rafmacalaba/datause-encoder-data huggingface dataset:
7 average real return on investments. Thus, expenses of the AFPs have consumed an average 5.3 percentage points. of the assets managed.

**Table 4: Internal Rates of Returns v. Average Real Pension Fund Returns under Selected Investment Horizons**

||Affiliate J<br>IRR'<br>to date|oined in 1982<br>Avg. of<br>Real Fund<br>Returns<br>2|Affiliate J<br>IRRi<br>to date|oined in 1986<br>Avg. of<br>Real Fund<br>Returns<br>2|Affiliate J<br>IRR<br>1<br>to date|oined in 1991<br>Avg. of<br>Real Fund<br>Returns<br>2|
|---|---|---|---|---|---|---|
|After5 Years|<br>0.3%|15.9%|1.6%|9.3%|2.1%|12.9%|
|After l0Years|7.9%|14.3%|6.6%|11.1%|||
|After 14 Years|<br>7.4%|12.7%|||||

Note: ' Returns reflect the IRR up to the corresponding year.

2 Annual average of the Real Pension Fund returns withou **t** he effect of fees or commissions for the corresponding time period. Source: Superintendent of Pension Fund Administrators, June 1996; D. Vittas, A. Iglesias, WPS867, February 1992.

Clearly, as a form of financial intermediation, privatized pension systems seems rather expensive not only in the initial period of affiliation, but also over the entire 14 year period. The implications of this are particularly disturbing for other countries, since the performance of the Chilean economy has been exceptional during this period. Other countries such as Argentina, Peru and Colombia who are encouraging, rather than forcing as in Chile, participation in the privatized system will undoubtedly face greater challenges in convincing the workers to join the privatized system if their capital seems to erode rather than grow in the initial period.

Costs **and Efficiency.** While the average cost for a contributing affiliate over 1982-95 seems rather high based on available data, it may be somewhat overstated for several reasons. The average cost data published by the Superintendency are highly processed, and some experts have suggested possibilities of errors (which were raised with but not confirmed or denied by the Superintendency). Also, while above costs are correctly interpreted for contributors, contributors subsidize non-contributing affiliates to the extent no fees are charged on balances maintained, as is the case in Chile since 1987. However, during 1982-86, the AFPs did charge fixed and variable fees based on the balance as well as on new contributions; thus, during this period, such distortions are relatively smaller. Also, the self-employed who contribute more irregularly than the salaried affiliates, and the holders of voluntary savings accounts (some 961,000 who hold only 1.3% of total funds) who don't pay any commissions "free ride" compared to the regular affiliates. Pension funds also pay retirement, death and disability pensions, but they charge a separate commission for this. Pension recipients have grown from zero at the initiation of the system to 289,452, still only 5.5% of the affiliates.

AFPs incur some expenses in following up missing or misapplied payments. Such costs cannot be isolated from available system-wide data, but perhaps are not an important factor. Collection problems, presumably worse in the earlier periods, do not
cash: Precautionary savings or foreign taxes. NBER Working Papers 23799, National Bureau of Economic Research, Inc, 2017.

- Murray Frank and Keer Yang. Does finance flow to high productivity firms? _University of Minnesota mimeo_ .

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- Amit Gandhi, Salvador Navarro, and David Rivers. How heterogeneous is productivity? a comparison of gross output and value added. _Journal of Political Economy_ , 2017.

- Xavier Giroud and Holger M Mueller. Does corporate governance matter in competitive industries? _Journal of Financial Economics_ , 95(3):312–331, 2010.

- Henry G. Grabowksi and Dennis Mueller. Industrial research and development, intangible capital stocks, and firm profit rates. _Bell Journal of Economics_ , 9(2):328–343, 1978. URL `https: //EconPapers` _._ `repec` _._ `org/RePEc:rje:bellje:v:9:y:1978:i:autumn:p:328-343` .

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- Robert E Hall. New evidence on the markup of prices over marginal costs and the role of mega-firms in the us economy. NBER Working Papers 24574, National Bureau of Economic Research, Inc, 2018.

