rafmacalaba/datause-encoder-data huggingface dataset:
regular issuers can turn secondary market liquidity into a lower coupon rate. Thus, the degree of primary market efficiency significantly affects economies of corporate bond finance.

# **4. Regulatory Focus on Non-retail Investors**

The regulator can differentiate the degree of investor protection by type of investors. The expeditiousness of offering procedures saves issuing expenses and opportunity costs and reduces risk. However, the issuance process period is often too long because the regulatory focus of corporate bond issuance has been placed on protection of the public. In reality, nonretail investors, especially institutional investors, are the core investor base for corporate bonds. Conventional regulation for corporate bond issuance may have been disconnecting corporate bond issuers and investors in emerging economies.

# **_4.1 Expeditious Procedures_**

The expeditiousness of offering procedures is a matter not only of expenses and opportunity costs but also of risk management. The immediacy of transactions has become increasingly important even to issuers in emerging markets to limit market risk. Most emerging economies have been integrated into the volatile global economy. Yet issuers in most emerging markets do not have means to hedge against market risk for seven to eight months or more. Even if they had it, it would be too expensive.

A typical stand-alone public offering of corporate bonds in an emerging market takes a few months or sometimes more than a year.<sup>23</sup> FOA (2005) reports that the issuance process takes 13 to 14 months in China. A survey of 28 countries done for this paper shows that the time for a stand-alone public offering ranges from 8 to 51 weeks (Box 4.1). By comparison, in the Eurobond market, a public offering for a new or infrequent issuer takes two months (eight weeks) (Table 4.1) and a few days or hours for a frequent issuer (Table 4.2). The reduction of the approval process from between 9 and 12 months to no more than 15 days seems to have contributed to the recent emergence of the Malaysian corporate bond market (BIS 2007).

> 23 This period is calculated from the time the issuer resolves the issuance of bonds until the closure of the issuance.

17
# **Appendix Table 4. Nutrition/health metrics for children and adolescents**

|**Outcome measure**|**Data source**|**Data**<br>**availability**|**Notes**|
|---|---|---|---|
|**Children under 5**||||
|**Neonatal mortality rate**and number of<br>neonatal deaths|UNICEF Global<br>Database||https://data.unicef.org/topic/child-survival/neonatal-mortality/|
|**U5 mortality rate**and number of deaths<br>of children U5|UNICEF Global<br>Database||https://data.unicef.org/topic/child-survival/under-five-mortality/|
|**Child stunting**(survey estimates,<br>updated Dec 2022)|UNICEF Global<br>Database|N=202<br>countries<br>(date depends<br>on survey<br>year)|https://data.unicef.org/wp-<br>content/uploads/2022/05/UNICEF_WHO_WB_Global_Expanded_Datab<br>ases_Stunting_Dec_2022.xlsx|
|**Child wasting**(survey estimates,<br>updated Dec 2022)|UNICEF Global<br>Database|N=202<br>countries<br>(date depends<br>on survey<br>year)|https://data.unicef.org/wp-<br>content/uploads/2022/05/UNICEF_WHO_WB_Global_Expanded_Datab<br>ases_Wasting_Dec_2022.xlsx|
|**Child overweight**(survey estimates,<br>updated Dec 2022)|UNICEF Global<br>Database|N=202<br>countries<br>(date depends<br>on survey<br>year)|https://data.unicef.org/wp-<br>content/uploads/2022/05/UNICEF_WHO_WB_Global_Expanded_Datab<br>ases_Overweight_Dec_2022.xlsx|
|**Child anaemia**: Percentage of children<br>aged 6−59 months with a Hb<br>concentration less than 110 g/L, adjusted<br>for altitude.|WHO Global Health<br>Observatory|n=124<br>countries,<br>2019|https://www.who.int/data/gho/data/indicators/indicator-<br>details/GHO/prevalence-of-anaemia-in-children-under-5-years-(-)|
|**Children and adolescents 5-19**||||
|**Overweight**|https://www.foodsystem<br>sdashboard.org/|2016|Proportion of 5-19 year olds with BMI-for-age > 1 SD above median of<br>WHO growth reference for this age group; Based on NCD-RisC<br>estimates|
|**Obesity**|As above||As above, but with BMI-for-age>2 SD above growth reference|
|**Underweight**|As above||As above, but with BMI-for-age<1 SD below growth reference|

