rafmacalaba/data-use-mentions-extended huggingface dataset:
|59%|58%|
|Serbia|||||||65%|||||||87%|
|Slovak Republic|61%|59%|63%|63%|61%|57%|52%|44%|48%|57%|59%|53%|43%|42%|
|Slovenia|71%|74%|75%|71%|72%|74%|66%|71%|74%|75%|72%|70%|73%|65%|
|Spain|52%|54%|54%|57%|59%|59%|56%|46%|46%|43%|49%|47%|49%|47%|

Note: Blanks correspond to country-year pairs for which the Eurostat’s SBS Database does not report information. Montenegro and Ukraine are excluded from this table as the SBS Database does not include information for any of the years in our sample.
# Introduction

Between 2010 and 2014, the Middle East and North Africa (MENA) region, experienced an extraordinary wave of energy and food subsidy reforms. These reforms did not achieve the objective of removing subsidies completely—far from it, but they were extraordinary in two important respects. They were unprecedented because no other period in the history of the region had seen such a wave of subsidy reforms and because they occurred during an extremely complex period from a social and political perspective—a period of war, revolutions, and social upheavals.

What triggered the reforms? Who reformed, when, how, and why? What are the pros and cons of reforms? These are some of the questions we discuss in this paper. Using the information contained in a cross-country comparative analysis and eight country case studies (Verme and Araar, 2016), this paper summarizes events and reflects on some of the choices made by policy makers and emerging (although still unfolding) lessons. A brief history of subsidies will show how their evolution followed a similar pattern across the countries of the region, a pattern mainly guided by oil prices and shifts in the dominant political views of the time. We then ask what triggered the reforms and try to pinpoint the key factors that eventually forced governments to take action on reforms. Next is a summary of the essential elements of the subsidy reforms in those countries that implemented reforms between 2010 and 2014. Last is a discussion of reforms and the challenges that remain in completing the reforms.

# A Brief History of Subsidies

Consumer subsidies are part of the history of the MENA region. In some countries, subsidies were already present during the colonial period and were part of the colonial heritage when countries became independent. Each case study in Verme and Araar (2016) briefly reviews the origin and evolution of subsidies during the post-independence period, and this section reviews some of the salient features of this history.

Consumer subsidies have evolved to serve different purposes depending on the country considered and the historical period. Most of
Although the methodology applied to collect the data is similar to time to export data from DB, the content of LPI is broader. The third indicator is an alternative for infrastructure measure. It measures the losses of firms within countries that resulted from power outages. These cross-sectional survey data, which are conducted only to firms in developing countries, are collected through Enterprise Surveys of the World Bank.<sup>14</sup> The final indicator is a measure of property rights from Index of Economic Freedom database. It measures the degree to which a country’s laws protect private property rights and the degree to which its government enforces these laws. This measure differs from the property rights measure in Economic Freedom of the World in its source of collection.<sup>15</sup> The data for infrastructure is available for 2005 to 2008 depending on the survey year. All other data are available for 2005-2007 periods. The results with these alternative variables are presented in Table 8. In all specifications, investment climate indicators have the expected signs and the interaction terms are significant. The findings are in close accordance with the main estimation results.

# **5. Alternative Specifications and Panel Estimation Methods**

The Economic Freedom of the World report publishes an index for the size of the trade sector relative to its expected size. Using regression analysis, an expected size of the trade sector (summation of import and export) is derived based on the population and geographical size of the country and its location relative to the concentration of world GDP. This expected size is compared to the actual size of trade. This procedure allocates higher ratings to countries with large trade sectors compared to what would be expected, given their population, geographic size, and location.<sup>16</sup> The level of knowledge capital in the country, the percentage of population speaking a major global language such as English, the legal origin of the country could affect the country’s export performance. Expected size of
>-0.060<br>-0.070<br>-0.080<br>-0.078<br>-0.090 -0.083<br>2009 2017<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017, 2009), INE and Fiscal Accounts and CEQ Methodology. Note: This paper scales-down the in-kind transfers, but Bucheli et al. (2013) did not because they used a previous version of the methodology, so the 2009 figures were recalculated using the updated methodology for comparability. Therefore, figures presented here for 2009 might not coincide with those from Bucheli et al. (2013).

In turn, poverty before fiscal interventions was higher in 2009 than in 2017 ( **Figure 8** ). And in both years, fiscal interventions (particularly direct transfers) have a reducing effect on poverty for either 24
# **B Description of Variable’s Construction**

This section describes how we constructed each of the variables in our main analysis.

# **B.A Outcome Variables**

Outcome variables were constructed using the data from the Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021.

