rafmacalaba/data-use-mentions-extended huggingface dataset:
correlated with an increase in public school expenditure, suggesting that formal electoral mechanisms can have an effect on service provision.<sup>103</sup> Such findings have likewise been found in the health sector, where enfranchisement of the poor has been shown to have a significant effect on health provision and outcomes. Fujiwara (2015) finds that the rise of electronic voting in Brazil resulted in the de-facto enfranchisement of poor and less educated voters, which had the secondary effect of increasing health care provision, including services for prenatal and newborn health.<sup>104</sup> Similarly, in terms of health outcomes, Kudamatsu (2012) utilizes surveys conducted in 28 African countries to compare infant survival rates before and after the emergence of democracy in these countries and finds that infant mortality declined after democratization,<sup>105</sup> which suggests the effect that formal democratic accountability channels may have on improving health outcomes, particularly in a developing country context.

# **_Gaps in the Literature_**

While the theoretical literature reviewed above provides an understanding of how the concepts of OGD, transparency, accountability and service delivery are defined—and how they relate to larger theoretical concepts and debates—the empirical literature that supports a relationship between these ideas is slightly less complete. As noted above, existing case studies are often aspirational or speculative in nature, and do not provide sufficiently rigorous evidence that OGD inputs translate into accountability or service delivery outcomes. Moreover, the situation-specificity of these cases provides limited external validity for other contexts. A final drawback to this literature is a paucity of analysis of the proximate conditions in place— that is, defining what is necessary and sufficient from a political economy perspective—to ensure that OGD has its intended impacts.

Likewise, with respect to the empirical literature, numerous studies have been completed—some using sophisticated large-n experimental and quasi-experimental designs—however, few have been able to link Open Data, and OGD in particular, to changes in levels of government accountability or
We control for additional country characteristics. We use the Ease of Doing Business index (EDBc) and the Trading Across Borders index (TABc) to control for the quality of the business environment in the exporting country. A country’s ranking on the former is based on the average of ten subindices–including starting a business, dealing with licenses, hiring and ...ring workers. More speci...cally, the Trading Across Borders index, captures the complexity of customs procedures faced by exporters. It accounts for the number of documents, the number of signatures and time necessary to export and import. The data for both indices is taken from the WBDBS. We use the Logistics Performance Index as a proxy for exporting country’s capacity to e¢ ciently move goods and connect with international markets (LPIc). The index is a weighted average of country scores on six key dimensions–including e¢ ciency of the clearance process, competence and quality of logistics services, ease of arranging competitively priced shipments. Additionally, we control for the quality of trade and transport infrastructures using the Infrastructure Index which enters the overall LPI index. The data comes from the World Bank Logistic Performance Indicator database for the year 2007<sup>16</sup> .

Product wise, we use a perishability index, to insure that our risk measure is not picking up on other product characteristics that may a¤ect their survival. The index takes value one if the product cannot be stored without refrigerator facilities, zero otherwise. Perishable products typically include, meat, ...shery products, fruits and vegetables. Correlation between our risk index and the perishability index is 0:15. Finally, data for the gravity variables come from the CEPII database.

# 4.3 Descriptive Statistics

Appendix 8:F reports some statistics at the ...rm level for each exporting country. "Risky" products account for an important share of ...rms total exports in all ...ve countries
regression function for the consumption of household _i_ in country _j_ at date _t_ on a vector of covariates _xijt_ using a survey sample of size _N jt_ : The PMT is then based on: The most common method in practice for estimating  _jt_ and  _jt_ in (1) appears to be Ordinary Least Squares (OLS) using log consumption per capita as the dependent variable. As usual, OLS 8
Policy Research Working Paper 9093

# **Abstract**

Entrepreneurs in developing countries report that unreliable electricity imposes a serious constraint, yet little evidence exists on how blackouts impact the micro firms that account for the majority of employment. This paper estimates the effects of outages on small firms using original firm-level panel data and finds evidence of differential effects by firm size. Firms without employees experience large reductions in revenues and profits. Outages have no measurable effect on the output of firms with employees, where worker hours increase, weekly wages paid decrease, and the analysis fails to reject that blackouts have no effect on (average firm-level) worker hourly wages.

This paper is a product of the Knowledge and Strategy Team, Development Economics. 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 morgan.hardy@nyu.edu.

