rafmacalaba/datause-encoder-data huggingface dataset:
-54 -

Table 2

# Trend of Key Financial Indicators

|a/<br>Financial Ratio|1966/73|1974/79|1980/85|Average<br>1966/85|
|---|---|---|---|---|
|Operating ratio|0.68|0.73|0.80|0.74|
|<br>Rate of return|9.2|7.9|6.0|7.9|
|Days receivable|77|97|112|96|
|<br>Self-financing ratio|24.6|18.6|17.2|20.1|
|<br>Debt service ratio|2.0|1.8|1.6|1.8|

a/ Financial ratios correspond to actual values at appraisal, completion, or supervision, depending on project status.

According to the OED review In 1972, there were higher rates ot return on assets and higher self-financing ratios for utilities In the 1960s. Most rates of return fell within the 8-9% range, compared wlth the much wider spread In the fi9lowing twenty years, while self-financing ratios averaged about 30% compared to 20% for the three periods above. Internal cash generation had Improved throughout the 1950s and 1960s for six out of the seven entitles studied which led OED to conclude that there would be a continual strengtheninsof utilfites' financial situation.

# Rate of Return on Assets

The financial rate of return on assets has always been one of the Indicators used by the Bank to monitor the financial performance of entities, and there Is a widespread practice of using a revenue covenant based on self-financing tOrgets In place of, or In addition to, a rate of return covenant. The rate of return should be reported on a pro forma basis where not formally required by the government and should be calculated on revalued assetE. It appears, however that many estimates In appraisal and audit reports (particularly for projects approved In the late 1960s and 1970s) were calculated on an historical value of assets base. Therefore, the comparison of rates of return at project completion with those forecast Includes cases where rate of return estimates were based on both revalued and historical assets.
economies may have contributed to greater risk-taking and increased leverage (Bekaert, Hoerova and Lo Duca 2013, Dell’Ariccia, Laeven and Suarez 2017, IMF 2013). This, in turn, contributed to greater cross-border credit intermediation and investment flows to developing countries (Fratzscher, Lo Duca and Straub 2016, Lim and Mohapatra 2016, Park, Ramayandi and Shin 2014).

Changes in US monetary policy and risk aversion are linked to powerful global financial cycles characterized by large common movements in asset prices, capital flows, and leverage (Miranda-Agrippino and Rey 2015, Rey 2015). Correa et al. (2018) find that monetary policy in source countries influences cross-border banking flows. The authors find evidence of a cross-border bank portfolio channel, whereby tighter monetary conditions erode the net worth of domestic borrowers, leading banks to reallocate credit to safer foreign borrowers. Jaramillo and Weber (2013) find that domestic bond yields in emerging economies are heavily influenced by global risk appetite and liquidity conditions. Cerutti, Claessens, and Puy (2019) find evidence of co-movements in bank, portfolio bond, and portfolio equity flows to emerging markets, but not for FDI flows. The authors report that emerging markets' reliance on global mutual funds, recipient market liquidity, and inclusion in global market indices increase the sensitivity of equity and bond inflows to common global factors. Earlier studies also suggest that time-varying global financial variables are likely to be driven by unobserved common factors conceived as a ‘world business cycle’ (for instance, see Albuquerque, Loayza and Serven 2005).

Several studies also find global risk aversion, captured by the VIX index, to be a significant determinant of capital flows (Bruno and Shin, 2014; Forbes and Warnock, 2012, Ghosh et al. 2014). Nier, Sedik and Mondino (2014) find that for low levels of VIX, country fundamentals are significant drivers of capital flows, but when the VIX index is high, country-specific factors become less important. Abrupt adjustment in global monetary and risk conditions can induce volatility in capital flows, exchange rates, and equity prices, as experienced during the summer of 2013 from expectations of tapering of quantitative easing (Eichengreen and Gupta 2015, Basu, Eichengreen and Gupta 2014). Such events can trigger a reappraisal of emerging economies by investors and result in increased differentiation among emerging economies based on their relative creditworthiness.

8
> annualization of working hours, i.e., calculating the weekly working time on a yearly basis (Ozaki, 1999).

> Second, the use of overtime is restricted by a relatively strict yearly limit of 120 hours, and by a high mandatory overtime premium (50 percent of base salary). The limit on the use of overtime is in Lithuania much stricter than in most other countries. For

> examnple, in Estonia and Hungary it is 200 hours. As regards mandatory overtime premia, they are high in most transition economies of Central and Eastern Europe, and Lithuania

> is not an exception. It should be borne in mind however that in many OECD countries overtime premium is only 25 percent, thus is half as high as in Lithuania. Moreover, in many countries efforts have been made to minimize the cost of overtime work. An increasingly widespread measure is to grant compensatory rest time instead of extra overtime payments (Ozaki, 1999).

