rafmacalaba/data-use-mentions-extended huggingface dataset:
Yu, H., and D. Robinson. “The New Ambiguity of Open Government,” _UCLA. L. Rev. Disc_ (2012): 59, 178–208.

# **_Books_**

Arrow, Kenneth J. 1951. 2nd ed., 1963. _Social Choice and Individual Values_ . Yale University Press.

Black, Duncan. 1958. _The Theory of Committees and Elections_ . Cambridge: Cambridge University Press.

Borowiak, Craig T. 2011. _Accountability and Democracy: The Pitfalls and Promise of Popular Control_ . Oxford: Oxford University Press.

Buchanan, James M. and Gordon Tullock. 1962. _The Calculus of Consent: Logical Foundations of Constitutional Democracy_ . University of Michigan Press.

Downs, Anthony. 1957. _An Economic Theory of Democracy_ . Cambridge: York: Cambridge University Press.

Keohane, Robert. 2011. _Global Governance and Democratic Accountability_ .

Lindblom, Charles E. and Robert A. Dahl. 1976. _Politics, economics, and welfare_ . Chicago: University of Chicago Press.

Olson, Mancur, Jr. 1965. 2nd ed., 1971. _The Logic of Collective Action_ . Cambridge: Harvard University Press.

Ostrom, Elinor. 2005. _Understanding Institutional Diversity_ . Princeton, NJ: Princeton University Press.

de Tocqueville, Alexis. 1956. _Democracy In America_ . Edited and Abridged Edition by Richard Heffner. Mentor Books.

Trapnell, Stephanie (ed.) 2014. _Right to Information: Case Studies on Implementation_ . World Bank.

# **_Reports_**

Global OGD Initiative. “Global OGD Initiative Survey and Interview Report.” Accessed March 23, 2015. <u>www.opendatainitiative.org/survey-and-interview-report/.</u> Hania Farhan, Debra D’Agostino, and Henry Worthington. _Web Index 2012_ , World Wide Web Foundation.

Hazell, R., and B. Worthy. “Impact of Freedom of Information on Central Government,” unpublished End of Award Report to the Economic and Social Research Council, University College London, Constitution
# - B.3. Causal Impact of the Covid 19 Lockdown on Congestion

After the World Health Organization (WHO) declared Covid-19 a global pandemic on March 11, 2020, many countries implemented restrictive policies of various degrees (stringent vs. lenient) and scope (national or subnational) to prevent further spread of the virus. These include, among others, social distancing, quarantine, isolation and community containment measures. As early as March 17, 2020, the French government banned all non-essential trips and required people leaving their homes for essential journeys to remain within a 1-km perimeter for up to 1 hour while carrying a signed and dated exception form. The initial 15-day stay-at-home order was later extended until May 5, 2020. The governor of New York, where the number of Covid-19 infections and deaths had drastically increased meanwhile followed suit and ordered that all nonessential businesses be shutdown on March 20, 2020. Both Colombia and India enforced similar countrywide stringent lockdown actions starting on March 25, 2020.

Using the above Bayesian structural time series model, we estimate the causal effect of the Covid-19 lockdown policies on traffic congestion in Bogotá, New Delhi, New York City, and Paris. All four metropolitan areas are consistently ranked among the most congested cities in the world. For example, Bogotá topped the Inrix rank of the world most congested cities in 2019, with drivers loosing on average the equivalent of 7 days and 23 hours a year (Reed, 2019).<sup>8</sup> Traffic congestion in New York City, the fourth most congested place in the United States according to Inrix, cost the city about $US11 billion in 2019. In Paris, a typical driver lost about 165 hours a year due to traffic congestion, making the city the 7th most congested in the world in 2019. Similarly, New Delhi was ranked the 8th most congested city of the world, according to the 2019 TomTom traffic congestion index. Together, while congestion remains a pressing policy challenge in these cities, existing
# **1 Introduction**

Taxing small and medium-sized businesses -otherwise known as small and medium-sized enterprises, or SMEs- can be challenging for a variety of reasons (e.g., see Hoy et al. 2024), with uncertain or, at best, unambiguously small fiscal gains, depending on the context. Moreover, the direct and indirect costs for governments from trying to enforce tax compliance among SMEs can be substantial. As a result, revenue authorities are increasingly investing in quasi-voluntary strategies to improve compliance among SMEs, such as greater engagement between tax officials and firms to help facilitate the tax payment process (Dom et al. 2022). However, relatively little is known about how SMEs respond to closer engagement and community presence by revenue authorities, particularly when this engagement transcends the usual audit-oriented interactions.

