rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure VI-3: Real Credit Growth and Medium-term Credit Cycle**

## **A- Oil exporter**

<!-- Start of picture text --> Kuwait: Real Credit<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br>100<br>50<br>0<br>-50<br><!-- End of picture text --> <!-- Start of picture text --> Algeria: Real Credit<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br>UAE: Real Credit<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br>100<br>50<br>0<br>-50<br>-100<br>60<br>40<br>20<br>0<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Iran: Real Credit<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br>Bahrain: Real Credit<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br>60<br>40<br>20<br>0<br>-20<br>30<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Oman: Real Credit<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br>100<br>50<br>0<br>-50<br><!-- End of picture text --> 34
also for consumers. Micro-credit programs have spread rapidly as a policy instrument for helping poor populations since the 1970s when they were first introduced. Initially, the loans were granted to both men and women, but in the 1980s there has been increased targeting of loans toward women, under the view that women are more likely to be credit constrained than men, that women have an inequitable share of power in household decision-making and that women may face discrimination in the regular labor market. Among the programs described below, some are gender targeted and some are not but are inclusive of women. In many cases, the programs also use poverty-based targeting mechanisms.

The Grameen Bank in Bangladesh is probably the best known of the microcredit programs. It provides loans for nonagricultural self-employment activities for poor borrowers, usually under a group lending arrangement, and has served as a prototype for similar lending programs around the world. In the case of the Grameen Bank, borrowers form small groups that are held jointly liable for repayment of loans, that is, all members of the group are ineligible for further lending if any one member defaults. It is thought that group members can better monitor each other‟s activities than can bank lenders, so that these programs may be able to overcome screening, monitoring and enforcement problems that can lead to market failures in the lending market. Microcredit programs often also provide other noncredit services, such as savings programs, training for skill development, literacy, investment strategies, health training, and programs aimed at changing women‟s attitudes. There are also relatively simple microfinance programs that increase access to credit for borrowers, without any group liability arrangement or training components.

The Grameen Bank and similar programs are often considered successful on the basis of high loan recovery rates, typically in excess of 90%, and high observed demand for loans, although these two indicators do not necessarily reflect productive use of funds. There are relatively few evaluation studies that assess directly the rates of return for microfinance programs, some of which are described below.

# **4.1  Microcredit/Grant Programs in Asia**

## **4.1.1  Group-based Microcredit Programs in Bangladesh**

There have been a large number of papers evaluating the effects of micro-lending programs in Bangladesh and the literature has reported some contradictory findings, even for studies based on 28
Table 5: Storm fatalities and institutional quality

