rafmacalaba/data-use-mentions-extended huggingface dataset:
for _within-country variations across firms_ . Columns 2 to 6 control for country characteristics (and remove country fixed effects). Overall, older firms are less likely to pledge collateral. One more year of operation reduces the chance of pledging collateral by about 0.1%. Small firms are 5-6% less likely to pledge collateral than large firms. One could also expect the opposite sign. However, if we consider the borrowing

> 6 Other commonly used variables such as Government Effectiveness or Control for Corruption yield similar results.

> 7 Depth of credit information index is part of Doing Business Report of the World Bank. It measures rules and practices affecting the coverage, scope and accessibility of credit information available through either a public credit registry or a private credit bureau. The index ranges from 0 to 6, with higher values indicating the availability of more credit information to facilitate lending decisions.

> 8 Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of non-equity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries, these claims include credit to public enterprises.

> 9 Interest rate spread is the interest rate charged by banks on loans to prime customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits.

10
We develop a simple model that outlines how households might adjust their borrowing and investment behaviour when offered the new labelled loan. The model explicitly incorporates the lower interest rate and also allows the label to influence households’ borrowing and investment choices. A key insight that emerges from the model is that when households are sensitive to labels, the label will induce them to make the investment associated with the new loan, and more so than when the loan is not labelled. As a result, introducing the sanitation loan will motivate sanitation investments. The lower interest rate, and additional supply of credit will also increase sanitation investments. Interestingly, the model also indicates that not all sanitation loans will result in sanitation investments if there are households that are not sensitive to the label. The lower interest rate will induce households to substitute away from more expensive credit sources without altering its investment choices. Thus, sanitation loan demand on its own is not necessarily informative of sanitation investments.

Insights from the model also provide a direct test for the loan label in influencing households’ choices. The test relies on the fact that if households are insensitive to loan labels, they would respond to the lower interest rate on the sanitation loan, and substitute away from more expensive loans, regardless of their investment behaviour. By contrast, households that are sensitive to loan labels will not substitute away from the business loan in a similar manner.<sup>2</sup> Rural India is a particularly apt context in which to study the adoption of household toilets. According to the WHO-UNICEF Joint Monitoring Program, around 57 percent of households in rural India practiced open defecation in 2015.<sup>3</sup> Lack of access to safe sanitation is even more pronounced in our study context – Latur and Nanded districts in Maharashtra. Only around 27 precent of study households had a private household toilet before the roll-out of the sanitation loan program.<sup>4</sup> Interestingly, households in this context typically identify the financial cost and
3 interrupt manufacturing processes thereby diminishing the returns to capital. Droughts cause food prices to rise and livestock prices to tumble (the latter in the face of collapsing demand for meat, Barrett (2001)). The effects of disasters may also be felt in the labor markets, with wages often substantially lower several years after the event (Jayachandran, 2006; Mueller and Osgood, 2009). Quisumbing and Mueller (2010) estimated for example that for each one foot deviation from the normal flood level agricultural and non‐agricultural wages were 4 to 7 percent lower five years after the 1998 flood of the century in Bangladesh.

Whether and the extent to which a household also sees its income and consumption reduced, depends further on its exposure to the disaster and its capacity to cope with it ex post (Dercon, 2002; Hasegawa, 2010). Those with irrigated fields or those earning their living outside agriculture are for example less likely to suffer from droughts, while domestic and foreign remittances and well targeted disaster relief can make up for a substantial part of the income loss. The better credit and insurance markets work, the better households are usually also able to cope with disasters and the less they see their consumption decline (Lopez, 2009). Furthermore, in the aftermath of the disaster, there may also be a temporary boost in certain sectors such as construction (building‐back) opening up opportunities for certain groups. The Keynesian type construction boom may even accelerate the overall growth rate if it results in an accelerated upgrading of the existing capital stock (building‐back‐better; Hallegatte and Dumas, 2009).

