rafmacalaba/data-use-mentions-extended huggingface dataset:
|
|Year effects|x|x|x|x|x|x|x|x|
|Country effects|x|x|x|x|x|x|x|x|
|Seasonal Dummies|x|x|x|x|x|x|x|x|
|Within R-squared|0.57|0.48|0.93|0.80|0.93|0.80|0.93|0.80|
|Nbr. obser.|396|392|392|392|392|392|392|392|

Note: Robust clustered standard errors in parentheses. Significant at (*) 10 per cent level; (**) 5 per cent level; (***) 1 per cent level.

(a) The currency ratio is the ratio of currency in circulation (oustide the banking system) over demand deposits and the reserves ratio is the ratio of excess reserves held by private banks over total deposists.

(b) SURE stands for seemingly unrelated least squares
**Plc,Planning, and Research**

**Ulescnomkc Adjustment and GrowthI**

Our knowlccige about the harmful effects of minimum wage regulations has been strongly influenced by statistical evidence from the industrial countries - which is often based on indirect estimates that do not take into account the many "discouraged job seekers" who withdraw from the labor force (and statistical samples) because of minimum wage regulations. Minimum wage regulations are probab:y more harmful than economists have assumed them to be.

Government regulation of the minimury wage is most likely to limit the job prospi.cts of young and uneducated or unskilled workers.

Proportionately fewer women than men are affected by the minimum wage because women withdraw from the labor force and remain unemployed - or work in the informal sector and productive activities not accounted for in labor statistics.

Minimum wage regulations should probably not apply to young or unskilled workers or apprentices. It is less important to guarantee unskilled workers a minimum wage and more important to provide them with training that will increase their chances of rising above the need for wage protection.

This paper is a product of the Macroeconomic Adjustment and Growth Division, Country Economics DepartmenL Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Raquel Luz, room N1 1- 057, extension 61762.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than ful!y polished. The fundings. interpietations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright i 1988 by the International Bank for Reconstruction and Developmenttlhe World Bank
> Our study contributes to the growing literature on the economic consequences of forced migration for host countries (Becker and Ferrara (2019)). Despite its enormous policy interest, this topic has remained understudied due to the scarcity of high quality data. Another challenge in the estimation of the effects on receiving communities is that the average impact on host communities is mixed because some native workers win while others lose, as noted in the review by Ruiz and Vargas-Silva (2013), In the last few years, the literature has concentrated heavily on the analysis of the

> 5 _Adequate employment_ refers to the full-time employed earning at least the minimum wage, and to workers with part-time jobs that do not wish to work more hours.

> 6Beine et al. (2019) have also used cell phone records in the context of Syrian refugees in Turkey, but their analysis is limited to refugees’ location choices and they do not analyze labor market outcomes.

4
formalize firms increases firm growth by allowing firms better access to formal credit, having multiple data points allows measurement of whether changes in credit precede firm growth or instead follow it. The second, and less practiced, use of more frequent data is to collect multiple measurements on noisy and weakly autocorrelated outcomes. For example, in a microenterprise survey, measuring several months of profits allows noise in a given month to be averaged out, increasing the power to detect genuine effects of a program.

With larger firms it may be possible to collect even more frequent data on key performance indicators like daily production, electricity usage, quality defects, and sales. Such data can then be used for both purposes above – averaging out daily fluctuations, and also 15
moderately unsatisfactory, this leads to further bunching in the middle of the distribution than would otherwise be the case.

Table 2 reports summary measures of project core characteristics in our two analytic samples. Two estimates of each measure are reported: the project-weighted and the dollar-weighted (by pledged loan volume) averages. The first information in the table depicts the distribution of the IEG project outcome scores. In the analytic data set there is a clear bunching of outcomes at the ‘satisfactory’ and ‘moderately satisfactory’ ratings while only 3% of projects are rated as highly satisfactory. Similarly, only 1% of projects were highly unsatisfactory and of the remaining 26% percent of projects deemed unsuccessful, 15% are ‘moderately unsatisfactory’. The fact that larger projects are slightly more likely to be rated successful is reflected in the fact that 76% of dollars was pledged to ultimately successful projects as opposed to 72% of projects.