||5.1|5.2|5.3|5.4|5.5|5.6|5.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDj,t-1_)<br>Ln(_GDPj,t-1_)<br>_CORR CONTjt_|0.784**<br>(0.342)<br>-0.673***<br>(0.169)<br>0.474<br>(0.485)|0.817**<br>(0.353)<br>-0.667***<br>(0.152)|0.726**<br>(0.307)<br>-0.627***<br>(0.138)|0.833*<br>(0.442)<br>-0.577***<br>(0.129)|0.767**<br>(0.332)<br>-0.658***<br>(0.175)|0.772**<br>(0.329)<br>-0.372**<br>(0.160)|1.075***<br>(0.345)<br>-0.208*<br>(0.115)|
|_GOV EFFjt_||0.389<br>(0.304)||||||
|_POL STABjt_|||0.532*<br>(0.202)|||||
|_REG QALjt_||||0.123<br>(0.572)||||
|_RULE LAWjt_|||||0.269<br>(0.419)|||
|_VOICE ACCjt_||||||-0.454<br>(0.373)||
|_DEMOCRACYjt_|||||||-0.116***<br>|
||||||||(0.025)|
|Ln(_POP jt)_|0.048|0.054|0.016|-0.001|-0.003|-0.081|<br>-0.032|
||(0.315)|(0.307)|(0.322)|(0.296)|(0.308)|(0.295)|(0.209)|
|Ln_(POPDENSjt)_|0.102|0.047|0.227|0.015|0.048|0.032|0.279|
|_OPENjt_|(0.236)<br>0.001|(0.250)<br>0.000|(0.262)<br>0.001|(0.282)<br>0.002|(0.260)<br>0.002|(0.246)<br>0.001|(0.170)<br>0.005|
|_MAGNITUDEijt_<br> _NATHAZ – RISKj_|(0.006)<br>0.012***<br>(0.003)<br>-0.200**|(0.005)<br>0.012***<br>(0.003)<br>-0.178**|(0.006)<br>0.013***<br>(0.003)<br>-0.208**|(0.006)<br>0.012***<br>(0.002)<br>-0.189**|(0.006)<br>0.012***<br>(0.003)<br>-0.168**|(0.006)<br>0.013***<br>(0.002)<br>-0.188***|(0.005)<br>0.012***<br>(0.002)<br>-0.225***|
|_LATITUDEj_|(0.081)<br>-0.031|(0.074)<br>-0.027|(0.066)<br>-0.034**|(0.075)<br>-0.015|(0.079)<br>-0.020|(0.071)<br>-0.018|(0.079)<br>-0.037***|
|_ELEVATIONj_|(0.028)<br>-0.001***<br>(0.000)|(0.020)<br>-0.001***<br>(0.000)|(0.016)<br>-0.001***<br>(0.000)|(0.020)<br>-0.001***<br>(0.000)|(0.020)<br>-0.001***<br>(0.000)|(0.014)<br>-0.002***<br>(0.000)|(0.011)<br>-0.002***<br>(0.000)|
|Obs.|<br>292|<br>292|<br>292|<br>292|<br>292|<br>292|<br>292|
|Log pseudolikelihood|-1387.450|-1387.637|-1386.003|-1388.219|-1388.010|-1387.101|-1377.600|
|Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|0.000|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

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Figure 9: Capital and its Rental Rate <!-- Start of picture text --> (A) Capital Stock Relative to 1968 (B) Rental Rate of Capital<br>ICT Capital ICT Capital<br>Non-ICT Capital Non-ICT Capital<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>Chain Index (1968=1)<br>Real Rental Rate (% of Output)<br>10<br>10<br>0<br><!-- End of picture text --> _Notes:_ Panel A graphs the stock of ICT and non-ICT captial relative to its 1968 level. Panel B depicts asset-specific real rental rates ( _Ri,t/Pt_ ) in % of final output, derived from expressions (A.5)-(A.6) in Appendix A. The underyling data are the BEA’s detailed fixed-asset accounts. The dashed vertical lines indicate the year 1968.

# 3.2. The Role of Housing

Rognlie (2015) has recently argued that housing may be the main driver for an increase in the net capital share—the capital income share, net of depreciation—since 1970. In light of this, we briefly discuss the role of housing within the context of our analysis. To this end, we use the methodology described in Section 3 to decompose the non-ICT share into residential and non-residential assets. Figure 10 illustrates the resulting decomposition, again based on the BEA’s fixed asset accounts.<sup>21</sup> This figure highlights that the non-residential non-ICT capital share is completely trend-less throughout the period. Consistent with the findings of Rognlie (2015), the increase in the nonICT capital income share in the post 2001 period is accounted for entirely by residential capital. Since the rising residential capital share is unlikely to be reflective of automation, our calibration assumes that the decline in the labor share which is attributable to ICT accumulation is only the portion that is directly countered by an increase in the ICT share.

> 21Note that this more disaggregated decomposition takes into account heterogeneous prices and depreciation rates for residential and non-residenital assets as measured by the BEA and depicted in Figure D.15 in Appendix D.

20
The research on the local accountability project included several economic and political outcomes, and so the questionnaire asked a range of different questions, including ones concerning demographics, asset ownership, consumption of alcohol and tobacco, and political party preferences.

