rafmacalaba/datause-encoder-data huggingface dataset:
(Bruno and Shin, 2017). However, little attention has been devoted to the maturity structure of this new debt. We provide evidence that much of the corporate debt that developing countries raised recently is at the longer end of the maturity spectrum, possibly diminishing the risks associated with foreign currency financing. As benchmark, we use the borrowing patterns of firms from developed countries.

Third, another strand of the literature focuses on the firm-level determinants of debt maturity. Existing theories are based on underlying hypotheses of agency costs, asymmetric information, liquidation risks, and differentiated tax treatments.<sup>3</sup> In particular, to the extent that larger firms are relatively less opaque, size and maturity will be positively correlated. Our results are consistent with this prediction as they show that larger firms issue bonds and syndicated loans at longer maturities than smaller firms. Hence, should smaller firms gain access to debt markets in a certain country over time, the aggregate maturity structure of that country would naturally decline (Custodio et al., 2012).

The remainder of the paper is organized as follows. Section 2 describes the data. Section 3 examines differences in aggregate maturity by developed and developing country firms. Section 4 analyzes how compositional factors can affect the maturity of debt issued, focusing on where and which types of firms obtain long-term finance. Section 5 uses balance sheet information to compare the type of debt issued by publicly listed firms across countries. Section 6 concludes.

# **2. Data**

To assess the debt maturity at issuance for firms from developing and developed countries, we assemble a comprehensive transaction-level data set covering capital raising activity in corporate bonds

> 3 Myers (1977), Diamond (1991), and Rey and Stiglitz (1993) provide theoretical foundations on why firm-specific factors can influence the maturity structure of liabilities. Several papers provide evidence consistent with these theories for individual (mostly developed) countries. See, for example, Barclay and Smith (1995), Guedes and Opler (1996), Berger et al. (2005), and Highfield (2008).

6
||(0.00552)|(0.00552)|(0.00551)|
|---|---|---|---|
|Improved water|-0.0454**|-0.0417**|-0.0418**|
||(0.0189)|(0.0188)|(0.0188)|
|Water treatment|0.00153|0.00228|0.00230|
||(0.0163)|(0.0165)|(0.0165)|
|Arsenic (WHO)|-0.0187***|||
||(0.00619)|||
|Arsenic (BD)||-0.0334***<br>(0.00780)||
|Arsenic - medium (11-50 ppb)|||-0.00224<br>(0.00732)|
|Arsenic - high (51+ ppb)|||-0.0418***<br>(0.0116)|
|Observations|7,906|7,906|7,906|
|Standard errors in parentheses||||
|*** p<0.01, ** p<0.05, * p<0.1||||

**Table 7. Correlates of child social development including three-specifications of arsenic exposure (N=7,693 children aged 3-5 years)**

|VARIABLES|(1)<br>WHO|(2)<br>Bangladesh|(3)<br>Risk Levels|
|---|---|---|---|
|Child ever breastfed|-0.0114|-0.0106|-0.00921|
||(0.0296)|(0.0294)|(0.0295)|
|Child is female|0.0492***|0.0493***|0.0491***|
||(0.0107)|(0.0107)|(0.0107)|
|Child's age (log)|0.108***|0.108***|0.108***|
||(0.0355)|(0.0356)|(0.0355)|
|Mother's education (primary/some secondary)|0.0267**|0.0270**|0.0263**|
||(0.0119)|(0.0119)|(0.0119)|
|Mother's education (secondary or higher)|0.0281|0.0300|0.0278|
||(0.0221)|(0.0220)|(0.0220)|
|Urban|0.0195|0.0170|0.0170|
||(0.0170)|(0.0170)|(0.0170)|
|Household size (log)|-0.00752|-0.00597|-0.00536|
||(0.0161)|(0.0161)|(0.0160)|
|Quintile 2|0.0177|0.0186|0.0186|
||(0.0159)|(0.0159)|(0.0159)|
|Quintile 3|0.00713|0.00877|0.00873|
||(0.0171)|(0.0170)|(0.0170)|
|Quintile 4|0.00147|0.00269|0.00307|
||(0.0182)|(0.0182)|(0.0182)|
|Quintile 5|0.0204|0.0181|0.0197|
||(0.0227)|(0.0227)|(0.0227)|
|Improved sanitation|0.00225|0.00155|0.00219|