This paper examines how SMEs react to increased local presence by tax officials by drawing on a novel field experiment in Tanzania. The World Bank and the Tanzania Revenue Authority (TRA) implemented a broadly representative in-person survey of SMEs across mainland Tanzania in December 2022. Among the 119 urban and peri-urban wards that were included in the study, half were randomly assigned to have tax officials present during the data collection exercise. This clustered randomized controlled trial introduced exogenous variation in the visibility and physical presence of tax officials among SMEs. While the tax officials did not directly survey the businesses (the survey was conducted by a local survey research firm and trained enumerators were tasked with conducting the interviews), simply having a tax official present was expected to have repercussions on respondents’ self-reported behavior (captured through the survey) as well as declaration behavior (captured through administrative data). As a consequence, it is valuable to examine both the impact of TRA presence on survey outcomes and ward-level indicators of compliance.

Tanzania is a useful context to explore strategies for improving SME taxation. Tanzania’s SMEs are overwhelmingly informal. Those that are registered with the TRA are primarily subject to a simplified, presumptive tax regime. However
# **1 Introduction**

In Latin America and the Caribbean, the average number of extreme weather events<sup>1</sup> has increased by more than 60% in the past two decades compared to the 1980s and 1990s, while in Paraguay it has more than doubled (OECD, 2022). These increases in frequency and intensity of extreme weather events are expected to continue as a consequence of climate change (IPCC, 2021). Due to its consequences on economic activities, ecosystems, and populations, which are significant and, in many cases, irreversible, climate change is considered one of the most systemic threats to our societies. Empirical evidence indicates that low- and middle-income countries (LMCs) are disproportionally affected by the consequences of climate change. In the most recent Global Climate Risk Index –an index that analyzes to what extent countries and regions have been affected by impacts of weather-related events– eight of the ten countries most affected by the quantified impacts of weather shocks in 2019 were LMCs (Eckstein, Künzel, & Schäfer, 2021). In the Global Climate Risk Index, Paraguay is ranked 20<sup>_th_</sup> worldwide (Eckstein et al., 2021).

Climate change is expected to worsen existing inequalities and poverty levels across the globe (Olsson et al., 2014). A recent cross-country study finds that a one-degree Celsius increase in temperature leads to a 9.1 percent increase in poverty (measured at the USD $1.90 poverty line), as well as a 0.8 percent increase in the Gini index (Dang, Cong Nguyen, & Trinh, 2023). Furthermore, existing socio-economic inequalities can lead to uneven impacts of weather shocks in LMCs, in particular, if vulnerable communities depend on natural resources for income generation, or when the poor are largely concentrated in environmentally risky areas (Reckien et al., 2017).

In Paraguay, climate-related shocks may have contributed to stalled poverty levels in the last decade.
: the first 12,000 Jordanian dinars (JOD) of an individual’s income is exempt; income between JOD 12,000 and JOD 24,000 is taxed at 7 percent; income between JOD 24,000 and 36,000 is taxed at 14 percent; anything above is taxed at 20 percent. There is a single deduction for dependents of an additional JOD 12,000. PIT is entirely borne by the worker.

**_Table 1. Jordan government revenues, 2018_**

|||**2018**<br>**Percent of total**||
|---|---|---|---|
||**JOD Million**|<br>**revenues**|**Percent of GDP**|
|_Total Domestic Revenues_|6,945|100|23|
|_Tax Revenues_|4,536|65|15|
|1 - Taxes on income and profits, of which:|965|14|3|
|**Individuals**|**53**|**1**|**0.2**|
|**Salaried Employees**|**150**|**2**|**0.5**|
|Income Tax from Companies & Projects|762|11|3|
|2 - Taxes on Financial Transactions (real estate’s tax)|93|1|0|

3 One exception where the household survey data allow this is Chile (see World Bank 2014).

4 The SST schedule for cigarettes is based on the number of cigarette packs purchased. Other types of tobacco products (cigar, aarghile, etc.) are subject to a tax per gram of tobacco. We assume that one cigar has approx. 2g, a pack of tombak is 40g and aarghileh 40g.

5 26 percent rate. 6 10 percent rate.

7 2.
appeal to the common timing of the IHDS data with the NSS data for 2004/05 and 2011/12, and the representative sampling design of both data sources, to suggest that the method is also likely to work well for mobility comparisons based on the NSS rounds.

Our validation exercise works with the consumption measure that is available in the IHDS surveys. As noted above, most distributional analysis conducted with the IHDS surveys has been based on a per-capita income measure of well-being. The IHDS surveys are the only nationally representative surveys that contain such income information. The IHDS consumption data, in contrast, are highly abbreviated in comparison with consumption data from the NSS surveys, and are rarely used for distributional analysis as a result. However, for our purposes it is appealing to validate our synthetic panel methods using the IHDS consumption variable. This is because implementation of the procedure will then resemble more closely its subsequent application with the NSS data.