The survey was conducted by 90 enumerators, divided into 4 teams, one for each language group of Uganda. Given the length of the survey and the fact that 8 individuals were to be interviewed each day, two enumerators were needed per village. During the data collection the authors randomized, within the teams, which village an enumerator would visit to conduct interviews. Thus, the pairing of enumerators visiting each village was random. Yet, it was not possible to randomize which specific individual within a village an enumerator interviewed. This could bias the analysis if enumerators could systematically choose individuals to interview of a specific type. However, in the analysis to follow, it is shown that this is unlikely to be the case.

During the morning of the day of data collection, the field manager or team leader assigned a village which was randomly determined using pre-developed randomization lists to each enumerator with the list of households to be interviewed.<sup>4</sup> Randomization was stratified by distance to ensure that if an enumerator went to a distant village the day before, he or she would go to a closer one the current day, and vice versa. That is, the field manager or team leader, before randomizing, split them into two groups: near and distant. An enumerator who went to a distant village the day before was given a randomly selected village from the near group. This process was done for each enumerator, making sure the

> 4 Each village was visited the day before by a mobilizer, who confirmed that pre-selected individuals will be available for interviews the next day.

7
O.A.M.D.G.

# **Sample Selection Issues**

There may be issues of sample selection in the database we use for our analysis. As explained earlier, we use the Familias survey as the backbone for our analysis and merge it at the municipality level with violence and municipal variables.

The households in the Familias survey have been identified from the government’s register for the lowest income stratum (the SISBEN dataset). When confirming the addresses of the around 20,000 households, the surveyors found that about 40% had left their municipality. It is not likely that these 40% left at random. Given that our work examines migration patterns and links them to violence, we need to test the assumption that the leavers censored the sample we ultimately use. In other words, we observe a sample of ‘stayers’ in municipalities that experienced (potentially systematic) displacement already. The reduced sample may be different from the original sample censored by out-migration, and display a stratification that is not representative. Therefore, our regression results may be different than with a complete sample. Likewise, peaceful locations will have experienced immigration of people marked by violence elsewhere.<sup>12</sup> Therefore, controlling for violence at the municipality level, rather than the personal history level may also bias our results.

There are various ways to remedy the bias in the empirical specification, depending on the specification used. If the specification is linear, and the selection process from the original sample is known, the standard remedy is Heckman’s (1979) two-step procedure.

> 12 In this context, the findings of Glaeser and Shapiro (2001) are interesting, which suggest that (terrorist) violence shapes the choice of settlement form, e.g. a dense urban setting allows more mutual protection and short transport times. In this sense the fairly urban structure of our sample (50% urban settlers) is likely to have been shaped by years of violence.
# **Box  5.1: Corporate Bond Issues in Singapore**

Singapore’s corporate bond market consists of Singapore-dollar (S$) and non-Singapore-dollar segments in terms of currency. Although the former is growing modestly, the latter is larger and growing fast (Figure 5.1). Despite its short history, the Singapore corporate bond market is sophisticated. Both domestic and foreign issuers in the market routinely use medium-term note programs and special-purpose vehicle structures. Professionals in the market are abreast of new financial technologies and techniques.

Nonpublic offerings are dominant in the market. Public offerings of Singapore-dollar corporate bonds accounted for 35 percent of the total corporate bond issuance in 1999, when the market saw policyinduced public offerings by statutory boards. However, their share fell sharply to 1 percent in 2002. Public offerings of non-Singapore-dollar corporate bonds accounted for only 3 percent of the total corporate bond issuance in 1999, further declining to 0 percent in 2002 (Table 5.3). Although the same data for 2003 onward is no longer available,<sup>47</sup> the dominance of nonpublic offerings is considered to have continued. Unlisted bonds accounted for 56 to 81 percent of issues of nongovernment debt securities in terms of issue amount from 2003 through the first quarter of 2007 (Table 5.4).

