rafmacalaba/datause-encoder-data huggingface dataset:
These global trends and the policies of the newly-independent government were reflected in the withering away of public health services, in a variety of ways, including:

# **_a) Neglect of public health regulations and their implementation_**

Public Health Acts, which constitute the legislative framework for public health service provision, have not been updated and rationalized since the colonial era. For example, five decades after Karnataka state was created out of several contiguous kingdoms and provinces, it has not developed a unified and updated Public Health Act — those for each constituent part from the colonial era are still on the books. In Tamil Nadu, the Madras Presidency’s Public Health Act of 1939 was still in place in 1999.<sup>13</sup> The central government developed a Model Public Health Act in 1950 and revised it in 1987, but did not influence the states to adopt it. As in the colonial period, major municipal areas continue to be privileged, in that they still have public health regulations in place, and some staff and facilities for implementing them. These are much less in evidence in small towns, and even less in rural areas.

The Prevention of Food Adulteration Act is one of the few pieces of public health legislation which is still widely known to be in force. However, the Act has several serious deficiencies which prevent it from effectively protecting food safety, beginning with the fact that it focuses almost exclusively on food adulteration. In a large volume of detailed regulation, only a few paragraphs pertain to food hygiene. Besides, the Act is geared more towards punishing offenders, than towards helping businesses understand and comply with the regulations. Given the very limited funds available to inspectors for purchasing food samples for testing, and the slow disposal of court cases, it is apparent to food sellers that the law is short on credibility. Food inspectors are also a shrinking category of staff, as they are of low priority for cash-strapped states.

# **_b) Diversion of funds from public health services_**

The distortionary implications of the fiscal and planning regime are illustrated by the effects of the family planning program. In the mid-1960s, the Indian government embarked on a massive effort to reduce population growth in the country, following some years of food shortages and Census results showing that population growth rates had accelerated sharply. To deliver sterilization and other contraceptive services, the network of public clinics was rapidly expanded. The central government is generous in supporting the family planning program, for example by covering the salaries of female outreach staff. The proportion of the central government health budget spent on this program has risen sharply, at the expense of other health programs. However, the states have to pay for maintaining the clinics and the salaries of doctors and other staff.

This heavy financial burden for the states has led to a progressive strangulation of funds for what ought to be routine public health services, to the point where these are often

> 13 <u>http://www.tnhealth.org/epidemics.doc</u>

6
statistics, which should not be a problem for Romanian customs authorities, but also access to statistics from trading partners. International cooperation between custom authorities is thus required, especially if something close to real-time monitoring is desired. Although statistics on bilateral flows can be obtained from sources such as the EFI Database, they are published with a delay of several years. Moreover, they are based on product definitions that might be too aggregate for monitoring purposes. For example, to design an effective targeted inspection program, Romanian customs authorities might require the precision of knowing that the statistical discrepancy for sawnwood exported to a particular country pertains to, say, beech rather the broad category of nonconiferous sawnwood.

23
process<sup>8</sup> . However, it is important to note that the samples underlying these surveys are designed to be representative at the department level rather than at the city level. Hence although, in the case of La Paz and El Alto, the population of these two cities represents two thirds of the population of the department; the results should nonetheless be taken as illustrative rather than entirely representative.

The impact on the poor can best be seen by focusing on access trends for the first quintile, which represent the poorest 20% of the population<sup>9</sup> . In the city of La Paz, the results show that there was little if any progress in coverage for the first quintile in water, sewerage or telephone services between 1989-94. Following 1994, however the access rates for all three of these services increased substantially by around 15 percentage points (Figure 3.9). It is particularly noteworthy that (fixed plus cellular) telephone coverage in the first quintile doubled from 12% to 24% between 1994 and 1999. Only in the case of electricitywhere in fact coverage has been close to universal since 1989was there no discernible change in the rate of service expansion before and after the reforms.