43
# **FIGURE 5: Advanced Reformers: New "Moderate" Inflation**

<!-- Start of picture text --> 250.0<br>+ Czech Republic<br>Hungary<br>200.0 * Poland<br>/- Slovakia<br>150.0<br>100.0<br>50.0<br>1991 1992 1993 1994<br>Source: IMF<br><!-- End of picture text --> The third necessary condition of an improving wage situation was met by the group of "advanced reformers" in all five countries by 1994. Originally, all four groups of countries have experienced a very sharp real wage decline when they initiated market reforms. Even the countries of Group I experienced overall real wage declines of the range of 25 percent for two successive years in 1990 and 1991 (see Figure 6). These sharp initial losses were only partially made up in the following years. Once again, we can see that Hungary which pioneered "market socialism" experienced less pronounced wage fluctuations and smaller year-to-year real wage losses. Yet, even the reported Hungarian wage losses were of the order of 5 percent annually. Such losses would create major political tensions in Western economies.

The demand for market mortgage finance has been minuscule due to the combination of three negative factors: job insecurity that results from a sharp contraction in output; high inflation with the higher risk, higher real interest rates. and its front loading of real repayments on long-term loans; falling real wages that cut back on the ability to save. Each one of these three factors would have been individually enough to curtail the demand for mortgage finance in well-established markets economies. In addition, housing finance in transition economies is facing other obstacles in the initial years. The development of long-term mortgage lending is also shaped by the speed and quality of privatization in the real estate sector and by the sustained quality of the financial reforms pursued by the government. These two other key policy areas must also be taken into account if we want to properly gauge the potential speed of development of the new housing finance system. We now turn to privatization.

8
# **1. Introduction**

1. **Importance of Reallocations** . The reallocation of irrigation water to alternative uses is a major, and increasing concern in many countries.  Irrigated agriculture is often by far the largest water user and usually includes the least economically valuable use of water.  Growing urban water demands and a rise in the values placed on the environment and instream flows are intensifying the competition for limited water supplies.  Climate change is worsening the situation in many regions.  With new water supplies increasingly difficult and costly to develop, water reallocations from irrigated agriculture will play an increasingly important role for meeting these changing water demands and for improving the economic efficiency of water resource allocation.

2. In recent years, transfers of irrigation water to non-agricultural and often non-rural sectors have been growing both in the developed and developing countries. While systematic studies from developing countries are missing, there are indications that irrigation water allocations are increasingly taking place in the surroundings of rapidly expanding cities and/or industrial development (Molle and Berkoff, 2009). This is especially the case for cities in arid environments that run out of water in their immediate vicinity (such as Amman) and need to contemplate alternative water supplies often involving costly and distant transfers. Even in more water-abundant regions, cities in upper catchments (such as São Paulo) or in small coastal catchments (such Manila) may face serious water supply problems. In times of drought, the demand for water for urban supplies usually takes precedent over competing demands such as from agriculture, and irrigation water is temporarily transferred.

3. For developed countries, especially those with active water markets, more information is available that shows the trend toward increasing reallocations. For Australia, for example, the activity of water markets is yearly reported by the National Water Commission (2010). For Chile a review was recently carried out by Cristi and Poblete (2010). For the United States, Brewer et al. (2007) presented comprehensive data on the extent, nature and timing of water transfers across 12 western states from 1987-2005. They looked at both agriculture-to-agriculture and agriculture-to-urban transactions, and found evidence of reallocation pressures in both price trends and the nature of transactions. Prices were higher for agriculture-to-urban than for within-agriculture transfers<sup>1</sup> , and prices for urban use were growing relative to agricultural use. Markets were responding in that the number of agriculture-to-urban transactions was rising, whereas the number of agriculture-to-agriculture transfers was not. Further, there

> 1 In part, this may reflect the differences in priority and reliability of rights purchased by cities, differences in transactions costs of moving water to distant jurisdictions and perhaps from higher differences in marginal values between the two uses.