26
The results reported for the EPZ surveys are simple unweighted averages due to the non‐randomness of the sample and the different coverage strategies described above. Furthermore, the small sample size of respondents and some item non‐response issues in some countries prevent the use of more sophisticated statistical analysis with the data. Finally, it should be noted that the small sample size of EPZ firms in some countries (and enclaves) makes our averages sensitive to the presence of outliers and firm and item non‐response. In the most extreme case – Tanzania, with only 17 operational EPZ firms – a relatively small number of outliers (or refusals to respond) can significantly bias our estimates.

It should be noted that even for the ten countries included in the study, the data are not comprehensive. In some zone programs (e.g. Nigeria) we were unable to get access to any reliable time series data on the free zone program. In other countries – e.g. Senegal – data are available on the small “free zone” program, but not always from the other parallel programs (the now‐defunct “points francs” and the large “EFE” regime). Moreover, the operation of single‐factory programs, especially in Ghana and Senegal but also on a smaller scale in Kenya (and a much smaller scale in Tanzania) makes direct comparisons (and in some cases, conclusions) difficult. Where possible, we have separated data on the zones programs delineated within specific industrial parks or enclaves, and dispersed through “single factory” licensing.

# **6. Assessing performance in African zones**

This section provides a summary and discussion of outcomes across the ten zone programs included in the study, with the specific focus on the six African zones.

## <u>Investment</u>

The first proximate measure of success of an EPZ program is the investment it attracts. Without investment there will be no employment or exports and no possibility of realizing structural economic benefits. In this section we review the scale and nature of investment in African EPZ programs to date. Note that we have very limited data on investment patterns in the economic zones of Senegal and Nigeria and so any comparisons which rely on time series data exclude these countries.

Table 2 summarizes the results on FDI in the EPZs. On measures of FDI stock and FDI per capita the non‐ African zones generally outperform the African zones. One important exception is Ghana, which experienced large‐scale investment in their free zones program during the 2000s. Most of the FDI in Ghana has come through “single unit” free zones<sup>8</sup> . These “single unit” firms are licensed as free zone companies but entitled to operate anywhere in Ghanaian territory. A similar program operates in Kenya, Senegal, and Tanzania. The last column shows the relative importance of the EPZ program as a source of FDI. What is striking is that – with the exception of Nigeria, whose free zone program has failed to attract significant investments by almost any measure – the African zone programs show

> 8 As opposed to investment in firms based in the Tema Free Zone

11
Solving Commitment Problems in Disaster Risk Finance<sup>_∗_</sup> _JEL classifications:_ F35, G22, H84, Q54.

_Keywords:_ Government commitment; disaster risk finance; insurance; disaster relief; Samaritan’s dilemma; index insurance

# **1 Introduction**

Natural disasters are responsible for huge amounts of damage across the world, with households, businesses and local governments all at risk of suffering important losses. Most of the deaths and serious injuries resulting from these disasters are in developing countries.<sup>1</sup> Despite many of these disasters being relatively foreseeable, current attempts to mitigate these risks appear to be grossly insufficient. Those at risk are generally under-protected, and frequently appear to under-invest in actions that would mitigate losses. Governments and donors provide some relief, but this is often too small and inefficiently allocated.

In this context, increasing attention is turning to risk transfer instruments as a potential solution (World Bank, 2014; WHO, 2016; Clarke and Dercon, 2016; OCHA, 2016). These in-

> _∗_ This paper is a product of the Disaster Risk Financing and Insurance Program (DRFIP), a partnership of the World Bank’s Finance and Markets Global Practice Group and the Global Facility for Disaster Reduction and Recovery, with funding from the UK Department for International Development. The authors would like to thank Tito Cordella and Elisabeth Sadoulet for helpful comments and suggestions.