**Figure 3.9: Evolution of coverage for poorest quintile in La Paz**

<!-- Start of picture text --> 100<br>90<br>80<br>Water<br>70<br>60 Sewerage<br>50<br>Electricity<br>40<br>30 Telephone<br>(fixed plus cellular)<br>20<br>10<br>0<br>1989 1994 1999<br>Coverage rate (%)<br><!-- End of picture text --> _Sources_ : Encuesta Integrada de Hogares (1989, 1994); Encuesta Continua de Hogares (1999) In El Alto, a similar pattern emerges except thateven for households in the same income bracketcoverage levels start from a lower base reflecting the recent foundation of the city (Figure 3.10). As before, the water, sewerage and telephone services show barely any progress in coverage between 1989 and 1994. However between 1994 and 8 The three surveys used were Encuesta Integrada de Hogares (1989, 1994) and Encuesta Continua de Hogares (1999). Together these make it posible to build-up a picture of coverage before, during and after the reform process. The coverage rates emerging from the household survey data are broadly consistent with those derived from utility connection data; however, they do not always coincide. This is for a variety of reasons, including: differences in the definition of what constitutes a connection; the presence of illegal connections in the survey data; and sampling error. In the case of telephone services, the 1989 survey did not include an explicit question about telephone connections, hence reported expenditure on telephone services was taken as evidence of access to a telephone.

9 National quintiles are used in this analysis. The implication is that the first quintile represents the same absolute income level both in La Paz and El Alto.

17
Table 3. Logit model (eq. 9) estimates STEP countries with non‐cognitive skill country

|country|Armenia|Bolivia|Colombia|Georgia|Ghana|Kenya|Vietnam|
|---|---|---|---|---|---|---|---|
|extraversion|0.03|0.01|‐0.02|0.07|0|0.1*|‐0.17***|
||(0.08)|(0.08)|(0.08)|(0.09)|(0.1)|(0.05)|(0.05)|
|conscientious|0.12|0.2**|‐0.09|0.05|‐0.03|‐0.04|‐0.11**|
||(0.09)|(0.09)|(0.1)|(0.09)|(0.11)|(0.06)|(0.05)|
|openness to exper.|‐0.14*|0.01|‐0.1|‐0.32***|0.02|0.06|‐0.12**|
||(0.08)|(0.09)|(0.09)|(0.1)|(0.09)|(0.06)|(0.06)|
|emot. stability|0.01|‐0.03|‐0.08|‐0.09|0.01|0.01|0.03|
||(0.08)|(0.09)|(0.08)|(0.08)|(0.08)|(0.06)|(0.05)|
|agreeableness|‐0.04|0.13|‐0.02|‐0.11|0.06|0.03|‐0.04|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.06)|(0.05)|
|literacy achievement|0.03|‐0.31**|‐0.06|0.03|‐0.39***|‐0.15*|‐0.18**|
||(0.09)|(0.12)|(0.11)|(0.11)|(0.12)|(0.08)|(0.08)|
|educational attainment (r|elative to ge|neral post‐s<br>|econdary)<br>|||||
|none|1.63|2.5***|1.39***|0***|2.08**|1.29***|2.1***|
||(1.43)|(0.39)|(0.46)|(0)|(0.84)|(0.31)|(0.35)|
|primary|1.05|3.04***|2.11***|1.1|2.7***|1.3***|1.58***|
||(1.37)|(0.66)|(0.37)|(1.13)|(0.52)|(0.24)|(0.2)|
|lower secondary|1.76***|2.74***|1.6***|0.9*|1.63***|0.89***|1.26***|
||(0.46)|(0.32)|(0.41)|(0.46)|(0.38)|(0.23)|(0.15)|
|vocational upper sec.|1.35***|2.11***|1.72***|1.17***|1.87***|0.49**|0.45|
||(0.3)|(0.31)|(0.4)|(0.38)|(0.46)|(0.24)|(0.3)|
|general upper sec.|1.07***<br>(0.2)|2.35***<br>(0.26)|1.44***<br>(0.29)|0.8***<br>(0.27)|1.27***<br>(0.33)|0.78***<br>(0.17)|0.98***<br>(0.14)|
|technical post‐sec.|0.92***|1.57***|0.91***|1.23***|0.71*|‐1.04***|0***|
||(0.2)|(0.26)|(0.27)|(0.24)|(0.4)|(0.38)|(0)|
|age|0.01*|0|0|0|0|0.01**|‐0.02***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0)|
|female|‐0.24|‐0.12|0.09|‐0.32|0.21|0.21*|0.18*|
||(0.15)|(0.14)|(0.19)|(0.2)|(0.19)|(0.11)|(0.11)|
|constant|‐0.92***|‐1.15***|‐0.59|‐0.4|‐0.9**|‐0.49**|0.18|
||(0.29)|(0.38)|(0.38)|(0.37)|(0.46)|(0.23)|(0.22)|
|observations|953|1641|1702|832|1019|2267|2303|

