i.e. 50kts) for _Vthres_ and a value of 278 km (i.e. 150kts) for _Vhalf_ . We depict the damage profiles of _FINDEX_ in Figure 1 as well as three consecutive bars reflecting the wind associated with Saffir-Simpson Scales [SSS] 1, 3, and 5. Wind speeds classified as SSS level 1 (119km/hr), 3 (178km/hr), and 5 (252km/hr) correspond to _f_ values of about 0.003, 0.090, and 0.39 respectively.

# _III.B Wind Field Model_

The damage function in (1) requires measures of local wind speed during a storm. In this regard, what level of wind a location will experience during a passing hurricane depends crucially on that location’s position relative to the storm and the storm’s movement and features, and thus requires explicit wind field modeling. In order to calculate the wind speed experienced due to a hurricane, we use Boose et al.’s (2004) version of the well-known Holland (1980) wind field model. More specifically, the wind experienced at time _t_ due to hurricane _j_ at any point _i_ = _1,....N_ , i.e., _Vi,j,t_ is given by: where _Vm_ is the maximum sustained wind velocity anywhere in the hurricane, _T_ is the clockwise angle between the forward path of the hurricane and a radial line from the hurricane center to the pixel of interest _i_ , _Vh_ is the forward velocity of the hurricane, _Rm_ is the radius of maximum winds, and _R_ is the radial distance from the center of the hurricane to point _i_ . The remaining ingredients in (3) consist of the gust factor _G_ and the scaling parameters _F, S,_ and _B_ , for surface friction, asymmetry due to the forward motion of the storm, and the shape of the wind profile curve, respectively.

10
- 13 -

employment; It rose from 28 percent In 1971 to 33 percert In 1981. _The_ Income profiles tracked by NCAER researchers likewise Identify the first substantial **boost** In rural nonfarm Incomes durlng the 19709 (Table 2). This colncides with the widespread adoption of green revolutlon wheat and rice varietles and provldes at least circumstantial evidence linking the big spurt In agricultural growth with erlargement of the nonfarm economy.

18. Urbanization has accompanied the rlsing prominence of nonfsrm activity. The nonfarm employment densities have remained constant in rural areas, rural towns and In large urban centers (Table 1). This, coupled with the rlsing natlonal share of nonfarm In total employment, can only be possible If rural towns and large cities Increase In relative size, as Indeed they have.

19. Yet nationally, the composition of nonfarm activity has shifted only slightly **durng** Its decade of growth. Because of changing labor force deflnitions, reported differences between 1961 and later years do not reliably track changes in economic activity. Between 1971 and 1981, w.an definitions remained roughly comparable, the only perceptible change has been a slight Increase In the prominence of nonhousehold manufacturing In both rural and urban areas (Table 8). Dlsaggregatlon at the state level, however, does reveal a more substantial shift, as Sectlon 1I1. _C._ will discuss.
**Figure 3. Gender Gap in Hourly Earnings in Kenya and Vietnam**

_Note_ . Earnings have been converted to 2011 PPP-adjusted U.S. dollars. The sample includes 25- to 64year-olds in self-employment or wage work. Employers, unpaid workers, part-time workers, and the top 1 percent of earners are excluded from the sample. _Source_ : STEP Surveys (2014).

Gender differences in educational attainment and skills, our primary explanatory variables for the gender wage gap, are presented in Tables 3 through 6. With regard to educational attainment, we find that in countries other than those in the Europe and Central Asia (ECA) region (namely, Armenia, Georgia, and Ukraine), women show lower educational attainment than men. Although these differences are small in magnitude, they are statistically significant. These differences in endowments are likely to translate into higher average wages among men.

**Table 3. Average Completed Years of Schooling for Men and Women, by Country**

||Me<br>|n<br>|Wome<br>|n<br>|||
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|N|_t_value|
|Armenia|13.77|3.30|13.78|3.10|530|1.76|
|Bolivia|11.56|4.58|10.68|5.05|653|4.93|
|Colombia|10.31|3.77|10.11|3.98|832|3.15|
|Georgia|15.30|2.66|15.56|2.74|481|-1.91|
|Kenya|10.10|4.73|8.91|4.90|1160|5.77|
|Ukraine|13.38|1.93|13.66|2.38|730|-1.45|
|Vietnam|11.35|4.28|10.81|4.40|1394|4.48|

_Note_ . The sample includes 25- to 64-year-olds in self-employment or wage work. _Source_ : STEP Surveys (2014).

