rafmacalaba/datause-encoder-data huggingface dataset:
On The Governance Of Public Pension Fund Management results do not change when controls for investment risk and portfolio size are taken into consideration (regression 2).

Indeed, two of the investment strategy variables-equity and international investingare associated with higher fund performance the following year, with a coefficient that is significantly different from zero at less than 1 percent level of significance.<sup>7</sup> From this analysis, the authors conclude that governance of public pension funds affects performance only indirectly by determining key investment strategies; these strategies are associated, in turn, with higher performance.

**Table 6. Determinants of public** pension **fund investment returns**

||Depend|entvariable.,-|
|---|---|---|
|t**Explanatoryvariable**<br>-<<br>,|**1990**<br> <br>return**-**|**Fivq-year**<br>**averagereturn**|
|<br>Pensionboardcomposition|||
|BDELAC|-0.01(0.02)|-0.02(0.01)|
|BDELRT|-0.17(0.07)*|-0.002(0.05)|
|Pensionmanagementpractices|||
|ADINVST|-0.34(0.89)|-1.24(0.58)**|
|INVINHS|0.80(1.36)|0.60(0.92)|
|TOP10MG|-0.49(1.32)|-0.32(0.86)|
|TOP10*EXT|-0.08(1.78)|1.03(1.17)|
|**Pensioninvestmentpractices**|||
|INSTATE|-0.08(0.04)*|-0.02(0.02)|
|PRUDMAN|0.83(1.20)|0.33(0.84)|
|STKMAX|-0.03(1.18)|0.57(0.86)|
|**Pensionreportingpractices**|||
|INDINVPF|-1.16(1.00)|-0.57(0.68)|
|FREQVAL|0.70(0.54)|0.13(0.56)|
|R 2|11.3|12.4|
|N|158|132|

Note: ****** pval > 0.01, * pval > 0.01.Both models also include a constant term as well as controls for plan type, plan size, and the fraction of the plan assets held in bonds and stock. Standard errors are in parentheses. Mitchell and Hsin (1997) give the following definitions of variables used: BDELAC, fraction of pension boards elected by active employees (percent); BDELRT, fraction of pension boards elected by retired employees (percent); ADIVNST, administrative costs charged to pension investment income; INVINHS, investment staff of pension portfolio partly (or fully) managed in-house; TOPIOMG, some investments handled by top-ten money managers; TOPI0*EXT, plan investments handled exclusively by top-ten money managers; INSTATE, fraction of pension investment that must be directed in-state; PRUDMAN, pension board required to act according to "prudent man" rule; STKMAX, maximum limitation on assets in the pension portfolio; INDINVPF, use of independent investment performance evaluations; and FREQVAL, frequency of independent investment performance evaluations.

Source. Mitchell and Hsin (1997).

Finally, Mitchell and Hsin (1997) use a survey of 201 U.S. pension systems conducted in 1991, covering 269 separate retirement plans, to link governance and information 7A non quantified selection bias may partly explain the results obtained. The authors note that data on all variables were available for only half of the plans surveyed. Plans providing full information were found to have the same average performance but higher shares of equities and foreign assets in their portfolios than plans providing only partial information.

-8-
Is it necessary to obtain any formal education in order to start a HE? Perhaps not. Reflecting the overall level of education of the labor force in low-income SSA, most HEs owners have not completed primary education **.** A weighted average across eight countries shows that half of all HE owners have either failed to complete primary school or have no education at all (Figure 11). In Uganda (a country with a slightly more educated labor force), only 17 percent of HE owners have never attended school, while in Burkina Faso more than 80 percent of HE owners lack education (Annex table 3). Reflecting the male advantage in education in the labor force, and the higher education levels in urban areas, male and urban HE owners are more educated than female and rural HE owners. Trends in Ghana indicate that as access to secondary education improved over time so did the education levels of HE owners (Fox et al, 2011).