- Integration: we measured the integration dimension with three different types of questions. The first included the individual reported answer from a 1 to 5 scale, where 1 means strongly disagree, and 5 strongly agree of the following questions: (i) ”Would you feel comfortable if your child or grandchild were to socialize or be friends with children of Refugee community people?”, (ii) ”Do you feel welcomed in this village/town/city?”, (iii) ”Do you think that everyone living in this village/ town/ city feels like they are a part of this village?”. The second, included the answer to the following question ”On a scale of 1 to 10, with 1 being not very much to 10 very much, how much do you feel that Tajikistan is your home?”, and the third, the total number of refugee, and Tajik friends among all of their friends, respectively.

The Integration Index is constructed using the methodology of Kling, Liebman and Katz (2007) with the variables explained above. It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again.

- Educational Attainment: we measured the educational attainment dimension using the following questions: (i) ”Can ¡name¿ read and write in any language?”, and (ii) ”What is the highest level of formal school ¡name¿ completed?”. With the answer to the question (ii), we generate an indicator variable corresponding to each level of education: _Some Education [=1]_ if the refugee has completed any level under the primary education level, _Primary Education [
Izquierdo, and Mejia, 2008; Calvo, Izquierdo, and Talvi, 2006; Calvo, Izquierdo, and LooKung, 2006; Guidotti et al., 2004; Ortiz et al., 2007). This literature studies the role of various types of macroeconomic imbalances in the incidence of sudden stops, and focuses on determining the role of ex-ante and ex-post policies in dampening their real consequences. The nature of the microeconomic frictions that transform sudden stops in output collapses is not only of academic interest, but also crucial for the correct design of policy responses to prevent and address these episodes and the lack of evidence on this regard is an important shortcoming. In reaction to this situation, Chari et al. (2005) conclude: _“The key frictions that generate output drops in the existing literature on sudden stops are subtle ones for which so far there is little evidence. Finding that evidence is a challenge for future research.”_ This paper provides evidence of the importance of financial frictions for the propagation of sudden stops to the real economy using detailed industry level data for a large set of developed and emerging countries. The paper exploits the heterogeneous response of different industries to the occurrence of a sudden stop, and the relationship between these responses and industry characteristics to identify the mechanisms at work in the propagation of the sudden stops to the real economy. This approach has several advantages over existing studies based on aggregate (country-level) behavior. Most importantly, the reverse causality between macroeconomic performance and the occurrence of sudden stop is much less of a concern when looking at the relation between detailed industry-level activity and aggregate shocks, such as sudden stops. Furthermore, the heterogeneous responses of industries to sudden stops allow us to use a difference-indifference approach to identify the empirical importance of various possible mechanisms of transmission of sudden stops to the real economy. In particular, the paper tests whether, according to most existing theories, sudden stops result in a larger output decline in industries that are more sensitive to financial frictions and less likely to
(6) yy<sup>II</sup> = yy<sup>AA</sup> −αα<sup>FF</sup> yy<sup>FF</sup> −αα<sup>OO</sup> yy<sup>OO</sup> where lowercase variables indicate relative change, is the estimated share of the formal sector in aggregate economic activity, and is the corresponding share for the illicit economy. αα<sup>FF</sup> In Afghanistan the informal sector has been estimated to account for 72 percent of total economic activity, αα<sup>OO</sup> with another 16 percent of it originating from illicit businesses (World Bank, 2005; SIGAR, 2018). These estimates are used in what follows for and respectively. αα<sup>FF</sup> αα<sup>OO</sup>

# **Data**

The model is estimated using non-traditional data from a variety of sources, most of which are extraneous to the Afghan official statistical system. This approach is consistent with a broader trend to rely on big data to generate finely granular high-frequency estimates of otherwise conventional economic variables.

_Aggregate economic activity_ A stable relationship has been shown to exist between aggregate economic activity and the sum of nighttime light per area. Global level estimates suggest that a 1 percent increase in nighttime light is YY<sup>AA</sup> associated with 0.267 percent increase in economic activity (Henderson et al. 2012). For South Asian countries the increase in economic activity has been estimated at 0.248 percent, quite close to the global figure (World Bank 2017). Building on these results, it is assumed in what follows that the elasticity of aggregate economic activity to nighttime light is 0.25. This elasticity implies that the relative change in economic activity , is only a quarter of the relative change in nighttime light. The raw nighttime light data are derived from the
25 ratios with acceptable norms whereas non-growth-oriented (bad?) firms would not be subject to this pressure. The negative coefficient on leveraging may be reflecting this.2'

# VI. **Conclusions**

State supported term lending institutions, particularly in developing countries, have been created with the objective of providing (often subsidized) access to term finance for industrial borrowers. As a contribution to evaluating this strategy, this paper has explored empirically the determinants and consequences of the maturity structure of debt, using firm level panel data.

