rafmacalaba/datause-encoder-data huggingface dataset:
# Table 5: Policy Questions

||||||**Simulation Modeling**<br>||
|---|---|---|---|---|---|---|
|**POLICY QUESTIONS**|**Case**<br>**studies**|**Micro‐econ.**<br>**Studies**|**Macro‐econ.**<br>**Studies**|**Fixed**<br>**employment**|**Full employment**|**Non‐full**<br>**employment**|
|Can policies be designed to mitigate|||||||
|costs associated with labor adjustment?|1,2|1,2||||2,3|
|Which policies lead to smaller<br>employment shifts among industries?||1,2||1,2|1,2|1,2|
|Which policies lead to smaller effects on|||||||
|unemployment?||||||1,2|
|Is there a role for training programs to|||||||
|mitigate labor adjustment costs?|1,2|1,2|||||
|How do we design policy to compensate|||||||
|industries harmed by climate policy?||||1,2|1,2|1,2|
|How do we design policy to compensate|||||||
|households harmed by climate policy?||1,2|||||
|How do we design policy to compensate|||||||
|workers who lose their jobs due to|||||||
|climate policy?||||||2,3|

## **LEGEND**

Normal text = topic addressed and generally well‐understood.

_Italic text_ = adequately addressed and/or adequately understood, room for improvement. <u>Underlined text = not addressed and/or not well understood.</u> 1 = method has been used answer this question 2 = this is a good method to answer this question 3 = this method could be used to answer this question 45
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Water and sanitation needs**

|$ millions/year||||
|---|---|---|---|
||Urban|Rural|Total|
|**_Water and sanitation_**||||
|_Total spending_|260.0|167.4|427.5|
|Capital spending|164.1|121.5|285.6|
|Expansion|67.3|47.4|114.7|
|Rehabilitation|96.8|74.1|170.9|
|Operation and maintenance|95.9|45.9|141.8|
|**_Water_**||||
|_Total spending_|242.9|128.3|371.2|
|Capital spending|151.4|88.4|239.8|
|Expansion|57.5|23.0|80.5|
|Rehabilitation|93.8|65.4|159.2|
|Operation and maintenance|91.5|39.9|131.4|
|**_Sanitation_**||||
|_Total spending_|17.2|39.1|56.3|
|Capital spending|12.8|33.1|45.9|
|Expansion|9.8|24.4|34.2|
|Rehabilitation|3.0|8.7|11.7|
|Operation and maintenance|4.4|6.1|10.5|

_Source:_ Banerjee and others 2009.

# Transport

## Roads

### **Achievements**

Zimbabwe has a relatively dense national road network. Total road density is 100 km/1,000 square kilometers (km<sup>2</sup> )—double the figure for neighboring Zambia and almost triple that of Mozambique—and the network spanned almost 44,000 km in 2008.<sup>8</sup> The classified network—roads under state jurisdiction— totaled 18,253 km in 2010, according to the recent Zimbabwe Road Condition Survey, while there are an additional 5,000 km of urban roads, and around 39,000 km of unclassified roads. The rural-accessibility index in Zimbabwe, at 46 percent, is more than double the regional averages (table 9).

Zimbabwe has strong road connections with the SADC. Zimbabwe is linked to its neighbor South Africa—the largest regional economy and home to the busiest port in Africa—and other countries such as Mozambique, Botswana, Zambia, the Democratic Republic of Congo, Malawi, and Tanzania through the North–South Corridor, the most extensive corridor system in the region. In addition, the North–South

> 8 Alternative sources, including several World Bank project documents, mention a total of more than 88,000 km, doubling AICD‘s estimates.

29
geographic targeting are far less marked than was observed when the FGT2 measure was our reference measure. In the baseline case, targeting at the district level in rural areas can be expected to achieve a nine percentage point further decline of the headcount rate over the uniform transfer – with commune level targeting the new FGT0 is 91% the value of the FGT0 that would have obtained had the budget been allocated uniformly across the rural population. In urban areas, the gains are more impressive: the new FGT0 is 65% of the value from a uniform allocation across the urban population. Again, when a particularly austere poverty line is selected (column 3) the gains from geographic targeting over uniform targeting are most pronounced.

# _Calculating “Equivalent Gains”_

In thinking about the “performance” of the transfer scheme we are interested not only in the poverty impact of a specific scheme, but also in how much more “expensive” a given poverty reduction would be without, as opposed to with, geographic targeting. To explore the latter we apply a variant on the simulation procedures described above whereby we calculate how much smaller the overall budget _S_ could be in order to achieve the same impact on the FGT2 measure with optimal targeting as with the untargeted uniform lump-sum transfer.

