rafmacalaba/datause-encoder-data huggingface dataset:
Investment Returns of Occupational and Personal Savings Plans. Tables 3.7, 5.3, and Issue Brief Table 2.3 report rates of return on occupational pension fund investments in various OECD countries as reported and elaborated upon in Davis (1993). For the purposes of this report, the annual rates of return which were used to calculate the simple annual averages which appear in Table 3.7 and Issue Brief Table 2.3 were taken from Davis (1993) and appear below in Annex Table 8. The period averages which appear in Table 5.3 are compounded rates of return after inflation.

All data on OECD occupational plan rates of return from Davis are simulation results which, as explained in the note to Figure 5.3, are produced by applying the known rates of return to various asset categories to the known portfolio allocation of the aggregate pension fund investments in each country. The portfolio allocation of the pension sector of each country is known for each year as are the rates of return for each type of investment, but in most cases data on actual rates of return to occupational pension plans are not available.

Annex Table **8 Real Annual Rates of Return to Occupational Pension Funds; Selected OECD Countries, 1967-1990**

|Year|U.S.|Canada|Germany|Denmark|Sweden|Netherlands|Switzerland|U.K.|Japan|
|---|---|---|---|---|---|---|---|---|---|
|1967|1.7|-3.0|7.9|-2.9|8.8|-|-|19.4|-2.9|
|1968|-1.9|-4.8|6.1|10.3|5.1|5.1|-|17.3|-5.7|
|1969|-8.6|-3.8|5.5|-10.1|-10.1|-1.5|-|-12.8|5.2|
|1970|-12.8|-1.6|0.3|-4.7|1.1|1.6|0.2|-|3.8|
|1971|14.8|13.2|5.4|8.6|2.7|-1.2|4.7|15.7|5.6|
|1972|8.9|5.4|3.3|8.3|1.0|6.5|0.8|9.6|12.5|
|1973|-4.5|-3.1|0.3|-15.5|0.6|-8.1|-8.1|-11.3|-6.3|
|1974|-21.2|-18.3|1.0|-14.8|-12.0|-4.6|-11.0|-36.4|-15.8|
|1975|-1.9|-3.1|6.6|7.1|-9.6|0.3|7.8|36.4|1.7|
|1976|11.0|0.3|4.1|-5.6|-4.3|-2.3|11.2|-7.9|-1.5|
|1977|-0.2|4.1|8.4|-0.7|-1.9|1.5|9.3|27.9|9.6|
|1978|-6.0|-1.3|5.9|**4.4**|-2.1|4.4|11.7|4.7|4.0|
|1979|-5.7|-1.2|-2.8|5.4|-3.1|5.4|-7.6|1.3|-15.0|
|1980|-8.3|-7.0|0.8|0.8|-15.1|0.9|-4.3|2.3|-3.2|
|1981|-6.4|-12.7|-0.2|15.9|-2.3|4.0|-8.1|-1.9|12.3|
|1982|3.5|-0.8|8.3|12.4|4.9|10.2|7.2|17.4|7.2|
|1983|26.0|25.8|11.6|28.4|11.2|11.8|3.3|17.4|11.2|
|1984|0.8|1.7|6.9|-2.9|4.0|11.5|0.8|15.0|12.3|
|1985|18.2|16.4|12.0|34.6|1.7|14.9|11.1|15.2|11.4|
|1986|27.6|15.9|14.7|-9.0|17.8|11.3|6.6|19.0|19.8|
|1987|8.9|7.2|5.3|5.5|3.1|3.4|0.1|12.1|16.6|
|1988|-3.1|-0.5|-1.4|-|**8.8**|**11.0**|7.6|0.9|**5.0**|
|1989|14.2|8.9|8.4|-|-7.0|7.5|-9.8|17.2|-|
|1990|2.9|-|4.7|-|0.7|-1.6|-4.3|-6.7|-|

47
In light of the difficulty of gathering treatment cost data for each country separately, the cost of treatment for a disease in each separate country was estimated using an indirect method based on such costs for the USA. The cost of treating a disease over the lifetime of the disease was first computed for the USA using data on all the health expenditures for treating cancer for 2009 (AMERICAN CANCER SOCIETY, 2009). These numbers were then made country specific by assuming that such costs were proportional to the per capita income in each country using Purchasing Power Parity (PPP) per capita GDP for each country. This is a conservative estimate since not all medical costs go down proportionately with the decrease in per capita income.<sup>18</sup> As a first step, the _direct_ costs of the health externalities of DDT were estimated by computing the extra incidence of _each_ disease from DDT exposure in a country given the respective RR in each of the three population groups: IRS spray operators, IRS exposed households and non-IRS exposed households. The additional incidence of a disease in a country was then multiplied by the cost of treating the disease per patient over the lifetime of the patient in that disease category in each country. The costs from all the diseases were then aggregated to estimate the total _direct_ cost of health externalities arising from the DDT exposure in a given country.