# **Constraints To Wage Adjustment**

> Constraints to wage adjustment come from the existence of the statutory minimum wage, as well as from other so called wage floors, such as unemployment

> benefit, which provide a floor for the wage distribution. Here we focus on the role played by the minimum wage. Since the mid 1990s the minimum wage in Lithuania has been relatively high (Table 22). Currently it accounts for about 40 percent of the average (mean) wage. This is high by standards of most transition economies of CEE, where the minimum wage tends to account for around 30-35 percent of the average wage, but for example in Estonia the minimum wage accounts for less than 30 percent of the average wage (IMF, 2001).

**Table 22 Evolution of the Minimum Wage, 1994-2000**

|Minimumwageas%of|1994|1995|1996|1997|1998|1999|2000|
|---|---|---|---|---|---|---|---|
|Grossaveragewage|15.5|26.1|35.7|45.8|41.4|40.0|39.5|
|Median wage||..|..|..|..|_50.1_||
|Bottomdecile||..|..|..|..|97.1||

> _Source. Earnings indices 1991-2000, Statistics Lithuania, Vilnius 2001. Earnings distribution in October 1999, Statistics Lithuania, Vilnius 2000._

65
<!-- Start of picture text --> A. CDF distribution of women-owned establishments<br>in 1994 across age categories, state averages<br>1.0<br>0.8<br>0.6<br>0.4<br>—# Pre-1996 implementation A ‘<br>~*-1996+ implementation<br>0.2 ——Never implemented<br>0.0<br>0-1 years 2-3 years 4-5 years 6-7 years 8-9 years 10-25years 26+ years<br><!-- End of picture text --> <!-- Start of picture text --> C. Share of establishments in age category that are<br>owned by women in 1994, state averages<br>0.4<br>~@ Pre-1996 implementation<br>—*- 1996+ implementation<br>0.3 ——Never implemented<br>0.2<br>0.1<br>0.0<br>0-1 years 2-3 years 4-5 years 6-7 years 8-9 years 10-25years 26+ years<br><!-- End of picture text --> <!-- Start of picture text --> B. CDF distribution of male-owned establishments in<br>1994 across age categories, state averages<br>1.0<br>0.8<br>0.6<br>0.4.<br>~TMPre-1996 implementation<br>0.2 —*- 1996+ implementation<br>——Never implemented<br>0.0<br>0-1 years 2-3 years 4-5 years 6-7 years 8-9 years 10-25years 26+ years<br><!-- End of picture text --> <!-- Start of picture text --> D. Normalized share of establishments in age category<br>that are owned by women in 1994, state averages<br>2.0<br>1.8 -@Pre-1996 implementation<br>16 —*- 1996+ implementation<br>14 —Never implemented<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>0-1 years 2-3 years 4-5 years 6-7 years 8-9 years 10-25years 26+ years<br><!-- End of picture text -->
**Table 4. Intention-to-treat effects on profits (total profits on products and services) and turnover (total revenues on products and services), past 6 months**

||Prof|its|Turnov|er (log)|
|---|---|---|---|---|
||All|Existing|All|Existing|
|Post|||||
|Cash|-0.068**|-0.073**|-0.077*|-0.067|
||(0.026)|(0.029)|(0.035)|(0.037)|
|Matching|-0.059**|-0.060**|-0.048|-0.045|
||(0.021)|(0.023)|(0.029)|(0.033)|
|ANCOVA|||||
|Cash|-0.057**|-0.057**|-0.058|-0.058|
||(0.023)|(0.023)|(0.034)|(0.034)|
|Matching|-0.045|-0.045|-0.013|-0.013|
||(0.028)|(0.028)|(0.035)|(0.035)|
|Control group mean<br>(in XOF, millions)|0.254|0.260|1.374|1.381|
|Standard deviation|0.386|0.394|2.078|2.107|
|S.E. clustered|Municipality|Municipality|Municipality|Municipality|
|Controls|Yes|Yes|Yes|Yes|
|Block fixed-effects|Yes|Yes|Yes|Yes|
|R2 (Post)|0.132|0.138|0.166|0.167|
|R2 (ANCOVA)|0.170|0.170|0.297|0.297|
|N (Post)|1,024|952|1,019|946|
|N (ANCOVA)|940|940|931|931|

_Note:_ ***p < .01, **p < .05, *p < .1. Profits are in XOF, millions (XOF 1 million = US$1,820), and turnover is in log. Profit and turnover have been trimmed at the 99th percentile. The lower number of observations compared to Table 3 is due to missing values for profits and turnover. All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned, and dummies for whether the owner is a household head and whether a member in the owner household is a community leader. The control group mean refers to the follow-up survey. The detailed regression results are provided in Table A.4 and Table A.5 in the appendix.