Authors' calculations using World Bank STEP data. Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted by ***, **, *, respectively.

That those with upper-secondary education are more likely than those with post-secondary 31
Table C.17: National Estimates of Effect of Years of Schooling on Number of Children Born - DHS - Combined Instrument

||**Years of**<br>**Schooling**<br>(First Stage)|**Number of**<br>**Children Born**<br>(Reduced Form)|**Number of**<br>**Children Born**<br>(2SLS)|
|---|---|---|---|
||(1)|(2)|(3)|
|Add’l Years of FPE or MTI|0.115|-0.037||
|w/out Script Change|(0.026)|(0.019)||
|(∆_Izy_)|[0.000]|[0.019]||
|�<br>Years of Schoolingizy|||-0.321<br>(0.156)<br>[0.040]|
|First Stage F-Statistic|19.56||19.56|
|Number of Clusters|58|58|58|
|N|24,898|24,898|24,898|

_Source_ : Author’s analysis based on data from the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016. _Note_ : The dependent variable is years of schooling in column 1 and is number of births � in the other two columns. Years of Schooling _izy_ is the predicted number of years of schooling, instrumented with the combined intensity measure, ∆ _Izy_ . All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age when multiple survey waves are included. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.36
Table IV.B Fraction of Price Change Due to Each Variable*

|Variable|Description|Fraction|
|---|---|---|
|�<sup>Apr</sup><br>t|Supply Private Signal|0:056|
|�<sup>Zpr</sup><br>t|Demand Private Signal|0:099|
|At|Productivity|0:018|
|"<sup>Z</sup><br>t|Quarter Demand Shock|0:089|
|Xt|Inventories|0:088|
|�<sup>Apu</sup><br>t|Supply Public Signal|0:009|
|�<sup>Zpu</sup><br>t|Demand Public Signal|0:019|
|Zy�1|Past Demand Level|0:043|
|At�1|Past Productivity|0|
|"<sup>Z</sup><br>y|Annual Demand Shock|0:274|
|Residual||0:305|

*in absolute values

# 4 Conclusion

This paper addresses the issue of agricultural commodity price changes throughout the past decades. In particularly, the paper takes the case of corn for US markets. It derives two analyses: long run and short run. In the ...rst, it initially runs a sign restricted VAR as a benchmark, ...nding that demand and speculation are the biggest drivers of annual changes. It then contrasts this with a simple model in which it simulates theoretical prices, such that demand and supply match each year. That is, it estimates the price that would have theoretically cleared the market (no inventory changes), then compares it against the observed prices. The intention was to develop what one could consider a proxy for a non-distorted price and compare to see whether the observed price was too far away from it. Results show that after the 1996 farm bill, the relationship between these two time series is very close. That is, there does not seem to be a major di¤erence between the marketclearing price and the observed price. Before 1996, observed prices clearly di¤ered from theoretical ones. There are several explanations for this, but a major one is that government agriculture policy 37
<!-- Start of picture text --> 6.0%<br>-4.0%a —AL calibrated / KS<br>3.0%aa ae 7 eatbracacseanet®<br>: we = -AL calibrated Common<br>OA on 7 os *, Crime/ KS<br>2.0% . oa _ -—- ~~ “ee, =<br>- --- petra t aa "ee<br>- °° Poe ~ ~<br>aeot® eee? ~- ~<br>1.0% =<br>0.0%<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br>20%<br>- OS<br>-14%12%aKN —AL calibrated Aggregate Illegal<br>10%Ynpe income. / reer eor<br>- re +++» AL calibrated Illegal Income<br>: “He from Cocaine / real GDP<br>8% +7 ne mae<br>woe . *s, = AL calibrated Illegal Income<br>6% pte A as tty, from Common Crime/ real<br>ee a GDP<br>- ~<br>4% = a<br>wenenene® ~ ~<br>2%<br>0%<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br><!-- End of picture text -->
**Table 6. Determinants of Human Capital Investment in Children's Education**