Cognitive skills, as measured by scores on the reading proficiency assessment, also show that men, on average, have slightly higher scores than women (except in Georgia and Ukraine, where women’s scores are higher; see Table 4). Differences in reading proficiency scores among 15
**Table 2. Share of Selected Partners in Exports, 1994,1998** _(Percent)_

||_Armenia_|_Azerbaijan_|_Georgia_|
|---|---|---|---|
|**1994**||||
|Armenia|X|--|8.5|
|Azerbaijan|--|X|9.7|
|Georgia|1.3|2.5|X|
|Russia|38.9|21.4|34.0|
|Other CIS|33.1|18.2|24.2|
|Turkey|0.1|2.5|14.9|
|Iran|6.8|39.4|3.2|
|UAE|0.1|0.1|0.0|
|EU|16.4|12.8|0.9|
|USA and Canada|0.2|0.0|1.3|
|Rest of world|3.4|3.1|_4.5_|
|**1998**||||
|Armenia|X|--|0.6|
|Azerbaijan|--|X|9.6|
|Georgia|4.3|12.7|X|
|Russia|18.1|17.5|28.7|
|Other CIS|14.1|8.1|16.8|
|Turkey|1.4|22.4|10.5|
|Iran|14.2|14.2|1.3|
|UAE|1.7|0.5|0.2|
|EU|34.6|21.7|19.1|
|USA and Canada|5.4|1.4|5.9|
|Rest of world|11.6|2.9|13.1|
|**Change between 199**|**4 and 1998**|||
|Armenia|X|--|-7.9|
|Azerbaijan|--|X|-0.1|
|Georgia|3.0|10.2|X|
|<br>Russia|-20.8|-3.9|-5.3|
|Other CIS|-18.9|-10.1|-7.4|
|Turkey|1.3|19.9|-4.4|
|<br>Iran|7.4|-25.2|-1.9|
|UAE|1.6|0.4|0.2|
|EU|18.2|8.9|18.2|
|USA and Canada|5.2|1.4|4.6|
|Rest of world|3.1|-1.6|4.0|

_Source: Statistical Yearbook of South Caucasus, 2000._ 7
zero at the five percent level, we find that the exporter productivity premium varies with the share of exports in total sales as follows:

- it increases (either both estimated coefficients have a positive sign, or the coefficient with a negative sign is statistically insignificant) in Austria, West Germany, East Germany, Italy, Republic of Ireland, and the UK;<sup>14</sup>

- it increases at a decreasing rate (the coefficient of the share of exports in total sales is positive, the coefficient of the squared value is negative, and the estimated maximum is reached for a value of the share of exports that is either higher than 100 percent, or very high compared to the average share of exports in total sales of the exporting firms according to Table 2) in Belgium, Chile, Colombia, Denmark, France, and Spain;

- it increases, reaches a maximum at around 50 percent, and decreases afterwards in China; and

- it decreases (the positive coefficient of the squared term is statistically insignificant) in Sweden.

As in the case of the exporter productivity premia estimated from the exporter status dummy variable, the degree of economic development of the countries does not appear to matter for the pattern of the relationship between export intensity and productivity when eyeballing the data. Note that the sign pattern often differs between the size classes, and that for some size classes the results point to no relationship between export intensity and productivity at all even when there is a statistically significant relationship for all firms taken together.

While eyeballing the results gives us some idea of what may or may not drive the differences, we also pursue a more rigorous approach by conducting a meta-regression

> 13 This is in line with findings from other studies using Slovenian firm data; for a discussion see Damijan, Polanec and Prasnicar (2004) and Damijan and Kostevc (2006).

> 14 In the UK data the sign pattern is “- / +”, but the estimated minimum of the parabola is 8.3 percent, so this indicates that the exporter premium is increasing in the share of exports in total sales in general.

10