_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
tems in Brisbane, Australia. Comparing BRT-based trips with trips taken in non-BRT urban public transit networks, the authors find that BRT-based trips exhibit significant spatial heterogeneity and are markedly different from ordinary bus travel.

# III. Traffic Flows and Externalities

The use of big data in economic analyses of transportation covers a variety of topics that can broadly be categorized as (i) measures of health and environmental externalities associated with congestion, (ii) economic effect of congestion (in terms of employment and productivity) and (iii) welfare costs. We sequentially review these applications, starting with welfare costs.

# A. Welfare Costs

Traffic flows can entail several externalities (e.g., accidents, health effects, delays), which are often directly related to or exacerbated by congestion. Congestion in cities can have very large economic and social costs. In its 2015 Urban Mobility Scorecard (UMS), for instance, the Texas Transportation Institute analyzes congestion in 471 urban areas in the United States and finds that urban dwellers spent an extra 6.9 billion hours in traffic and wasted 3.1 billion gallons of fuel (Schrank et al., 2015). Converting these congestion-induced travel delays and fuel consumption into monetary values, the study concludes that the total cost of urban traffic congestion in the United States represented $US160 billion in 2014. In Cairo and Kuala Lumpur, two highly urbanized cities in the developing world, congestion costs estimated using similar approaches reach up to 3.6 and 2.2 percent of national GDP, respectively (Zachau et al., 2015). These figures mostly reflect the industry estimation standard in which the direct costs of congestion are computed as the aggregate travel delay—including time unreliability—relative to a free-flow benchmark. However, despite being widely referenced, economists are often skeptical 6
Policy Research Working Paper 10944

# **Abstract**

This paper examines the impact of implementing a carbon tax on aggregate total factor productivity in the Dominican Republic through the resource allocation channel. It incorporates energy inputs—electricity and fuel—into firms’ production functions, allowing predictions of potential changes in resource allocation due to the carbon tax. The theoretical implications of the model indicate that the carbon tax has a heterogeneous effect on firms’ input choices, contingent on the level of firms’ existing input market distortions. Moreover, the model suggests that in economies in which more productive firms are less distorted, the carbon tax can decrease aggregate total factor productivity. In contrast, when more productive firms are more distorted, the carbon tax can increase or decrease aggregate total factor productivity. Utilizing detailed firm-level data from 2009 to 2018, covering up to 118,000 firms, this paper finds that a carbon tax is more effective when levied on fuels rather than electricity. For the majority of sectors in the sample, the paper finds that existing distortions in energy consumption are positively correlated with firmlevel productivity. Moreover, the quantitative results show that the introduction of the carbon tax shifts the burden of market distortions from high productivity firms to low productivity ones, generating aggregate total factor productivity gains for most sectors in the Dominican Republic. Overall, this study underscores the importance of considering existing input market distortions when analyzing the impact of environmental taxes.

This paper is a product of the Finance, Competitiveness and Innovation 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 fzeni@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
Targeting in Tax Compliance Interventions: Experimental Evidence from Honduras Giselle Del Carmen Edgardo Enrique Espinal Hernandez Thiago Scot<sup>_*_</sup>

**Keywords** : Tax compliance, Tax evasion, Firms & Productivity, Targeting **JEL Codes** : H26, O17

> _*_ Development Impact Evaluation (DIME), World Bank. Corresponding author at tscot@worldbank.org. Giselle del Carmen (Enodo): g.e.delcarmen@gmail.com. Edgardo Enrique Espinal Hernandez (Servicio de Administración de Rentas (SAR) de Honduras): eespinal@sar.gob.hn. We acknowledge the collaboration of the team at the Honduran Tax Authority (SAR), in particular Oziel Aron Fernandez Herrera, David Fernando Pineda Pinto, Gabriel Ricardo Perdomo Zelaya, Wilman Alonso Ponce Nuñez, Milton Alexander Maldonado Castillo, Pedro Rafael Zuniga Figueroa, Roldan Manuel Enamorado Irias, Carlos Mauricio Zelaya Guzman, Ximena Fernanda Venis Dilworth, Mario Josué Ramos Canales, Fabián Alfredo Gómez Guillen. This project also benefited from comments from Marta Ruiz-Arranz, Martin Ardanaz, Jordi Prat Cordero, Ricardo Perez-Truglia, Reed Walker, Guo Xu, Steve Tadellis, Ernesto Dal Bo, Frederico Finan, Muhammad Yasir Khan, Petr Martynov, Arianna Legovini and participants at the UC Berkeley Development Lunch. The main email experiment was submitted to UC Berkeley Internal Review Board (IRB) under ID 2019-12-12772 and was deemed "not Human Subject Research". The forecast survey was submitted to UC Berkeley IRB under ID 2020-03-13079 and approved on March 25th 2020. This study has been enrolled in the AEA Trial Registry (RCT ID: AEARCTR-0005285)
<!-- Start of picture text --> Price Price General 50% Price<br>linearization experiment price increase experiment<br>Beginning End<br>ry - ry "> a<br>Water Baseline Quarterly Monitoring<br>consumption data survey 1,465 households . _<br>Every month from July 2015 1.488 Every 3 months from November 2015 to May 2018<br>to May 2018: 1488 households<br>households<br>Subsidized price rollout<br>Jun-Dec 15 Jan-Jun 16 Jul-Dec 16 — Jan-Jun 17 Jul-Dec 17 Jan-May 18<br>Group 1 100% 50% 25% 100% 150% 150%<br>Group 2 100% 100% 50% 100% 37.5% 150%<br>Group 3 100% 100% 100% 100% 75% 37.5%<br><!-- End of picture text -->
0.471|0.468|0.216|0.236|0.225|