||Dependentva<br>Largeshoc|riable.YearsofSchooling<br>k (>Y 5,000)|Completedbyl1-15year<br>Smallshock(Y|old children<br> 1,000- 5,000)|
|---|---|---|---|---|
|Maledummy|0.435***|0.431***|0.413**|0.432***|
||(2.72)|(2.71)|(2.57)|(2.73)|
|Headmigration dummy|-0.311*|-0.359**|-0.331*|-0.371**|
||(1.73)|(2.00)|(1.82)|(2.07)|
|ChildrenMigrationdummy|-0.069|-0.059|-0.110|-0047|
||(0.36)|(0.31)|(0.58)|(0.25)|
|Percapitapaddyland|0.364|0.387|0.412|0.680**|
||(1.31)|(1.40)|(1.43)|(2.22)|
|Per capitaupland|0.188|0.203|0.274|0.258|
||(0.74)|(0.81)|(1.09)|(1.03)|
|Percapitaotherland|-0.081|-0.070|-0094|-0.109|
||(0.97)|(0.84)|(1.11)|(1.32)|
|Parents'yearsofeducation|0.124***|0.122***|0.127***|0120***|
||(365)|(3.62)|(3.73)|(3.55)|
|Experienced shock(yi)|-0.677**|-1.449***|-0.116|-I.034***|
||(1.98)|(2.63)|(0.65)|(268)|
|Guizhoudummy*shock**_(T2)_**||1.934**||0.887*|
|||(2.44)||(I89)|
|Yunnandummy*shock(y3)||0.429||1.473***|
|||(0.50)||(3.11)|
|Guizhoudummy|-0.165|-0.281|-0.155|-0.343|
||(0.78)|(1.30)|(0.72)|(1.45)|
|Yunnandummy|-0.713***|-0.761***|-0.718***|-1.095***|
||(2.78)|(2.94)|(2.77)|(3.84)|
|Constant|3.634***|3.680***|3.565***|3.700***|
||(10.83)|(10.99)|(10.60)|(11.05)|
|No.ofobservations|258|258|258|258|
|R-squared|0.43|0.45|0.42|0.45|
|**Testfor neteffectofshockbyprovince:a**<br>|||||
|_r,+r2=_<sup>_0_</sup>||(2.25)**||(0.86)|
|_ri+y3=0_||(0.15)||(1.29)|
|Absolutevalueofrobust tstatisticsinparent|heses||||

* significant at 10%; ** significant at 5%; *** significant at 1%

' Test statistic reported is the t-value.

Note: Age dummies included but not reported 30
and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

- Francou, B., E. Ramirez, B. Caceres et J. Mendoza (2000) “Glacier evolution in the Tropical Andes during the last decades of the 20th Century: Chacaltaya, Bolivia, and Antizana, Ecuador.” _Ambio_ , 29(7): 416 -422

- Gonzales Iwanciw, J., J. Cusicanqui Giles & M. Aparicio Effen (n.d.) “Vulnerabilidad y Adaptación al Cambio Climático en las Regiones del lago Titicaca y los Valles Cruceños de Bolivia: Sistematización de los Resultados de la Investigación Participativa, Consultas y Estudios de Caso.” Ministerio de Planificación del Desarrollo, Programa Nacional de Cambios Climáticos, Bolivia.

- Hoffmann, G., E. Ramirez, J. D. Taupin, B. Francou, P. Ribstein, R. Delmas, H. Dürr, R. Gallaire, J. Simões, U. Schotterer, M. Stievenard & M. Werner (2003) “Coherent isotope history of Andean ice cores over the last century.” _Geophysical Research Letters_ , **30** (4): 1179-1182.