To capture both direct and indirect effects and illuminate the implicit costs associated with some of the coping strategies, this study pursues a fully reduced form analysis at the household level (very much in the spirit of Dercon, 2004). In particular, it examines the effect of natural disasters on household consumption, using disaggregated (objective) disaster measures derived from geo‐ referenced meteorological data and controlling
HK. Poverty and malnutrition in Africa: a conceptual analysis. Nutr Food Sci. (2017) 47:754–64. 10.1108/NFS-02-2017-0027

- Battese, G. E., Harter, R. M., & Fuller, W. A. (1988). An error-components model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association, 83(401), 28-36.

Brown, C; van de Walle, D, and M. Ravallion. 2017. Are Poor Individuals Mainly Found in Poor Households? Evidence using Nutrition Data for Africa. NBER Working Paper No. 24047

- <mark>Delisle HF. Poverty: the double burden of malnutrition in mothers and the intergenerational impact. Ann N Y Acad Sci. 2008;1136:172-84. doi: 10.1196/annals.1425.026. PMID: 18579881.</mark>

Edochie, I., D. Newhouse, T. Schmid, and N. Wurz, “Povmap: Extension to the emdi package for small area estimation”, available at Comprehensive R Archive Network Galasso, E & N. Umapathi (2009) Improving nutritional status through behavioural change: lessons from Madagascar, Journal of Development Effectiveness, 1:1, 60-85, DOI: 10.1080/19439340902727669 16 | P a g e
resulted in technical and financial problems. Today, the community has reverted to using river water for bathing and washing and private tanker water for drinking. Residents complained that at times, the river water is brackish, sour, and sticky when bathing due to palm oil waste. Gastroenteritis broke out in 2016 and caused fatalities.

The presence of various forms of governance failure—capture, competition, or apathy by the village authorities—meant that project facilitators were operating in an environment where the coordination complexities of carrying out community socialization as well as the legitimacy of the project was compromised. Lack of proper socialization meant that though the community was interested in receiving the water service, they were not ready with a mind-set to contribute funds or pay for house connections, did not know who would carry out the operations and management, and saw the service as someone else’s responsibility. In some villages, elite capture simply resulted in communities opting out of the system altogether, resorting to private individual solutions for water. What allowed village leaders the space for apathy is their source of authority, which as the statements above revealed, emanated not from local community support but from external sources.

# Conclusion

This paper sought to understand the conditions under which projects can create sustainable community management of water services. Through analysis of project- and village-level data, we found that community participation is indeed associated with sustainable villages where infrastructure has been managed by communities for 6 to 10 years without rehabilitation support. We also found that socioeconomic conditions and water conditions of the village have a strong impact on community management. We then sought to answer why we still see pockets of success and failure as a way of understanding exactly what mechanisms participation works through, that is, why we still see sustainable community management in places characterized by difficult water and socioeconomic conditions and unsustainable management in structurally more enabling conditions.

We found that collaboration between frontline providers—village authorities and water user groups—is an important influence on the nature of participatory implementation. The degree and nature
# _Discussion_

The school readiness intervention was found to have a positive effect on overall school readiness measured by the TEHCI for girls of mothers with no high-school education. It also affected the school readiness literacy domains of girls and boys in this group. Children of mothers without high-school education had lower levels of school readiness compared to children of higher educated mothers in the baseline TEHCI; hence, the school readiness intervention seems to have benefited the more vulnerable children.

Table 5. Effect size per 100 USD

||all|mothers w<br>school e|ithout high<br>ducation|mothers<br>school e|with high<br>ducation|
|---|---|---|---|---|---|
|||girls|boys|girls|boys|
|average TEHCI score|0.16|0.79**|0.32|‐0.08|‐0.28|
||(0.48)|(0.79)|(0.63)|(0.55)|(0.4)|
|average TEHCI literacy domains|0.28|0.75**|0.67**|‐0.08|‐0.12|
||(0.4)|(0.63)|(0.55)|(0.48)|(0.4)|

Standard errors included in parentheses. Statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively. Estimates are calculated by dividing the effects presented in Table 4 by the per child cost of the intervention in 100s.

