rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9970

# **Abstract**

Infrastructure is critical to economic development. When infrastructure companies are owned and operated by the government, however, they create significant sources of fiscal risk. These fiscal risks can be sizable, but they are often preventable with proper planning, risk assessment, and strict rules and procedures for corporate and fiscal governance. This paper examines fiscal risk stemming from state-owned enterprises (SOEs) in the infrastructure sector in a sample of 135 firms in 19 countries from an original database of SOE financials for 2009–18. The paper develops a typology of fiscal risks and their determinants, builds new measures of fiscal injections to SOEs, and documents them using the novel database. The results show that governments support SOEs through a remarkably wide range of fiscal instruments. The fiscal cost of supporting infrastructure SOEs is usually below 1 percent of gross domestic product. Support is more prevalent and frequent than previously thought. The findings show that fiscal risk stems not only from “tail risk,” but also from the everyday operation of infrastructure SOEs. The paper calculates the Altman Z” score (a measure of default risk) and shows that it can be used to forecast the need for fiscal injections in SOEs.

This paper is a product of the Infrastructure Chief Economist Office. 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 author may be contacted at mdappe@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
. and Meiselman, D., 1963. _The Relative Stability of Monetary Velocity and the Investment Multiplier in the United States, 1897-1958_ in Stabilization Policies, a Series of Research Studies Prepared for the Commission on Money and Credit. Englewood Cliffs, NJ: Prentice-Hall, pp. 165-268.

Friedman, M. and Schwartz, A., 1963. _A Monetary History of the United States, 1867-1960._ NBER Books, National Bureau of Economic Research, number 63-1.

Friedman, M., 1968. _The Role of Monetary Policy_ . American Economic Review, N 58, 1-17 Galati and Moessner, 2011. _Macroprudential Policy – A Literature Review._ Bank for International Settlements. Working Paper N. 337. Basel, Switzerland.

Garcia, Marcio G.P. (2011). “ _Can Sterilized FX Purchases under Inflation Targeting Be Expansionary?_ ”, Department of Economics – PUC-RIO, April.

Gelain, P., Lansing, K., and Mendicino, C., 2012. _House Prices, Credit Growth, and Excess Volatility: Implications for Monetary and Macroprudential Policy._ Federal Reserve Bank of San Francisco Working Paper N. 2012-11.

Ghosh, S., Gonzalez del Mazo, I., and Ötker-Robe, I., 2012. _Chasing the Shadows: How Significant Is Shadow Banking in Emerging Markets?_ Economic Premise n.88, September.

Global Financial Stability Report (GFSR), 2010. International Monetary Fund – World Economic and Financial Surveys, October. Washington DC.

Goodhart, 2011. _The Macro-Prudential Authority: Powers, Scope and Accountability_ . OECD Journal: Financial Market Trends. Volume 2011 – issue 2.

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migration and destination. But their emergence often also depends on distance in the first place. Some distance-related aspects, such as transport costs and information, may be more amenable to policy, and can reduce friction even over short distance, as illustrated by the effect of cash transfers on seasonal migration in Bangladesh (Bryan, Chowdhury and Mobarak, 2014). Others may take more time (such as the development of social safety nets). Together it would suggest that the effect of distance on destination choice is likely to continue for some time.

Finally, two potential spill-overs following the interplay between distance, towns and migration may further accelerate the structural transformation and deserve further attention. First, many new towns are arising in Tanzania, as they are across much of the African continent. OECD/SWAC (2020) combines demographic data with satellite and aerial imagery to document a total of 7,617 urban agglomerations of at least 10,000 people in Africa in 2015. In 1950 that number stood at just 624. Growth in the number of urban agglomerations has been especially strong in recent years. From 2000 to 2015, 2,475 new urban areas came into existence through in-situ urbanization. With every new small town (a subset of) rural dwellers will see the distance between their home village and the nearest urban agglomeration shrink. Given the primacy of distance as a determinant of destination choice documented here, we would expect frictions to rural-urban migration to come down through the rise of small towns. Such rural-urban migration is an important component of structural transformation, so the rise of small towns is not only a consequence of this process, but could actually speed it up. Relatedly, there are good reasons to believe other frictions, for example related to the flow of goods, services and information between urban and rural areas will also come down with the emergence of small towns. While important studies like Dorosh and Thurlow (2013, 2014) and Gibson et al. (2017) have studied how urban growth in large versus
# **Table 6: Alternative questions and enumerator characteristics for placebo test**