- Jarrad Harford, Sattar A. Mansi, and William F. Maxwell. Corporate governance and firm cash

33
Table 8: Estimating the effect of migrants’ legal status on labor supply, unconditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Hours of<br>work/week|
|---|---|---|---|---|
|Age|0.012***<br>(0.001)|-0.079**<br>(0.033)|-0.018***<br>(0.007)|0.913***<br>(0.130)|
|Age squared|-0.000***|0.001**|0.000***|-0.013***|
||(0.000)|(0.000)|(0.000)|(0.002)|
|No education|0.021***|0.481***|-0.035*|1.097*|
||(0.004)|(0.113)|(0.021)|(0.622)|
|Primary or preparatory|0.031***|0.435***|0.038*|2.298***|
||(0.004)|(0.123)|(0.022)|(0.598)|
|Secondary|0.017***|0.385***|0.026|1.914***|
||(0.003)|(0.102)|(0.018)|(0.504)|
|Rural|0.013***|0.178**|-0.006|-2.460***|
||(0.003)|(0.074)|(0.014)|(0.337)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.297***<br>(0.097)|||
|Confict|||0.054***<br>(0.014)||
|Illegal status||||-2.765<br>(1.945)|
|Legal status||||1.467<br>(1.502)|
|Observations|14,879|1,955|2,071|10,588|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted hours: Non-mig.: 49.206;|Illegal mig.=4|6.030; Legal mig|.=50.603||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models labor supply upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality of the number of hours of work/week, unconditional on return migration. The predicted number of hours of work/week for non-migrants, illegal migrants, and legal migrants are reported in the last row.

42
3 in 2 out of 3 cases at the request of the operators. The renegotiation<sup>incidencewas,in fact,much</sup> higher in the transport and water sectors.<sup>6</sup> These stylized facts may seem like a puzzle in which the pieces<sup>do notfit together.The</sup> main purpose of this paper to show how the various pieces<sup>actuallydo fit together.Inparticular,</sup> we examine the extent to which the decision to adopt<sup>pricecapsinthe highlyuncertain</sup> environments with weak regulatory capacity that characterized<sup>Latin America,affected(down)</sup> the investment levels, the cream skimming strategies, the efficiency<sup>levels,the high renegotiation</sup> rates, and the overall sector performance. The paper draws<sup>on the collectivefield experience of</sup> its authors in Latin America, the slowly growing literature<sup>oninfrastructureregulationin</sup> developing countries and on the preliminary results of<sup>arecent researchprojectoncontract</sup> renegotiations in the region.

The paper is organized as follows. In section 2, we first review<sup>brieflythediversityof</sup> infrastructure reform experiences and the relative role of<sup>incentive basedregulationinLatin</sup> America. In section 3, we draw on the main lessons from<sup>arecentempirical analysisofthe</sup> impact of the adoption of price caps on the odds of contract<sup>renegotiations.Insection4,we</sup> review the empirical evidence on the efficiency gains that<sup>wereassociated withtheadoption of</sup> incentive based regulatory regimes in the region. In section 5 we present Argentina's experience to discuss the allocation of the efficiency gains<sup>betweenthe variousactorsinvolvedin</sup> the regulatory game. In section 6, we discuss the impact<sup>of theadoptionof pricecapregimeson</sup> the cost of capital Section 7 discusses how other factors<sup>contributesignificantlytosolvethe</sup> puzzle. We conclude in section 8 on what the review suggests<sup>in termsof theinheritanceleft by</sup> Prof. Littlechild in Latin America's infrastructure sector.

# **2. Latin America's infrastructure privatization and regulation**<sup>**mechanisms**</sup>

While casual observers continue to associate infrastructure<sup>reforrnswithprivatization-</sup> divestiture of assets-Latin America's reality is more subtle.<sup>Whatcasualobserversusuallyrefer</sup> to as privatization often did not actually imply any change<sup>inproperty.Divestitureis,infact,</sup> only one of the four main categories of contracts generally<sup>associated withprivatization.The</sup> others are management contracts, BOT/O (Built operate<sup>andtransfer/own) contractsand</sup> concessions. Table 1 shows that 2 out of 3 contracts during<sup>the1990swere concessioncontracts</sup> 6 see Guasch (2003) as well as Guasch, Laffont and Straub (2003) and Guasch,<sup>KartachevaandQuesada.(2000)</sup>
introduction 7 stock, and (3) the decreasing returns in consumption–that is, the fact that people who live on $2 per day are more affected by a $1 loss than are richer individuals.