Table 3 demonstrates that in rural Morocco even a poverty map at the regional level would have permitted the same reduction in the FGT2 as the uniform transfer at only 76% of the cost of the uniform transfer. With a more detailed rural poverty map that allows for disaggregation down to the district level, the same impact could have been achieved at only 28% of the cost of the uniform transfer. In urban areas, with district level targeting, the same

> difference in the estimated FGT2 based on optimal targeting at the 3<sup>rd</sup> administrative level vis-à-vis targeting at the uniform, 1<sup>st</sup> and 2<sup>nd</sup> administrative levels.  In all cases, these differences are strongly significant.

14
needs to be more expansive than focusing only on the cognitive, academic type skills measured by international tests.

The objective of our paper is threefold. Rather than focusing on cognitive academic skills alone, we focus on a more expanded notion of “skills.”<sup>3</sup> Our first objective is to develop a composite indicator of skills for developing countries in Asia like the European Skills Index (ESI) developed by the European Centre for the Development of Vocational Training (CEDEFOP).<sup>4</sup> The Asian Skills Index (ASI) intends to measure the performance of a similar group of Asian countries in terms of how effectively labor market skills from post-secondary education level and beyond are being formed (“skills formation”), how effectively such skills are utilized in labor markets (“skills activation”) and finally, how effectively labor market demand-supply mechanisms match skills to abilities (“skills matching”).

The motivation to develop such an index is threefold. First, the ASI considers the _entire skills system_ - ranging from how skills are formed, to how skills are activated, to how skills are ultimately deployed for economic growth. This is particularly important in the Asian context given that skills formation alone may not be the primary constraint since a significant pool of educated are unable to find productive employment. We also invoke the concept of deadweight loss for the education and skills development system due to a highly educated population with no jobs. It should be noted that a major difference between previous proxies for human capital such as “years of schooling” is that they essentially focus only on skills formation. But skills formation by itself is redundant if it is not activated through participation in the labor market and

> 3 The term “Skills” is used in this paper to broadly define the acquisition of marketable competencies and attributes as applicable beyond secondary education and encompassing tertiary education (both technical vocational education and training and higher education at university level).

> 4 - <u>https://www.cedefop.europa.eu/files/esi_ _technical_report_2020.pdf</u>

4
force in 1980, (ii) one which controls for the log of GDP per capita in 1980, and (iii) one which controls for both variables and year fixed effects simultaneously. In each of these regressions we control for year fixed effects. Similar to when estimating equation (5), we estimate these specifications both through OLS and through the pseudo maximum likelihood method proposed by SSTW, which takes into account the truncated nature of the left hand side variable. The results of the estimation of the four sets of regressions are presented in Table 2.

The results presented in Table 2 confirm the hypothesis set forth at the beginning of this subsection— small and poor countries have higher turnover in their export baskets, as they open and close export lines at a higher rate. In particular, columns (1)-(3) and (7)-(9) of Table 2, Panel A show the OLS estimates of the creation and destruction rates (opened export lines relative to the size of the export basket in _t_ and closed export lines relative to the size of the export basket in _t-1_ ), respectively. The coefficients for size and income per capita are negative in each of these columns, which implies that small and poor countries present high year-to-year turnover in their export baskets. Clearly, the results are partly driven by the bounded nature of creation and destruction rates; columns (4)-(6) and (10)-(12) present the SSTW estimates, which have a smaller magnitude compared to the OLS estimates. However, the effect of size and GDP per capita remains significant under the SSTW estimation. Indeed, except for the coefficient on size in the regression for the creation rate (column (4)), all of the coefficients are statistically significant.

The results for creation and destruction relative to the initial export basket, presented in Panel B of Table 2, reinforce the conclusion that small and poor countries have higher rates of creation and destruction relative to the initial export basket than bigger and richer countries. Overall, the results in this subsection suggest that the disproportionately large latent basket relative to cross-sectional baskets observed in poor and small countries is indeed a byproduct of the large turnover observed in their cross-sectional baskets.

# **6. Latent Trade Diversification and Macroeconomic Volatility**

Concentration has been deemed by academics and policy makers as a possible cause of economic volatility. For example, Acemoglu and Zilibotti (1997) and Imbs and Wacziarg (2003) highlight, theoretically and empirically, that poorer countries, which on average are more volatile, have more concentrated economic structures. More recently, di Giovanni and Levchenko (2012) show that poorer countries have more concentrated firm size distributions. Under certain assumptions, this implies that they are more vulnerable to the amplification of shocks hitting big firms.

22
33 constant. Note, consistent with Levine and Zervos (1998), the size of the domestic stock markets is not related to the excess growth of firms. After conducting a wide-array of robustness checks, DM conclude that the proportion of firms that grow at rates exceeding the rate at which each firm can grow with only retained earnings and short-term borrowing is positively associated with stock market liquidity and banking system size.

