rafmacalaba/datause-encoder-data huggingface dataset:
Claessens and Peters (1997) concluded that the biggest losses are concentrated within the largest firms, which is in accordance with Table 5.

**Table 6 Estimation of the investment equation with liquidity constraints**

|Dependent<br>variable<br>EB/K<br>Gross investment|Small firms|Large firms|
|---|---|---|
|C, Constant term|0.229|0.227|
|T-statistic|(27.37)|(11.34)|
|Probability|(0.00)|(0.00)|
|ASAL/K , First diff. of sales to capital|0.013|0.042|
|<br>T-statistic|(2.87)|(2.78)|
|Probability|(0.00)|(0.006)|
|CF/K,CashFlowto capital|0.034|0.097|
|<br>T-statistic|(2.36)|(1.49)|
|Probability|(0.018)|(0.137)|
|AdjustedR'|0.031|0.064|
|F-statistic|14.22|7.33|
|Prob.(F-statistic)|0.0|0.0|
|Sample size: 1993/95<br> <br>|818 companies,<br>dNEMPL<709|185 compalues<br>NEMPL>709|

_ _________________________ Numbers in brackets below the estimated coefficients are the t-ratios. Standard errors are robust Whitestandard errors.

Table. 6 presents the estimation results for equation (1') for small and large firms. The first column shows that the cash flow coefficient is positive and significant for the small firms. The second column presents the estimation results for large firms. For these firms we obtained an insignificant cash flow coefficient. Again, this is in line with the idea that large firms are less profitable (lower cash flow) and thus have relatively less internal funds at their disposal to finance their investment. This need not to be a problem for these firms for they can rely relatively more upon external funds. Whether this reliance does reflect lower information costs, as one would expect in the case of large firms in '"Western" economies, or simply the inheritance from the centrally planned economy of an inefficient financial sector and the continuation of soft budget constraints remains an open question. One should, however, be careful and not overemphasize the difference not have any infornation on the banks that have extended these loans. Dobrinski (1996), however, gives infonnation about the segmnentation of the banking sector in bad (consisting of predominantly state-owned banks that have large part of non-performing loans in their portfolios) and good (consisting mainly of newly created private banks which did not have such loans in their portfolios).

17
**Table 2. Step 1: Determinants of Capital Ownership among Firms**

|(1)<br>Coefficients|(2)<br>Marginal effects|
|---|---|
|Business association<br>0.209**<br>(0.0972)|0.0548|
|Financial knowledge<br>0.151***<br>(0.0585)|0.0397|
|Family tradition<br>0.104<br>(0.0896)|0.0273|
|Male(=1)<br>0.0887*<br>(0.0522)|0.0233|
|Age firm<br>0.00571<br>(0.0129)|0.00150|
|Age owner<br>0.00370<br>(0.00805)|0.000972|
|Age squared owner<br>-0.000<br>(0.000)|-0.000|
|Constant<br>0.802***<br>(0.184)||
|Sector fixed effects<br>yes||
|Region fixed effects<br>yes||
|Pseudo-R2<br>0.1138||
|Observations<br>4,410|4,410|
|Standard errors in parentheses *** p<0.01, ** p<0.05,|* p<0.1|

In the next step, the paper derives the predicted probabilities from the estimation of (1) to calculate the inverse Mills ratio, that is, the ratio of the probability density function to the cumulative distribution function, as follows<sup>5</sup> : The paper then includes this ratio in estimating the following equation: Top where Yi is the binary variable indicating a firm that is a top performer;  is the cumulative distribution function of the logistic distribution; β1 is the vector of coefficients that defines the link between owner and firm characteristics and the probability that the firm is a top performer; and  i is the vector of owner and firm characteristics. The vector  i encompasses the predetermined factors that are correlated with identity as a top performer. To address the issue of endogeneity, predetermined variables are included, as follows: age, age squared, gender, educational attainment, and the motivation of the entrepreneur to set up the business, which is measured by a dummy variable taking the value of 1 if the entrepreneur created the firm because s/he could not find a paid job in a large company. Besides these predetermined variables, which allow to control for the firm’s age and avoid selection against younger firms with a high potential, a sector dummy is included.

> 5 Box A1 (in appendix A) provides a detailed explanation about the inverse Mills ratio, the probability density and the cumulative distribution functions.

