rafmacalaba/datause-encoder-data huggingface dataset:
15 Tables 2 and 3 present the two stage Least square<sup>estimates of the model using samples that cover</sup> two periods, namely, 1975-85 and 1979-83.<sup>The reason for estimating the 1979-83 period was that the</sup> trade regime data covers this period<sup>and thus we were interested in checking the stability of the</sup> parameters estimated using the longer time<sup>period. The estimates in Tables 2 and 3 were obtained after</sup> excluding 3 and 2 observations, respectively<sup>that turned out significant at</sup><sup>_5%_level of significance when</sup> performing the D exclusion text for<sup>influential observations (Besley, Kuh and Welsh, 1980).9</sup> In general, the goodness-of-fit of the two<sup>set of estimates is very good with adjusted R2 in the</sup> 0.42-0.62 range and high level of significance<sup>of most variables considered. The sign pattern of the</sup> coefficients is generally consistent with a<sup>priori expectations with the only exception of the sign of the</sup> coefficient of the export dummy in the capital<sup>growth equation. Moreover, as shown by the equations</sup> presented in Tables 2 and 3 as well as several<sup>other estimates not reported in them, the coefficients are</sup> quite robust to changes in data and specification.<sup>The lack of significance of the coefficient associated</sup> with the labor force variable in the per capita<sup>GDP growth equations suggests that the hypothesis of</sup> constant returns to scale cannot be rejected.

We use a Chow test to analyze the stability<sup>of the coefficients through time. In particular we</sup> tested whether they remained constant in<sup>the period 1981-85 compared with the 1975-80 period. The</sup> hypothesis of stability of the coefficients could<sup>not be rejected at 5% level of significance. Similarly, the</sup> hypothesis that the coefficients were constant<sup>in the period 1979-83 could not be rejected at 5%. Thus,</sup> we will henceforth refer mostly to the estimates<sup>in Table 2 that cover a larger time period.</sup> The most striking result of Table 2 is<sup>the high significance and negative sign of the export</sup> restriction dummy (DX) in the output growth<sup>equation and its positive effect on the capital growth</sup> equation. The import restriction dummy<sup>(DM) does not appear to have a significant effect on output</sup> It is important to note that the signs of the<sup>statistically significant coefficients were not affected</sup> by the exclusion of the observations.
**The discrepancy between reported income and true income, a common issue in tax data, indicates the extent to which people underreport their income on their tax returns or tax evasion; in Romania, this is higher among low-income individuals.** The reported income, also known as imputed tax income, is the income that individuals or companies report on their tax returns. In contrast, the survey income, also known as true income, is the actual income earned by individuals or companies **.** The results show that underreporting is exceptionally high for individuals in the bottom half of the income distribution, where income is likely easier to hide. At the median, the gap between reported and true income is 10%, which means that people report only 90% of their "true" income. At the 25th percentile, the gap is 17%, indicating that people in the lower quartile report only 83% of their true income. However, at the 10th percentile, the gap is still 4%, which means that even those with the lowest income still report less than their true income. Here, we should notice that at the bottom of the income distribution, the possibility of paying employees via envelope payments is limited because enough income must remain to pay the minimum wage. Otherwise, formal employment relations would be lost.  There could be several reasons for underreporting, including tax evasion, an informal economy, and criminal activities. Individuals may underreport incomes to the tax authorities to reduce their tax liability. Alternatively, there may be a high incidence of informal economy activities, including "envelope payments," where individuals are paid in cash, and the payment is not recorded. Criminal activities, such as drug trafficking or money laundering, may also result in underreporting of income.

**Our data and method used in this note are also not good at capturing the underreporting of income** . **at the top of the distribution because top earners tend to be underrepresented in the survey data** While surveys can provide helpful information about income trends and distributions, they may not accurately capture the incomes of the wealthiest individuals, leading to potential gaps and biases in the data. Wealthy individuals may be missing from the sample (due to coverage errors, sparseness, or unit non-response) or -- even if they are included-- the income information is missing (due to item nonresponse), underreported or censored (Lustig, 2019; Ravallion, 2022; Yonzan et al., 2022) To somehow control for this problem, we restrict the distribution of survey income and imputed income at the 99<sup>th</sup> percentile of the income in the EU-SILC (see Bendek & Lelkes, 2011). In this case, the mean income in the EU-SILC is 11,231 EUR, and the mean imputed tax income is 10,193 EUR. The average

23
## _The ENE98 and ENECE99 Surveys_

Two other surveys are used in this paper, The National Employment Survey (ENE) and the National Employment, Schooling, and Training Survey (ENECE). The first is representative at a national level and by urban and rural areas. It has rich information on individual labor market characteristics. The ENE98 has a sample size of nearly 200,000 individuals. The second survey is a module of the National Employment Survey. The 1999 sample size was 164,550 individuals. The ENECE is also representative at the national level and has useful additional information on the professional profile of the individuals and the training status, such as type of training received, training time, date of training, place of training, etc.