No effects were found for the index of home activities, though positive effects were found for specific activities. Of course, the data do not provide a comprehensive picture of the quality of the activities or caregiver-child interaction more generally.
increase government involvement in the financing of the operation of the SOE, not transfers to fund investments.<sup>2</sup> Using these new measures of fiscal injections, we show that the fiscal costs of supporting infrastructure SOEs across countries is usually below 1 percent of GDP<sup>3</sup> but that support—and the performance of SOEs—is volatile and needed more often than Bova et al. (2019) acknowledge. We identify 44 country-year events of fiscal injections to infrastructure SOEs that are above 0.2 percent of GDP (see table A.2 in the appendix for a comprehensive list). We document a slow dripping of support in the average country during the average year that, over a handful of years, turns into points of GDP. This evidence indicates that fiscal risk is not only about “tail risk” but also about the risks associated with the daily operation of infrastructure SOEs.

We also show the variation in fiscal costs by sector and the variety of instruments used to support infrastructure SOEs in different countries. Average annual fiscal costs range from 0.04 percent of GDP for airlines and airports to 0.12 percent for railways and 0.24 percent for power and roads. There is also wide heterogeneity in how governments support SOEs.

We use conventional tools from the corporate finance literature on defaults to assess the effectiveness of forward-looking measures of fiscal risk to predict the need of fiscal injections in SOEs and provide a simple tool that can help prevent fiscal disasters stemming from the operation of SOEs. We rely on the Altman Z-score, a traditional measure of default risk, as applied to firms that are not publicly traded, a measure known as the Z” score. We calculate the Altman Z” score using the Infrastructure SOE Database and show that it can be used to forecast the need for fiscal injections in SOEs. Our estimates reinforce the idea that ex ante monitoring of SOE financials can significantly reduce the need for fiscal injections and thus reduce overall fiscal risk
0.258|
|Mega (500+ employees)|0.001|0.028|0.251|
|Total unorganized share|0.761|0.733|0.090|
|Total organized share|0.239|0.267|0.911|

Notes: See Table 1a. U.S. descriptive statistics taken from 1997 Census of Services. "Ownaccount enterprises" (OAE) are firms that do not employ any hired worker on a regular basis.
_W_ ) is a CES function of _leisure_ , _investment_ , and a consumption good _C_ , and _C_ is a composite good produced by a CES function of _untaxed_ , _domestic_ , and _imports_ . The remaining panels are the production functions for _domestic_ , _export_ and _untaxed_ . The bottom parts of these diagrams represent the nested CES functions over the factors of production, with the top levels representing capital/labor substitution, and the bottom levels representing formal/informal substitution. The top parts of the diagrams represent the CET functions between the final good and investment. The relevant elasticities, used in robustness testing of the model, are labelled by sigma and tau and appropriate sub- and super-scripts.

Taxes are imposed on _domestic_ , _exports_ , _imports_ , _formal capital_ , and _formal labor_ . There are no untaxed traded exports or imports. This is not meant to imply that no smuggling occurs in African countries. Rather, the official figures for trade are based on customs data, which typically reflects taxed goods. An implication is

> 8The modelling differs from the standard 1-2-3 model. The standard model starts with the definition that _GDP_ = _C_ + _G_ + _I_ +( _E − M_ ) (see section 3.2), and combines _C_ + _G_ + _I_ into a single domestic good, giving _GDP_ = _D_ + _E − M_ . In the IMF figures used in this study government spending is already allocated to consumption or investment, so the original data are represented as _GDP_ = _C_ + _I_ + ( _E − M_ ). It would be simple to make the figures for investment disappear (since investment serves no role in a static model) by simply following the standard model. But it seems wrong to suppose that all investment goes toward domestic consumption goods, and that no investment goes into the production of export goods. We have thus included an explicit investment good, ensuring that the full value of GDP is reflected our model.