The analysis presented in this paper applies this approach to floods in Sri Lanka. It combines flood maps from a global model developed by SSBN (now Fathom) with the Sri Lanka Household Income & Expenditures Survey (HIES) to model flood impacts at the household level, using an agent-based model to represent the recovery dynamics [1]. The full methodology and detailed model description is provided in the technical appendix to this document, and in [2].

The analysis provides information to better target DRM interventions both spatially (where should we act?) and sectorally (which type of intervention is the most efficient?). Compared with previous work in Sri Lanka, the analysis innovates by measuring disaster risk with several metrics that complement asset losses (or the cost to repair or replace damaged buildings, infrastructure, productive equipment, etc.). These metrics include (1) traditional asset losses, which highlight the experience of the wealthy; (2) poverty-related measures such as poverty headcount, which highlight the experience of the poor and near-poor; (3) wellbeing losses, which provide a balanced estimate of the impact on poor and rich households; and (4) socioeconomic resilience, an indicator that measures the ability of the population to cope with and recover from asset losses. This broad perspective is intended to complement traditional, more spatially-detailed risk assessments in Sri Lanka.

One major conclusion of this work is that alternative and new metrics of disaster impacts–including poverty headcount, poverty gap, and wellbeing losses–can be used to quantify the value of interventions currently outside the traditional riskmanagement toolbox. Asset-focused risk-management strategies focus primarily on protection infrastructure, such as dikes, and the position and condition of assets, for instance with land-use and codes. can plans building Wellbeing-focused strategies utilize a wider set of available measures, such as financial inclusion, private and public insurance, disaster-responsive social safety nets, macro-fiscal policies, and disaster preparedness and contingent planning. Even if they do not reduce asset losses, these types of measures can bolster communities’ socioeconomic resilience, or their to with and recover and reduce the capacity cope from asset losses when they occur, wellbeing impact of natural disasters.
competitive student will put more effort in order to increase her grades and avoid falling behind her peers.

We test these predictions using data on the scores of a subsample of our study participants in Portuguese and Math exams of _Prova Rio_ . Those are standardized exams administered to all 3<sup>rd</sup> grade students from public schools (run by the local government) who were in attendance on the examination date.<sup>43,44</sup>

|**Table 2: Choic**|**es in Sharing Task Predict**<br><br><br><br>_Math Grade_|**Test Scor**<br><br>_Port_|**es**<br><br>_uguese Gr_|_ade_|
|---|---|---|---|---|
||(1)<br>(2)<br>(3)|(4)|(5)|(6)|
|Aversion to Disadvan. Inequality|0.25<br>0.12<br>0.11|0.25|0.14|0.13|
||(0.04)<br>(0.04)<br>(0.04)|(0.04)|(0.04)|(0.04)|
|Cognitive Function|0.44<br>0.41||0.37|0.34|
||(0.03)<br>(0.03)||(0.03)|(0.03)|
|Risk Tolerance|0.00|||-0.01|
||(0.02)|||(0.02)|
|Aversion to Advan. Inequality|0.05|||0.09|
||(0.04)|||(0.04)|
|Delayed Gratification|0.01|||0.01|
||(0.00)|||(0.00)|
|Violations of Transitivity|0.00|||-0.01|
|in Toys Task|(0.01)|||(0.01)|
|Violations of Transitivity|-0.01|||-0.03|
|in Sharing Task|(0.03)|||(0.03)|
|Dominated Risk Choices|-0.04|||-0.02|
||(0.02)|||(0.02)|
|Violations of Transitivity or|0.00|||0.00|
|_Notes_: This table investigates wheth<br>Monotonicity in Risk Task|er aversion to disadvantageous ine<br>(0.02)|quality is as|sociated w|ith scores<br>(0.02)|

_Notes_ : This table investigates whether aversion to disadvantageous inequality is associated with scores in standardized tests. The dependent variable in the first three columns is the student’s standardized score in _Prova Rio_ ’s Math exam. In the last three columns, it is the student’s standardized score in _Prova Rio_ ’s Portuguese exam. The measure of aversion to disadvantageous inequality is the number of times that the participant chose the equitable allocation in the two DI trials. The measure of cognitive function is the Wechsler Intelligence Scale for Children-IV. All regressions control for gender. _N_ = 1,006.