# **VI.1 Methodology**

In order to compare CONALEP graduates to a control group, this paper examines labor force participation, employment status, earnings, training and hours worked for both the CONALEP group and the control group. To construct the control group, this paper uses the statistical approach of propensity score matching. As discussed by Ravallion (1999) and Todd (1999), the idea behind matching is to find a comparison group that is as similar as possible to the treatment group in terms of the relevant observable characteristics such as age, sex, education, region of residence, as summarized by the propensity score. In calculating the propensity scores, we followed Ravallion's methodology (1999) and Gill and Dar (1995).

First, we chose two representative sample surveys of eligible non-participants as well as one of the participants. The two surveys of eligible non-participants are The National Employment Survey of 1998 (ENE98) and the National Education, Training, and Employment 24
# **Non-Renewable Resources,**

# **Fiscal Rules, and Human Capital**<sup>_∗_</sup>

Paul Levine<sup>_†_</sup> Giovanni Melina<sup>_‡_</sup> Harun Onder<sup>_§_</sup> June 4, 2016 _JEL Codes_ : Q32; E22; E62; F34 _Keywords_ : Natural Resources, Public investment, Human Capital, Debt Sustainability, Developing countries, DSGE

> _∗_ This paper was prepared as a background note to the World Bank Country Economic Memorandum (CEM) for Kenya “From Economic Growth to Jobs and Shared Prosperity”. Giovanni Melina acknowledges the support from U.K.’s Department for International Development (DFID) under the project Macroeconomic Research in Low-Income Countries, with project ID number 60925. We are grateful to Diarietou Gaye, Albert Zeufack, Apurva Sanghi, Alan Gelb, Auguste Kouame, Havard Halland, Borko Handjiski, and Jane Bodoev for useful comments and suggestions to the version of this paper that appeared in the CEM. All remaining errors are ours.

> _†_ School of Economics, University of Surrey, Guildford, Surrey GU2 5XH, United Kingdom. E-mail: p.levine@surrey.ac.uk.

> _‡_ International Monetary Fund, 700 19th Street N.W., Washington, D.C. 20431, United States; and Department of Economics, City University London, UK. E-mail: gmelina@imf.org.

> _§_ Macroeconomics and Fiscal Management Global Practice, World Bank, 1818 H Street, N.W., Washington, DC 20433. E-mail: honder@worldbank.org.
# **Figure 12: Growth in Extensive Margin of US Imports from China, 1992-2005**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Feenstra index of net variety<br>Extensive margin share of export growth<br>Percent<br><!-- End of picture text --> Note: HS 10-digit US imports from China.