1
to Duflo’s paper which focuses on expansion of primary schools, this paper focuses on shocks that primarily influenced high school and college attainment. Using instrumental variables to identify the true returns to higher levels of educational attainment in developing countries is of particular interest because ability bias in such settings is likely to be substantial given the highly selective procedures often used to allocate scarce entrance slots in institutions of higher education to large pools of potential students. Developing country governments facing severe resource constraints must make difficult choices between investing in higher education or primary and secondary education. Accurate information on the true private returns to different levels of schooling can better inform such choices.

Studies that have estimated the returns to schooling in urban China using Mincertype specifications generally have found that the returns to schooling remained very low by international standards well into the reform period, which began in 1978, but that starting in the mid-1990s there was a steady and dramatic increase in the returns to schooling, reaching very high levels even in comparison to other countries. The best set of consistent estimates over time uses repeated cross-sectional urban survey data from 6 provinces in different regions from 1988 to 2001 and finds that over this period the returns to a year of schooling in urban China grew from 4 percent to over 10 percent (Zhang et al., 2005). In 2001, the return to high school education compared to middle school was 21 percent, and the return to college education compared to high school was 37 percent (Zhang et al., 2005).

These simple estimates of the returns to schooling in urban China are subject to several sources of potential bias, including measurement error in self-reported educational attainment and unobserved ability or school quality. A recent paper by Li et al. (2005) attempts to deal with these problems by comparing earnings differences between identical twins with different levels of educational attainment to reduce bias caused by unobserved 4
special credit institutions. This category is supposed to capture non-bank financial institutions that specialize in certain lending activities that might be more attractive for small and mediumsized enterprises, such as leasing and factoring. The final category is deposit-taking or commercial banks.

We use the FSAP data to construct two indicators. The **asset share** is calculated as each type’s assets relative to the sum of low-end financial institutions, specialized non-bank financial institutions and commercial bank financial assets gauges the importance of each segment within the financial system by dividing the total assets of each category by total financial assets of these three segments in the country. The three asset shares add up to 100.<sup>6</sup> The **average size** is computed by dividing the total amount of assets per category by the number of institutions per category.

Both indicators vary widely across our sample countries. The share of banks varies from almost 99% in Ukraine to 61% in Colombia. The share of specialized lenders varies from 38% in Colombia to less than one percent in Senegal, Ukraine, Bolivia, and Madagascar. The share of low-end financial institutions varies from 21% in Burkina Faso to less than one-half percent in Chile and Latvia. The average size of banks in USD ranges from 3.5 billion in Turkey to 10 million in Guinea-Bissau. The average size of specialized lenders varies from 350 million USD in Chile to less than one million in Mongolia. The average size of low-end financial institutions varies from 800 million in Turkey to less than one million in Mongolia.

We combine the financial structure data with data from the World Bank/IFC Enterprise Surveys. The Enterprise Surveys collect firm level-data from key manufacturing and service sectors in over 120 countries since 2002.<sup>7</sup> Countries are surveyed every three to four years but not simultaneously. To ensure data consistency and inter-country comparability we only use data from countries in the standardized dataset 2006-2010 which contains data for 100 countries.<sup>8</sup> The number of firms surveyed in each country depends on the size of the economy with more firms

> 6 There are other categories such as insurance companies or pension funds that we do not include in our analysis. 7 Only private sector firms are surveyed; fully state-owned firms are excluded.

> 8 Due to changes in the questionnaire data from the earlier years cannot be easily compared to data collected in the more recent years. In the six instances where multiple years of data are available for a given country, we keep only the most recent year of data.