> _†_ World Bank, Washington DC, USA. dclarke2@worldbank.org

> _‡_ Paris School of Economics, Paris, France. liam.wren-lewis@parisschoolofeconomics.eu

> 1UNISDR (2015) estimates that economic losses from extreme natural events are now reaching an average of US$250-$300 billion a year. According to Germanwatch (2015), of the ten most affected countries from 1994 to 2013, nine were in the low-income or lower-middle-income country group, while only one was classified as an upper-middle-income country.
Families and communities have shared this burden, and it might be time to institute government support to help households cope.

# _REFERENCES_

Antman, Francisca M. 2013. “The Impact of Migration on Family Left Behind.” Forthcoming in _The International Handbook on the Economics of Migration_ , eds. Amelie Constant and Klaus F. Zimmermann, Chapter 16. Edward Elgar. Attanasio, Orazio, Emla Fitzsimons, Ana Gomez, Maria I. Gutiérrez, Costas Meghir, and Alice Mesnard. 2010. “Child Education and Work Choices in the Presence of a Conditional Cash Transfer Program in Rural Colombia.” _Economic Development and Cultural Change_ 58 (2): 181–210.

———, Erich Battistin, and Alice Mesnard. 2012. “Food and Cash Transfers: Evidence from Colombia.” _Economic Journal_ 122 (559): 92–124.

Beegle, Kathleen, Rajeev Dehejia, and Roberta Gatti. 2006. “Child Labor and Agricultural Shocks.” _Journal of Development Economics_ 81 (1): 80–96.

———, Joachim De Weerdt, and Stefan Dercon. 2006. “Orphanhood and the Long-Run Impact on Children.” _American Journal of Agricultural Economics_ 88 (5): 1266– 72.

———, Joachim De Weerdt, and Stefan Dercon. 2010. “Orphanhood and Human Capital Destruction: Is There Persistence into Adulthood?” _Demography_ , 47 (1): 163–80.

26
In each of the participating districts, the IE team randomly selected 10 pairs of villages. Each pair consisted of one treatment village and one comparison village from the same _kecamatan_ (sub-district). Villages in Indonesia have various communities or sub-villages, and the project intervention occurred at the sub-village level. At least one community in the treatment village received the full project intervention. No communities in the comparison villages received the project intervention.

# **4. SAMPLING STRATEGY**

# _4.1 Selecting Sub-villages_

From each of the eight districts, 10 treatment and 10 control villages were randomly chosen to participate in the IE. Local government offices from each district gave the IE team a list of at least 30 villages in which the program could be implemented.<sup>5</sup> Most district offices gave the IE teams lists of 40 to 70 villages. These are villages the districts had chosen to participate in the project based on sanitation needs, poverty levels, access to water, and so forth. Using a random number generator in STATA, the IE team randomly selected 10 treatment and 10 control villages from each district list. The IE team then sent the list of 20 villages back to the district government office (without telling them which villages had been selected as control and treatment villages). The reason for this is that the project is actually implemented at the _dusun_ , or sub-village level. Villages generally have two to three sub-villages. Wanting the same selection criteria to be used for the selection of sub-village for both the treatment and control villages, the IE team asked each district office to provide the sub-village names for all 20 villages. District offices were told that some would be the treatment and others the control. The IE has internal validity but not external validity in that villages were not randomly chosen from the universe of villages. Different districts chose villages on the basis of different indicators. For example, some districts chose to include villages that had recently participated in water supply programs, whereas other districts explicitly chose to exclude such villages. The sample thus reflects the variety of ways in which government officials generally choose villages for a sanitation program so internal validity is sufficient under these circumstances. That is, the evaluation will provide estimates of the average impact expected given the way governments select villages for such programs. The impact of the different bases for the choices can be examined as part of the evaluation.