26
|P057352|1999|2000|6/14/2000|3 Closed|China|
|---|---|---|---|---|---|
|P057352|1999|2001|6/22/2001|5 Closed|China|
|P057352|1999|2002|5/6/2002|7 Closed|China|
|P057352|1999|2003|6/4/2003|9 Closed|China|
|P057352|1999|2004|5/21/2004|11 Closed|China|
|P057352|1999|2005|4/27/2005|13 Closed|China|
|P057352|1999|2006|11/30/2005|14 Closed|China|
|P057352|1999|2007|12/22/2006|15 Closed|China|
|P057369|2002|2002|6/5/2002|2 Closed|Colombia|
|P057369|2002|2003|6/16/2003|4 Closed|Colombia|
|P057369|2002|2004|6/7/2004|6 Closed|Colombia|
|P057369|2002|2005|5/3/2005|8 Closed|Colombia|
|P057369|2002|2006|1/4/2006|9 Closed|Colombia|
|P057369|2002|2006|6/26/2006|10 Closed|Colombia|
|P057378|1999|1999||1 Closed|Madagascar|
|P057378|1999|2000|6/30/2000|3 Closed|Madagascar|
|P057378|1999|2001|6/28/2001|5 Closed|Madagascar|
|P057378|1999|2002|5/7/2002|7 Closed|Madagascar|
|P057378|1999|2003|12/23/2002|8 Closed|Madagascar|
|P057394|2002|2002|4/30/2002|1 Closed|Gambia,The|
|P057394|2002|2003|6/2/2003|4 Closed|Gambia,The|
|P057394|2002|2004|6/2/2004|6 Closed|Gambia,The|
|P057394|2002|2005|1/26/2005|9 Closed|Gambia,The|
|P057394|2002|2005|5/10/2005|10 Closed|Gambia,The|
|P057394|2002|2006|7/19/2005|11 Closed|Gambia,The|
|P057394|2002|2006|12/28/2005|12 Closed|Gambia,The|
|P057394|2002|2006|6/29/2006|13 Closed|Gambia,The|
|P057394|2002|2007|2/23/2007|14 Closed|Gambia,The|
|P057394|2002|2007|6/28/2007|15 Closed|Gambia,The|
|P057394|2002|2008|12/27/2007|16 Closed|Gambia,The|
|P057394|2002|2008|6/27/2008|17 Closed|Gambia,The|
|P057394|2002|2009|12/25/2008|18 Closed|Gambia,The|
|P057394|2002|2009|6/30/2009|19 Closed|Gambia,The|
|P057394|2002|2010|12/24/2009|20 Closed|Gambia,The|
|P057396|1998|1998||0 Closed|Bolivia|
|P057396|1998|1999||3 Closed|Bolivia|
|P057396|1998|2000|6/30/2000|6 Closed|Bolivia|
|P057396|1998|2001|6/8/2001|9 Closed|Bolivia|
|P057396|1998|2002|5/30/2002|11 Closed|Bolivia|
|P057396|1998|2003|6/6/2003|13 Closed|Bolivia|
|P057396|1998|2004|6/4/2004|16 Closed|Bolivia|
|P057396|1998|2005|2/28/2005|18 Closed|Bolivia|
|P057416|2001|2001|6/25/2001|1 Closed|Bolivia|
|P057416|2001|2002|6/12/2002|3 Closed|Bolivia|
|P057416|2001|2003|6/9/2003|5 Closed|Bolivia|
|P057416|2001|2004|6/1/2004|7 Closed|Bolivia|
|P057416|2001|2005|5/6/2005|9 Closed|Bolivia|
21 TABLE 1 Equation (4): -y For Alternative Definitions of Experience

|XL|X2|X3|X4|X5|
|---|---|---|---|---|
|0.26|0.25|0.25|0.09|0.04|
|(2.49)|(2.19)|(2.41)|(1.00)|(1.97)|

|Country and Region Exper|ience Simultaneously|
|---|---|
|=u2|**X6**|
|0.25|-0.035|
|(2.22)|(-1.02)|
|R2 of each equation is near .55.<br>The numbers in parenthesis are the "t"|values.|

TABLE 2 Equation (4): Alternative Cutoff Values for the Sample

||1296-75|12i8-76|1968-77|
|---|---|---|---|
|**7**|0.44|0.44|0.52|
||(2.71)|(2.91)|(4.15)|
|R2|0.64|0.64|0.62|

X2 definition of the experience variable is employed and the numbers in the parenthesis are the "t" values.
2 Implementing the riglt policies vas also often harder because of structural rigidities caused by weak institutions (Sub-Saharan Africa) or by distributional conflicts (Latin America and the Caribbean). The outcome was an average annual per capita GDP growth of -2.2 percent in Sub-Saharan Airica and -0.6 percent in Latin America and the Caribbean during the 1980s. For Sub-Saharan Africa, per capita GDP growth during 1973-80 had been barely positive, but for Latin America and the Caribbean, average per capita GDP growth had been 2.6 percent. It is, therefore, no surprise that the past decade has been called a lost decade for the poor in these regions. The Worl Development ReRort (World Bank, ?"^3) estimates that in 1985, 30 percent of the population in Africa and 12 percent in Latin America was in extreme poverty.