11
9 2005). On the other hand, about 700,000 Tutsi people returned to Rwanda from exile in Uganda shortly after the genocide (Newbury 2005). This group of _old caseload refugees_ either fled Rwanda during waves of ethnic violence against Tutsi since independence or were the offspring of Rwandan exiles.

Grasping the demographic imbalance in numbers, Fig. 1 depicts sex ratios for five-year age groups calculated from the (pre-genocide) 1991 Census and the (post-genocide) 2002 Census for Rwanda. The graph allows comparing the relative distribution of men and women across age at the two points in time. Clearly, in 2002 there are shortages of men that _may be_ attributable to genocide-related excess male deaths (i.e. shortages of men even larger than prior to the genocide for some age groups). The shortage of men is most pronounced in the groups of 20-45 year olds and the elderly older than 55 years. An immediate implication that follows from the unbalanced sex ratios is the reduced chance of women to get married to men of similar age for women in the age group most affected by genocide or to remarry after being divorced or widowed. This is hence a topic that we will investigate in more detail below.

# **4 Data**

## **4.1 Rwanda Demographic and Health Surveys**

The analysis builds on three cross-sectional Rwanda Demographic and Health Surveys (RDHS) collected in 1992 (before the genocide) (ONAPO and Macro International 1994), 2000 (after the genocide) (ONAPO and ORC Macro 2001) and 2005 (INSR and ORC Macro 2006). The data in each survey is representative of households at the national and in 1992 and 2005 at the provincial level, based on a stratified survey design. In the 2005 RDHS, each of Rwanda’s twelve provinces was divided into an urban and a rural stratum, resulting in 23 strata (the province of Kigali City only consists of urban areas). In a first stage, primary sampling units were drawn from a listing of enumeration areas prepared for the 2002 Census. Primary sampling units were selected with probability proportional to size regarding the number of households in each enumeration area. This exercise was conducted separately in every stratum. In a second stage, 20 and 24 households within each urban and rural primary
Central Asia, into components that corresponded to inefficiencies with respect to certain aspects of managing the utility.

Recent studies of the composition of the quasi-fiscal deficit (QFD) of state utilities in Sub-Saharan Africa (Briceño-Garmendia et al., 2008; Alleyne et al. 2013; Trimble et al., 2016) and the Middle East and North Africa (Camos et al., 2017) provide an important insight into the magnitude of certain inefficiencies. The QFD of state-owned public utilities can be defined (Saavalainen and ten Berge, 2006) as: _[the] value of the implicit subsidy computed as the difference between the average revenue charged and collected at regulated prices and the revenue required to fully cover the operating costs of production and capital depreciation._ The QFD can be broken into components (the so called “hidden costs”) to highlight the major sources of underperformance of a utility: (a) excess transmission and distribution losses; (b) overstaffing costs; (c) bill collection failure; and (d) underpricing. The first three items are defined relative to an “ideal” performance target for that country or region. Trimble et al. defined T&D losses of 10% and bill collection of 100% as achievable for a well-managed utility. The target number of staff was calculated from studies on Latin American distribution utilities. The underpricing element is then determined as a residual. Camos et al. used a similar approach. Other possible sources of inefficiency, for example, generation plant run in a sub-optimal fashion, are not shown explicitly and are therefore included in the underpricing element. Other shortcomings, such as the rate of access, are not measured by such a calculation.

Most of the countries included in these studies had not undertaken deep reforms of the electricity sector, so that the costs identified give an insight into the magnitude of certain shortcomings of unreformed power sectors. The few countries where there has been long-standing private engagement (beyond management contracts) are shown with an asterisk in the following tables.

Table 1 shows the hidden costs as a percentage of GDP for Sub-Saharan countries for a reference year (between 2013 and 2015). The median QFD was around 1% of GDP–with a maximum country value of 6%. Underpricing was the most important item, accounting for about 40% of the total QFD. Bill collection was the smallest item, but it may be the easiest to rectify. However, for 13 countries the underpricing component was negative. This can be interpreted as showing that had the other costs (incomplete bill collection, excess T&D losses, and overstaffing) been removed by some policy action, the tariffs being charged would have more than covered costs.