# **2.6. Country case studies**

Dropping the cross-country dimension and focusing on an individual country often increases the confidence in the results by reducing potential biases due to measurement error and reducing concerns about omitted variables and endogeneity. In a study of individual regions of Italy, Guiso, Sapienza and Zingales (2002) use a household dataset and examine the effect of differences in local financial development on economic activity across different regions. They find that local financial development enhances the probability that an individual starts a business, increases industrial competition, and promotes growth of firms. And these results are stronger for smaller firms which cannot easily raise funds outside of the local area. Another example is Haber’s (1997) historical comparison of industrial and capital market development in Brazil, Mexico and the United States between 1830 and 1930. He uses firm level data to illustrate that international differences in financial development significantly affected the rate of industrial expansion.

Perhaps one of the cleanest ways of dealing with identification problems is to focus on a particular policy change in a specific country and evaluate its impact. One example of this approach is Jayaratne and Strahan’s (1996) investigation of the impact of bank branch reform in individual states of the United States. Since early 1970s, U.S. states started relaxing impediments on their intrastate branching. Using a difference-in-difference methodology,
# **Corporate Debt Maturity in Developing Countries: Sources of Long- and Short-Termism**

Juan J. Cortina Tatiana Didier Sergio L. Schmukler<sup>*</sup>

**JEL Classification Codes:** F34, F65, G0, G10, G15, G21, G32

**Keywords:** capital raising, corporate bonds, domestic and international debt markets, developing countries, firm financing, issuance maturity, long-term debt, short-term debt, syndicated loans

* We received very helpful comments from Andreas Antoniades, Roberto Fattal Jaef, Stephany Griffith-Jones, Ruth Llovet, Andrea Presbitero, María Soledad Martínez Pería, Luis Servén, two anonymous referees, and participants at related presentations held at the ASSA Annual Meetings (Chicago), Bocconi University, Borsa Istanbul, Central Bank of Argentina, Central Bank of Chile, CSEF-University of Naples Federico II, Georgetown University, the International Monetary Fund, University of Buenos Aires, University of Cape Town, and the World Bank (Washington, DC and Malaysia Knowledge and Research Hub). For financial support, we are grateful to the World Bank Knowledge for Change Program (KCP) and Strategic Research Program (SRP) and to the Assonime/CEPR Research Programme on Restarting European Long-Term Investment Finance (RELTIF). The paper is part of the background work prepared for the World Bank 2016 Global Financial Development Report and is forthcoming in _World Economy_ . Email addresses: jcortinalorente@worldbank.org, tdidier@worldbank.org, <u>sschmukler@worldbank.org.</u>
<!-- Start of picture text --> Pollcy Research<br>N~~~~~~<br>Public Economlos<br>WPS 1030<br><!-- End of picture text -->

- This paper- a product of the Public Economics Division, Country Economics Deparunent is part of a larger research project funded by the Bank's Research Support Budget on "Pollution and the Choice of Policy Instruments in Developing Countries" (RPO 67648). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Peggy Pender, room N10067, extension 37851 (November 1992, 77 pages).

Mandating emission control devices in new cars is only one of the most obvious steps to address the problem of vehicle emissions. Others range from taxes on gasoline and paridng to incentives to scrap old cars or move businesses out of the cities.

importance of nonmotorized modes, of mopeds, of old vehicles, and of work-related trips, greater growth in urbanization, and greater growth in the urban vehicle stock) allow us to assess how well models from developed countries apply in industrial countries.

There are models to simulate the "engineering" implications when changes are made to the vehicle fleet (such as the U.S. Environmental Protection Agency's "MOBILE 4"), but other models are needed to capture individual behavior, for two reasons. First, behavior - for example, using certain vehicles - affects emissions, and thereby the effect of policies on pollution. Second, behavioral relations determine how much consumer welfare is affected by - different policies through other channels than the effect on air pollution.

Krupnick reviews existing models of urban transport and evaluates their ability to simulate the effects of different policies on emissions and on other variables relevant to welfare. He finds that: Little modeling work is done on developing countries, but some stylized facts (the greater

* Models vary greatly in complexity. The central question for users is whether they want detailed coverage of the spatial nature of pollution and congestion. The most comprehensive and detailed models also require the **niost** data.

Krupnick proposes eclectic use of several models, since a model incorporating long-tern responses, shorter-term responses, and emission consequences is not easily tractable.

Krupnick acknowledges the many complex links between policies (on the one hand) and welfare and air pollution (on the other), but says that research can often be narrowed according to available policy instrunents, data availability, and the implications considered relevant. Often, simple models can improve the basis for policy evaluation, particularly when there are limited data and resources for research.

