rafmacalaba/datause-encoder-data huggingface dataset:
After this introduction, we present the recent trends in sovereign bond issuances to illustrate the increase in the funding denominated in EUR. In section III, we describe the market for EURdenominated debt and its particularities compared to the USD market. In section IV, we summarize the motivations behind EUR-denominated issuances, and in section V, we present selected cases of countries that have recently stepped up their issuance in EUR. Finally, section VI concludes. In addition, the annex summarizes the calculations to compare the cost of a bond issued in EUR with the cost that would result from issuing a bond in USD and then swapping the obligation into EUR.

# **II. Recent trends in sovereign bond issuances**

Compared to the EUR market, the USD market for both sovereign and corporate EM issuers is much more established and liquid. While in 2016 EM sovereigns issued close to USD120 billion in USD, the placements in EUR were close to USD30 billion, about one-fourth. As illustrated in figure 1, the USD market has traditionally been much larger, but the market for EUR-denominated obligations has gained relevance in the EM space after 2013.

**Figure 1: EM sovereign issuances denominated in EUR and USD (billions)**

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>USD EUR<br>USD billions<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Source: Bloomberg The continued expansionary policy of the ECB has enticed emerging economies, both near and far from the Eurozone, to step up their borrowings in EUR. This has been the case of Turkey, the Russian Federation, Poland, Romania, Croatia, the Czech Republic, and Hungary;<sup>4</sup> all of them

> 4 Examples of countries close to the Eurozone that have significantly switched their funding from USD to EUR sometime after the Global Financial Crisis include Hungary: USD funding from 2005 to 2011 and EUR from 2010

4
**Table A3: Dynamics Selection into who responds to the long-run surveys**

|||<br>Full sa|mple|||<br>Control|group||Pers|<br>onal initiativ|e training g|roup|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Both|Only one|Neither||Both|Only one|Neither||Both|Only one|Neither||
||surveys|survey|survey|p-value|surveys|survey|survey|p-value|surveys|survey|survey|p-value|
|_Round 4 survey variables_|||||||||||||
|Answered Round 4|0.97|0.88|0.63|0.000|0.97|0.85|0.67|0.000|0.97|0.90|0.63|0.000|
|Open in Round 4|0.93|0.89|0.80|0.000|0.94|0.90|0.83|0.027|0.91|0.88|0.83|0.208|
|Above Median profits round 4|0.57|0.50|0.39|0.000|0.55|0.49|0.37|0.013|0.60|0.54|0.46|0.134|
|Personal initiative in round 4|4.52|4.52|4.49|0.828|4.49|4.45|4.43|0.503|4.57|4.60|4.58|0.721|
|Business practices in round 4|0.63|0.59|0.46|0.000|0.59|0.56|0.48|0.002|0.65|0.59|0.50|0.001|
|_Known operating status in long-run_|||||||||||||
|Self-employed after 7 years|0.97|0.92|0.06|0.000|0.96|0.92|0.09|0.000|0.97|0.95|0.04|0.000|
|Monthly Profits after 7 years|248|167|||204|145|||289|213|||
|Sample size|915|336|249||279|115|106||334|101|65||

Notes: Both surveys denotes firms that replied to both in-person and phone 7-year survey. Only one survey denotes firm replied to only one of the two survey types, and neither denotes firm did not respond to either survey type.

Round 4 survey was last short-run survey conducted, at 2.5 years post-training. Self-employed after 7 years is based on those who responded to long-run survey, or who had operating status reported by proxy report, were dead or internationally migrated, but is only available for 36% of those responding to neither survey. Monthly profits after 7 years not available for those not answering any surveys.

p-value is for test of equality of means across the three groups (both, only one, neither).