**Figure 11 Half of HE owners have not completed primary education...**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>None<br>60%<br>50% Incomplete Primary<br>40% Completed Primary<br>30%<br>20% Incomplete Secondary<br>10% Completed Secondary<br>0%<br>Tertiary or other<br>Public Private 6 1 to 5<br>Wages Micro HE Farming<br><!-- End of picture text -->

# **but HE ownership is most likely among those who complete primary and go no further**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10 Educational distribution of HE<br>workers<br>5<br>Share of population within<br>education level working in HE<br>0<br>Percentage<br><!-- End of picture text --> _Source: Annex table 1._ However, _the probability of being a HE owner is higher among those that completed primary education._ For a given level of education **,** the likelihood of being a HE owner (seen as the share of the population with a given education level in the bottom half of figure 12) is highest 18
Although this information by itself is valuable, it is not precisely the most relevant question for a policymaker. A policymaker would like to know what is the impact of signing Open Skies Agreements; that is, if by implementing this type of agreements, transport costs are reduced over time.

To answer this question, our second set of results relies on panel data and includes country fixed effects in order to isolate the time-series dimension of Open Skies Agreements and their impact on air transport costs, leaving out the cross-sectional variation. Thus, time-invariant country specific variables such as distance between a foreign country and the US will be subsumed in these country fixed effects. In addition, to some extent, the inclusion of country dummies addresses potential endogeneity problems that would arise if countries following a cost-benefit analysis tend to invest in an Open Skies Agreement only with partners with which they have high air transport costs, since the potential benefits for these countries derived from more competition will be higher. If this is the case, a cross-sectional analysis would underestimate the effect of Open Skies Agreements on transport costs. Indeed, as will be shown below, a comparison of our panel results with those obtained when we use cross-section regressions suggests that the latter in fact understate the impact of Open Skies Agreements on air transport costs.<sup>22</sup> It is important to point out that the use of country dummies does not fully eliminate the endogeneity bias. It is possible that countries decide to sign an open skies agreement following a substantial increase in their air transport costs. The shorter the period used to estimate the effect since the implementation of Open Skies Agreements, the less severe are the remaining concerns about endogeneity.<sup>23</sup> In any case, if endogeneity were a problem, one would expect our estimates to underestimate the effect of Open Skies Agreement on transport costs.

The relation between transport costs and imported volume causes an additional endogeneity problem. To control for this endogeneity problem, in the empirical section, following the gravity literature on trade, we use the foreign country’s GDP as an instrument for the volume of imports.

> 22  Glick and Rose (2001) and Micco, Stein and Ordoñez (2003) use this same technique to identify the effect of currency union and EMU on trade, respectively.

12
full amount. As a result, when oil revenue increases and is subsequently transferred equally among people, private saving will increase.

However, there is a counteracting, and much stronger effect on private saving that tends to crowd out the capital stock; since current workers expect to receive an “oil transfer” from the government when they retire, there is less need to save for retirement. This mechanism reduces the incentive for workers to save and consequently crowds out the capital stock.

If the government decides, on the other hand, to invest a certain share of its oil revenue in physical capital — or human capital for that matter — there will be a direct injection of capital into the existing capital stock. This would further drive down private saving, but the capital stock would be maintained at high level that is conducive to growth. The question is how much of the available oil revenue that should be invested; if, for example, oil revenue as a share of GDP increases from 2 percent to 5 percent as a result of the Pre-Salt oil discoveries, how much of this revenue should be invested and how much could be consumed? This would depend on what is efficient from a macroeconomic perspective; which investment share would be most conducive to welfare and growth? The aim of this section is to analyze this question in depth using the macroeconomic model for Brazil developed in the previous section.

**<u>Figure 3. All Oil Revenue Should Be Invested Unless it Exceeds 10 Percent of GDP or More</u>**

<!-- Start of picture text --> 350%<br>300%<br>250%<br>200%<br>150%<br>100% 105% 97.8% 92% 87% 83% 80% 77%<br>50% -5% 2.2% 8% 13% 17% 20% 23%<br>0%<br>2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%<br>-50%<br>Oil revenue as a share of output<br>-100%<br>-150%<br>-200%<br>Investment share Transfer share<br>-250%<br>Investment and Transfer Shares<br><!-- End of picture text --> Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

The derivation of the efficient investment share is carried out in the appendix (available upon request), but basically involves solving the model for the investment share under the condition that the capital stock must equal the golden rule capital stock. This leads to the condition (22) explained in section 2.6, ∗ ∗ γ = γ ( **x** ) , telling us which share of oil revenue should be invested and which share should be consumed, given that the distribution of consumption is in a completely equal proportion across generations: the same dollars per person in the population (no matter which generation you belong to).