> 43 Exams are also administered to 7th graders but our cohort had not reached the 7th grade yet.

44 Survey data were linked to the test scores data using the child’s name and, if necessary, the child’s date of birth and the names of the child’s mother and father. Of the 2,113 children who participated in our study, 1,213 children were linked. For 1,006 of them, we also have a measure of IQ available. We suspect that many children could not be linked because they are either attending private schools or are attending public schools run by the state government rather than the local government.

26
57

# Table II.7

# Source of Contraceptives, by Vomee'z Education and Area of Residence, for Selected Countries

|Contr||Source of Co|ntraceptives|a|
|---|---|---|---|---|
|uy<br>Location<br>Education|Governrmen<br>(Percent)|t Co.mercial<br> <br>(Percent)|b<br>**NGO**<br> <br>(Percent)|Otherc<br>(Percent)|
|Egypt (1984)|||||
|Urban|||||
|None|28.5|68.5|1.9|1.1|
|<Primary|29.8|66.7|2.3|0.8|
|Primary<br>Comp.<br>|26.8<br>|69.9|1.6|1.6|
|Secondary +|15.6|82.4|1.4|0.6|
|Rural|||||
|None<br>|36.2<br>|62.2|0.5|1.1|
|<Primary|36.6|61.9|0.4|1.1|
|Primary<br>Comp.|17.1|80.0|0.0|2.9|
|Secondary<br>+|16.4|80.6|1.5|1.5|
|Colombia<br>(1986)|||||
|Urban|||||
|None|50.0|15.6|31.3|3.1|
|< Pr,mary<br>|37.0|34.7|27.3|0.9|
|Primary<br>Comp.|32.6|36.3|28.9|2.1|
|Secondary<br>+|17.5|57.9|23.4|1.2|
|Rural|||||
|None|78.8|15.2|3.0|3.0|
|< Primary|55.5|34.2|8.9|1.4|
|Primary<br>Comp.<br>|58.8<br>|23.5|17.6|0.0|
|Secondary<br>+|46.2|53.8|0.0|0.0|
|Morocco (1984)|||||
|Urban|||||
|None|58.7|39.7|--|1.6|
|< Primary|51.1|46.0|**--**|2.8|
|Primary Comp.|36.6|62.1|**--**|1.4|
|Secondary<br>+|28.9|71.1|**--**|0.0|
|Rural|||||
|None|69.6|27.9|--|2.5|
|< Primary|55.6|44.4|**--**|0.0|
|Primary Comp.|76.9|23.1|**--**|0.0|
|Secondary +|100.0|0.0|**--**|0.0|

FP: Table **111.7**
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Table 4: Validation of Population Estimates

|_R_<sup>2</sup>|Census|Facebook|WorldPop 2010|A. Sub-district<br>WorldPop 2015|Level<br>GPW 2010|GPW 2015|GHSL|LandScan|
|---|---|---|---|---|---|---|---|---|
|Census|1||||||||
|Facebook|.996|1|||||||
|WorldPop 2010|.753|.758|1||||||
|WorldPop 2015|.999|.995|.757|1|||||
|GPW 2010|.985|.982|.748|.986|1||||
|GPW 2015|.986|.989|.770|.988|.990|1|||
|GHSL|.994|.996|.766|.995|.982|.990|1||
|LandScan|.015|.013|.026|.015|.022|.017|.014|1|
|_Average discrepa_|_ncy relati_|_ve to census_|||||||
|Persons|0<br>|2468<br>|15107<br>|10360<br>|4948<br>|3663<br>|2778<br>|46936<br>|
|Percent|0%|4.02%|21.03%|13.38%|6.72%|5.37%|4.79%|69.58%|
|||||B. Village L|evel||||
|_R_<sup>2</sup>|Census|Facebook|WorldPop 2010|WorldPop 2015|GPW 2010|GPW 2015|GHSL|LandScan|
|Census|1||||||||
|Facebook|.841|1|||||||
|WorldPop 2010|.107|.093|1||||||
|WorldPop 2015|.988|.833|.107|1|||||
|GPW 2010|.404|.466|.049|.420|1||||
|GPW 2015|.595|.634|081|.604|.595|1|||
|GHSL|.672|.801|.076|.671|.408|.527|1||
|LandScan|.0006|.0009|.0008|.00007|.00007|.0009|.0007|1|
|_Average discrepa_|_ncy relati_|_ve to census_|||||||
|Persons|0|292|733|197|669|553|515|1684|
|Percent|0%|22.62%|72.12%|13.5%|51.02%|45.77%|42.42%|197.37%|