Since the IHDS surveys are panel surveys, we split the IHDS panels into two randomly drawn subsamples (each representing half of the total sample). Call these subsamples A and B. Then we can use subsample A in the first round and subsample B in the second round as two repeated cross-sections to which we apply our method. We can then compare the mobility results obtained from using subsample A to impute round 1 values for subsample B against the results we would get by using the genuine panel for subsample B. We use panels with the same household heads only for the genuine panels. As is common in pseudo-panel-based analysis, we restrict our attention to households whose head (in 2004/05) is in the 25–55-year age range.

It is useful to briefly note the estimation of ρ before discussing estimation results. As in Dang and Lanjouw (2018), we form cohorts by interacting household heads’ age with a dummy variable indicating whether they belong to scheduled castes. The partial correlation coefficient ρ is estimated to be 0.39 and strongly statistically different from zero.
categories, censorship does not appear to be a significant concern when estimating expenditure demands.

# **Conclusions**

In this paper we have presented an empirical model to test for and quantify differences in expenditure demands between migrant and nonmigrant households using new household data from rural Mexico. The modeling approach we propose is more general than standard consumer models, remittance-use studies, and recent work extending consumer models by including direct remittance effects. It controls for both censorship in demands and the endogeneity of migration while offering a comprehensive test of migration’s effects on expenditure patterns. Our findings indicate that migration reshapes household demands in ways that are independent of total income. Three key insights emerge from this analysis.

First, migration has complex effects on household expenditures. Past studies, which focus on remittance use or include remittances as explanatory variables in household demand models, capture one (albeit potentially important) component of these migration effects. Migration, in addition to contributing to household income, links village households to new markets, societies and cultures; it may induce changes in consumption technologies and induce a substitution of purchased for home-produced goods in response to lost labor and other effects; and it may alter households’ information set, risk profile, and preferences in ways that affect marginal utilities of consumption and 28
4 Empirical studies undertaken as background for the World Trade Organization’s ongoing Doha round of multilateral trade negotiations suggest that in 2001, when that round was launched, policy-driven distortions to agricultural incentives contributed around twothirds of the global welfare cost of merchandise trade barriers and subsidies (see, e.g., Anderson and Martin 2005). While such empirical studies did not have access to comprehensive estimates of distortions to farmer and food consumer incentives in developing countries other than applied tariffs on imports, a more recent study that draws on a new database of distortions to agricultural incentives has confirmed that earlier result: Valenzuela, van der Mensbrugghe and Anderson (2009) suggest agricultural price and trade policies as of 2004 accounted for 70 percent of the global welfare cost of those and other merchandise trade policies. This is a striking result, given that the shares of agriculture and food in global GDP and trade are only 3 and 6 percent, respectively. The contribution of farm and food policies to the welfare cost of global trade-distorting policies for just developing countries is estimated by those authors to be even greater, at 72 percent – of which two-thirds is due to policies of developing countries themselves. Even so, the estimates of price distortions that went into that modeling study show that many developing countries protect their less-competitive farmers from import competition, so some farm households might be hurt if all markets were opened (Anderson 2009, Ch. 1).

The World Bank’s recent study of price distortions (Anderson 2009) shows that the rate of assistance to farmers relative to producers of non-farm tradables has fallen by onethird for high-income countries since the latter 1980s (from 51 to 32 percent) while in developing countries it has all but disappeared (rising from -41 percent in the early 1980s to +1 percent in 2000-04). The latter trend for developing countries is mainly because of the phasing out of agricultural export taxes, since assistance via import restrictions has risen over the period shown. In both high-income and developing countries there remains a large
was stratified by type of clinic, which included family health centers, general health centers, and small hospitals (this stratification was motivated by the idea that different infrastructure and human resources across types of health centers may play an important role in the delivery of the program).

Within each clinic, a sample of 18 families was randomly drawn from a potential wait-list of participants formed by facilitators (which usually contains between 45 and 60 potential participants per center). Potentially eligible families<sup>8</sup> were identified during regular health visits to the center and added to the wait-list just prior to the administration of the baseline survey.

> 8 The recruiting procedure is as follows. Facilitators construct and review the waitlists checking the clinical records of each family, with the purpose of selecting those families that satisfied both the inclusion and exclusion criteria of eligibility for NEP. Families in crisis such as detected domestic violence, severe mental health problems, or child developmental delays that require clinical attention are excluded from the group sessions. Health professionals at the health center derive families in these cases to individualized attention. Once identified, parents of eligible families were invited to participate through home visits or through direct recommendation made by a health professional. Eligible families were enrolled after an interview with an NEP facilitator, where they were informed about this study’s intentions and about the randomized process of assignment to groups. Also, they were given the chance read the informed consent clause (or received an assisted reading of the same when they declared difficulties to read, poor reading skills or illiteracy). After accepting and signing the clause, they were included in a general database and the facilitators proceeded to randomly assign them to one of the groups. Parents who refused to participate continued receiving their usual health care offered at their usual health center and were encouraged to reconsider their entry for a future wave of NEP.