16
|**Table 2: Baseline Airtel Dat**|**a on Mobile Money Trans**<br>Control group<br>|**actions by T**<br>Treatme|**reatment Sta**<br>nt group<br>|**tus**<br>P-value of<br>|
|---|---|---|---|---|
||||||
|Number of monthly transactions|Mean<br>tanar<br>deviation|Mean|tanar<br>deviation|erence<br>in means|
|(June to December 2016)|(1)<br>(2)|(3)|(4)|(5)|
|Any transaction|2.383<br>22.627|0.734|4.075|0.253|
|Send P2P transfer|0.040<br>0.367|0.034|0.341|0.788|
|Receive P2P transfer|0.056<br>0.379|0.034|0.269|0.333|
|Cash in|0.221<br>1.471|0.153|1.217|0.515|
|Cash out|0.203<br>0.995|0.160|0.819|0.556|
|Bill pay|0.077<br>0.926|0.017|0.215|0.222|
|Airtime or data top up|1.731<br>21.708|0.321|2.208|0.307|
|Observations|1589|1|743|3332|
|Number of months|7||7|7|
|Number ofphone numbers|227|2|49|476|

Notes: This table displays data for 476 Airtel phone numbers reported by our survey respondents. We do not include other phone numbers in the Airtel transactions data since we do not have the necessary information to map these to our study locations. Column 5 shows the p-value of the difference in treatment and control group means, clustered at the EA cluster level and conditional on strata and month dummies.

22
7 to private sources, but few households affected by lower real income and rural households with limited access to private services will be able to switch. Because urban households are more likely to suffer reduced real income than are rural households, adjustment should affect a Asures to prevent illness more in urban areas than in rural areas. This effect is likely to be reinforced by urban areas' higher use of and access to services. (But the effects of adjustment policies may be reversed by the emphasis in the 1980s on primary health care, which has a large prevention component and enjoys support from several large international donors.) The effects of adjustment on curative services -- on availability of medical supplies, on expansion of services, on quality of care, on higher user fees, and on costs as a result of higher import prices -- are likely to have a negative effect on health. This effect will be most marked for those who, in earlier periods, used the services most intensively. The urban population -- perhaps most of all the urban middle class -- may thus be most affected.

# HEALTH INDICATORS IN SUB-SAHARAN AFRICA

Sub-Saharan Africa is perhaps the worst region of the world in which to investigate complex and subtle relations between economic and demographic measures. No country in the region (except Mauritius) has a vital registration system complete enough to provide even a time series of numbers of vital events for numerator analysis, let alone a time series of demographic rates. Nor are there subpopulations for which time series of morbidity data, anthropometric information, or data on use of health services, exist. Even basic mortality indicators, reasonable proxies for health status, are weak (Hill and Hill 1988).
with high government debt than in those with low debt (World Bank 2015). In 2020, in part because of massive monetary policy accommodation by advanced-economy central banks, such a divergence was not evident: fiscal support packages were similar in countries with higher and lower debt (see discussion above).

Not only the size but the effectiveness of fiscal policy could be constrained when governments face high debt levels. Empirical evidence suggests that fiscal multipliers are smaller when government debt is higher or fiscal positions are weaker, whether defined in terms of debt or deficits (Huidrom et al. 2020; Ilzetzki, Mendoza, and Vegh 2013).

# 3.2.4. Slowing growth of output and investment

Higher debt typically comes with higher debt service. Since 2014, long-term interest rates for EMDEs have declined but their interest payments have risen (Figure 4). In commodityexporting EMDEs, interest payments, as well as interest rates, surged around 2015 as these economies rapidly accumulated debt to buffer the impact of a steep plunge in commodity prices (Supplementary Appendix Figure S2; Baffes et al. 2015). After the spike, a gradual decline in interest payments has been accompanied by an upward trend in long-term interest rates.

Spending on higher debt service needs to be financed through some combination of increased borrowing, increased taxes, and reduced government spending. Spending cuts may even include spending on critical government functions such as social safety nets or growthenhancing public investment (Debrun and Kinda 2016; Obstfeld 2013; Reinhart and Rogoff 2010).