7
# **Figure 2.3. PIQ-F index by EMDE groups and regions**

<!-- Start of picture text --> A. PIQ-F by country groups, simple averages<br>Index<br>0.04 0.035<br>0.02<br>0.005<br>0.00<br>-0.007<br>-0.02<br>-0.018<br>-0.04<br>Commodity Commodity EMDEs LICs<br>exporters importers without LICs<br><!-- End of picture text -->

## **B. PIQ-F by regions, simple averages**

<!-- Start of picture text --> Index<br>0.06<br>0.044<br>0.04 0.030<br>0.02<br>0.006<br>0.00<br>-0.02 -0.010<br>-0.018<br>-0.04<br>-0.06<br>-0.08<br>-0.083<br>-0.10<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

## **C. PIQ-F by country groups, GDP-weighted averages**

## **D. PIQ-F by regions, GDP-weighted averages**

<!-- Start of picture text --> Index Index<br>0.061 0.10 0.079<br>0.06<br>0.044<br>0.05<br>0.01 0.002<br>0.00<br>-0.04 -0.020 -0.05 -0.037 -0.029<br>-0.10 -0.079<br>-0.09<br>-0.105 -0.15<br>-0.14 -0.167<br>Commodity Commodity EMDEs LICs -0.20<br>exporters importers without LICs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

## _Source:_ Authors’ estimates.

_Note:_ EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean; LICs = low-income countries; MNA = Middle East and North Africa; SAR = South Asia; SSA = Sub-Saharan Africa. Higher index values indicate better public investment quality. PIQ-F index ranges from -0.6 to 0.5. A.C. The sample includes 118 EMDEs: 24 LICs, 73 commodity-exporting EMDEs, and 45 commodity-importing EMDEs. B.D. The sample includes 118 EMDEs, including 8 SAR, 41 SSA, 23 ECA, 11 MNA, 22 LAC, 13 EAP countries. C.D. GDP-weighted averages are based on 2000-21 real GDP.

13
Figure 4. Real Cost of Debt: Average for Two Moshavim and Two Supply Cooperatives <!-- Start of picture text --> 100 percent<br>50<br>-50<br>-100 .4<br>1971 1973 1975 1977 1979 1981 1983 1985 1987<br><!-- End of picture text --> The real effective cost of outstanding debt of one of the largest industrial corporations in Israel was around 20 percent (before-tax) in the 1964-1970 period. In 1978, with accelerating inflation, the real effective cost of debt of this corporation plunged tonminus 20 percent (again before-tax), reflecting the erosion of the massive financing from non-linked government-directed sources (Levy 1979). For a sample of 13 industrial corporations, the real effective cost of all loans declined from -6 percent in 1971 to -40 in 1980 (Levy and Lerman 1987).

Preliminary results obtained for four agricultural cooperatives (two moshavim and two secondary regional cooperatives) reveal a similar picture (Figure 4)0. The real effective cost of outstanding loans from all sources became progressively more negative between 1971 and 1984. In 1983-1984, with inflation rates rising to 300-400 percent per annum, the nominal average cost of debt soared, but still not fast enough, so that the real cost of debt reached an all-time low of between minus 40 and minus 60 percent in 1984. But then inflation slowed down by government policy, and nominal rates were caught on their upward path °The time development of the cost of debt of the 4 cooperatives was almost identical; the figure depicts average cost.

34
of pair-wise correlations of these variables. All data except the data on natural disasters are from World Bank’s World Development Indicators, WDI, (2007).

Four dependent variables are considered. For comparison with other studies, regressions are first run using the growth rate of real per capita Gross Domestic Product (GDP) as dependent variable. Subsequently measures of the growth rate of real per capita value added in the three major sector of the economy, that is, agriculture, industry and services are used. All of them are measured as the five-year average of the log differences of per capita output (in 2000 US dollars). Per capita output is obtained by dividing the value added of each sector by the total population.

From Appendix 1 it emerges that the growth performance of different sectors has been diverse: the service sector has grown the fastest (1.83 percent per year), followed by industry (1.73%), and agriculture (0.33%). The disparity across sectoral growth performance would be consistent with the view that natural disasters have diverse effects on the different sectors of the economy. Three groups of growth determinants are considered: 1) variables that measure transitional convergence, structural and stabilization policies, and institutions; 2) variables that proxy the role of external conditions that may affect the growth performance across countries; and 3) natural disasters, which form the subject matter of the paper. To control for transitional convergence, in each regression the corresponding initial value of output per capita (in logs) for the five-year period is used. This is crucial to test whether the initial position of the economy is important for its subsequent growth, all things equal. A negative sign would suggest that poor economies tend to catch up and grow faster than rich economies.