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18

> firms could have potential with downsizing and debt reacturimg.

<!-- Start of picture text --> Profits and Costs Dynamics AAA<br>I<br>0.9<br>0.7<br>0.6 8.S<br>0.5_<br>0.4<br>0.3 0.2<br>0.2<br>1990 1991 1992<br>|MbtE M r U tw |ors<br>|-° M PrOtlht l<br><!-- End of picture text -->

<!-- Start of picture text --> Profits and Costs Dynamics A<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1.2<br>.<br>0.4<br>'.: 1990 1991 1992<br>M MoE M Lobor gEnWtest|<br>|p M OH Proftlhs<br><!-- End of picture text -->

# I.7 EXpOrts as an Index of Adjustment

Hard currency exports took off in 1990 leading to an almost embarrassingly large increase in reserves that rendered use of the one billion dollar Zloty Stabilization Fund unnecessary.' Ever since, two questions have arisen: (a) what motivated this increase in exports? And (b) can hard currency export performance be taken as a signal that Polish firms are capable of competing at international levels?

Based on firm-level evidence, an important finding in the first phase of this study was that the export boom was a result of a switch to the West owing to the domestic demand barrier, not a response to higher export profitability. In fact, export profitability feU to low and even negative levels by firstquarter 1991, mirroring the real appreciation of the zloty during 1990 and early 1991 (when the exchange rate remained fixed at its lamuary 1, 1990 level).

Can the export boom be interpreted as a confirmation of the suipply response capacity of Polish firms and their ability to meet western standards? It does not matter whether the diversion was from the domestic market or eastern export markets, as both had the same quality requirements, and diversion in either case would represent ability to meet the more stringent standards of the west.

Diversion from the domestic and eastern export markets to the west is obvious from aggregate data: hard currency exports grew by some 40% while total industrial sales fel by 23% and CMEA 6<sup>81</sup> hMs does _wt_ mea ditu de fiud was Irelevat, a it provided an or _aw_ signal of credibDit .
3 education (e.g., reading at nighttime), and health (e.g., clean cooking), the lost load has both direct and indirect values. Second, households can partially avoid power outages by investing in energy backup or storage (e.g., small diesel or solar generator, power inverters, and batteries) that often carry significant option value. Third, the data requirements for estimating a theoretically consistent system of electricity demand functions (Sanghvi, 1983) are exacting and unavailable in most locations. Fourth, the willingness to pay for reliable power supply is likely to be affected by unobserved heterogeneity in household preferences for electricity-using appliance choices (McRae, 2010). Fifth, in surveys, households may report lower (or even zero) willingness to pay for improved power supply even if they value it, in principle, because they object to the practices of the utility or disagree with certain aspects of the provision of the improved supply (Mitchell and Carson, 1989; Beenstock et al., 1998). Sixth, stated preference studies in developed economies (Layton and Moeltner, 2005; Carlsson and Martinsson, 2007, 2008; Hensher et al., 2014; Ozbafli and Jenkins, 2016) suffer from the problem that people have little experience with outages and with the arrangements that they would have to make to adjust to them. Finally, technical constraints and ethical considerations make it very difficult to randomly assign load-shedding schedules and prices, preventing experimental inference of the WTP increasingly common in other infrastructure settings (Berry et al., 2020; Do and Jacoby 2020; Grimm et al., 2020).<sup>3</sup> For these reasons, the literature on assessing the WTP from residential customers for electricity reliability in developing countries is comprised of only a

> 3 One potential approach to experimentally elicit the WTP for reliable power supply is direct load management, where households voluntarily agree that utility takes control of some of the appliances (e.g., air conditioners) in critical peak hours (see Faruqui and Sergici, 2010, and references therein). This approach cannot work in Nepal due to technical unsophistication of national electricity grid and low power requirements of main electric appliances in Nepal. Additionally, one would expect a very high public distrust, which turned out a major obstacle to success of such experiments even in developed economies (Stenner et al., 2017).
_Table 4: Household expenditure, calorie intake, and vulnerability to poverty_