Once the IE team received the sub-village lists from the district offices for all 20 villages, they told district offices which villages were in the treatment group and which were in the control group. The district offices promised to do everything possible to make sure the treatment _dusun_ were treated and the control _dusun_ remained untreated. There was some concern by local

> 5 At least two villages were listed for every sub-district on the district lists. This allowed pairing of treatment and control communities with sub-districts as described above.

7
Policy ReseaRch WoRking PaPeR 4328

# **Abstract**

Multilateral development banks are frequently accused of “defensive lending,” the practice of extending new loans purely in order to ensure that existing loans are repaid. This paper empirically examine this hypothesis using data on lending by and repayments to the International Development Association (IDA), which is the largest provider of concessional development loans to low-income countries. The authors argue that key institutional features of IDA both (i) potentially create incentives for defensive lending, and (ii) enable particularly sharp tests of the defensive lending hypothesis. The authors find that there is a surprisingly robust partial correlation between disbursements on new IDA loans and repayments on existing loans. However, a closer look at the evidence suggests that defensive lending is unlikely to be a major explanation for this partial correlation.

This paper—a joint product of the Development Research Group and the Economic Policy and Debt Department in the Poverty Reduction and Economic Management Network—is part of a larger effort in the latter department to study external debt issues in low-income countries. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The authors may be contacted at akraay@worldbank.org and cgeginat@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
# **UNEMPLOYMENT AND MORTALITY:**

# **EVIDENCE FROM THE GREAT RECESSION**

Ha Nguyen and Huong Nguyen<sup>1</sup> Development Research Group The World Bank JEL codes are: I10, J64 Keywords: Unemployment, Mortality, Great Recession

> 1 The authors acknowledge financial support from the World Bank’s Research Support Budget. We thank Luis Serven and Aart Kraay for very helpful comments and feedback. Contract address: Ha Nguyen, Development Research Group, The World Bank. Email hanguyen@worldbank.org.
<!-- Start of picture text --> External debt as a share of GNI<br>CPI inflation (concurrent 10yr average)<br>(concurrent 10 year average)<br>.0<br>.04<br>.04<br>.03<br>.03<br>.02<br>.02<br>.01 .01<br>0 0<br>Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income<br>Below Median Average Above Median Average Below Median Average Above Median Average<br>Future 10 Year Growth Avg Future 10 Year Growth Avg<br><!-- End of picture text --> <u>Figure 13: Inflation (left) and external debt (right)</u>

# **_Governance and politics_**

Democracy has little if any predicted effect on growth for either lower or upper middle income countries. Similar to the previous results, autocracy seems to help growth at lower middle income levels but harm growth at upper middle income levels.

<!-- Start of picture text --> Democracy indicator (beginning of period) Autocracy indicator (beginning of period)<br>.0 .05<br>.04 .04<br>.03 .03<br>.02 .02<br>.01 .01<br>0 0<br>Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income<br>Below Median Average Above Median Average Below Median Average Above Median Average<br>Future 10 Year Growth Avg Future 10 Year Growth Avg<br><!-- End of picture text --> <u>Figure 14: Democracy (left) and autocracy (right)</u>

# **_Inequality and demographics_**

Both the level of demographic characteristics at the beginning of the period and changes in demographic characteristics over the period affect growth. Lower dependency ratios result in faster growth, and declining dependency ratios (the “demographic dividend”) also translate into faster growth.