31
_Yijt_ = _β_ 0 + _β_ 1 _Femaleijt_ + _β_ 2 _SchoolClosedjt_ + _β_ 3 _SchoolClosedjtxFemaleijt_ + _β_ 4 _AcademicBreakjt_ + _β_ 5 _AcademicBreakjtxFemaleijt_ (3) + _γ_ 1<sup>_′Xijt_+</sup><sup>_γ_2</sup><sup>_Vjt_+</sup><sup>_γ_3</sup><sup>_lnGDP_2019+</sup><sup>_δr_+</sup><sup>_αt_+</sup><sup>_εijt_</sup> where _Yijt_ is the outcome of interest for firm i in country j at time t. _Femaleijt_ is a dummy variable equal to 1 if the business owner is female; 0 if male. _SchoolClosedjt_ is a dummy variable equal to 1 if schools were fully or partially closed in the country due to COVID-19 at time t of the survey wave. _Xijt_ is a vector of controls of firm i which includes controls for sector of business, firm size and age, role in the business, and demographics of the business owner, including marital status and types of dependents. In order to control for other factors that may be influencing both school closure and firm outcomes, we also control for _AcademicBreakjt_ and _AcademicBreakjtxFemalejt_ that indicates whether the country j was in a scheduled academic break at the time of survey wave t. _Vjt_ is a set of mobility controls in country j at time t. _lnGDP_ 2019 is the natural log of the Gross Domestic Product (GDP) per capita in country j for the year 2019 i.e. before the pandemic. _δr_ indicates region-of-the-world fixed effects, _αt_ are time fixed effects in the form of survey wave dummy variables. _εijt_ is the error term. We use robust Eicker-Huber-White (EHW) standard errors throughout.

Finally, we look at whether the school closure shock is either mitigated or accentuated by gender norms through the OLS estimation of equation 4: Variables are defined as in equation 3, with the addition of norms from the 2019 round of the FoB data set. In this analysis we split the sample by gender of the firm owner for ease of interpretation. It is worth noting that the control for GDP here helps control not only for pre-existing economic conditions, but also any effect that levels of national wealth might have on norms.

17
According to the well-known literature of ‘insurance’ motives and fertility decisions, uncertainty of children’s survival may not only have an effect on school enrolment but also on fertility decisions. Kalemli-Ozcan (2006) examines the impact of the epidemic on fertility decisions in a panel of 44 Sub-Saharan African (SSA) countries and concludes that HIV/AIDS affects the total fertility rate positively and school enrollment rates negatively. She argues that those results are consistent with theoretical models of precautionary demand for children in the face of uncertainty about child survival. That is, in a high mortality environment, parents will choose to have more children and provide each child with less education. Hence, HIV/AIDS would contribute to reversing the fertility transition and accumulation of human capital leading to significant negative long-run effects on growth and welfare. In contrast, Young (2005) uses micro data from 27 SSA countries and finds that the HIV epidemic decreases fertility and has no systematic influence on human capital. He argues that this decline is associated with behavioral changes such as increased use of condoms. Young’s regressions might be picking up a specific fertility pattern and response to the epidemic. This is more so if there are substantial variations within and across African countries in HIV prevalence that make empirical results difficult to generalize (Beegle and de Walque, 2008). We are less convinced about Young’s calculations that the decline in fertility brought about by the epidemic will create sufficient resources for medical care to fight the epidemic. He argues that a decline in fertility will lower dependency ratios, increase savings rates, provide future cohorts with more capital per person, and make it possible to allocate resources for medical care. However, resources are not only needed to fight the epidemic but to combat extreme poverty and malnutrition, which were already pervasive before the onset of the epidemic and have contributed to its transmission. This trade off and changes in labor force composition (as a consequence of the epidemic) should also be factored in his calculations.

Besides the direct health costs of the disease, the nature and the way HIV/AIDS is transmitted has mainly created: a) social stigma and discrimination frequently attached to infected individuals which threaten to break family and community ties (Gaffeo, 2003) and b) market failures because of asymmetric information and externalities (Gersovitz and Hammer, 2003). The scope for public intervention arises from the uncertainty 6
Table 10: Corruption and decentralization: controlling for fixed country effects

||paid bribe|bribe<br>bur-<br>den|informal<br>gift|bribe%sales|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|main dec. index|-0.02|-0.44*||-0.13**|
||-0.03|-0.26||-0.07|
|corruption perception,|-0.02***|0.05***|-0.36***|-0.01***|
|2000|||||
||0|-0.02|-0.01|0|
|exp. dec., 2000|-0.01|-0.58*|3.35***||
||-0.04|-0.3|-0.63||
|aux. dec. index|||-1.77***||
||||-0.29||
|aux. dec. index, 2000||||-0.10***<br>-0.02|
|Observations|23301|23079|25254|23408|
|R-squared|0.07|0.03|0.02|0.02|