**Figure  5.1: Size of Singapore Corporate Bond Market (1997–2006)**

<!-- Start of picture text --> 140 131.1 100<br>89 .8 90<br>120<br>76.6 80<br>100 64.5 95.0 66.7 70<br>80 58.3 60.2 60<br>47.7 48.0 5 0 91 . 0 50<br>57.0<br>60 50.0 40.8 47.9 40<br>34.0 33.1<br>40 27.0 36.1 30<br>25.7<br>20 1.7 6.713.018.0 5.2 3.912.020.0 10.39.216.0 1 4 .415.5 22.0 19.218.2 19.3 21.7 20.0 2010<br>0 0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>Non-S$ debt issuance (left scale) S$ debt issuance (left scale)<br>Outstanding S$ debt (right scale) Outstanding non-S$ debt (right scale)<br>Issuance Amount (S$ billion)<br>Outstanding Amount (S$ billion)<br><!-- End of picture text --> _Source:_ Monetary Authority of Singapore

**Table  5.3: Private Placements and Public Offerings of Corporate Bonds in Singapore (1999–2002)**

|||_1999_|_2000_|_2001_|_2002_|
|---|---|---|---|---|---|
|_S$ issues_|Private placement|65%|85%|96%|99%|
||Public offering|35%|15%|4%|1%|
|_Non-S$ issues_|Private placement|97%|98%|99%|100%|
||Public offering|3%|2%|1%|0%|

_Source_ : Monetary Authority of Singapore. .

**Table  5.4: Percentage of Issue Amounts of Unlisted Bonds in Issues of Nongovernment Debt Securities in Singapore (2003–07)**

|_2003_<br>_2004_|_2005_|_2006_|_2007Q1 _|
|---|---|---|---|
|56%<br>68%|75%|67%|81%|
|_Source:_Monetary Authority of Singapore.||||

47The Monetary Authority of Singapore discontinued the collection of bond data on the basis of the breakdown between private placement and public offering in 2003.

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surveys, and section 5 concludes.

# **2 Background on ECD in Nepal**

Nepal has made large investments in early childhood education in recent years, with enrollment in early childhood education and development (ECED) centers increasing from 12% in 2000 to 86% in 2017 (Poudyal et al. (2019)). In Nepal, ECE centers are either privately run or government run. The government-supported centers are either community-based or school-based. Our sample includes teachers in school-based centers, which are located near and connected to primary schools in the area. These centers work to promote the cognitive, physical, emotional, social, and language development of children in them, as well as educate parents about these realms of development. The constitution of Nepal states that "every child shall have the right to early childhood development" and Nepal has adopted a goal for every child to receive education in an ECED center by 2030.

The number of ECED centers was over 36,000 in 2017, serving almost a million children (World Bank (2020)). The ECED gross enrollment ratio (GER) has increased from 63.4% in 2007 to 84.1% in 2017, and the percentage of grade 1 students with ECED experience has doubled from 33% in 2007 to 66.3% in 2017. While ECD expenditure has been increasing, the budget for ECD services is still less than 0.1% of GDP and is between 1-3% of Nepal’s expenditure on education (Unicef (2019)). Despite the rapid expansion of ECED access, quality remains a concern. In 2014, less than one third of Nepalese children aged 36-59 months were developmentally on track in literacy and numeracy and only 64% were on track in the social-emotional domain according to UNICEF’s Early Child Development Index (Unicef (2019)). ECED facilitators are required to have completed grade 10 and ECED customized teachers’ training curriculum was released in 2018; this training was developed as a refresher curriculum targeting teachers who have already taken the basic ECED training in the past. However, the percentage of facilitators with one-month of training was only 3
rainfall datasets produced by long-running Earth-observing satellites are often lower resolution, have spatial or temporal gaps in data coverage, and exhibit variation in the fidelity and methodology of measurements over time.

In our empirical analysis, our preferred measure of precipitation shocks is a variable we call `dry days’, which varies at the annual and secondary sub-national administrative unit (ADM2) level. To construct this variable, we start with the daily total precipitation values observed in all ERA5 grid cells that are contained within the ADM2 units where we observe at least one firm in the ES data. Then, to focus on contemporary climate and match the temporal coverage of the ES data, we restrict the daily data to the period from 1990 to 2021. We then compute the long-run (1990-2021) mean and standard deviation for each day of the year in each grid cell. Using these moments of the local climate distribution, we then classify whether the precipitation observed on each day is more than 1 standard deviation below the long-run average for that particular day of the year. Days that satisfy this condition are considered `dry’ days. Finally, we sum across days within grid-years and average across ERA5 grid cells within ADM2 units to arrive at the final measure of dry days that we use in our primary empirical analysis. In many of our specifications, we also include an analogous measure of `wet’ days, which captures the number of days in a year where rainfall was more than 1 standard deviation above the long-run day-of-year average.

