rafmacalaba/datause-encoder-data huggingface dataset:
all pairwise flows taking place across the world, nor does it provide details on “zero” tourism bilateral flows. Therefore, we are unable to apply recent approaches in the trade gravity equation literature to correct selection bias problems.<sup>10</sup> The next challenge is to come up with indicators about the adoption of digital tourism platforms with variation across countries and over time. We use two alternative sets of proxies for the use of digital planning tools. First, we use information on the percent of the population with Internet access in the origin country, as an approximation to the extent to which would‐be travelers can use digital tools for travel planning. In addition, from the perspective of the destination country, we use an indicator of the extent to which businesses use the internet to reach costumers, in the economy as a whole and not limited to the tourism industry. This measure consists of an index from 1 to 7 in response to the survey question “In your country, to what extent do businesses use the internet for selling their goods and services to consumers?”, with 1 being “not at all” and 7 “to a great extent”. The data come from the World Economic Forum’s _Travel and Tourism Competitiveness Reports_ for 2015, 2017, and 2019, with information dated two years prior to each report. As a result, in the first case, the econometric exercise uses only data for the years 2013, 2015 and 2017.

Figure 5 shows that there is a strong correlation between the extent of business‐to‐consumer (B2C) internet use and “digital demand” for tourism services. The latter come from WEF (2019) and consist of two indicators (0‐100) that measure total online search volume of either nature‐related or cultural “brandtags”. Nature tourism brandtags include terms such as “beaches”, “diving”, “hiking”, “protected areas”; whereas examples of cultural and entertainment brandtags include “historical sites”, “museums”, “religious tourism”, “local gastronomy”, and “nightlife”. More than 3.8 million destination‐specific keywords correlated to tourist activities and attractions were analyzed across nine languages to build each indicator. Figure 5 shows that greater B2C internet use in the destination country is correlated with greater online search for tourism‐related terms in that destination.

In the second approach, we use _Google Trends_ search data. Choi and Varian (2012) and Varian (2014) advocate for the use of _Google Trends_ data to forecast values of economic variables, with illustrations of applications that include travel destination planning. In this paper, we focus on internet queries on Google for the names of five of the most common online tourism platforms: “TripAdvisor”, “Booking.com”, “Travelocity”, “Expedia”, and “Orbitz”. We downloaded search terms from 2004 to the present, with

> 10 That is, Poisson Maximum Likelihood estimation methods, as proposed by Santos Silva and Tenreyro (2010).

Page **14** of **34**
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30
that there are no further compliance costs associated with the existing NTMs facing UK ...rms should the UK leave the EU.

It should be noted that this paper will not talk about services trade, “passporting”and the role of London as a ...nancial center post-Brexit, or immigration and the social aspect of Brexit.<sup>3</sup> It also will not speculate about the optimal timing to invoke Article 50 and “soft Brexit”. Finally the long-run impact of Brexit could depend on whether the UK can continue to retain and attract foreign direct investment, which surely will shape the UK’s exports for years to come, which is beyond the scope of the current paper. Recent ...rm survey evidence from Bloom and Mizen (2017) suggests that while most ...rms expect a negative impact of Brexit on sales, investment and costs, only larger ...rms and those that are more exposed to international markets are likely to think that they might move part of their business abroad. Thus, there are reasons to be optimistic post-Brexit.

The paper proceeds as follows. Section 2 brie‡y describes the theoretical framework underpinning OTRI. Section 3 provides a data description for the paper. Section 4 presents the OTRI of the UK’s major trading partners, while Section 5 concludes the paper.

# 2 Overall Trade Restrictiveness Index

The Overall Trade Restrictiveness Index (OTRI) summarizes the trade policies stance of a country. For a country, c; OTRI takes into account the trade responsiveness of all product n; "n;c , with respect to its trade policy, Tn;c; weighted by its total import, mn;c :

> 3For the impact of Brexit on services trade, please refer to Mulabdic, Osnago and Ruta (2016).