|||**All Iraqi r**|3 N<br>**esidence**|4 Diff||**Displaced**|7 Diff<br>|
|---|---|---|---|---|---|---|---|
||(1) All|(2) Displaced|() on-|() erence|(5) PDS|(6) Non-PDS|() erence|
|Variables|<br>Mean [SD]|<br>Mean [SD]|Displaced<br>Mean [SD]|(2 - 3)<br>b (se)|<br>Mean [SD]|<br>Mean [SD]|<br>(5 - 6)<br>b (se)|
|A. Monthly expenditure (per capita 000 IQD)||||||||
|Poverty rate (expenditure below poverty line)|0.18|0.33|0.15|0.18***|0.31|0.42|-0.11|
||[0.38]|[0.52]|[0.35]|(0.04)|[0.46]|[0.76]|(0.11)|
|Total Expenditure (log)|5.19|4.96|5.24|-0.28***|4.98|4.91|0.07|
||[0.53]|[0.55]|[0.51]|(0.05)|[0.47]|[0.89]|(0.12)|
|Food expenditure (log)|4.25|4.00|4.31|-0.31***|4.00|4.01|-0.01|
||[0.48]|[0.48]|[0.47]|(0.04)|[0.41]|[0.72]|(0.09)|
|Non-food expenditure (log)|4.65|4.44|4.70|-0.25***|4.47|4.34|0.13|
||[0.63]|[0.68]|[0.61]|(0.06)|[0.57]|[1.11]|(0.15)|
|Expenditure on education (log)|0.44|0.32|0.47|-0.16**|0.35|0.15|0.20***|
||[0.90]|[0.73]|[0.92]|(0.07)|[0.67]|[0.72]|(0.08)|
|B. Calorie intake (per capita per day)||||||||
|Total calorie intake (log)|8.16|8.06|8.18|-0.11***|8.08|7.99|0.09|
||[0.36]|[0.37]|[0.36]|(0.03)|[0.32]|[0.58]|(0.06)|
|Calories from ration food (log)|6.41|5.47|6.61|-1.14***|6.74|0.00|6.74***|
||[2.15]|[3.28]|[1.83]|(0.24)|[1.46]|[0.00]|(0.13)|
|Calories from non-ration food (log)|7.68|7.62|7.70|-0.08|7.53|7.99|-0.45***|
||[0.54]|[0.63]|[0.52]|(0.06)|[0.57]|[0.58]|(0.08)|
|C. Vulnerability to Poverty||||||||
|Probability of poverty in the next period (p)|0.18|0.31|0.15|0.17***|0.29|0.41|-0.11|
||[0.28]|[0.36]|[0.25]|(0.03)|[0.31]|[0.53]|(0.08)|
|High vulnerability (p > 0.5)|0.15|0.26|0.12|0.14***|0.24|0.35|-0.11|
||[0.36]|[0.49]|[0.32]|(0.04)|[0.42]|[0.74]|(0.10)|
|Moderate vulnerability (0.2 < p < 0.5)|0.12|0.26|0.09|0.17***|0.24|0.33|-0.09|
||[0.32]|[0.49]|[0.28]|(0.04)|[0.42]|[0.72]|(0.09)|
|_Observations_<br>_Sample = "Short-form"_|1500|327|1173|1500|209|118|327|

Note: Standard deviations are in brackets and standard errors are in parentheses. Significance levels are denoted as follows: * p<0.10 ** p<0.05 *** p<0.01.

_Table 5: Subjective wellbeing fixed-effect model_

|Variables|(1) Current<br>economic situation -<br>good or very good<br>c|(2) Economic situation<br>ompared to before Jan-2014 -<br>slightlyor much better|(3) Expected situation<br>in the next 2 years -<br>slightlyor much better|(4) Subjective<br>wellbeing index<br>(1 + 2 + 3)|
|---|---|---|---|---|
|Displaced with PDS|0.006|0.020|0.030|0.056|
||(0.015)|(0.024)|(0.026)|(0.043)|
|Observations|1,849|1,849|1,849|1,849|
|Adjusted R-squared|0.068|0.193|0.136|0.103|
|Qhada fixed effects|Yes|Yes|Yes|Yes|
|Number of clusters|597|597|597|597|
|Control mean(Displaced without PDS)|0.0826|0.177|0.270|0.529|

Note: Standard errors are clustered at census blocks . Significance levels are denoted as follow: * p<0.10 ** p<0.05 *** p<0.01. Sample is limited to displaced households from the full "short-form" sample.

33
**Figure 4.3 Pension-system assets invested in real estate: Signs of a more diversified portfolio approach**

Percent of total <!-- Start of picture text --> 30<br>25<br>20 2001<br>2006*<br>15<br>10<br>5<br>0<br>Ghana Kenya Madagascar South Africa Zambia<br>%<br><!-- End of picture text --> Sources: Pension-system asset data sourced from national pension funds and financial authorities.