Note: GPW=Gridded Population of the World; GHSL=Global Human Settlement Layer. Discrepancy is the absolute value of the difference between population estimates and the census at the village level. Percent relative to the census is (absolute-discrepancy/census-population) X 100.

22
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<!-- Start of picture text --> .<br>-=<br>1<br>e° ° anee mcImpact on rents (%)<br># “8 tal me 25-0<br>5 ogee eep.e oe<br>or oebet oh a0. Tee g°eee . Jobs> 28 - 1904<br>- 2467<br>oeoTSoe ", me*t toOe0° ©©> 246712551904- 3275<br>+ aeeo 5 fieo >i, tas~~1G 7 ©© 3275-70767076-6 -37525<br>oe 23 eo° F Se '<br>e 'e =° "oie e a<br>s - @ ,<br><!-- End of picture text -->
Overall, results in this section suggest that an SOE is more likely to be decentralized if it lies far away from the oversight government. The effect of distance is larger when there is more uncertainty about the firm and when communication costs are higher.

# **4. Conclusions**

China’s decentralization of SOEs represents a unique chance to test the implications of Hayek’s insight on the importance of local information for designing an economic system. As the opening quote of Hayek suggests, when a society is experiencing rapid changes, the ultimate decisions should be left to those familiar with the particular circumstances, and “some form of decentralization” is needed. Indeed, we find that a larger information asymmetry between the original oversight government and the SOE, as proxied by their physical distance, is associated with a greater likelihood of decentralization. Moreover, the positive effect of distance on decentralization is larger where SOE performance is more uncertain and communication costs are higher. Our findings suggest that Hayek’s insight on local information is a key for understanding the efficiency of firms in general and economic systems in particular.

# **Reference**

Agarwal, S. and Hauswald, R. 2010. “Distance and Private Information in Lending,” _Review of Financial Studies_ . 23(7): 2757-2788.

Acemoglu, D., P. Aghion, C. Lelarge, J. Van Reenen, and F. Zilibotti, 2007, “Technology, information, and the decentralization of the firm”, _Quarterly Journal of Economics_ , 122(4), 1759-1799.

24
individual-level module on employment (or have labor statistics collected for) were all those aged 10 and older.

For the first dimension, we developed a detailed labor module and a short labor module. The short labor module reflects the approach in shorter questionnaires, such as the Core Welfare Indicator Questionnaire (CWIQ). The CWIQ approach was developed by the World Bank as an off-the-shelf survey that country statistical agencies could implement on a large sample in a short time frame. Many countries regularly field CWIQ-type surveys (in many cases they are called by another name, such as Welfare Monitoring Survey), especially with increasing demands to produce sub-regional household survey statistics. This shorter module is often used in generating statistics with a higher frequency, for example with annual regularity, in lieu of multi-topic household surveys which are too demanding to implement on an annual basis. The detailed labor module reflects the approach in longer questionnaires typically used in multipurpose household surveys, that are often modeled on the Living Standards Measurement Study (LSMS) surveys (Glewwe and Grosh, 2000) in developing countries.

In our survey experiment, the detailed module differed from the short module in two ways: in the set of screening questions to determine employment status and in asking about second and third jobs. Here we focus on the former issue. The detailed module starts with three questions to determine employment status: specifically, whether the person has worked for someone outside the household (as an employee), whether s/he has worked on the household farm, and whether s/he has worked in a non-farm

> the data – see, for example, Judge and Schechter (2009) who study the first digits of numbers, or Benford’s law.

11