**Notes:** This table reports a set of cross-country regressions where the dependent variable is the average growth rate of real annual GDP per capita and the explanatory variables are: net FDI inflows as a percentage of GDP; credit to the private sector by deposit money banks as a percentage of GDP (this variables is scaled by 100); the log of average years of secondary schooling in adult population; the log of initial GDP per capita; the log inflation; the log of export plus import over GDP; the log of government expenditure over GDP; the ICRG investment risk index; the log of the black market premium; and a dummy that takes value one for countries located in Sub-Saharan Africa. Columns (1)-(3) focus on developing and emerging economies for which we have data starting in 1970. Columns (4)-(6) focus on developing and emerging economies for which we have data starting in 1975. Robust standard errors are in parentheses. Significance levels are denoted as: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

7
# **Figure 2 – Bank Lending and Interest Rates to Local Governments with Low vs High Public Debt**

Panel A plots bank quarterly lending to state governments, relative to 2016Q2, for states with high and low public debt. A state is defined with high (low) public debt if its debt as a share of income in the quarter prior to the reform is above or below the median. Panel B plots the monthly interest rate on bank loans paid by state governments with high and low <u>public debt. The vertical lines in both panels mark the introduction of the FD Law.</u> <!-- Start of picture text --> Liabilities Interest Rate<br>2015m1 2016m1 2017m1 2018m1<br>2014q1 2015q1 2016q1 2017q1 2018q1 Time<br>Time<br> States Low Public Debt States High Public Debt<br> States Low Public Debt States High Public Debt Monthly Data<br>Lending Relative to 2016Q2 - Quarterly Data<br>5 7<br>0 6<br>-5 5<br>Index Percent<br>-10 4<br>-15 3<br><!-- End of picture text --> 39
Policy Research Working Paper 9221

# **Abstract**

The fast spread of severe acute respiratory syndrome coronavirus 2 has resulted in the emergence of several hot-spots around the world. Several of these are located in areas associated with high levels of air pollution. This study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands. The results show that atmospheric particulate matter with diameter less than 2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. The estimates suggest that expected COVID-19 cases increase by nearly 100 percent when pollution concentrations increase by 20 percent. The association between air pollution and case incidence is robust in the presence of data on health-related preconditions, proxies for symptom severity, and demographic control variables. The results are obtained with ground-measurements and satellite-derived measures of atmospheric particulate matter as well as COVID-19 data from alternative dates. The findings call for further investigation into the association between air pollution and SARS-CoV-2 infection risk. If particulate matter plays a significant role in COVID-19 incidence, it has strong implications for the mitigation strategies required to prevent spreading.