* End-2006 or most recent available; end-2005 for Madagascar and South Africa; end-2007 for Zambia.

There is no comprehensive data set on the investment allocation practices of national insurance sectors in the focus countries, especially for investments in infrastructure services. Even in South Africa’s relatively well-developed insurance sector (where the FSB has compiled and publicly released the most recently available data for the one-year period ending 2005), it is clear that insurance companies did not serve as major sources of financing for new projects. South African insurers strongly preferred investments in securities issued by blue-chip corporates (which received most of the sector’s 51 percent of total assets invested in equities) and real estate investments (the destination of $6.96 billion, or 4 percent of total assets). This is despite the high income from life insurance premiums in South Africa’s sector, which is ranked among the highest worldwide, at 12.4 percent of GDP in 2005 (EIU 2006a).

According to the most recently compiled data, several insurance sectors in the focus countries placed significant amounts of their assets in real estate. For two countries (Tanzania and Mozambique), the percentage of total assets held in real estate exceeded 40 percent. For a further seven countries— Cameroon, Cape Verde, Kenya, Madagascar, Malawi, Niger, and Senegal—the percentage exceeded 20 percent. Among the few countries for which a quantitative breakdown of insurance sector asset allocation was not available, such as Chad and the Democratic Republic of the Congo, industry sector analysts reported from available market information that real estate constituted a major investment vehicle for assets held by these national sectors.<sup>35</sup> DRC life insurer SNAV, for example, reported that it invested its assets in real estate and bank deposits. In Chad, real estate, government bonds issued within the region, and bank deposits are the popular options, but there are limited government securities vehicles available, particularly taking into account demand from insurers elsewhere in the region.

> 35 Axco country reports.

36
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**Table 1. Correlation Between** PCOM & **Poverty Indicators**

||PCOM|**IHDRPOOR**|WBPOOR|P2185|P3085|**P6085**|
|---|---|---|---|---|---|---|
|PCOM|**1.00*****<br>0.0000<br>(321)|-0.382***<br>0.0001<br>(164)|0.252**<br>0.0209<br>(84)|-0.034<br>0.6492<br>(183)|-0.013<br>0.8590<br>(183)|0.043<br>0.5596<br>(183)|
|HDRPOOR|-0.382***<br>0.0001<br>(164)|**1.00*****<br>0.0000<br>(172)|0.642***<br>0.0001<br>**(65)**|**0.248****<br>0.0111<br>(104)|0.350***<br>0.0003<br>(104)|0.613***<br>0.0001<br>(104)|
|WBPOOR|0.252**<br>0.0209<br>**(**84)|0.642***<br>0.0001<br>(65)|1.00***<br>0.0000<br>( 99)|0.330***<br>0.0026<br>( 81)|0.366***<br>0.0008<br>( 81)|0.513***<br>0.0001<br>( 81)|
|P2185|-0.034<br>0.6492<br>(183)|0.248**<br>0.0111<br>(104)|0.330***<br>0.0026<br>(81)|1.00***<br>0.000<br>(190)|0.964***<br>0.0001<br>(190)|0.778***<br>0.0001<br>(190)|
|P3085|-0.013<br>0.8590<br>(183)|0.350***<br>0.0003<br>(104)|0.366***<br>0.0008<br>(81)|0.964***<br>0.0001<br>(190)|1.00***<br>0.0000<br>(190)|0.8918***<br>0.0001<br>(190)|
|**P6085**|0.0434<br>0.5596|0.613***<br>0.0001|0.513***<br>0.0001|0.778***<br>0.0001|0.892***<br>0.0001|**1.00*****<br>0.0000|
||**(183)**|(104)|**81)**|**(190)**|**(190)**|**(190)**|

Note: Pearson Correlation Coefficient is the first entry, below it is the significance level, followed by the number of observations in parentheses.

***' Significant at the .01 level or better

** Significant at the .05 level or better

# A. Summary Statistics

The analyses are run separately for central budget and consolidated **budget per capita** infrastructure expenditures, as noted earlier. Table 2 shows the means and standard deviations **of all** variables included in the analyses for countries where data on central budget infrastructure expenditures exist, while Table 3 provides the means and standard deviations **of** variables when data on consolidated budget infrastructure expenditures exist. The mean **per** capita expenditure **on** infrastructure in the sample is (1980) **US** $17.73 for the central budget and (1980) **US** $19.67 for the consolidated budget.