We see that while World Bank investment projects are more numerous in Sub-Saharan Africa, when weighting by dollars they are greatest in the East Asia and Pacific region, followed by Latin America and the Caribbean. The Sustainable Development (SD) sector dominates by project count and by dollar volume but still almost half of projects are in the Equitable Growth, Finance, and Institutions (EFI) and Human Development (HD) sectors.<sup>10</sup> Slightly more than half of investment projects occur through the IDA instrument but 44% of projects fall within the less-concessional IBRD vehicle. In contrast, by dollar volume 64% of lending over the period studied was offered through IBRD.<sup>11</sup> The average real project size at time of approval stands at 89.6 million USD (measured in 2015 USD), while the slightly lower total amount committed, at 87.3 million USD, reflects the actual amount transferred including both partial cancellations and any increases in loans size that took place during project restructuring. The intended length at approval stands at five years,
sample data. We encourage further research on comparing the performance of different methods, contexts, and outcomes. Further research could also shed light on whether models are sufficiently stable to use for intertemporal prediction. It is also useful to evaluate the performance of these methods in richer data contexts where we can compare models using satellite-based variables versus those using variables coming from household surveys and censuses.

Last but not least, a practical challenge with implementing machine learning methods involves limitations with analytical capacity of national statistical offices. We note that in these contexts, using existing tools to investigate modeling issues is good practice and should be done as part of the standard data checking and model exploration process before employing more advanced ML methods. For these methods to be employed more widely in a responsible way to better address data gaps, we call for more efforts by various stakeholders to improve local analytical capacity. For example, donors can support leading experts to jointly collaborate with national statistical staff to produce statistical training and better results. Providing ungated, public access to the latest research results and well-documented software packages (e.g., the _EMDI_ and _povmap_ 42
COVID-19. Before COVID-19, children living in urban areas and under the age of 15 are more to attend school than children in rural areas that have the same likely living age. From the age of 15 onward, no difference in school attendance is observed between urban and rural children. After schools reopened, i.e., during COVID-19, no difference is observed between urban and rural children, although before the age of 15, urban residents are more still likely to attend school than rural residents.

Overall, Figure 2 suggests that COVID-19 decreased school attendance among all age groups, but even more so, among those whose education is no longer compulsory (agegroup 15-18). Figure 2 also suggests that COVID-19 did not discriminate between the genders or between rural and urban children.

# **2.2.6 Learning Activities and Contact with Teachers during the School-Closure Period**

In the analysis of the heterogeneous effects, we are interested in whether the impact of temporary schools’ closure on attendance differs by the opportunity of being engaged in any learning activities or in contact with their teachers during the school closure period in Nigeria. We hypothesize that people who were engaged in any learning activities during the lockdown are more likely to return to school than those who were not. Indeed, losing contact with the education system over the whole period of schools’ disruption is expected to lower students’ motivation to schooling by making other options (like employment and marriage) more attracting alternatives.

# **_(i) Learning Activities_**

To capture learning activities during the school closure period, we exploit the first five rounds of the COVID-19 NLPS in Nigeria, using the question asking if children have been in education or activities the seven engaged any learning during days preceding each round survey.<sup>16</sup> Therefore, we construct a dummy variable equal to 1 if a child resides in a household where children have been engaged in these types of activities in 16The question was asked in each of the first
two stages. First, we estimate the income process given by (19) and construct the quasi-innovations to the present value of income growth; then we use the latter to estimate the risk-sharing model (13).

## **4.1 Estimation of the income process**

As already explained, we estimate three different specifications of (16). The first one imposes **A** (L) = **0** and **β** (L) = **0** (which in turn implies that **B** = **0** in (19)), so that each country’s per capita GDP growth rate is an iid process. The second specification allows 19Specifically, from the former dataset we take the KAOPEN financial openness indicator, while from the latter we use the ratio of total foreign assets and liabilities to GDP.