||Age|Alcohol and tobacco<br>consumption|
|---|---|---|
|Enumerator gender (male)|-0.003|0.011|
||(0.514)|(0.009)|
|Enumerator education|-0.048|0.003*|
||(0.064)|(0.002)|
|Enumerator age|0.556|0.010|
||(0.952)|(0.016)|
|Enumerator from urban locality|-0.025|-0.002|
||(0.418)|(0.013)|
|Work experience as enumerator|-0.004|0.000|
||(0.005)|(0.000)|
|Observations|6,693|6,687|
|R-squared|0.315|0.454|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : This table presents OLS regression results for equation (1). For each column, the outcome variable is indicated in the first row. _Alcohol and tobacco consumption_ is a dummy that takes value 1 if the respondent consumes alcohol or tobacco and 0 otherwise. _Work experience as enumerator_ is the number of months previously worked as an enumerator. Each regression includes as additional control variables: _Want people to know how good can be at work; Strongly motivated by the wage can earn; Enjoy handling new problems; Enjoy trying to solve complex problems; Curiosity is a driving force for your actions_ (for the definitions see Table 5). as in Table 3. Each regression includes community fixed effects and a constant. Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are
4

**Table 1: Evolution of GNP per Capita (in US$) for a Selected Group of CEE Countries.**

||**GNP Data**<br>**1990**|**Based on W**|**B Atlas M**<br>**1992**|**ethodology**|**1994**||**GNP-PPP**<br>**1994**|**Estimate**|
|---|---|---|---|---|---|---|---|---|
||Level|Share<br>of EU<br>Average|Level|Share<br>ofEU<br>Average|Level|Share<br>of EU<br>Average|Level|Share<br>ofEU<br>Average|
|Czech|3490|22|2570|14|3200|17|8900|53|
|Hungary|2910|18|3120|17|3840|20|6080|36|
|Poland|1700|11|1950|11|2410|13|5480|33|
|Slovakia|3330|21|2250|12|2470|13|6450|38|
|Slovenia|9500|59|6770|37|7020|37|6230|37|
|EU Average|16185||18532||19005||16757||

Source: The GNP data for 1994 in PPP terms is from the 1996 World Development Report.

Considerable thinking has gone into the elaboration of policy agendas that would allow sustained medium-term growth. Not surprisingly, given the relative similarity of initial structural conditions, the prescriptions for most CEE countries have been fairly similar. They include (i) strengthening property rights and the administration of government services, including the adequacy of general legal provisions to functioning of market
the sample to individual's ages 15-64 in every survey round, and the final number of individualround observations becomes 33,858.<sup>20</sup>

# **3.2. Data on Refugees**

Data on refugees in Ethiopia come from the UNHCR. The data contain the location (GPS coordinates) of each of the refugee camps or settlement sites in the country and yearly time series data on the refugee population hosted in each of these locations from 2008 to 2018.<sup>21</sup> The refugee population is the end-of-year measure reflecting the number of refugees recorded in December<sup>22</sup> of the corresponding year, except for 2018 it reflects the number of refugees recorded in August. Moreover, the data include yearly time-series information on the aggregate number of refugees hosted in Ethiopia by country of origin.