The strikingly different performance between the East and South Asian countries on the one hand, and the African and Latin American and Caribbean countries on the other raises several issues. Was the difference mostly attributable to market characteristics (flexible vs. non-flexible economies), levels of development (middle-to-high income vs. low income), institutional development (economies financially developed and integrated :.nto the world financial markets vs. financially underdeveloped economies)? It would be useful to investigate how important these various differences are in practice. In particular, how will growth and income distribution respond to similar (or identical) policies in these different economic environments? Is it likely that poverty and income distribution indicators will react in the same way in the African and Latin American economies?

This paper is concerned with the interaction of "typical" stabilization and structural adjustment policies on income distribution in what we shall refer to as "typical" Latin American and African economies. This label is, of course, only suggestive and one could just as well think of our archetypes as representative of low-income and middle-income economies or of flexible and rigid economies. Because of stylized differences between the two archetype economies, the same packages or shocks can be expected to have different
one percent of the labor force with the exception of Ecuador, with approximately four percent of the labor force in unpaid home-based work.<sup>11</sup> Due to the small number of professionals and unpaid workers, and their unique situations – professionals have high human capital and unpaid workers may be considered as having misreported their earnings<sup>12</sup> – the rest of the analysis will only consider informal entrepreneurs and contract workers who have no more than a completed secondary level of education.

# **4.2 Demographic Characteristics of Home-based Workers in the Sample**

Women are over-represented in the home-based work sector in all three countries analyzed (Tables 3a-3c). In Mexico, while women comprise 35.5 percent of all workers, they are nearly 64.8 percent of informal entrepreneurs and 41.9 percent of contract workers. The proportions are higher in Brazil, with 74.7 percent and 81.7 percent, respectively, as compared to 39.6 percent in the rest of the economy, and Ecuador with 74.1 percent and 61.5 percent, respectively, compared to 34.9 percent of all workers.

In general, home-based workers have less human capital than do workers in the rest of the economy. Informal entrepreneurs tend to be older and thus have more potential work experience, than workers in the rest of the economy while contract

> 11 Data from 1993 in Brazil, 1994 in Mexico and 1995 in Ecuador show that the proportion of home-based workers declined over the second half of the 1990s in Brazil, but increased in Ecuador and Mexico.  In Brazil, home-based workers were over seven percent of the labor force in 1993, but this declined to 5.5 percent by 1999, primarily due to a decrease in informal self-employment and the “other” category.  Most of the decrease was seen among women, whose share fell from 13.2 percent of the female labor force to 9.9 percent seven years later.  In contrast, home-based work has increased slightly in Mexico and Ecuador. This is due to an increased propensity of both men and women to be home -based workers in the later period.  In Ecuador, the growth occurred in the unpaid sector while in Mexico, both men and women increased their informal self-employment.

> 12 Unpaid workers may be considered as misreporting their earnings since most work in family firms and thus receive in-kind remuneration.  For example, the household head may own the household firm and report firm profits as his/her income, but she/he uses this income to buy food, clothing, lodging, and other material goods for other household members who were partly responsible for generating this income

12
Urdal & Hoelscher: Urban Youth Bulges and Social Disorder The last two interaction variables are presented in Table 4. The economic growth variable has been inverted to create an interaction term where a high value indicate large youth bulges and low economic growth, expected to be associated with higher levels of disturbance. However, neither urban male bias nor low economic growth appears to influence the relationship between urban male youth bulges and disturbance in the expected direction. On the contrary, the interaction term between youth and economic growth is negative and statistically significant at the 10% confidence level.