> 20Foreign asset and liability positions, relative to GDP, provide an alternative way to assess the trends in de facto international financial integration. They also exhibit a marked rising trend over the sample period and, like in Figure 1, the rise is steeper among advanced than among emerging countries.

16
, bicycles, cycle trailers and tricycles, motorcycles, and tractors.

19 Additional information can be found at the World Bank’s website on rural transport services at <u>www.worldbank.org/transport/rural_tr/rts_imt.htm</u> 20 Although the idea has been discussed in some developing countries, the authors are not aware of any results so far. Indirectly, though, privatization of state-owned transportation enterprises through competitive tenders in some countries may have provided subsidies to new rural operators, for example through below-cost sale of assets or initial tax exemptions. Also, where the construction or maintenance of infrastructure is undertaken by the private sector, the work is usually contracted through a tendering process which encourages competition for the price to be paid, including any element of subsidy. Such competition is being extended to various forms of BOOT (build, own, operate, and transfer) contracts (e.g.. South Africa).

21 For example, several rural routes could be licensed together and groups of operators encouraged to form companies to bid for these licenses, which would be awarded to the company that requires the lowest subsidy. Route frequencies, fares, and subsidy amounts would be made explicit in the bidding process. See Starkey et al. 2002.

22 Whether this proves to be a concern (or a business opportunity) will depend on the economic homogeneity of the target population. In small rural settlements it is typical for most inhabitants to be living close to or under the poverty line so this may be unimportant.

23 About 2-3 percent of community income is often used for initial analysis of rural telecommunications programs. Surveys of rural communities in countries as different as Argentina, India, Nicaragua, and the Philippines show that households spend about five percent of monetary income in energy, fairly consistently across countries and with even higher proportions for the lower income households. The World Health Organization’s target is that water supply should not cost more than five percent of household income.

24 An initial
in the middle range within the same sample

> 11The figure refers to 2014 according to World Bank Data on emissions.

> 12In Indonesia the subsidy has been largely phased out in the public budget at the end of 2014, but energy prices continue to be implicitly subsidized by the state owned monopolists of electricity production and distribution and of fuels distribution, which also generates concerns for their economic sustainability. In 2014 the Mexican government has started implementing a gradual reform of the energy sector and it introduced a carbon tax, although the tax was limited in scope.

5
Adding to the partial analysis of corporate debt, the different debt statistics available, with very different methodologies and coverages, fail to provide a comprehensive picture of the various components of aggregate debt levels and their evolution across countries. Overall, understanding the determinants of the financial architecture of corporations is key because the costs associated with economic crises depend on the type of debt instruments used (Hale, 2007). More analyses on the changes in the composition of corporate debt can be relevant to identify how financial distress can be transmitted throughout the economy and help to design policies to prevent and mitigate its impacts.

Second, further understanding the characteristics of firms issuing debt is also key to accurately analyze debt exposures across borrowers. For instance, larger firms have better capacity to repay their debt obligations than smaller corporations. In addition, larger firms can raise financing from different instruments and markets, allowing them to better mitigate negative shocks. Another important distinction is between privately-owned and state-owned firms. For the latter, implicit government guarantees can be relevant when assessing their credit risk.

Third, the type of creditors holding nonfinancial corporate debt can also impact firms’ risk profile. For instance, investors with long-term horizons that can hold illiquid instruments up to maturity, such as defined benefit pension and insurance funds, might be more inclined to rollover debt relative to short-term investors looking to obtain rapid gains and following market-to-market practices that are monitored frequently (Opazo et al., 2015). Similarly, a large share of debt held by highly speculative investors (the so-called “vulture funds”) could hinder efforts to renegotiate the outstanding debt in case of default.