I present some of the descriptive statistics (Figures 1 and 2) using all the available data. However, the main empirical analyses make use of the refugee data for the period 2009-2014, when the number of refugees in the country increased from 125,910 in 2009 to 660,987 in 2014. About 98% of these refugees are hosted in 23 refugee camps and 11 semi-formal settlement sites, including transit centers and entry points, and informal settlement sites with fairly precisely known locations in five of the 11 administrative regions of the country—Afar, Benishangul Gumuz, Gambella, Somali, and Tigray—where approximately 15% of the county’s population resides. The rest are spontaneously settled refugees located mainly in Addis Ababa, and small others in the Tigray region and Borena zone in the Oromia region.<sup>23</sup> Most of these spontaneously settled refugees in Addis Ababa and Tigray regions are either urban assisted refugees or those under the OCP, while those in the Oromia region are refugees living in semi-formal and informal settlements.

> 20About 24% (19%) of the sample household/individual-round observations reside in
through a group of independent peers (when the research-awarding process is not captured by rent seekers). One advantage of this approach is that it allows coordination of research efforts with little or no excess duplication. With respect to dissemination, publicly-produced knowledge should generally be made freely available, which is socially desirable to ensure efficient use once produced. A key shortcoming of government-funded R&D is that, as research moves from basic to more applied phases, incentives are not strong to reflect information from markets about what consumers want and are willing to pay for.

As highlighted in Section 1, frontier green (and non-green) innovations that are dependent on significant formal R&D support have to-date largely been concentrated in high-income countries and a few more technologically advanced developing countries, with most developing countries having little such innovations as indicated by patent data. So there is likely a more limited role for formal R&D support for frontier innovations in most developing countries, to the extent that such spending reflects underlying technological capabilities.<sup>20</sup> Box 3 illustrates one such area, the development of smart grids, where major direct government funding for R&D, typically in public-private partnership mode, is taking place in many more technologically-advanced countries, and where the benefits are expected to eventually be reaped by all countries as these technologies diffuse and are absorbed by a broader range of firms.

# **<u>Box 3. Smart grid R&D and expected green benefits</u>**

̳Smart grid‘ means computerizing the electric utility grid, similar to the way today‘s ̳smart phones‘ have a computer inside. It includes adding two-way digital communication technology to all devices associated with the grid to ensure two-way flow of electricity and information between all power plants and consumers and all points in between, with sensors to gather data on incoming power from wind, solar and other renewable that have constantly varying power outputs, broken equipment and leakages, more intelligent management of generation outages,
We detect strong program impacts on monthly earnings. We measure earnings as an individual’s total earnings in the past month, including income from all IGAs, but not including unearned income.<sup>52</sup> We observe a statistically significant (at the 1 percent level) increase in monthly earnings for the treatment group by 850 to 921 NRs (≈ 12 USD), from a baseline average of 1,272 NRs (≈ 17 USD).<sup>53</sup> In percentage terms, this earnings increase translates to a 72 percent for the pooled sample.

With alternative measurements of earnings, we detect even larger program impacts. To account for the highly skewed nature of earnings distributions, we examine the impact on logged earnings and we find impacts of over 100 percent. A third way to examine the impact on earnings is to consider the proportion of participants who earned a “decent living.” The Employment Fund considers 3,000 NRs per month (≈ 40 USD) as “gainful employment” and considers this amount as “being productively employed.” At baseline, only about 20 percent of the sample was “gainfully employed”. The EF training program increased the “gainful employment” rate increases by 13 to 14 percentage points, a result statistically significant across all three models. The surveys of the 2011 and 2012 cohorts provide data on self-employment and work outside of home, conditional on having non-farm employment. We find an increase in self-employment (significant at the 5 percent level) and no increase in the proportion of people working outside the home.