Note<sup>∗</sup> - significant at 5% level,<sup>∗∗</sup> - significant at 3% level,<sup>∗∗∗</sup> - significant at 1% level. Dependent variable: column (1) - paid bribe; columns (2) - paid bribe; columns (3) - informal gift; columns (4) - bribe%sales (see definitions in Table 2). Estimation method: linear probability (OLS). Heteroscedasticity robust standard errors are reported in brackets.

they are simply not reported. Therefore, severe measurement error could inhibit any signs of predicted behavior. The same role could be played by a small variation of decentralization in our main samples - GCB concentrates mostly on high income countries, while WBES - on low income countries. However, there may be a problem with the fixed effect estimation - fixed variable, which drives both decentralization and corruption, may be not that fixed after all. In this case, an exogenous source of variation for a change in decentralization is needed to provide consistent estimates.

# 8 Conclusion

This paper has pursued rigorous quantitative analysis to explore the impact of decentralization (empowering of local governments) on the incidence of corruption in a sample of 182 countries. The paper has shown that decentralization, when properly measured to mean moving government closer to people by empowering local governments, has a significant negative (positive in the sense of good governance) effect on corruption regardless of the choice of the estimation procedures or the measures of corruption used. In terms of various dimensions of decentralized governance, political decentralization matters even when fiscal decentralization is controlled for. Further voice (political accountability ) is considered more important in combating corruption than exit options made available though competition among jurisdictions. Shah (2006)

# References

- Ahlin, C. (2000). Corruption, aggregate economic activity and political organization, University of Chicago, processed.

34
day (conditional on going to work) are worked. This distinction makes sense in the presence of fixed commuting costs per day (Gutierrez-i Puigarnau and van Ommeren, 2010), which is the case treated here. This decision however is not entirely voluntary in this case given that the fixed costs of commuting are in certain days (i.e. during severe closures) prohibitive. For this reason the direct effect of the barriers is expected to reduce the number of days worked over a period of time. This prediction is also consistent with the recent theoretical and empirical evidence on the effect of an increase in commuting costs and time. Gutierrez-i Puigarnau and van Ommeren (2010) show that an increase in daily commuting time induces workers to reduce their overall commuting time by reducing the number of workdays. On the other hand once the worker reaches the workplace she may want to work more hours to compensate for the lower number of working days. Therefore the direct effect of closures may be positive on the hours worked per day conditional on the worker reaching the workplace, a prediction confirmed by the evidence in Gutierrez-i Puigarnau and van Ommeren (2010).

Again, the indirect channel of closure is expected to have a negative impact on the number of hours and days worked as the firms’ production and demand for labor decline. While a sizable share of the labor adjustment by the firms following the fall in production could happen by downscaling employment (as argued above), some adjustment could also occur via a reduction in the average per worker labor inputs. The empirical analysis below explores to what extent that is the case.

# **4. Data**

The bulk of our data come from two main sources: labor force surveys data collected by the Palestinian Central Bureau of Statistics (PCBS) and GIS data on various types of physical barriers to movement over time collected by ARIJ. We also complement the latter data with data collected by OCHA.