Previous studies have often measured rainfall shocks in terms of (normalized) deviations of total cumulative annual or seasonal rainfall from location-specific long-run averages. Standardized drought indices, such as the SPEI or PDSI, have also been widely used. Relative to these standards, our dry (wet) days measure has two key advantages. First, our measure captures extreme dryness (wetness) events even when they occur over a period of just a few days (or even just a single day). This helps us identify nonlinear effects which 8
# 5. Corporate Stress Test Results

**We focus on the sub-sample with non-missing ICR and QR, which tends to contain larger, more leveraged corporates.** Of these, 9,656 firms in 86 countries do not have missing ICR and QR data (of which 56 countries have five or more firms). Together, these firms represent US$ 15.3 trillion in total assets and US$ 4.31 trillion in debt. Firms that have non-missing ICR or QR data tend to be larger compared to firms that have missing data (total assets of the median firm are US$ 84 million vs. US$ 45 million) and they are also more leveraged (median debt to assets is 17.7 percent vs 9.4 percent).

**By the end of 2019, about 15 percent of debt was associated with firms that experience ICR challenges. Progressive shocks to corporate earnings result in a non-linear increase in total debt at risk according to the ICR.** Exhibit 5 presents D as a function of the magnitude of negative corporate earnings shocks. In the absence of any earnings shock, 16 percent of corporate IICCII debt has an ICR less than 1 and is thus considered “at risk”. This suggests that interest expenses DD are relatively small compared to EBIT for most firms. However, debt at risk increases non-linearly with the magnitude of earning shocks. For example, a 10 percent shock to earnings increases the fraction of debt at risk from 16 percent to 19.5 percent, a 19 percent increase. However, a 40 percent shock to earnings almost leads to a doubling of debt at risk. In the case of a 50 percent earnings shock, 52 percent of all corporate debt is considered at risk, an increase of 220 percent.

**Before the pandemic, relatively weak QRs were a fairly common phenomenon. As a result, total debt at risk according to the QR is less responsive to progressively larger shocks than for ICRs.** Exhibit 6 presents D as a function of the magnitude of negative receivables shocks. Absent a shock to receivables, 60.3 percent of corporate debt is considered vulnerable due to the QQII DD inability of current assets (minus inventories) to cover current liabilities, suggesting elevated vulnerabilities to liquidity strains. A 10 percent shock to receivables results in the largest relative increase in debt at risk to 71.7 percent, a 35 percent increase. Subsequent larger shocks lead to much smaller relative increments of debt at risk. A 50 percent shock to receivables renders 91.8 percent of corporate debt being at risk, a 52 percent increase.

11
shown inconsistent gradient between child mortality and wealth quintiles, which could be due to data errors or the construction of the wealth index itself.<sup>16</sup> Third, the prevailing wealth-related inequalities in some services are not narrowing. Despite the pro-poor progress, the gap between the poor and the rich is still large; the utilization of these services is more concentrated among richer individuals. This is particularly the case for antenatal care visits and skilled birth attendance. For these services, the concentration curves for the latest survey are far below the equality line. This shows that the poor still faces lower odds of making the required antenatal care visits from a skilled health professional and delivering with the assistance of a skilled birth attendant. However, given the narrowing trends of the inequality, the poor would benefit proportionally more from further expansion of these services.

Fourth, decomposition of inequalities shows that differences in wealth ranking are the most important contributor to the inequity. Other major contributors to health inequity include education. It is either maternal education in child health models or own education in maternal health services. The decomposition also shows that location variables, including regional differences and place of residence, matter.

> 16 Observations from child mortality data compiled from DHS stat compiler <u>http://beta.statcompiler.com/</u>

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