- Horowitz, J. K. (2006) “The Income-Temperature Relationship in a Cross-Section of Countries and its Implications for Global Warming.” Department of Agricultural and Resource Economics, University of Maryland, Submitted manuscript, July. - -

- <u>http://faculty.arec.umd.edu/jhorowitz/Income Temp i.pdf</u>

- Masters, W. A. & M. S. McMillan (2001) “Climate and Scale in Economic Growth,” _Journal of Economic Growth_ , **6** (3): 167-186.

- Mendelsohn, R., W. Nordhaus & D. Shaw (1994) “The Impact of Global Warming on Agriculture: A Ricardian Analysis,” _American Economic Review_ , **84** (4): 753-71.

- PNUD (2004) **Índice de Desarrollo Humano en los Municipios de Bolivia.** Informe Nacional de Desarrollo Humano 2004, La Paz, Bolivia.

- Ramirez, E. (2008) “Impactos del cambio climático y gestión del agua sobre la disponibilidad de recursos hídricos para las ciudades de La Paz y El Alto.” _Revista Virtual LEDESMA_ , **2** (3): 49-61.

- Ramirez, E., Francou, B **.** , Ribstein, P., Descloitres, M., Guerin, R., Mendoza, J., Gallaire, R., Pouyaud, B., & Jordan, E. (2001) “Small glaciers disappearing in the tropical Andes. A case study in Bolivia: Glaciar Chacaltaya (16°S).” _Journal of Glaciology,_ **47** (157): 187-194.

- Tol, R. S. J. (2005) “Emission abatement versus development as strategies to reduce vulnerability to climate change: an application of FUND.” _Environment and Development Economics_ , **10** : 615-629.

- Trenberth, K.E., P.D. Jones, P. Ambenje, R. Bojariu, D. Easterling, A. Klein Tank, D. Parker, F. Rahimzadeh, J.A. Renwick, M. Rusticucci, B. Soden and P. Zhai (2007) “Observations: Surface and Atmospheric Climate Change.” In: Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.) **Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth  assessment Report of the Intergovernmental Panel on Climate Change.** Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

- Quiggin, J. & J. K. Horowitz (1999) “The Impact of Global Warming on Agriculture: A Ricardian Analysis: Comment,” _American Economic Review_ , **89** (4): 1044-45.

19
The transition process did not simply lead to demand and supply shocks. It also led to a deep institutional restructuring. It is plausible that the transition process, by removing the existing coordinating mechanisms, led to an increase in search costs, and in turn to a decline in aggregate activities. Some Bank surveys-for example, Ickes and Ryterman (1994), Qimiao Fan (1994), De Melo (1 997)-have attempted to measure these changes. The data collected however suffer of the limitations emphasized above, focusing mainly on few of the aspects of the transition process.

Another dimension of particular interest to policy-makers is firn responses to macro shocks. Since this type of analysis has a less defined set of theoretical hypotheses and may be more specific in scope, it is difficult to evaluate the coverage of the World Bank surveys. Instead, we discuss the advantages and disadvantages of recent surveys undertaken in response to macro-economic crises.

Research on transition economies mentioned above is a natural starting point since they provide some information on how firns react to shocks or changes in the environment in which they operate. Earle, Estrin and Leshchenko (1996), for example, focus on the privatization process and its impact on firm performance. Using survey data collected in Russia in 1994, the authors analyze the effects of different ownership structures on firm behavior. Their findings suggest that outsider-owned and state-owned firms differ significantly in terms of perforrnance and but less in terms of restructuring behavior. The latter result may be partly a function of the timing of the survey (it was conducted only two years into the transition process) and, consequently, the restructuring process had made only limited progress.'<sup>4</sup> Recanatini and Ryterman (1999a and 1999b) study the reaction of firns to the institutional vacuum left by the removal of government institutions. Their analysis suggests that, because of the sudden loss of information and the underdeveloped legal system, firms reacted by by an underdeveloped banking system, initially postponed the inevitable restructuring process. The decline in real wages and the increase in interest rates appear to be therefore responsible for the collapse in output.

> 1 The timing of these surveys and their "once for all" approach seriously limit the usefulness of these data sets. Especially when focusing on macro-economic issues it is important to analyze cross-country time series. Unfortunately, most of these surveys are not repeated over time, making it difficult to test both the existing alternative theories and the validity of the survey instruments used.