This paper is a product of the Strategy, Analytics, Financing Solutions and Knowledge Unit. 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 bandree@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
16|0.23|

Source: Author’s calculations The unit record data underlying table 2.3 were generated randomly from the beta distribution. The probability density function is defined by the following expression (Johnson, Kotz and Balakrishnan 1995): where B(q,r) stands for the beta integral (also known as the Euler integral of the second kind) defined as:

> Setting _x_ = ( _y_ − _a_ ) /( _b_ − _a_ ) leads to the following standard form of the beta density function:

The corresponding probability integral up to **x** is given by the incomplete beta ratio or _incomplete beta integral_ : 12
DHS data. Further, while they may not predict stunting accurately, the impact of a drought may vary across wealth quintiles. Figure 3-a shows the distribution of HAZ scores for the lowest quintile, contingent on a drought. Compare this with Figure 3-b, which shows the drought-contingent HAZ distributions for the top four wealth quintiles. First, the density close to the stunting threshold is higher for the lowest wealth quintile. Second, the shift leftward appears to be more pronounced for the lowest wealth quintile. Tables 7 and 8 Table 7 starts with the regression reported in Table 5 (specification 1) and estimates this regression for different sub-samples of wealth quintiles and countries. Since sample sizes are limited, we group the second and third lowest quintiles and the wealthiest two quintiles. For all the five countries taken together, the impacts are muted for the top two wealth quintiles. The top two quintiles exhibit a 1 percentage point increase in stunting, considerably smaller than the 6 percentage points impact for the bottom quintile. In Mozambique, Malawi (for the season after birth) and Tanzania, the differences in impacts across wealth quintiles are large. The magnitudes of the drought impact for the lowest quintile is more than double the impact for the wealthiest two quintiles. However, in Zimbabwe and Zambia, the drought impacts are comparable across wealth quintiles.

It may be that in Tanzania, Malawi and Mozambique, children in the wealthier quintiles have z-scores significantly above the stunting threshold, and are still vulnerable to drought shocks, even though they do not fall below the threshold. Figure 2 shows that the z-scores for children in the wealthiest quintile in these three countries are, in fact, lower than the z-scores for the comparable children in Zambia and Zimbabwe. There are smaller proportions of children in the wealthiest quintiles in Zambia and Zimbabwe below the -2 standard deviation cut-off.

Table 8, repeats the exercise reported in Table 7, except using height for age z-scores, rather than stunting outcomes as the dependent variable. The results are very similar. In
have engulfed all household assets. According to Tim Harris (2014), founder of Real Change, an alternative newspaper in Seattle, homelessness in the city is a crippling form of institutionalized dehumanization that produces a highly stigmatizing narrative in which the homeless are portrayed as ‘filth’ and ‘contamination’, as ‘being in the way’ while the rich voice their angst about feeling ‘unsafe’. Such a narrative, carefully crafted to protect the cherished notion of American individual success, opens the door to particular sets of policies that, instead of addressing the precarious adaptive needs of homeless people, produce misdemeanor criminals, often highly racialized, who are rendered even more invisible by being removed from the streets (e.g. laws on panhandling) and incarcerated. Such criminalization of homeless citizens then shapes who is considered as ‘deserving poor’ and protected, for instance in the case of cold spells and heat waves, and who is not.

Let us consider the recent increase in homeless people in France. In only ten years (2001‐2011), the number of people without a home ( _sans domicile fixe_ ) has doubled, now amounting to 141,500, with roughly one‐fourth of them employed and more than half of foreign origin (Yaouancq et al. 2013). Being without shelter counts as lack in physical assets. Not having a bed or a roof over one’s head is without any doubt measurable in national statistics. What remains largely invisible, though, are the social and urban planning policies that discount these unwanted poor by prescribing their removal from urban landscapes of daily interactions. As summarized in a newspaper article right after Christmas 2014, anti‐ homeless efforts in some French cities include the fencing of benches to make them inaccessible, the replacement of such sought‐after sleeping spots with iron spears, mini cactus gardens, stone mounts, and other pseudo‐artistic creations, and the barely noticeable downward tilting of plastic chairs in the Paris metro so that disgracefully falling asleep on these
if countries with a comparative advantage in product A also have comparative advantage in product B with high probability, this implies that products A and B demand the same production capabilities and hence are close to one another. The indices, described in detail in the Appendix, are obtained for each new 7-digit ISIC product relative to the weighted average of the past products of the plant, accounting for the relative weights of the different past products in total plant revenues. We estimate our main specifications allowing differential effects on survival for new products that are closer versus more distant from the plants‟ past expertise. The results shown in columns (4)-(6) of Table 7 suggest that new products that are closer to the plants‟ expertise and thus pose lower technical risks do not impact plant survival any differently than new products that are more distant from the plants‟ expertise. A shortcoming of this approach is that the product proximity indices do not cover all Chilean innovators due to difficulties in establishing concordances between the 7-digit ISIC level and the HS classification of the trade data.

18