As an illustrative example, the increased _direct_ costs from DDT exposure across all countries in Sub-Saharan Africa are shown in Figure 2.

# [ **Insert Figure 2]**

Under the _widespread DDT use_ scenario, estimates indicate an increased _direct_ cost of more than $3.03 billion, while a _restricted DDT use_ scenario will result in a cost increase of nearly $0.60 billion. However, it should be noted that this is an underestimate as it does not include the effect of DDT in all disease categories because of the non-availability of data for some of the diseases and the costs should be used as a lower bound.

> **18** The present estimation therefore provides a lower bound for such costs and the actual costs are likely to be higher.

12
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-5-

# Table 1. Intensity **and** Dlotribution of Major Trade PolIcy Retorm Propose' Among Forty Countries Receiving World Sank Trade Adjustmnt Loans

|Area of reform|Presnt|Not<br>presnt|Strong|Moderate|Mild or<br> <br>absent|
|---|---|---|---|---|---|
|Exchange rate|as|2||||
|Export prog3otionb|**is**|7||||
|Proteetion<br>studies|2P|12||||
|Overall export policy|||16|15|10|
|Imports forexport.|||17|16|S|
|Overall Import policy|||14|15|11|
|Nonprotective<br>quantitative<br>rostrictions|||14|16|10|
|Protective<br>quantitative<br>restrictionse|||14|15|11|
|Tariff leveiC|||7|21|12|
|Tariff disporsion|||7|24|9|
|Schedule of future action|||6|29|6|
|Overall reduction in antl-xport<br>bias|||17|12|11|

**a.** Often **these were** not explicit conditions, but understandings under the program.

b. Removal of restrictions, provision of export credit., insurance, guarantee, institutional developmnt, **and the** Ilke

**c. Whero** reforme Include a replacement of quantitative restrictions, they **are** cousted in both those lines.

# Source: World Bank data.

restrictions were large in some cases but modest on average across countries in the case of both items competing with domestic production and noncompetitive items (luxuries, for example). Over one-third of the eighty-one trade-related loan operations reviewed included technical assistance components or were accompanied by technical assistance loans to help in implementing reforms or carrying out studies.

There has been less attention under adjustment lending, however, to reforms that would promote greater internal competition. Because most
18 relative prLces that prevailed in 1987 (estimated in the Harrese-Wittenberg study) continued to prevail in later years, with the exception of energy prices. For 1990 his estimate (with $21 per barrel oil) _o'_ $1.9 billion is at the upper range of our comparable estimate (row 2 table 2<sup>).0</sup>

# 5. CONCLUSIONS

This paper has surveyed the literature on the Soviet "subsidy" to Eastern Europe and concluded that, given the overall income reducing effect of the Soviet presence, a Soviet subsidy can only be interpreted in the limited context of a static terms-of-trade loss. This paper has introduced a new data set, which is based on interviews of experts in the respective product categories, for the purpose of analyzing the static terms-of-trade loss that Hungary is likely to suffer as a result of the switch-over in 1991 of its trading relationship with the Soviet Union. We believe that these data are at least as reliable as the principal alternative data sources, which are based on selection of unit values from markets defined as comparable by the researcher. We have analyzed the 'Seminar entitled, "The Cost to Central-East Europe of the Disintegration of the CMEA and the 1990 Oil Price Increase," at the OECD-World Bank conference on "the Transition to a Market Economy in Central and Eastern Europe," Paris, November 28-30, 1990. Marrese's estimates for 1988, however, are considerably less than ours; in fact, he finds that Hungary "subsidized" the Soviet Union in 1988. Since, as we explain in appendix 2, for the same relative prices, Marrese's estimates of the Soviet subsidy to Hungary will be larger than our lower bound estimate, the fact that he has a lower estimate than us for 1988 of the Soviet subsidy must derive from different relative price estimates. Marrese, however, did not update his and Wittenberg's microeconomic investigation of relative prices (other than in energy) from 1987.