7
<sup>denotethePDforbank</sup><sup>_j_</sup> holding asset class _i_ under scenario S, where S is now re-defined as S = _{TTC_ , _PIT_ , _Stress}_

> 25Note that we work here with the NPL ratio in terms of the volume of the non-performing loans while a better measure to use would be the number of NPLs per total number of loans. Due to the unavailability of relevant cross-country data, we leave this for future research or to the specific country cases.

33
the Green Goods and Services (GGS) survey.<sup>22</sup> The GGS survey was a firmlevel survey on a sample of 120,000 establishments in industries identified as potentially producing green outputs—333 industries out of 1,200 detailed at the six-digit American Industrial Classification System (NAICS). The selected industries represent 23 percent of all establishments and 20 percent of employment in the U.S. economy. Specifically, the GGS survey asks establishments for the percentage of revenues (or, for firms that do not generate revenue, the percentage of employment) deriving from green outputs according to the BLS definition. This self-reported percentage was then decoded into an establishment’s green percentage. Most sampled establishments ended up not having revenues or employment associated with a green output (confirming limitations of relying solely on standard classifications), only around 2 percent of the U.S. workforce was estimated to do so.<sup>23</sup> Lastly, replicating a survey like the GGS in developing countries would represent a costly endeavor, not only in terms of sampling a substantial number of firms to have statistical power but also in explaining sophisticated questions to respondents.

The BLS could overcome the critical limitation mentioned above regarding occupational information by linking the GGS survey to the Occupational Employment Survey (OES), a well-established survey designed to produce employment and wage estimates for nearly 800 narrowly defined occupations

> 21 To the best of our knowledge, the U.S. is only country that collects data on green outputs and links it to occupational data.

> 22 BLS (2013a), BLS (2013b), and BLS (2012a).

> 23 BLS (2013a).

9
# **Table 6. Drivers of the skill content of jobs, developing countries sample (STEP index). Controlling for income group trends.**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Non‐routine<br>analytical|Non‐routine<br>interpersonal|Routine<br>cognitive|Routine<br>Manual|Non‐routine<br>manual|
|GDP (lagged, log)|0.00364|‐0.0200|‐0.0189|0.0548|0.0396|
||(0.0405)|(0.0394)|(0.0204)|(0.0341)|(0.0322)|
|Industry VA (% of GDP), change|0.164|0.154|‐0.104|0.112|0.0942|
||(0.131)|(0.127)|(0.0660)|(0.110)|(0.104)|
|Services VA (% of GDP), change|0.0225|0.0496|‐0.0548|‐0.0317|0.00334|
||(0.102)|(0.0997)|(0.0516)|(0.0863)|(0.0814)|
|Skilled (% of working age population), change|‐0.0575|‐0.0729|0.0227|‐0.0471|‐0.00529|
||(0.0480)|(0.0467)|(0.0242)|(0.0405)|(0.0382)|
|Females (% of employment), change|
post-incentive price is lower than the sample average post incentive price in all regions, as expensive models tend to have lower market shares. Finally, in North America, the sales-weighted average size is slightly larger than the sample average, which may suggest that consumers prefer larger vehicles. Comparing across regions, EVs sold in North America have the largest salesweighted average battery capacity. EVs sold in China have lower post incentive price, battery capacity, and size on average, likely driven by both consumer preferences and purchase subsidies based on driving range (Li et al., 2021). EV models in Europe and Japan tend to have higher prices than other markets.

From 2015 to 2020, the number of EV models increased from 90 to 370 in the world, reflecting the ever growing choices for consumers.<sup>3</sup> There appears to be strong preference towards local

> 1Norway, Sweden, Netherlands, Switzerland, Germany, France, United Kingdom, Austria, Spain.

> 2United States and Canada.

> 3Source: IEA Global EV Outlook 2021.

2
✓|✓|
|First Stage F-stats for Price||||105.23|121.44|
|First Stage F-stats for Charging Station|||||36.34|
|Underidentifcation Test||||53.38|62.30|
|Weak Identifcation Test||||36.49|24.08|
|Overidentifcation Test||||58.89|66.87|
|Observations|4528|4528|4528|4528|4528|

_Notes:_ The regressions are based on data for 13 countries from 2013 to 2020. Observations for China in 2020 are excluded due to the impact of COVID19. The dependent variable is log(sales). _Price - Incentive_ variable is constructed from tax inclusive price subtracting total incentive received. Column (4) shows 2SLS estimates using instruments for consumer prices using the battery supplier dummy interacted with battery capacity as well as BLP instruments including the number of models and models’ size, battery capacity, and range for both own brand and rival brands. Column (5) in addition instruments for the number of charging ports using heavy duty EV stock, construction labor costs, and their interactions. Standard errors are clustered at the country by year level and reported in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 S7