The Palestinian Labor Force Survey (PLFS) of the West Bank and Gaza Strip began in 1995 following the creation of the Palestinian Authority (PA) and since then it has been administered every quarter to a nationally representative sample of households. The PLFS surveys each household four times over a period of six quarters: each household stays in the sample for two consecutive quarters, and after a break of two quarters it returns to the sample for 12
Policy Research Working Paper 9805

# **Abstract**

Do Sahelian countries face specific risks of water-related conflict? Sahelian countries face growing fragility and climate challenges—especially those belonging to the Group of Five Sahel States (known as the G5 Sahel)—Burkina Faso, Chad, Mali, Mauritania, and Niger. This study examines how their relation to water availability and irrigation infrastructure factors in. It documents that the G5 Sahel countries, given their high baseline water scarcity and state fragility, face a higher risk of conflict over water resources compared to the rest of Africa. This is demonstrated through empirical analyses using geospatial data and exploiting (i) climate-induced variation in water availability, and (ii) an event study analysis of conflict trends, which sharply increased post-2010 in the region following the Arab Spring and the rise of the Boko Haram. Irrigated areas are found to be important for buffering against weather shocks but are also more prone to targeting during conflict events compared to non-irrigated regions. The evidence suggests that this reflects increased competition for scarce (fertile) resources between state and rebel groups on this climate frontier with a well-documented history of agropastoral conflict. Other regions of Africa are not found to experience similar conflict related to water resources. These findings are especially pertinent for informing projects and policy interventions in fragile countries as post-COVID-19 recovery and climate action plans are rolled out.

This paper is a product of the Water Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at akhan31@worldbank.org and arodella@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# 4 Conclusions

The disruption of bank lending following the global financial crisis that erupted in 2007 resulted in sharp output contractions in both emerging and advanced countries. This brought up the question of how the duration of credit tightening and impaired bank intermediation affect output and its recovery patterns. Recent trends in private sector deleveraging and balance-sheet adjustment suggest that subdued credit growth during the recovery represented an obstacle for growth. Against this background, policy responses have been substantial, ranging from increases in fiscal spending, sizable monetary expansions, and bank recapitalizations.<sup>11</sup> Despite these interventions, in many countries worldwide recoveries have been weak, and often accompanied by a modest pick-up in credit flows.

In their pioneering study on sudden stops in capital flows, Calvo et al. (2006) were the first authors to document credit-less recoveries in emerging markets, naming them Phoenix Miracles. Subsequent empirical literature shows that these episodes are a common feature of business cycles in both advanced and emerging economies, and that they bring relevant costs for countries. Output growth is between 2 and 3 percent lower than during "normal" recoveries, and it takes longer for it to recover to its pre-crisis levels.

An open question remains whether weak credit growth is a result of demand or supply constraints to lending activity. Providing evidence on this point is important not only for economic theory but also for policy design. The lack of comprehensive empirical evidence in this area has limited economic research in providing detailed insights on the role of policy responses following these episodes.

The primary goal of this work was to analyze demand and supply drivers of bank credit growth during economic recoveries. Building on Chadha et al. (2010), we analyze how liquidity shocks impact on credit conditions in an endowment economy model. In our framework, credit-less episodes are correlated with demand and supply shocks in real and financial markets and with the pace of private sector deleveraging. As in Biggs et al. (2009), we show that a recovery in output can occur in the presence of negative credit growth as long as the pace of deleveraging is decreasing. We use panel data on output, credit and the external finance premium for a sample of 42 advanced and emerging countries between 1980 and 2014 to identify demand and supply shocks in real and financial markets. Through panel Probit model techniques we analyze the relative impact of these shocks in predicting the onset of a credit-less recovery.

Findings show that in the sample of advanced and emerging countries, stagnant credit growth during credit-less recoveries has been (on average) primarily demand-constrained. These episodes appear the outcome of weak demand for liquidity by the private sector in the aftermath of recessionary

> 11See for example Jiménez et al. (2017) for evidence on the effect of countercyclical bank capital buffers and credit supply.