The analysis reveals that one of the most important determinants of debt maturity is asset composition with a higher proportion of fixed assets in the asset mix supporting higher maturity debt. The positive coefficient on the working capital (liquidity) variable in the maturity equation is consistent with term finance going to firms with the healthier balance sheet position. It may also suggest that firms face term finance constraints. Everything else equal, the effect of cash flow on maturity is weak, although the bivariate association is positive but not large. There is some evidence in the maturity equation to suggest that larger firms prefer a debt composition with relatively less long term debt, though the bivariate association between size and length of maturity is positive.

Although the results in this paper are not conclusive, they seem to support the conventional 27 The adverse consequences of high leveraging have also been mentioned by the Narsimhan Committee (1991) in its report. At p. 102 of its report the committee notes: "While the liberal lending norms, particularly the high leverage in the form of a debt equity of 2:1 did indeed help in attracting new entrepreneurs, it acted as a deterrent in making the projects financially viable particularly in the context of fiscal and market uncertainties. In a way this could be attributed to the fiscal system which favoured debt rather than equity as a source of investment financing as a result of which most of the projects were seriously undercapitalised thereby affecting their ability to withstand difficult times."
Hotte, L., Van Long, N. and Tian, H. (2000) International trade with endogenous enforcement of property rights, _Journal of Development Economics_ , **62** , 25–54.

Ivanova, K. (2007) Corruption, illegal trade and compliance with the montreal protocol, _Environmental and Resource Economics_ , **38** , 475–496.

- Kreickemeier, U. and Richter, P. M. (2014) Trade and the environment: The role of firm heterogeneity, _Review of International Economics_ , **22** , 209–225.

- Leblois, A., Damette, O. and Wolfersberger, J. (2017) What has driven deforestation in developing countries since the 2000s? Evidence from new remote-sensing data, _World Development_ , **92** , 82–102.

- L ́opez, R. and Galinato, G. I. (2005) Trade policies, economic growth, and the direct causes of deforestation, _Land Economics_ , **81** , 145–169.

- Maggi, G. (2016) Issue linkage, in _The Handbook of Commercial Policy_ (Eds.) K. Bagwell and R. W. Staiger, Elsevier, Amsterdam, Netherlands, vol. 1B, chap. 9, pp. 513–564.

- Managi, S., Hibiki, A. and Tsurumi, T. (2009) Does trade openness improve environmental quality?, _Journal of Environmental Economics and Management_ , **58** , 346–363.

- Mattoo, A., Rocha, N. and Ruta, M. (2020) _Handbook of Deep Trade Agreements_ , World Bank, Washington, DC.

- McKenzie, R., Bernhard, G., Liley, B., Disterhoft, P., Rhodes, S., Bais, A., Morgenstern, O., Newman, P., Oman, L., Brogniez, C. _et al
<mark>estimated speed differences also differ substantially from an unconditional comparison of speeds as presented in T</mark> able 2 <mark>, emphasizing the importance of a local comparison.</mark> <mark>The speed reduction is most pronounced and significant for the Esprit de Mort transport network, which operates under wet conditions on average with speeds about 4 km/h slower than under dry conditions. For the Esprit de Vie and the Taxi Jaune network average speeds are estimated to lie about 2 km/h and 0.5 km/h below dry condition speeds respectively, but effects are not significant.</mark> <mark>Considering the characteristics of the three different transport modes the estimated effects appear reasonable: The Esprit de Mort transport network is provided through old vehicles that are most likely to be affected through rain, mud, and wet conditions. On the Esprit de Vie network more modern mini-buses are operational. Taxi Jaune is the most flexible type of public transport infrastructure in Kinshasa and it is likely that the small vehicles can circumvent traffic jams due to flooding and easily reroute, hence obtaining speeds similar to dry conditions.</mark> <mark>Additional results from the DDD regressions suggest that travel speeds are more affected on route segments that are more likely to be flooded. When considering for example the Esprit de Mort network under wet conditions, average travel speeds are about 3.7 km/h below dry condition speeds on all route segments outside the 10-year flood plain. Within the 10-year-flood plain, the speed reduction is more pronounced, and speeds are on average an additional 6.1, i.e. 9.8 km/h in total, lower than during dry conditions. However, this effect cannot be estimated with sufficient precision to be significant, most likely due to the small sample size: only 5.5% of the mapped Esprit de Mort network lie within the 10-year flood plain.</mark> Table 4: Effects of rain
inequities in human development outcomes.