23
**Figure 16: Percentage of ninis who decide to start their own household with children and do not search for work in LAC, by gender and age, 1992-2010**

Panel A: 15-18 years Panel B: 19-24 years <!-- Start of picture text --> 39.2 39.7<br>17.5 37.3 36.0 37 . 3<br>16.2<br>14.7 29.5 28.5 27 . 9 29.6 29.3<br>12.5 12.8<br>10.5 11.2<br>9.8<br>8.4 8.8<br>0.1 0.2 0.4 0.4 0.6 2.4 1.4 1.7 2.0 1.8<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>Figure 17: Ninis who decide to start their own household with children and do not search for work in LAC, by<br>gender and age, 1992-2010 (as % of ninis not searching for work)<br>Panel A: 15-18 years Panel B: 19-24 years<br>19.9 48.9 49.0<br>15.6 17.2 18.8 43.2 37.7 45.039.8 46.2 39.4 42.1 41.1<br>14.2 13.4 13.8<br>12.5<br>11.8<br>10.4<br>0.1 0.3 0.7 0.6 0.9 4.5 3.4 4.1 5.1 4.1<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>20.0 40.0<br>15.0 30.0<br>10.0 20.0<br>5.0 10.0<br>Ninis starting own household (% relevant Ninis population) Ninis starting own household (% relevant Ninis population)<br>0.0 0.0<br>with children & not searching for work with children & not searching for work<br>20.0 50.0<br>40.0<br>15.0<br>30.0<br>10.0<br>20.0<br>not searching for work) 5.0 not searching for work) 10.0<br>Ninis starting own household (% Ninis in relevant population Ninis starting own household (% Ninis in relevant population<br>0.0 0.0<br>with children & not searching for work with children & not searching for work<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015)

# **6. Conclusion**

This note has offered a descriptive diagnostic of the nini phenomenon in Latin America and the Caribbean—the patterns at a regional level and within individual countries, as well as the evolution of the phenomenon between 1992 and 2010. The analysis draws on data from 238 household surveys collected together and harmonized in the SEDLAC database supplemented by surveys assembled in Cárdenas, de Hoyos and Székely (2015). While this analysis is just a first cut at the data, it reveals 23
# **Table 21 Proportion of average male annual wages replaced by different hybrid pension systems, when contributions are based on joint income in marriage.**

# (21a) All individuals

||N|PAYG=0<br>DC=20|PAYG=5<br>DC=15|PAYG=10<br>DC=20|PAYG=15<br>DC=5|PAYG=20<br>DC=0|
|---|---|---|---|---|---|---|
|Dl|319|.27|.31|.36|.40|.44|
|D2|320|.34|.37|.39|.42|.44|
|D3|319|.37|.39|.41|.42|.44|
|D4|320|.41|.42|.43|.43|.44|
|D)|319|.41|.42|.43|.43|.44|
|D6|320|.44|.44|.44|.44|.44|
|D7|320|.49|.48|.47|.45|.44|
|D8|319|.53|.50|.49|.46|.44|
|D9|320|.60|.56|.52|.48|.44|
|D1I|319|.80|.71|.62|.53|.44|
|ALL|3195|.47|.46|.45|.45|.44|

(21b) Men

||N|PAYG=0<br>DC=20|PAYG=5<br>DC=15|PAYG=10<br>DC=20|PAYG=15<br>DC=5|PAYG=20<br>DC=0|
|---|---|---|---|---|---|---|
|DI|13|.10|.19|.27|.36|.44|
|D2|15|.22|.28|.33|.39|<br>.44|
|D3|52|.27|.31|.36|.40|.44|
|D4|76|.30|.33|.37|.40|.44|
|D5|164|.35|.38|.40|.43|.44|
|D6|189|.38|.40|.41|.43|.44|
|D7|209|.43|.43|.44|.44|.44|
|D8|243|.49|.48|.47|.45|.44|
|D9|263|.57|.54|.51|.47|.44|
|D1O|284|.78|.70|.61|.53|.44|
|ALL|1508|Q|.49|.47|.46|.44|

65