We also note that Marrese's estimate of the terms-of-trade loss to Hungary at $26 per barrel is 27 percent greater than our midpoint estimate; this indicates that his estimates are more sensLtive than ours to a change in the price of oil, apparently due in part to an assumption of a greater percentage pass-through to the natural gas and electricity sectors than we have assumed.
In addition, to study the link between trade and financial flows in LAC we explore the sectoral dimension of the different datasets. The information on trade flows we retrieve from Comtrade has bilateral sectoral-level data, covering the 1990-2012 period for 205 source and recipient countries. This database has yearly information at the five-digit SITC (Standard International Trade Classification) level. For financial flows, the Thomson Reuter’s SDC Platinum and the Financial Times’ fDi databases contain transaction-level information at the four-digit SIC (Standard Industrial Classification) level. Hence, for each year, we aggregate the transaction-level observations into a bilateral sectoral-level dataset. That is, for each country pair in each year, we track financial flows in different sectors. We then match these data on foreign investments (both M&A and greenfield) and syndicated loans at the bilateral sectoral level with trade data with the same level of aggregation. Notice however that the sectoral classification of the data in the different datasets is not directly comparable: the SITC is a classification of goods while the SIC identifies the industry. We use Eurostat’s conversion tables to obtain for each SITC code the associate four-digit ISIC (International Standard Industrial Classification) code. Our final dataset is aggregated into the following 14 sectors: agriculture, hunting, forestry, and fishing; mining; crude petroleum and natural gas; manufacturing of food, beverage, and tobacco; textiles and apparel; wood and paper related products; manufacturing of refined petroleum and related; chemicals and plastics; non-metallic minerals; metals; machinery and equipment; transport equipment; utilities and infrastructure; and other manufacturing.<sup>6</sup> The M&A dataset at the sectoral bilateral level contains 21,199 observations, while the greenfield and syndicated loans datasets encompass 31,925 and 39,386 observations, respectively. Of these observations, 2,635 involve a LAC country (as a sender or a receiver) in the M&A dataset, 3,598 observations in the greenfield dataset, and 3,970 in the syndicated loans dataset.

We also obtain information on gravity control variables at the bilateral level. In particular, we gathered country-pair information on the geographical distance between countries (from CEPII’s GeoDist database), differences in latitude and longitude (calculated based on data from the CIA World Factbook), differences in time zones, whether they share a common language (CEPII’s GeoDist database), whether they have a common legal origin (from La Porta et al., 1998), and whether the receiver (sender) country is (or have been) a colony of the sender (receiver) (from CEPII’s GeoDist database).

> 6 It is worth pointing out that that we do not explore the data on the broadly defined services sector - SIC codes [15002000) and [5000-9900) - because of the lack of trade data in services at the same level of aggregation. Hence, we drop all transactions with a classification in one of the following sectors: financial intermediation; public sector; and other services.

6
<!-- Start of picture text --> Periods 1−5 Periods 6−10<br>LL 0.21 0.45 LL 0.01 0.72<br>LH 0 .15 0.39 LH 0.07 0.59<br>Efficient<br>coordination<br>HH0.3 6 0.19 HH 0.21 0.51<br>−0.9 0 0.9 −0.9 0 0.9 Miscoordination<br>Period 5 Period 10<br>Inefficient<br>coordination<br>LL 0.20 0.67 0.07 0.80<br>LH 0.07 0.40 0.07 0.60<br>HH0.38 0.19 0.33 0.47<br>−0.9 0 0.9 −0.9 0 0.9<br><!-- End of picture text --> Figure 2: Outcomes for fixed pairs in the Stag Hunt, by pair type. Black bars are proportional to the number of pairs with outcome ( _Hare_ , _Hare_ ), gray bars are proportional to the number of pairs with outcomes ( _Hare_ , _Stag_ ) or ( _Stag_ , _Hare_ ), and light gray bars are proportional to the number of pairs with outcome ( _Stag_ , _Stag_ ).

the final period of a pairing, i.e., periods 5 and 10. A striking pattern is that pairs with low-caste players achieved much more efficient outcomes than pairs with high-caste players. Over the course of the entire experiment, _LL_ pairs spent 59 percent of periods in efficient coordination ( _Stag_ , _Stag_ ), versus 49 percent for _LH_ pairs, and only 34 percent for _HH_ pairs. The contrast is even starker for periods 5 and 10, after the fixed pairs have had ample time to form a convention. In these final periods of a pairing, the proportion of pairs playing ( _Stag_ , _Stag_ ) was 73 percent for _LL_ , 50 percent for _LH_ , and only 32 percent for _HH_ . All of these differences are statistically significant at the 5 percent level.<sup>5</sup> In addition, _HH_ pairs spend much more time in inefficient coordination on ( _Hare_ , _Hare_ ) than do _LH_ or _LL_ . In particular, _HH_ pairs spent 29 percent of all periods and 35 percent of final periods in inefficient coordination. The comparable numbers for _LH_ and _LL_ pairs pooled are 11 percent and 9 percent. We reject the hypothesis that _LL_ and _LH_ pairs were in inefficient coordination at the same frequencies as _HH_ at the 5 percent level of significance, both for all periods pooled and for final periods.