7
employed/individual businesses, and professionals is €2.36 billion, with an additional €1.36 billion for the social insurance payments.<sup>10</sup> Additional funds were allocated to benefits for unemployed workers. On March 20, a measure was introduced to extend payments of the regular unemployment benefit, the long-term unemployment benefit, and the unemployment allowance for the selfemployed by 2 months for those whose entitlement ended on March 31. The measure was then extended to cover those whose entitlement ended at the end of April and at the end of May. In addition, a lump sum stipend of €400 was introduced for 155,000 long-term unemployed individuals, registered with the public employment agency (Hellenic Manpower Employment Organization (OAED)) from April 1, 2019 who were maintaining their status until April 16, 2020 and were not receiving any other benefit from the state. The budget allocation for these measures related to unemployment benefits is about €300 million.

In terms of numbers affected and financial commitment, the government’s mitigation measures have emphasized the preservation of employment in enterprises where operations were suspended. A key condition of the benefits provided to affected businesses was that they were obliged to maintain the same headcount. In fact, layoffs in designated industries were temporarily prohibited from March 18 until the restriction was lifted on June 16.

# **4. Data and methods**

This section briefly presents the data sources, relevant indicators, and the methods that are used to assess labor market adjustments in Greece due to the COVID-19 pandemic and the relevant mitigation policies.

## **4.1. Labor market indicators**

We report monthly estimates from the Labor Force Survey (from ELSTAT) on the labor force participation rate, employment to population ratio, and unemployment rate for the periods January-April 2019 and January-April 2020 to see differences in trends before and during the lockdown.<sup>11</sup> We also use the LFS data to identify how other aspects of the Greek labor market have been affected by the COVID-19 crisis. In addition, to assess the degree of labor market slackness, we calculate the extended labor force indicator which is simply the active labor force plus the “potential additional labor force” (PALF), which takes into account persons seeking work but not immediately available and persons available for work but not seeking work.

> 10 <mark>Information regarding fiscal responses to the economic fallout from the coronavirus was provided by the Bruegel data sets; see</mark> <u><mark>here.</mark></u>

> 11 It should be noted that the pandemic and mitigation measures affected the LFS data collection process, to some extent. From mid-March 2020 onwards, the LFS data collection switched from a blended style of personal and telephone interviews to solely telephone interviews. This decreased the response rate compared to previous months, especially in urban areas. The relevant ELSTAT press release is here.

15
Malaysia, despite the Asian Crisis during late 1990s and the recent financial crisis.<sup>17</sup> The rapid transformation of the native labor force’s educational attainment is particularly impressive. In 1990, 62 percent of the native labor force had primary school education or less, and only 29 percent had high school education or more. By 2010 the share of the labor force with at most primary school education declined to 20 percent while 66 percent had at least high school education. Among the new entrants to the labor force, age group 20-25, over 80 percent of this group have at least a high school degree.

Between 1990-2010, the share of immigrants in the labor force increased from 3.6 to 10.6 percent according to the LFS. Immigrant workers are disproportionately employed in agriculture and construction. Their share in manufacturing also increased during this period, while they are under-represented in relatively skill-intensive service sectors such as health, education and public administration. Immigrants are significantly less educated than Malaysians: 91 percent had (at most) primary school education in 1990. Even though this number had fallen to 66 percent by 2010, only 19 percent had completed high school. Around 55 percent of all immigrants are from Indonesia, 20 percent from the Philippines and the remainder from countries such as Bangladesh, Cambodia, India, Lao PDR, Myanmar, Sri Lanka, Thailand, and Vietnam.

The income supplement includes information on numerous forms of income. The wage measure we use is the total income derived from all labor related sources in the previous month divided by the number of days worked in that month. The income measure also includes all employment related forms of non-monetary compensation, such as housing

> 17Malaysia’s GDP per capita in current US dollars went from $2,418 in 1990 to $8,691 in 2010 (World Bank national accounts data).

18
5.19 Sahn (1989) associates initial s,. or financing improvements with adjustment: education's share of the total government *,udget increased (from 11.5 percent to 15.0 percent) as did real education spending (from and index of 103 to 110) between the period before structural adjustment (up through 1980) and the period after (1981-84). However, they fell to 10.6 percent and 105 respectively in 1986-87. The rapid growth of enrollments between 1965 and 1980 slowed after 1980. Absolute primary enrollments actually dropped by 35,000 children (4 percent) between 1981 and 1983, corresponding closely to a period of drought and falling agricultural output.