Understanding these patterns is crucial for ensuring gender equitable outcomes for children from a life-course perspective, particularly in settings where this research remains limited. Untangling the relationships of ECD and child gender is complicated by numerous interrelated factors, including links to biological processes; social processes, such as gendered caregiving and peer norms and expectations; and even country-level measures of gender inequality (Rey-Guerra et al., 2023)—all of which vary across countries and sociocultural contexts.

In Pakistan, stark gender inequities persist in employment, education, and training (World Bank, 2023). As of 2018, only 23% of women participated in the labor force compared to 81% of men (World Bank, 2023). As of 2019, more girls aged 5-16 were out of school than boys (37% vs. 25%, respectively), and boys spent on average 1.4 more years in school than girls (World Bank, 2023). Although ECE enrollment is low for all children aged 3-5 years, with a national average enrollment rate of 19% as of 2020, across provinces enrollment rates are between 1 and 3 percentage points lower for girls (Tomlinson et al., 2023). A prior study examined associations between risk and protective factors with child development and school readiness outcomes in Pakistan, finding strong associations between better child outcomes and higher levels of psychosocial stimulation and lower levels of parental distress. However, relationships with gender remain unexplored (Hentschel et al., 2023) in part because of the complexity of the interconnections among ECD, risk and protective factors, and child gender. A study in rural Sindh examined gender differences in the associations of ECD outcomes with measures of brain activity and found that brain activity and verbal ability were positively related in 48-month-old girls but not boys. The authors present several possible explanations for this including biological, environmental, or sociocultural factors, but call for future research to examine these relationships (Tarullo et. al., 2017).

This
social security contributions amounts are the contributions of the general government to social security, which appear in the medium-term fiscal framework of the Ministry of Finance and Public Credit

# **_a) Direct taxes and contributions to the social security system_**

The personal income tax in Colombia taxes the income of individuals, once it is cleaned to account for certain exemptions and deductions under law. Law 1819 of 2016 established an income type system for the filing and payment of personal income tax. Thus, income is classified according to its origin, with different deductions and exemptions applied accordingly. Consequently, income is divided into labor, pension, capital, non-labor, and dividend income. According to the Central National Government figures, revenue from income tax during 2017 was $ 56.655 billion.

> 5 For more information on the reasons why the survey differs from the administrative data, see annex I and II in Nuñez et al. (2019).

5
Policy Research Working Paper 9052

# **Abstract**

Visible Infrared Imaging Radiometer Suite (VIIRS) nightlights are used to model damage caused by earthquakes, floods, and typhoons in five Southeast Asian countries (Indonesia, Myanmar, the Philippines, Thailand, and Vietnam). The data are used to examine the extent to which for each type of hazard there is a difference in nightlight intensity between affected and nonaffected cells based on (i) case studies of specific disasters, and (ii) fixed effect regression models akin to the double difference method to determine any effect that the different natural hazards might have had on the nightlight value. The results show little to no significance regardless of the methodology used, most likely due to noise in the nightlight data and the fact that the tropics have only a few days per month with no cloud cover.

This paper is a product of the Poverty and Equity 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 eskoufias@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
41 Table 13. Stationary Population for ALl Couwntries or Econamies

|Stationary<br>p|opulation|<br>Year||Stationary<br>p|opulation|Year|
|---|---|---|---|---|---|---|
|<br>Country or ecronWy<br>Millions<br>1|As X of<br>985 pop|when<br>NRR-1|Cowtry or economy|<br>Millions<br>1|As X of<br>985 pop|<br>when<br>NRR=1|
|Africa<br>2573|A60|2045|Mali|39|522|2040|
|Botswana<br>5|441|2025|Mauritania|9|527|2040|
|Burundi<br>24|504|2035|Niger|36|570|2040|
|Comoros<br>2|522|2030|Nigeria|529|530|2035|
|DJibouti<br>2|613|2045|Sao Taom and Principe|<br>0.3|310|2020|
|Ethiopia<br>205|486|2040|Senegal|30|454|2035|
|Kenya<br>124|608|2030|Sierra Leone|18|480|2045|
|Lesotho<br>6|398|2030|Togo|16|527|2035|
|Madagascar<br>52|506|2035|Other West Africa|0.01|186|2010|
|Nalawi<br>42|590|20'0|West Africa total|1038|510|2045|
|Mauritius<br>2|168|2000|||||
|Nazambique<br>74|537|2040|Algeria|81|374|2025|
|Namibia<br>5|478|2035|Egypt, Arab