# **c. Non-Employment Impacts for the Full Sample**

The last four rows of Table 8 present the estimated impacts on savings and borrowing behavior. We find no systematic impacts of the EF training program on loans. The program had a significant and positive impact on individual total savings – a year into the program, savings increased by 901 to 1,171 NRs (≈ 12 to 15 USD).
manufacturing GVCs, including those in textiles and apparel, metal products, transport equipment, and electrical goods. Results from the analyses of the World Bank Enterprise Surveys show that domestic actors in these linkages are typically relatively large manufacturers of 100 or more employees that have been in operation in their respective industries five years or longer. As participants in GVCs, these manufacturers are more likely to have foreign equity holders or hold foreign technology licenses than other enterprises.

The current geographic configuration of these linkages evolved over the last 25 years, during which linkages of China and India to the United States, Western Europe, and the rest of the developing world developed. The paper shows that Africa accounts only for a tiny fraction of the volume of trade in manufactures of any one of China, Western Europe, or the United States, with linkage rates to Africa along manufacturing GVCs consequently being extremely low. Yet Africa’s manufacturing GVC linkage rate with the three larger economies is comparable to the corresponding linkage rates to economies of other developing regions including the external comparators namely, Bangladesh, Cambodia, Indonesia, and Vietnam.

At the same time, linkage rates across manufacturing GVCs do vary significantly between countries within Africa and have changed substantially over the past decade. A major factor in cross-country variation in linkage rates within the region is natural resource endowment, with countries that are rich in non-oil natural resources having greater linkages to manufacturing GVCs. Linkages to manufacturing GVCs have also increased over the period in non-oil resource rich countries but have declined quite steeply for non-resource rich countries as a group. This development needs to be seen against the premise that governments’ efforts across Africa for promoting job growth through industrialization would succeed only in as far as they facilitate entry of domestic firms into manufacturing GVCs at links deemed to maximize expected gains in jobs and productivity.

The level and dynamics of linkage rates to manufacturing GVCs also vary between countries within the resource rich group and between the non-resource rich. This is partly due to differences in the levels of natural resource endowment across
generally due to marriage.

> 11It is important to note that the difference in observations between the married mothers (7,480) and the married fathers (5,496) is due to the number of observations as provided in the official 2008-09 Albanian DHS. In other words, there are more observations of women than men. A detailed description of the LSMS surveys and the DHS survey for Albania and on the construction of the subsamples and some key variables is provided in appendix A. See Table A-1 in the Appendix for more information.

> 12This measure of fertility, _number of children ever born_ , also includes the deceased children.

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A key issue in analyzing any intervention using survey data is the extent to which the treatment changes self-reporting of outcomes holding actual outcomes constant. For example, if respondents try and give socially-desirable outcomes, they may say they have saved after the training, even if they have not, because the training emphasized savings as a desirable activity. We use two main approaches to help mitigate this problem and ensure that any effects measured are likely to be genuine. First, since much of our analysis involves a comparison across treatments, this form of potential bias is likely to be lower since we can effectively difference out the common effect of treatment on self-reporting. Second, we trace out the causal chain from knowledge, to behaviors and then outcomes and look for consistency in impact throughout this chain.

# **3. Analysis**

To analyze the impact of financial literacy training on different outcomes of interest, we estimate the following regression equation: Outcomei,t = a+ b∗TAi + c∗TBi + d∗TCi + e∗Outcomei,0 + f∗Roundt + εi,s (1) Where _TAi_ , _TBi_ and _TCi_ are dummy variables indicating assignment to treatment A (Migrant Only), B (Family Only), and C (Migrant and Family), respectively. To increase power we pool together the three follow-up rounds (McKenzie, 2011).<sup>7</sup> Survey Round indicators are included in the regressions. Robust (White-corrected) standard errors, clustered at the individual level, are reported in parentheses under the coefficients in the tables. Since the regressions are based on original assignment to treatment, all coefficients are estimating _intention to treat_ (ITT) effects. The ITT estimate is the relevant measure to focus on for overall policy impacts.<sup>8</sup> The pooled sample takes into account all available data and we analyze an unbalanced panel with 394 clusters.<sup>9</sup> Since our surveys contain a number of questions
Figure 4: Policy Impact on Education Outcomes <!-- Start of picture text --> Non−Experimental Schools Experimental Schools All Schools Non−Experimental Schools Experimental Schools All Schools<br>.08<br>.15<br>.06<br>.1<br>.04<br>.05 .02<br>Treat Effect on Rates of Child Literacy<br>Treat Effect on Secondary School Enrollment 0<br>0<br><!-- End of picture text --> Panel A: Secondary School Enrollment Panel B: Child Literacy _Notes_ : The bars in the figure represent the OLS estimates of the assignment to the API program during the government implementation of the _Plus_ modality on school-level secondary school enrollment rates (Panel A) and child literacy rates (Panel B). Vertical lines overlaid on each bar displays the 95 percent and 90 percent confidence intervals, respectively. Confidence intervals are based on asymptotic inference.