High debt could also weigh on growth because it is a source of uncertainty about macroeconomic and policy prospects (Lo and Rogoff 2015). This includes risks that the government may need to resort to distortionary taxation to rein in debt and deficits. Higher interest rates and uncertainty would tend to crowd out productivity-enhancing private investment and weigh on output growth. Crowding-out of private investment due to high public debt is documented in Huang, Pagano, and Panizza (2020), and with data for China, and Panizza, Huang, and Varghese (2018), with firm- and industry-level data across 69 countries.

# 4. Understanding debt booms

The costs associated with debt are often amplified by financial crises triggered by rapid debt accumulation as discussed in the previous section. This section presents an empirical analysis of the evolution of macroeconomic outcomes and fiscal positions during rapid government debt accumulation episodes—“debt booms”—over the past five decades. Many countries have been experiencing debt booms over the past decade, making the current global debt wave the largest, fastest, and most synchronized one of the past five decades. We also compare the features of the ongoing debt booms with those of the past episodes.

A debt boom is defined as a period during which the government debt-to-GDP ratio rises from trough to peak of the episode by more than the maximum of ten-year rolling standard deviations during the episode. This identification approach for debt booms follows Kose et al.

12
Weill (2010) use quarterly data from the financial information agency Interfax and the Central Bank of Russia to calculate the Lerner index for banks in Russia between 2001 and 2007. The paper examines whether competition depends on the ownership type of banks by comparing market power across private, government-owned, and foreign banks. The authors find that bank competition in Russia is similar to that in other European Union countries and has only slightly improved during the period studied. Furthermore, they find no differences in market power across private, government, and foreign-owned banks.

Our study adds to the work of Fungáčová, Solanko and Weill (2010) in several ways. Most importantly, rather than limiting our analysis to only one non-structural measure of competition, we pursue different approaches to ascertain the degree of banking sector competition in Russia. First, we examine various measures of concentration. Second, because concentrated banking sectors can be competitive as long as they are contestable (e.g. the Canadian banking system as shown by Allen and Engert, 2007), we analyze the regulations and supervisory practices affecting the ease of entry and exit into the banking sector to assess the degree of contestability. Third, as an alternative non-structural measure of competition, we calculate the Panzar and Rosse H–statistic. Finally, while Fungáčová et al. (2010) simply compare the Lerner Index for Russia with other authors’ estimates for European Union countries (Fernandez de Guevara and Maudos, 2007 and Cabo-Valverde et al., 2009) and contrast the estimates for banks in Russia across ownership types, we conduct rigorous tests to evaluate

> Mkrtchyan (2005), which focuses on Armenia; Prasad and Ghosh (2005), which investigates the case of India; Mamatzakis et al. (2005), Drakos and Konstantinou (2005), and Yildirim and Philippatos (2007) that study competition in Central and Eastern European countries; Levy Yeyati and Micco (2007), which analyzes banking competition in Latin America; and Anzoategui, Martínez Pería, and Rocha (2010) that focus on competition in the Middle East and North Africa region.

5
**Figure 4.6: Typical structure of water bills**

<!-- Start of picture text --> (a) Large cities (b) Small towns<br><!-- End of picture text --> <!-- Start of picture text --> 10 10<br>9 9 San Sebastian<br>(Retalheuleu)<br>8 8<br>7 EMPAGUA 7 San Martin<br>6 (domestic) 6 (Retalheuleu)<br>5 EMPAGUA 5<br>(social) San Agustin<br>4 Quetzaltenango 4 Acasaguastian<br>3 3 (El Progreso)<br>2 2 San Cristobal<br>1 (El Progreso)<br>1<br>0<br>0<br>water consumption (m3/mo.) water consumption (m3/mo.)<br>0 10 20 30 40 50 60 0 10 20 30 40 50 60<br>water bill (US$/mo.) water bill (US$/mo.)<br><!-- End of picture text --> <!-- Start of picture text --> Source: IADB<br><!-- End of picture text --> Not only are water tariffs very low, but survey evidence suggests that revenue collection rates are also extremely low. On average, as many as 30% of those with piped water reported that they did not pay for the service during the last month, compared with only 8% for the electricity service in spite of the fact that average monthly electricity bills are almost 10 times as high as average water bills (US$12.97 versus US$1.48). Among the poorest, non-payment rate rises to 46%.