9 The _informal_ good uses only _informal capital_ and _informal labor_ .

7
|18|17|40|
|**Total**|57|31|56|38|35|65|
|**_Market Shares:*_**|||||||
|**World**|18%|31%|23%(**)- 34%(*)|23%(**)- 31%(*)|39%|37%|

* The market share represents the average of the sample market but only in countries in which the firms of the sample have an activity. The standard deviations are given for regional and country measures.

First it shows that we tried to report data on the sectors as unbundled as possible. Second, for the electricity and telecommunications, we managed to get market data close enough to what a competition agency would consider appropriate. For water and sanitation, we relied on two proxies: urban population and total population. The first probably gives a lower bound and the second an upper bound of the populations affected by the large operators covered by the database. We should thus get respectively a further level of upper and lower bound for the concentration index from these two measures. Third, the country coverage is relatively evenly distributed between OECD and non-OECD countries which ensures a reasonable sense of robustness when comparing the two country groups. Finally, the combination of the market shares and of the number of players suggests that there is no outrageous domination of any sector by a very limited number of actors and hence that the approximation method used here is unlikely to be very distorted at the global level.

At a more substantive level, a simple look at the line on the number of players suggests that for electricity and telecoms, the concentration issue is subject to debates since the number of firms is large enough according to the rule of thumb discussed earlier. For water and sanitation, the information on the number of firms already points to a fairly high concentration level. The rest of this section
financial firms and banks, given that there is lack of information on the maturity structure of time deposits and we are particularly interested about debt maturity. We also eliminate from the sample firms for which we have information for less than three periods. Given that available data only exist for publicly traded firms, we are mostly studying large companies.' To measure financial integration at the firm level, we construct indicators of access to international bond and equity markets. First, we use data on international bond issues **by** firms from emerging economies. The data come from the database of H. Kalsi and **A.** Mody, World Bank Prospects Group, and **JP** Morgan. The data measure the access to international bond markets. Second, to capture access to international equity markets, we use the proportional value traded on American Depositary Receipts (ADRs), in the New York Stock Exchange, and on Global Depositary Receipts (GDRs), in the London Stock Exchange. This proportion is calculated relative to the total value traded for that firm's equity in all markets. Data on ADRs and GDRs come from Bloomberg.' To measure financial liberalization in these economies, we employ the index of financial controls constructed **by** Kaminsky and Schnukler **(1999).** This is a qualitative multidimensional index of financial liberalization. The index takes into account controls on interest rates, legal restrictions for firms and banks to borrow in foreign markets, level of

> **_5_** Appendix 2 presents, for each country, the number of firms and time periods covered in the sample. **6** Data on publicly traded firms exist because firms have to submit their balance sheets regularly to the stock market authorities of each country. Accounting standards for other firms are different and there is no centralized agency that collects such data. **If** the data existed, it would be very interesting to analyze those firms.

> **
# Exhibits

Figure 1: Import market concentration in natural resource-dependent countries and a potential mechanism <!-- Start of picture text --> (a) Import market concentration (b) Trade protection<br>GNB GAB<br>LAO<br>TLS<br>BDI STP BGD NPLBENMAR CMR GNB<br>MUS TGO SENMDG GABCIV RWACPVMWI TGO ETHCPV<br>BWAMKD CRI CMR ZMB PAKLKA KENSEN RWASTP<br>SLVDOMJORALBBENMAR TZA GTMKENGEOUGA ETH KHM TZA EGY MDGUGABDI COLCIV ECU<br>KHM NPLLKAHRVSRB EGY URYCOMCOLPER CHL ECUPRY DOMJORIND ZAF URYCOMPER CHL ZMBMWIPRY<br>ZAF MEX MUS GTMGEO<br>ROU IDN MKDSLV BGRSRB TLS<br>PAK BGR VNM ROU ALBHRV CRI IDN<br>BGD VNM BWA LAO<br>MEX IND<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Commodity export share of total merchandise exports Commodity export share of total merchandise exports<br>.15<br>3000<br>2000 .1<br>.05<br>1000<br>Average HHI of country's import markets<br>Tariff rate, applied, weighted mean, all products<br>0<br><!-- End of picture text --> Notes: Panel A displays the average HHI of a country’s import markets and the country’s average commodity export share, where averages are taken over years. Within years, the HHI is the import value-weighted average of the log of the HHI across HS 6-digit products for a given country. The slope of the best fit line in Panel A is 0.86 (standard error = 0.19). Panel B displays the import value-weighted average tariff rate of a country and the country’s average commodity export share, where averages are taken over years. The slope of the best fit line in Panel B is 0.031 (0.022).
In this paper, we focus on the criteria of policy selectivity and poverty selectivity that largely guide IDA allocations, and that reflect a widespread belief about country characteristics conducive to the productive use of aid funds.