**<u>Table 4 Interaction Models</u>**

|**Explanatory**<br>**Variables**|**Model 15**|**Model 16**|**Model 17**|**Model 18**|
|---|---|---|---|---|
||**Lethal**<br>**events**|**Nonlethal**<br>**events**|**Lethal**<br>**events**|**Nonlethal**<br>**events**|
|Urban male β<br>youthbulgesz|0.001<br>(0.07)|-0.022<br>(-1.51)|0.005<br>(0.27)|-0.025*<br>(-1.74)|
|Urban male<br>youthbias|0.223**<br>(2.09)|-0.051<br>(-0.61)|||
|Urban yb* male<br>bias|-0.366<br>(-0.74)|0.075<br>(0.19)|||
|Economic<br>growth(inv), t-1|||0.017**<br>(2.51)|0.013**<br>(2.26)|
|Urban yb* ec<br>growth(inv) , t-1|||0.001<br>(0.48)|-0.002*<br>(-1.66)|
|City pop|0.005**<br>(1.96)|0.003<br>(1.49)|0.005**<br>(2.16)|0.003*<br>(1.65)|
|Real gdp pc|-0.037<br>(-1.03)|0.029<br>(1.32)|-0.028<br>(-0.77)|0.025<br>(1.14)|
|Real gdp per<br>capita growth|-0.018***<br>(-3.15)|-0.010*<br>(-1.79)|||
|Regime type|0.005<br>(0.48)|0.015*<br>(1.86)|0.005<br>(0.47)|0.014*<br>(1.74)|
|Regime squared|-0.011***<br>(-5.84)|-0.007***<br>(-4.60)|-0.011***<br>(-5.60)|-0.008***<br>(-4.77)|
|Missing regime|0.134|-0.062|0.092|-0.041|
|<br>data|(0.56)|(-0.22)|(0.39)|(-0.15)|
|Ongoing armed<br>conflict|0.572***<br>(5.30)|0.216**<br>(2.24)|0.542***<br>(5.08)|0.220**<br>(2.31)|
|Lagged<br>dependent|0.100***<br>(6.37)|0.090***<br>(8.84)|0.101***<br>(6.43)|0.089***<br>(8.92)|
|Constant|-0.175|-0.277*|-0.261|-0.273**|
||(-0.97)|(-1.95)|(-1.51)|(-1.99)|
|N|2,042|2,089|2,041|2,088|
|No. of cities|<br>50|<br>52|<br>50|<br>52|
|LogLikelihood|-1537.77|-2006.78|-1539.71|-2005.23|
|<br>*Sign at 0.1**Sig<br>growth has been in<br>economic growth.|n at 0.05***Sig<br>verted so that<br>|n at 0.01. Inter<br>a high value on|<br>action and cons<br>the interaction|titutive terms are centered on their means. Economic<br>term reflects high urban youth bulges and low|

16
<!-- Start of picture text --> Use of Bank Accounts<br>(1) (2) (3) (4) (S) Li]<br>Micro -0.16%* * -O.1 7 #** -0.18*** -0.16*** -0.16*** -O.19***<br>(-5.544 (-5.904 (-6.56) (-5.73) (-5.55) (-6.60)<br>Owner's Education 0.10*** 0.08 *** 0.05 *** 0.07 *** 0.07 *** 0.05 ***<br>(-4.594 (-231) (-229) (-3.75) (-3.92) (-.58)<br>Owner's Job in Formal Sector 0.06* o.08** 0.09*** 0.13*** O.12*** 0.09**<br>(-1.76) (-2.34) (-2.58) (-3.58) (-3.34) (-2.55)<br>Lig Age 0.003 0.002 0.0035 0.01 0.005 0.003<br>(-0.354 (-0.21) (-O.31) (-0.60) (-0.28) (-0.36)<br>Female OwnerDectision Maker -0.01 -0.01 -0.01 -0.005 -0.0003 -0.01<br>(-0.353 €-0.813 (-O.37) €-0.25) €-0.02) (-0.34)<br>Manufacturing -0.01 0.004 -0.01 -0.004 o.0035 -0.01<br>(-0.524 (-0.20) (-0.48) (-O.21) -0.16 (-0.38)<br>Lac Region -0.16 %* * -O..2 7 ee -0.20%*** -0.26*** -0.26*** -O.27 ***<br>(-3.514 (-12.75) (-14.28) (-11.69) (-11.76) [(-13.29)<br>Private Credit to GDP 0.00***<br>(-3.413<br>Financial Freedom o.003***<br>(-4.46)<br>Contract ViabilitExpropriation 0.05 ***<br>(-3.83)<br>Law and Order o.02*<br>(-Ley7)<br>Property Rights 0.002 ***<br>(-3.15)<br>Pseudo R2 O.21 O.21 0.19 O.22 O.21 0.19<br><!-- End of picture text -->