Malawi: education indicator5

||1975|1980|1981|1982|1983|1984|1985|1986|1987 1988|
|---|---|---|---|---|---|---|---|---|---|
|Finance:||||||||||
|% of public budget<br>|9.4<br>|9.0<br>|11.1<br>|14.3<br>|13.4<br>|12.3<br>|11.0<br>|10.8<br>|10.0 10.4<br>|
|% of GDP<br>|2.5|3.1|3.9|4.1|3.9|3.4|3.5|3.6|3.1|
|real expenditures (index)|63|100||107|||101||_105_|
|Intrasectoral finance:<br>||||||||||
|recurrent budget share(%)<br>|92<br>|76|||||72|||
|primary share of budget (%)|45|39|||||41|||
|<br>higher share of budget (%)|||||24|||34||
|**QjjRut**(enrollment rates--%):||||||||||
|gross primary<br>|_56_<br>|60<br>|65|62|60|60|62|_65_|66|
|net primary|36|40||||43|44|48|49|
|gross secondary||3|4|4|4|4|4|4|4|
|Ouality:||||||||||
|per student spending (index)|111|100|||||104|||
|teacher salaries (index)||100|96|99|96|94|96|||
|primary repeaters (%)|17|17|15|14|16|16|18|21||
|<br>grade 4 completion (%)|67|59|60|53|52|47|53|68|64|
|primary male enrollment(t)<br>||72||73|69|69|70|73|73|
|primary female enrollment (%|)|48||52|50|51|53|57|59|

5.20 Sources disagree on public per student expenditure trends after 1980. Fuller (1989) describes declines at all levels between 1980 and 1987 (in 1980 US$): by over onehalf in primary education, and by three-fourths in secondary and tertiary education. At the same time, Lockheed, Verspoor, et.al. (1990) describe a slight increase from 1980 to 1985 at the primary level (1985 US$). Public recurrent expenditure per primary teacher also declined ten percent from 1980 to 1988 (Lewin and Berstecher, 1989). The ratio of teacher salaries to manufacturing worker salaries declined through 1982 (from 0.91 in 1980 to 0.67), 42
the nonfarm business sector, divided by the population measure. Consequently, the di↵erence in the variation of _HRt_ over _HCt_ is due to variation in employment.<sup>24</sup> Figure 9 displays the annual growth rate of total hours versus the annual growth rate of total employment. As the figure shows, the two series are highly correlated displaying similar fluctuations which suggests that employment is the main driving force of total hours. For this reason, we also present _alternative measures_ of _HRt_ and _HCt_ , computed simply as employment divided by population.<sup>25</sup> As in Fisher (2006), the price index of capital investment, _PK_ , corresponds to the price of total investment and is constructed with the equipment deflator and the NIPA (National Income and Product Accounts) deflators for residential and nonresidential structures, consumer durables and government investment. The equipment deflator was constructed by Gordon (1990) for the years up to 1980 and was extended by Cummins and Violante (2002) for the years up until 2000. We extend the Gordon-Cummins-Violante index further to 2007 using the pattern of NIPA investment price series. The rest of the data were taken from the NIPA tables. The price index, _PGDPt_ , used to deflate the price of capital investment is the implied deflator from chained real GDP. Aggregate output in the consumption good sector is nominal GDP net of R&D investment as reported in the BEA-NSF satellite account, deflated by the implied GDP deflator. Outputs _YRt_ and _YCt_ are obtained by dividing real R&D investment and real aggregate output in the consumption good sector by the population

> 24Previous studies also indicate that most variation in total hours is due to variation in employment than variation in individual hours (e.g. Hansen (1985), Castro and Coen-Pirani (2008)), especially at annual frequencies.

> 25The theory could also be summarized by an indivisible labor model a la Hansen (1985) and Rogerson (1988). In that case, the optimality conditions for labor supply in the theoretical model would be slightly di↵erent but the main theoretical arguments would remain una↵ected.