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# **Appendices**

## **A Tables**

Table A1: Self-selection into completion of follow-up surveys

||_Mean_|_Mean_|_Mean_|_Norm.Dif._|_Norm.Dif._|_Dif=0_|_Dif=0_|
|---|---|---|---|---|---|---|---|
||Complete<br>only<br>baseline<br>N=4232<br>(1)|Complete<br>short<br>term<br>N=606<br>(2)|Complete<br>medium<br>term<br>N=619<br>(3)|Complete<br>ST vs.<br>baseline<br>(4)|Complete<br>MT vs.<br>baseline<br>(5)|_(p-value)_<br>Complete<br>ST vs.<br>baseline<br>(6)|_(p-value)_<br>Complete<br>MT vs.<br>baseline<br>(7)|
|**Panel A: Outcomes**||||||||
|Baseline index|-0.029|0.091|0.124|0.164|0.209|0.000|0.000|
|Father determines sex|0.268|0.281|0.320|0.020|0.081|0.511|0.009|
|Stricter control daugthers|0.556|0.491|0.464|-0.092|-0.131|0.003|0.000|
|Women should be virgin|0.742|0.720|0.706|-0.036|-0.056|0.281|0.088|
|Justify beating if unfaith|0.370|0.294|0.266|-0.115|-0.160|0.000|0.000|
|Women wear whatever|0.952|0.965|0.956|0.048|0.013|0.103|0.665|
|Ban kitchen during period|0.382|0.298|0.305|-0.126|-0.116|0.000|0.000|
|Tell anyone if friend beat|0.854|0.895|0.916|0.088|0.138|0.004|0.000|
|Climate change is a threat|0.598|0.670|0.696|0.105|0.146|0.000|0.000|
|Work in the future|0.720|0.744|0.721|0.038|0.000|0.212|0.998|
|**Panel B: Controls**||||||||
|Age (years)|20.939|20.969|20.889|0.011|-0.018|0.725|0.547|
|English self-assess (0-10)|5.449|5.576|5.667|0.029|0.050|0.338|0.092|
|Survey in english|0.372|0.368|0.449|-0.006|0.111|0.833|0.000|
|Female|0.240|0.272|0.275|0.052|0.056|0.097|0.073|
|Primary|0.073|0.069|0.050|-0.010|-0.067|0.754|0.019|
|Secondary|0.437|0.413|0.425|-0.036|-0.018|0.246|0.557|
|University|0.462|0.492|0.498|0.042|0.050|0.177|0.102|
|HH-head primary|0.186|0.208|0.183|0.039|-0.006|0.206|0.849|
|HH-head secondary|0.338|0.325|0.321|-0.020|-0.025|0.522|0.408|
|HH-head university|0.357|0.353|0.384|-0.005|0.041|0.869|0.182|
|Hindu|0.804|0.809|0.827|0.008|0.042|0.783|0.156|
|Muslim|0.134|0.149|0.116|0.029|-0.038|0.352|0.198|
|Christian|0.012|0.003|0.008|-0.068|-0.025|0.004|0.376|
|Sikh|0.031|0.023|0.024|-0.034|-0.029|0.238|0.318|
|General caste|0.534|0.526|0.564|-0.010|0.043|0.742|0.157|
|OBC caste|0.285|0.284|0.252|-0.001|-0.052|0.963|0.082|
|SC caste|0.133|0.152|0.152|0.039|0.039|0.214|0.209|
|Student|0.622|0.652|0.667|0.043|0.066|0.156|0.028|
|Employed|0.132|0.130|0.124|-0.004|-0.016|0.906|0.589|
|Self-employed|0.093|0.081|0.079|-0.031|-0.035|0.305|0.235|
|Live in Delhi|0.562|0.587|0.559|0.036|-0.005|0.237|0.882|
|Member of organization|0.180|0.135|0.155|-0.087|-0.047|0.003|0.112|
|Currently dating|0.334|0.333|0.309|-0.000|-0.038|0.988|0.208|
|Married|0.065|0.061|0.050|-0.011|-0.045|0.724|0.125|
|With sisters|0.675|0.733|0.709|0.090|0.053|0.003|0.078|
|Daily freq. social media|15.558|15.917|15.845|0.023|0.018|0.486|0.578|
|Daily freq. watch videos|2.288|2.389|2.388|0.067|0.066|0.023|0.026|
|Male friend beating|0.146|0.140|0.157|-0.012|0.021|0.691|0.503|
|Female friend beated|0.146|0.155|0.158|0.019|0.025|0.542|0.415|

Notes: Table shows sample means at baseline for different categories of respondents: those who only completed baseline, those who completed the short-term survey and those who completed the medium-term survey.

26