Figure 3 shows the evolution of behavior across different pair types. In the first five periods, the proportion of _LL_ pairs with the outcome ( _Stag_ , _Stag_ ) increased from 27 to 67 percent, a statistically significant change ( _p <_ 0 _._ 001). In contrast, there was no statistically significant time trend in the proportion of efficient outcomes among _HH_ and _LH_ pairs, and

> 5All standard errors for the pair analysis are clustered by village.

10
difference-in-differences model captured greater exposure and showed that there were modest and sustained impacts on child development, especially for children from more disadvantaged backgrounds.

The present paper focuses on the younger cohort who, based on their age, became eligible to enroll in playgroups later in the project’s implementation, in 2011 and 2012. Similar to the previous analysis for the older cohort (Brinkman et al. 2017b), this paper estimates the impact of the intervention for the younger cohort at age 5 and at age 8 in terms of enrollment in pre-primary education and child development outcomes. We also capitalize on the two cohorts that experienced the project to contrast the impact estimates for the younger cohort with those of the older cohort. For the older cohort, in addition to estimates presented in Brinkman et al. (2017b), we report new estimates of the impact of exposure to early childhood education and development services on test scores in language (Bahasa Indonesia) conducted when the children were aged 8 and in the early grades of primary school.

4
<!-- Start of picture text --> 3<br>129<br>388<br>8<br>SE<br>8<br>35<br>15 =<br>204 5 1 5 7 1 4 4 4<br>fo}<br>45 51 52 61_~«63_—«64_—65 70717273874<br><!-- End of picture text --> <!-- Start of picture text --> 435<br>=}g<br>313<br>°<br>g°8<br>3<br>Paa8<br>8<br>= 74<br>51 43<br>18 23 4 05 2 23 6 | 1 2 10 10<br>i=}<br>40 45 51 52 55 60 63 64 65 67 70 71 72 73 74 80 85 92 97<br><!-- End of picture text -->

<!-- Start of picture text --> Extensive _ Intensive<br>DV: Switch to services as primary All Margin margin<br>product (dummy) | rr(1) a(2) a(3)<br>Exposure to imports 0.536*** —0.561*** 0.531***<br>(0.091) (0.101) (0.103)<br>Lagged Servitization 0.671*** 0.256***<br>(0.196) (0.046)<br>Total factor productivity -0.227*** -0.152* -0.152*<br>(0.070) (0.082) (0.083)<br>Total observations 3156 2546 2546<br>Number of firms 404 344 344<br>Notes: These estimations regress an indicator variable for switch into services. All<br>estimations include standard vector of firm traits, firm and year fixed effects and report<br>standard errors clustered at the firm-level. Significance levels are indicated with *** (at<br>1 % level), ** (at 5% level) and * (at 10% level).<br><!-- End of picture text -->

|||Extensive|_Intensive|
|---|---|---|---|
|DV:Switchtoservicesasprimary<br>|All<br>|<br>Margin<br>|<br>margin<br>|
|<br>product (dummy)|| rr <br>(1)|a <br>(2)|a<br>(3)|
|Exposure to imports|0.536***<br>(0.091)|—0.561***<br>(0.101)|0.531***<br>(0.103)|
|Lagged Servitization||0.671***<br>(0.196)|0.256***<br>(0.046)|
|Total factor productivity|-0.227***<br>(0.070)|-0.152*<br>(0.082)|-0.152*<br>(0.083)|
|Total observations|3156|2546|2546|
|Number offirms|404|344|344|
|Notes: These estimations regress an indicato<br>estimations include standard vector offirm tr<br>standard errors clustered at the firm-level. Si<br>1%level),**(at5%level)and*(at10%le|r variable for s<br>aits, firm and <br>gnificance leve<br>vel).|witch into serv<br> year fixed effe<br>ls are indicate|ices. All<br>cts and report<br>d with *** (at|
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21