31
Figure 5: Impact of AGOA on Mauritius apparel exports: firm-level evidence <!-- Start of picture text --> AGOA impact on apparel: firm-level evidence<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br>Year<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Notes: the figure shows coefficients and 95 percent confidence intervals based on robust standard errors, clustered by HS 6-digit product obtained by estimating Eq. (3). The regression is based on 4,916,706 observations at the firm-HS6-destination-year level (with the three destinations being US, EU and ROW) including zeroes in export flows. These observations correspond to the export transactions of 5,566 firms. The first dotted vertical line indicates the switch into period (B1) with strict ROO while the second dotted vertical line indicates the switch into period (B2) with liberal ROO.

Table 1. US Tariff Schedule, GSP and AGOA for African countries Panel A. Number of Tariff Lines and Share of Exports to the US by African Country Sub-Group

||<br><br>Number of US tari|<br><br>ff lines (HTS 8-digit)|<br><br>% of Expor|<br><br>ts to US|
|---|---|---|---|---|
||LDC|Non-LDC|LDC|Non-LDC|
|MFN Zero|3,131|3,131|9%|28%|
|GSP duty-free|3,507|3,507|1%|4%|
|GSP LDC duty-free|1,670||79%||
|AGOA Apparel|555|555|11%|3%|
|AGOA Non-LDC||1,610||64%|
|AGOA Only|225|225|0%|0%|
|No Preference (MFN>0)|1,096|1,156|0%|1%|
|Total|10,184|10,184|100%|100%|

Panel B. US MFN tariff rates on GSP LDC-eligible or AGOA-eligible HS 6-digit products

||<br>GSP<br>LDC<br>(810)|<br> <br> <br>AGOA<br>Non-LDC<br>(769)<br>AGOA/GSP|<br>AGOA<br>Only<br>(91)<br>Countries|<br> <br> <br>AGOA<br>Apparel<br>(239)|
|---|---|---|---|---|
|Mean<br>US MFN triff rt|5.0%|4.3%|7.3%|11.2%|
|described in Section 2.<br>Maximum<br>a ae|21.9%|10.0%|13.3%|15.6%|

Source: US database described in Section 2.

Notes: in Panel A the number of tariff lines and total exports are for 2001 and the LDC and Non-LDC African country exports include only the exports of the 46 countries that are AGOA-eligible between 2001 and 2017 (therefore they do not account for country-year specific eligibility and they also do not account for preference utilization). Panel B shows the average and maximum MFN tariff computed across the HS 6-digit products part of the category listed in the column heading. The total number of eligible HS 6-digit products in each category is shown in parentheses.

44
# Contents

|**Introduction**|**1**|
|---|---|
|**1 Macroeconomic fundamentals**|**2**|
|Size of the economy and volume of savings<br>2||
|Domestic and external debt 6||
|**2 Financial intermediation and bank lending**|**7**|
|Assets of financial intermediaries<br>8||
|Ratio of private bank credit to GDP as an indicator of financial depth<br>14||
|**3 Syndicated bank lending for infrastructure development**|**21**|
|**4 Institutional investors as a potential source of infrastructure financing**|**27**|
|**5 Domestic capital markets**|**41**|
|Government bonds<br>41||
|Corporate bond markets<br>47||
|Equity markets<br>53||
|**6 Conclusions and policy recommendations**|**61**|
|Macroeconomic stability, financial depth, and infrastructure financing<br>61||
|Growing potential role of institutional investors<br>63||
|Local capital markets: bonds and equities<br>64||
|The importance of corporate bonds issued to finance infrastructure<br>65||
|**References**|**69**|
|**Appendix 1 Sovereign credit ratings**|**72**|
|**Appendix 2 Basic macroeconomic data**|**77**|
|**Appendix 3 Official development assistance as a source of infrastructure financing**|**82**|

ii
<!-- Start of picture text --> Partial Retail Competition Structure<br>f cemeacr | bassinet i]<br>ununy ’ “|<br>Utility Buyer WOLESALE MARKETERS<br>Transmission<br>RoneeCustomers SrnaCustomersCommercial gaelCustomers dora CustomersLdseeenl<br><!-- End of picture text -->

<!-- Start of picture text --> Full Retail Competition Structure<br>RETAIL MARKETERS — UTILITY RERRIETERGWHOLESALE<br>Transmission<br>Distribution<br><!-- End of picture text -->