The Policy Research Working Paper Series disseminates the fmdings of work under way in the Bank. An objective of the series is to get these findings out quickly, even if presentations are less than fuDly polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
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- Tzannatos, Z. and G. Psacharopoulos, “Latin American Women’s Earnings and Participation in the Labor Force,” World Bank Policy Research Working Paper 856, 1992.

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_Economla_ , 38(114), 127-53, August 2001.

23
in the sample is 11. There are two concerns regarding the accuracy of the hospitalization data. The AIH form is also completed if a patient requires a procedure; so, some nonovernight hospitalizations may also be entered in the system. However, few procedures are associated with assault and are thus unlikely to bias the results. The other concern is that hospitals cannot report more patients than the number of beds they have registered with the centralized health care system. The system may therefore be underreporting incidents in crowded hospitals.

_Violent incident reporting by health workers._ In 2011, Brazil standardized and expanded its mandatory notification system (VIVA) for cases of domestic violence and violence against women, children, and the elderly to every type of health care facility in the country (Ministry of Health 2016). The reports are filled out at the health care units by the attending medical personnel (for example, doctors, nurses, dentists, psychologists, and social workers) and taken to the municipal health care administrative office, where they are digitized and entered into the Information System for Notifiable Diseases <mark>(Sistema de Informação de Agravos de Notificação</mark> , SINAN).<sup>7</sup> These are to be filled out whether the patient admits to being a victim or if the patient is merely suspected of being a victim. Although psychological, financial, sexual, and physical violence may be reported, the analysis only included SINAN cases involving sexual or physical violence that was reported because these cases are included in the ICD-10 codes used in creating the hospitalization and homicide variables. Observations are dropped from the analysis if the wound is described in the report as self-inflicted because suicides are not included in the homicide data.

Because the number of facilities reporting has risen since the passage of the 2011 law, only mandatory reports of physical violence ae included that have been supplied by facilities that had at least one such report in 2011. Focusing on facilities that have already submitted the mandatory reports avoids confounding an increase in reports with an increase in events. Indeed, figure 1 indicates rather consistent aggregate levels of reporting by these facilities.

Around one-third of the mandatory reports are delivered by clinics, while twothirds originate from hospitals or emergency rooms. This implies that there may be some double counting with the reports on hospitalizations for assault, but these would only involve cases in which the injury is so severe as to require an overnight stay. The cases

> 7 See “Violência Interpessoal/Autoprovocada,” Sistema de Vigilância de Violências e Acidentes (Viva), Sistema de Informação de Agravos de Notificação, Secretaria de Vigilância em Saúde, Brasília, http://www.portalsinan.saude.gov.br/violencia-interpessoal-autoprovocada.

9
experiences an average of 424 storm-induced outages per year, lasting 40 hours and affecting 13,500 consumers on average.

What share of outages are caused by natural hazards?

Unlike in the US, data on overall outages is not readily available for Bangladesh. One reason for this is that outages are occurring at such a high frequency that does not allow for reliable monitoring. For instance, the 2013 Enterprise Survey for Bangladesh suggests that the majority of firms experience more than two outages per day.<sup>17</sup> In order to get a sense of the relative importance of storms as a cause of outages, we retrieve firm-level outage data from the Enterprise Surveys for 2007 and 2013. The survey covers six Bangladeshi divisions – Chittagong, Dhaka, Rajshahi, Khulna, Barisal, and Sylhet– and is representative at the country level. However, to ensure comparability with the BREB dataset, we illustrate the share of outages caused by storms for two selected divisions: Chittagong and Dhaka. Figure 3.7 compares the number of outages due to storms (as reported by BREB) with the average monthly number of all outages (reported in the Enterprise Surveys). In 2007, storms were responsible for approximately 1% of all outages in Chittagong and 24% in Dhaka. In 2013, those percentages increased in both regions: storms caused 4% of all outages in Chittagong and 41% in Dhaka.

**Figure 3.7: Number of outage reports per month in Chittagong and Dhaka**

<!-- Start of picture text --> Chittagong - 2007 Chittagong - 2013<br>120<br>40<br>100 35<br>30<br>80<br>25<br>60 20<br>15<br>40<br>10<br>20 5<br>0<br>0<br>Storm-induced outages Storm-induced outages<br>Annual average (all causes) Annual average (all causes)<br>Number of outages per month Number of outages per month<br>April May June July April May June July<br>January February March August September October November December January February March August September October November December<br><!-- End of picture text -->

> 17 Detailed figures may be found at:

http://www.enterprisesurveys.org/data/exploreeconomies/2013/bangladesh#infrastructure 14