16
**Figure 14: Geographical structure of employment and wages, 1995-2012**

<!-- Start of picture text --> (a) Rural-Urban wage gap (in %) (b) Share in employment<br>1.00 100%<br>0.80 80%<br>0.60 60%<br>54%<br>0.40 40%<br>28%<br>0.20 20%<br>0.00 0%<br>Rural‐Urban wage gap Rural Urban<br>1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> _Source:_ RLMS (Rounds VI to XXI). _Notes:_ all the indicators have been calculated based on the subsample of currently employed wage workers in the age group 18-60, reporting more than 140 worked hours in the last 30 days and with non-missing data in wages. The distribution of hourly wages was trimmed at the bottom and top 0.25 percent in each round, to remove outliers. Wages for rounds 6 and 7 were divided by 1,000 to account for the nominal revaluation of the ruble in January 1998 (1,000 old rubles = 1 new ruble). The RLMS was not administered in 1997 and 1999.

# **2.4.   Demand-side factors: Industry affiliation of workers and occupation premium**

Demand-side dynamics appear to have played a critical role in explaining the changes in the wage distribution during Russia’s transition to a market economy and in the aftermath of the economic crisis of 1998. Lukiyanova (2006) finds that, together with regional variables, industry affiliation is one of the factors accounting for most of the variation in wage structure. In particular, the author finds negative wage premiums for agriculture, public health and education; coupled with higher wages in traditional industries than in the service sector.

Recent evidence suggests that, over the last decade, the reduction in wage inequality was driven by changes in the industrial composition of employment, with a significant reallocation of labor between industries towards higher-paying jobs, particularly, workers moved out of agriculture and manufacturing and into the service sector (Gimpelson, 2015). These changes were associated with a shift in production, from tradable to non-tradable sectors, as economic growth was fostered by high oil prices. Changes in the industrial affiliation of workers were accompanied by 17
investments in children's health and education. In Bangladesh, the presence of one or more adult sons in the household was associated with reduced mortality for elderly women but not for elderly men (Rahman 1999). However, the (log of) household size had no relation with BMI for Indonesian men or women aged 20-69 (Frankenberg, Thomas and Beegle 1999).

**_Community and policy variables._** We expect higher food prices to reduce food intake or the quality of food purchased and a higher price of medical care or lower quality medical care to reduce its use, reducing BMI. Among adults aged 20-60 years in CMte d'Ivoire, higher food prices were associated with lower BMI, and these effects were greatest for those living in rural areas (Thomas, Lavy and Strauss 1996). The availability and quality of health services in the community had no effect (individually or jointly). In the sample of men and women in CMte d'Ivoire and of women in Ghana, those living in urban areas had significantly higher BMI than those living in rural areas (Alderman 1990).

# **III. Setting, dataset, descriptive statistics and econometric model**

Our data come from the Kagera region of Tanzania, located to the west of Lake Victoria, adjacent to Uganda, Rwanda, and Burundi, in an area of high HIV prevalence and high adult mortality. The 1988 census reported more than 1.3 million people living in the region, with more than 80 percent residing in rural areas. Most of the population is engaged in agriculture - tree crops in the north and annual crops and livestock in the south. Household consumption expenditure in 1991 was US $217 per capita based on data used from this study, ranging US$118 to US$357 across Kagera's six districts.

Kagera is at the epicenter of the African AIIDS epidemic. The first case of AIDS in the region was diagnosed in 1983, although HIV was most likely present at least a decade earlier. A population- based seroprevalence survey conducted in 1987 found that about one in four adults in the regional capital (Bukoba) were infected with HIV, and one in 10 adults in rural areas surrounding Bukoba (Killewo et al 1990). In the rural south and southwest of Kagera, in contrast, fewer than 1% of adults were infected; to the west the adult infection rate was **_5%._** These levels of infection are not unlike those projected nationally at present-about 8% of prime-aged adults. A follow-up study in 1993 found that HIV infection had declined from 24% to 18% in Bukoba town among those 15-54 years of age, and in the rural area surrounding Bukoba, from 10% to 6.8% (Kwesigabo et al 1998). However, in the latter case, the only population group that registered a significant decline was rural women aged 1 5-24.5 5 Since HIV infection is life-long, the only way that prevalence can decline over time in a cohort is for the mortality rate to exceed the new infection rate (incidence) or for HIV-positive people to migrate out of the study population.

7