Three actors are involved in the recent trust fund trend: donor countries, the World Bank, and recipient countries. Donor governments may use trust funds to target their foreign aid to priority countries and development issues, while, and in contrast to bilateral aid, delegating responsibility for its management and implementation to the multilateral organization. Evidence collected by the Bank’s evaluation unit (IEG 2011a) through structured interviews with 55 officials of eight donor countries finds that six out of eight donor countries use trust funds to target priority issues or countries. From the perspective of the multilateral organization, trust funds allow expanding its global role and operations, and increasing its staff and assets under management.

From a recipient country perspective, trust funds may have several positive implications. For middle‐income countries seeking technical assistance but are reluctant to borrow for this purpose, trust funds make technical assistance available at grant terms (IEG 2011a: 7). Trust funds have also supported post‐conflict and post‐disaster countries and territories that are ineligible to borrow from the IDA or the International Bank for Reconstruction and Development (IBRD) (e.g., Timor‐Leste, Aceh in Indonesia, West Bank and Gaza). Moreover, trust funds have encouraged the provision of

> 4 Programs funded from FIF are typically monitored by separate agencies, such as GAVI, the Vaccine Alliance, or the Global Fund, and not by World Bank staff. The Bank mainly provides financial administration for FIFs. 5 The fiscal year at the World Bank runs from July 1 to June 30.

> 6 This information is based on contributions data with donor‐specific information.

3
22 values on immunization status for any of the relevant vaccines, we conservatively assumed that the particular vaccine was not given.

A child is categorized as being born in a facility if it was born in a public or private health facility.

The DHS data on antenatal care was collected for the last birth only. For a child who is not the last birth, the value was approximated by its lastborn sibling. In our final sample (4668 observations), there are 1046 children (22.4%) who are not lastborn. We consider three dummy variables of antenatal care. All of them are considered not received if the mother responded "don't know." Protection against neonatal tetanus is achieved if a pregnant woman without any previous tetanus immunization receives at least two shots of tetanus vaccine during her pregnancy. One or zero tetanus shots during the pregnancy might be sufficient if the mother received previous tetanus immunization and depending on the number and timing of the previous tetanus vaccinations (KNBS and ICF Macro 2010). Due to unavailability of the information on previous tetanus immunization in the DHS 2003, we consider a child protected against neonatal tetanus only if its mother received at least two shots of tetanus vaccine during her pregnancy. The dummy variables on intake of any type of anti-malarial drugs during pregnancy and on intake of iron supplement during pregnancy are self-explanatory. Malaria or lack of iron during pregnancy might lead to anemia of both mother and child and to low birth weight, which in turn increases both neonatal and postneonatal mortality (KNBS and ICF Macro 2010, Snow and Marsh 2002).

Information on the source of drinking water and on the type of sanitation facility is represented by a set of three dummy variables thus distinguishing three stages of quality. In both cases, there is an "improved" category. The concept of improved source of drinking water and improved sanitation facility was proposed by the WHO and UNICEF in 2004 and implemented in the DHS 2008. An attempt was made to approximate the "improved" categories in the