29
Policy Research Working Paper 9401

# **Abstract**

This paper examines the influence of sovereign credit ratings and relative risk ratings on private capital flows to 26 emerging and frontier market economies, using quarterly data for 1998–2017. A dynamic panel regression model is used to estimate the relationship between ratings and capital flows after controlling for other factors that can influence capital flows, such as growth and interest rate differentials and global risk conditions. The analysis finds that while absolute ratings were an important determinant of net capital inflows prior to the global financial crisis in 2008, the influence of relative risk ratings increased in the post-crisis period, which was characterized by easy monetary policies and global liquidity, on the one hand, and greater caution and discretion on the part of investors on the other. The post-crisis effect of relative ratings appears to be driven mostly by portfolio flows. These findings imply that emerging and frontier markets need to pay greater attention to their relative economic performance and not just their sovereign ratings. Tracking changes in relative ratings could help predict macroeconomic disturbances resulting from volatile portfolio capital movements.

This paper is a product of the Migration and Remittances Team, Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at sde@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/
|Region‐Year FE|yes|yes|yes|yes|
|---|---|---|---|---|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|140,594|140,594|140,594|140,594|
|R‐squared|0.215|0.216|0.169|0.170|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same microregion and county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch employment definition.

# 3.3 Robustness checks

To test the robustness of our results, we first investigate whether the measured relationships changed after the Great Recession in 2007 (Table 8). We find that HGF share among buyer industries matters in both periods, though somewhat more after the Great Recession. The results also indicate that HGF in supplier industries had been significant before the recession and has become less important afterwards.

The results are largely robust when controlling for autocorrelation (Table A3). The larger coefficient following the crisis comes from the 2009‐2012 period, a period of sluggish recovery. Firms with potential buyers who have been high growth even during the crisis, have increased their productivity rapidly.

## **Table 8 Before and after the Great Recession**

||**Laborpro**<br>|**Productivity (3**<br
in the STRI: rescaled_WALI = (50 - WALI) * 2. This is an imperfect approximation of policy restrictiveness. WALI does not reflect the MFN policy, but rather is a synthetic measure of openness which relates to all services agreements an economy may have signed, and it pre-dates the policy information collected for 2016.<sup>28</sup> However, the importance of cross-border supply for air transport services means that the use of an imperfect approximation results is a less serious distortion than the omission of Mode 1 altogether.

This completes the aggregation of scores from the level of individual measures to the subsector and economy level, respectively. We then aggregate subsector STRIs to obtain sector-level and economy-level STRIs, respectively, using the weights provided in table 6.1 of Annex 6. These are based on the value-added weights of services sectors of individual economies’ total value added for 2013.

# **Decomposition of changes in STRI scores in 2008-11 vs 2016**

In this section we offer a comprehensive decomposition of differences in STRI scores across 200811 and 2016, respectively, to transparently identify the reasons for those changes. It is tempting to attribute any observed change in STRI scores between two periods in time to a corresponding change in policy regime, but in fact it could also be due to improvements in the quantification method (i.e. introduction of the algorithm and use of the CES aggregation method), or changes in the sectoral scope of the STRI. Hence, for the 55 economies covered in both periods, we map the scores from the earlier (2008-11) World Bank database against the new scores in the STPD as of 2016 and disentangle the three components that each play a role—to a varying degree—in driving the overall observed change in scores: (i) changes in the quantification methodology, (ii) actual changes in policy regime, and (iii) changes in sectoral scope.

- i. For this first level of analysis we use the 2008-11 raw policy data and aggregate
-0.01|0.01|0.09|-0.01|
||(0.12)|(0.12)|(0.12)|(0.15)|(0.12)|
|Post-graduate degree|2.07|2.55|3.25|5.35|2.14|
||(3.71)|(3.69)|(3.85)|(4.41)|(3.67)|
|Studying abroad|-7.92**|-8.10**|-6.24|-5.49|-8.10**|
||(3.75)|(3.73)|(3.84)|(4.81)|(3.76)|
|Experience in large business|2.93|2.64|2.44|2.90|2.47|
||(2.80)|(2.78)|(2.87)|(3.28)|(2.79)|
|Management Z-score|0.34|0.53|0.34|-1.47|0.53|
||(1.86)|(1.87)|(1.92)|(2.26)|(1.87)|
|Observations|885|891|853|605|892|
|R-squared|0.21|0.21|0.21|0.24|0.21|

Table B4: Regressions of sales on digitalization and quantiles of tech sophistication. _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1 Robust standard errors in parentheses.

33
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