As a result, water utility revenues are extremely low, both with respect to the likely cost of providing water and sanitation services, and with respect to the likely willingness and ability to pay of the population.

Although there is no available information about the cost of potable water in Guatemala, international benchmarks would suggest a full cost of around $0.30 to $0.40 per cubic meter, exclusive of sewerage. This suggests that at current tariff levels, water utilities are unlikely to be covering their operating costs, let alone their capital costs.

**Table 4.6: Comparison of expenditures on piped and bottled water**

||National|Bya|rea|B|ycons|umption|quinti|le|
|---|---|---|---|---|---|---|---|---|
|||Urban|Rural|1|2|3|4|5|
|% of households that bought bottled water<sup>1</sup>|.17|.33|.04|.02|.03|.08|.23|.47|
|Among those who bought bottled water|||||||||
|Quetzales spent on piped water|16|24|5|1|5|7|12|34|
|Quetzales spent on bottled water|47|50|30|10|30|30|39|56|
|Expenditure on piped water as % of consumption|.004|.005|.002|.001|.003|.003|.004|.006|
|Expenditure on bottled water as%of consumption|.01|.01|.01|.01|.01|.01|.01|.01|

1: Refers to last two weeks before the survey. _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística - Guatemala In terms of willingness to pay, the WHO has traditionally recommended an affordability threshold of 5% of income for water and sanitation services, about 10 times as high as what households in Guatemala  currently spend. Recent research in Central America involving willingness to pay surveys in  Nicaragua, Panama, and El Salvador has provided empirical confirmation of the WHO threshold (Walker _et al_ ., 2000). Further confirmation of willingness to pay for water in Guatemala comes from expenditure on bottled water. The ENCOVI survey shows that 20% of households purchase bottled 35
TABLE 1: RECENT PSD STUDIES OF CONSTRAINTS TO ENTERPRISE GROWIH AND OPERATION

|STUDIES^:<br>COUNTRY/SECTOR/YEAR||BINDING CON|STRAINTS'||
|---|---|---|---|---|
|[AUTHOR]|Leading<br>Constraint|Second<br>Constraint|IThird<br>Constraint|Fourth<br>Constraint|
|Brazil/Garments/92<br>[Stone/Levy/Paredes]<br>L_______________|Political and<br>Policy<br>Uncertainty|Inflation and<br>Price<br>Instability|High Taxes|Tax<br>Bureaucracy|
|Chile/Garments/92<br>[Stone/Levy/Paredes]|Lack of<br>Competent<br>Workers|Political<br>Uncertainty|Inflation and<br>Price<br>Instability|Lack of<br>Technicians|
|Kenya/multiple sectors/92<br>[GTZ, Schankerman, Stone]|Inflation|Access to<br>Finance|Political and<br>Policy<br>Uncertainty|Infrastructure|
|Tanzania/SMEs in Furniture,<br>Construction, Horticulture/<br>1991 [Levy]|Lack of Access<br>to Finance|Bureaucratic<br>Procedures of<br>Regulation|Lack of<br>Access to<br>Industrial<br>Sites||
|Sri Lank2/SMEs in Leather,<br>Ceramics, Gemstone<br>Calibration/1991[Levy]|Lack of Access<br>to Finance|Lack of Access<br>to Materials|||
|Egypt/Food, Textiles,<br>Engineering/1991 [Ghalal]|Political<br>Uncertainty|Tax Structure|Tax Adminis-<br>tration|Access to/<br>Cost of<br>Finance|
|Cote d'Ivoire/SMEs in<br>multiple sectors/1991<br>[Rueda-Sabater and Stone]|Tax and Fee<br>Administration<br>and Cost|Policy<br>Uncertainty|Access to<br>Finance||
|Ghana/multiple sectors/1991<br>[Steel and Webster]|Lack of Access<br>to Fnance|Taxes|Lack of<br>Demand|Economic<br>Uncertainty|

# **Source:** CECPS

- Notes: a. Reports only those studies using constraint rankings. Frischtak's study of Malawi, 1990, utilizes a similar targeted field survey, but does not report results in a comparable manner.

- b. Omits "high cost" constraints. Reports only those obstacles with a mean normalized rating of .5 or more or an average ranking of at least a "moderate obstacle."

3