||||Use of Bank|Accounts|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(S)|Li]|
|Micro|-0.16%* *|-O.1 7 #**|-0.18***|-0.16***|-0.16***|-O.19***|
||(-5.544|(-5.904|(-6.56)|(-5.73)|(-5.55)|(-6.60)|
|Owner's Education|0.10***|0.08 ***|0.05 ***|0.07 ***|0.07 ***|0.05 ***|
||(-4.594|(-231)|(-229)|(-3.75)|(-3.92)|(-.58)|
|Owner's Job in Formal Sector|0.06*|o.08**|0.09***|0.13***|O.12***|0.09**|
||(-1.76)|(-2.34)|(-2.58)|(-3.58)|(-3.34)|(-2.55)|
|Lig Age|0.003|0.002|0.0035|0.01|0.005|0.003|
||(-0.354|(-0.21)|(-O.31)|(-0.60)|(-0.28)|(-0.36)|
|Female OwnerDectision Maker|-0.01|-0.01|-0.01|-0.005|-0.0003|-0.01|
||(-0.353|€-0.813|(-O.37)|€-0.25)|€-0.02)|(-0.34)|
|Manufacturing|-0.01|0.004|-0.01|-0.004|o.0035|-0.01|
||(-0.524|(-0.20)|(-0.48)|(-O.21)|-0.16|(-0.38)|
|Lac Region|-0.16 %* *|-O..2 7 ee|-0.20%***|-0.26***|-0.26***|-O.27 ***|
||(-3.514|(-12.75)|(-14.28)|(-11.69)|(-11.76)|[(-13.29)|
|Private Credit to GDP||0.00***|||||
|||(-3.413|||||
|Financial Freedom|||o.003***||||
||||(-4.46)||||
|Contract ViabilitExpropriation||||0.05***|||
|||||(-3.83)|||
|Law and Order|||||o.02*||
||||||(-Ley7)||
|Property Rights||||||0.002 ***|
|||||||(-3.15)|
|PseudoR2|O.21|O.21|0.19|O.22|O.21|0.19|
|Tab|le 3.4:Inv|estmentR|eturns o|nCPP A|ssets,20|00-2007|||
|---|---|---|---|---|---|---|---|---|
|Fiscal Years|2000|2001|2002|2003|2004|2005|2006|2007|
|percent|||||||||
|CPP Assets|3.2|7.0|4.0|-1.5|17.6|8.5|15.5|12.9|
|||Source: C|PPIB Ann|ual Report|s||||

Annual data on excess returns over the benchmark portfolio show that very large excess returns of the order of 8 percent were realized in 2001 (Table 3.5). This is explained by the decision to reduce exposure to Nortel Networks that accounted at the time for 35 percent of the Toronto market and whose share price collapsed when the bubble burst. Excess returns were substantially negative in 2004 and 2006, but rebounded strongly in 2007, when they reached 2.5 percent of assets.

However, the benchmark used for the 2007 calculation is the new simplified Reference Portfolio, which is not designed to reflect the full riskiness of the actual portfolio. A Risk/Return Accountability Framework is used to measure excess returns, net of fees, on a risk-adjusted basis. However, it is not clear how the volatility of excess returns on investments in private equity, real estate and infrastructure is taken into account. Over the whole period, excess returns equaled 0.9 percent (90 basis points). If the special result of 2001 is not taken into account, the average excess return becomes a negative 0.1 percent (minus 10 basis points).

||Tabl<br>|e 3.5:Ex<br>|cessRetu<br>|rns,200<br>|0-2006<br>||||
|---|---|---|---|---|---|---|---|---|
|Percent of assets|2000|2001|2002|2003|2004|2005|2006|2007|
|Canadian Equities|-0.2|10.9|1.2|0.2|-2.7|0.2|-2.6|n/a|
|Foreign Equities|0.5|0.7|0.5|0.4|-5.0|1.8|-0.7|n/a|
|Real return assets|||1.5|-59.9|35.2|1.1|-7.0|n/a|
|Nominal Fixed Income||||||-0.7|-0.8|n/a|
|Total Portfolio|0.8|8.4|1.0|-0.8|-2.7|0.8|-2.1|2.5|

Source: CPPIB Annual Reports The experience of the past few years suggests the need for a closer examination of the decision to expand active management and engage in principal investing. The performance of the past few years may reflect both large initial expenditures and delayed realization of potential gains. However, such patterns need to be analyzed and evaluated carefully and to be closely monitored.

Unlike the NBIM in Norway, the CPPIB does not document the contribution of internal and external management to the generation of excess returns over the benchmark targets. This may reflect the growing focus on alternative asset classes, which involve high upfront investment management expenses.

In practice, investment expenses, including external management fees, are included in the capital cost of projects. Public securities are marked-to-market on a daily basis, but assets that are not traded on public markets are subject to ‘fair value’ accounting on a quarterly or annual basis. Fair values are obtained from external managers and are based on 43