the assignment of the mentors under the _Plus_ modality at scale. Each bar represents the treatment effect of the policy ( _α_ 1 in equation (6)) for a different sample of schools in the state of Chiapas. For the sample of schools that did not participate in the second experiment, we find that the program increases the fraction of children who enroll in secondary schools by 4.5 percentage points ( _p_ -value = 0.039), which represents an increase of 6.5 percent with respect to the sample mean. For the schools that were part of the experiment, the impact of receiving the program during the government implementation is larger (+8.5p.p., _p_ -value = 0.031), although the two estimates are statistically similar. These effects on secondary school enrollment are in line with the experimental findings on the enrollment in seventh grade (+12.4 percentage points, see Table 2). We interpret this result as evidence that the program at scale is effective in
Table 6: Components of Time-use Index

|||Panel A||
|---|---|---|---|
||(1)|(2)|(3)|
|Dependent Variable:|Studying/Homework<br>Outside School|Household work|Sleeping|
|Goal-Setting|0.042|-0.056|-0.087**|
||(0.040)|(0.038)|(0.038)|
|Goal-Setting + Recognition|0.014<br>(0.043)|-0.106***<br>(0.040)|-0.039<br>(0.041)|
|Observations|13,273|13,250|13,219|
|Baseline Outcome Controlled|Yes|Yes|Yes|
|||Panel B||
||(1)|(2)|(3)|
|Dependent Variable:|Games/Leisure time<br>Outside school|Studying extra for<br>Endline exam|Studying Math<br>Outside school|
|Goal-Setting|-0.043|0.022|0.067*|
||(0.027)|(0.048)|(0.039)|
|Goal-Setting + Recognition|0.004|0.023|0.059|
||(0.031)|(0.059)|(0.047)|
|Observations|13,258|13,310|13,241|
|Baseline Outcome Controlled|Yes|Yes|Yes|

_Notes_ : This table reports the result of estimating equation 1 on individual components of Time-use Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p
<!-- Start of picture text --> Figure 8. Linkage to manufacturing GVCs by industy (%): All non-<br>resource rich<br>100<br>90<br>80<br>70<br>60 62<br>58<br>50 48 51 50<br>40<br>36 37<br>30<br>24<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>Upstream Downstream Δ GVC 2015/1995<br>Δ Upstream 2015/1995 Δ Downstream 2015/1995 GVC linkage 2015<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

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time _t_<br>External capital acquired by the rm at time _t_|
|_O_<sup>_t_</sup><br>_r_|Share of the market risk covered by reinsurer _r_ at time _t_|
|_π_<sup>_d,t_</sup><br>_r_|Probability of default of the rm at time _t_|

iteration involves the approximate solution of a large linear system using the method of Preconditioned Conjugate Gradients (PCG).

# 4.2 Scenario

## Reinsurance loss coverage

To build the insurance market demand for reinsurance, we proceed in the following manner. We use model-based aggregate insured loss data from Risk Management Solutions Inc. for the four main reinsurance disaster markets: Japan Earthquake, European Windstorm, US Hurricane and US Earthquake, including four types of risk covered (agricultural, commercial, residential and 10