Similar to the cross-country growth specifications by Levine, Loayza, Beck (2000) and Dollar and Kraay (2004) the areas of education, financial development, monetary and fiscal policy, and trade openness are considered to capture the role of structural and stabilization policies, and institutions. Education is approximated by the log of the gross rate of enrollment in secondary school, which is the ratio of the number of students enrolled in secondary school to the number of persons of the corresponding age. Financial depth is measured by the ratio of private domestic credit supplied by private financial institutions to GDP. The government burden is measured as the ratio of 14
# **_Table 7b: Heterogeneous Impact of COVID-19 School Closures by Women’s Beliefs on Gender Equality (Conservative versus Liberal Societies)_**

## **_Married Sample with Child Dependents_**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||||Tim|e Spent on(<br>|Hoursper d<br>|ay)...<br>||
||Busines|s closed|||Care and|Domestic|Care and|Domestic|
||<br>(1=Yes|<br>; 0=No)|Paid|Work|<br>Respons<br>(Se|<br>ibilities<br>lf)|<br>Respon<br>(Spo|<br>sibilities<br>use)|
||_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|
|School closure due to COVID-19=1|0.007|-0.124|10.186|-1.072|15.769***|0.289|9.871**|-5.878**|
||[0.35]|[0.13]|[6.82]|[3.36]|[4.45]|[1.82]|[3.97]|[2.76]|
|Women's Individual Beliefs (Share 0-1)|-0.087|0.068|11.150***|5.201**|2.510|4.295***|4.456**|-5.570***|
||[0.16]|[0.10]|[3.25]|[2.12]|[3.24]|[1.28]|[2.00]|[1.72]|
|School closure due to COVID-19*Women's Individual|||||-||||
|Beliefs|0.031|0.128|-10.726|1.476|15.868***|-0.166|-10.364**|6.715**|
||[0.37]|[0.15]|[7.18]|[3.58]|[4.67]|[1.94]|[4.18]|[2.93]|
|GDP per capita (Ln)|-0.001|-0.025***|0.196*|0.535***|0.160*|0.027|-0.280***|-0.079|
||[0.01]|[0.00]|[0.11]|[0.09]|[0.09]|[0.05]|[0.08]|[0.07]|
|Constant|0.099|0.150|-4.134|-1.471|-0.068|-1.060|1.749|11.082***|
||[0.18]|[0.11]|[3.47]|[2.31]|[3.15]|[1.35]|[2.05]|[1.88]|
|Observations|11692|23578|6794|13759|11692|23578|8806|17960|
|Adjusted R-squared|0.10|0.09|0.07|0.09|0.14|0.04|0.06|0.06|
|Mean|0.14|0.13|6.83|7.95|6.38|4.09|3.60|6.21|
|Sample|FEMALE|MALE|FEMALE|MALE|FEMALE|MALE|FEMALE|MALE|
|Notes:* significant at 10% level, ** significant at 5% level, *** sig<br>[1] School Closure due to COVID-19 is a dummy variable where 1 = scho<br>agreed with the statement that_men and women should have equal oppo_|nificant at 1% le<br>ols closed in coun<br>_rtunities_measure|vel.<br>try at time of s<br>d prior to the C|urvey wave. W<br>OVID-19 pande|omen's Individ<br>mic.|ual Belief is the|proportion of|women in the c|ountry who|

<mark>[2] OLS pooled regressions include Region of the world and Time Fixed Effects and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls</mark> (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents). Academic Break control is included in the regressions but not shown in the table.

[3] Sample is restricted to business owners who are married or living with a partner with any child dependents aged 18 years or younger in the household and is split by gender with FEMALE (Columns (1), (3), (5), and (7)) versus MALE (Columns (2), (4), (6), and (8)).

44