2
<u>Table 13: Percent change in emissions in 2030 relative to the baseline</u>

||<br>CO2|<br>CH4|<br>N2O|<br>FGAS|<br>BC|<br>CO|NH3|
|---|---|---|---|---|---|---|---|
|World|0.5|0.7|0.9|-1.8|0.6|0.8|0.5|
|BRI Area|0.6|0.6|0.9|-0.7|0.6|0.9|0.4|
|Cambodia|17.7|2.8|1.5||8.6|5.5|3.2|
|China|0.0|-0.1|0.1|-0.6|0.0|0.1|0.1|
|Indonesia|0.3|0.3|0.1|0.0|0.6|0.5|0.1|
|Lao PDR|7.7|3.1|-0.8||3.4|0.5|-0.6|
|Malaysia|5.1|-2.8|-2.8|16.0|-1.0|3.6|-4.3|
|Philippines|0.7|0.0|0.3|-2.3|0.1|0.3|-0.1|
|Thailand|4.1|-0.8|3.3|-3.1|-8.6|0.1|-2.4|
|Vietnam|1.8|-0.8|0.6|-0.1|2.3|1.4|0.1|
|Rest of East Asia|0.8|2.0|1.2|2.6|1.8|1.5|1.3|
|Bangladesh|5.8|3.8|6.4|0.0|18.2|15.2|5.8|
|India|0.1|0.4|0.2|0.0|0.0|0.2|-0.3|
|Nepal|-0.3|0.5|0.5||0.6|0.4|0.4|
|Pakistan|7.3|7.3|10.9|16.8|11.0|11.2|8.8|
|Sri Lanka|1.8|0.6|0.6|0.0|3.2|2.6|1.0|
|Rest of S. Asia|1.6|1.4|1.6||1.7|0.9|1.0|
|EU ECA|0.1|0.3|0.4|-4.6|0.1|0.2|0.7|
|Russian Federation|1.1|1.0|0.8|-1.0|0.8|1.1|1.4|
|Poland|0.1|-0.1|0.3|-4.7|0.1|0.2|0.5|
|Rest of E. Europe|0.7|0.7|0.8|-3.9|0.7|0.5|1.6|
|Kazakhstan|0.2|0.7|2.1|-5.6|0.5|0.7|2.4|
|Kyrgyz Republic|6.8|-0.8|-2.9|-5.1|7.0|1.5|-3.4|
|Rest of C. Asia|1.0|0.6|1.3|-1.6|0.6|0.7|0.9|
|Iran,Islamic Rep.|3.9|1.8|3.9|1.9|2.1|3.0|2.1|
|Egypt,Arab Rep.|-0.1|0.1|0.1|-1.8|0.2|-0.1|0.2|
|Turkey|2.7|1.2|2.3|-2.1|-0.8|2.4|2.3|
|Rest of MENA|1.1|1.2|0.8|-5.3|1.1|1.2|0.6|
|Kenya|2.1|3.0|3.5|-8.3|1.7|2.5|0.9|
|Tanzania|3.5|2.9|3.1||4.4|4.4|2.8|
|Non-BRI Area|0.3|0.8|0.9|-3.2|0.6|0.6|0.8|
|Ethiopia|1.3|2.2|2.5|-14.5|1.8|1.7|1.2|
|Rest of SSA|0.0|0.7|0.8|-3.0|0.8|0.9|0.7|
|Lat. Am. & Car.|0.1|0.9|1.1|-1.8|0.3|0.5|0.9|
|United States|0.2|0.8|0.9|-1.4|0.1|0.3|1.4|
|Rest of High-inc.|0.7|0.2|0.6|-2.6|0.9|0.8|0.7|
|Rest of W. Eur.|0.2|0.2|0.2|-6.4|0.1|0.2|0.3|

- 56 -
7 of the elasticities of substitution for imported factors of production. Imported intermediate goods and domestic value added are found to be complements; **the** estimate of the elasticity of substitution between these two factors is 0.57. Price Table 2: Selected elasticity and parameter estimates

|value;<br>t-vilue:<br>restriction'<br>mn simulation::|
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0.66coM tru elatiit aruze18)ad ofauly, (197 [4pu 37ogru s eo h Yi elasticity of Xubdmto 61te 40oedoladdedmstccptlsok(o h changes do affect f-actor-input relations. As the estimate is sigmificantly different from 0 and 1, the general CES formulation can neither be approximated by a fixedproportion nor a Cobb-Douglas fumction.! Subsdtittability between the two capital stocks is significantly weaker. 'MTe estimate has the expected positive sign but is 8 Othe studies estimating the elasticity of substitudonbetween imported intermediate goods and domestic factors of productionL also describe these factor pairs as complements. Conway (1986, exploring the same relationship as depicted above for Turkey, attains an estimat of 0.66; Marquez (1984) and Marquaz and Pauly (1987) compute a long-run estimate of the elasticity of substitution between iEmported oil and the domestic capital stock (for the developing countries as a group) of 0.73.

- 9 We assume replacement costs of new investments to represent the relevant user cost of the capital SEocks.
**Table 1: Treatment arms and treatment assignments**

|**Treatment**|**Type**|**Message**|**N**|
|---|---|---|---|
|Control|No message||47,555|
|T1|Reminder|“Dear taxpayer, TRA reminds you to pay your property tax.<br>Pay before 30th June. For more information:<br>dial * 152 * 00 #, visit your nearest TRA ofce<br>or call 0800780078. Thank you.”|47,502|
|T2|Reciprocity|[Reminder] + “Your tax facilitates access to social<br>services and infrastructure. Together we build our nation.”|47,547|
|||[Reminder] + “Non-taxpayers are not contributing||
|T3|Social Pressure|to national development and thus hindering<br>development of their communities.”|47,538|
|T4|Enforcement*|[Reminder] + “Pay your tax early to avoid penalties.”|47,553|

***Note:** Subjects randomly allocated to T4 were mistakenly sent T1. See sub-section 3.3 for details. Original messages were sent in Swahili.

4 (Enforcement), essentially doubling the size of the first treatment arm. Thus no enforcement messages were sent as a part of the experiment (although a few were sent independently by the TRA). Second, instead of using the list of cleaned and prepared phone numbers they were provided, the firm chose an unformatted list which contained the same numbers, but in some cases were not usable due to missing pre-fixes or mistakenly included county codes. As a result, between 22-33 percent of each treatment arm was not sent the intended message. Using data from the text message delivery, we can account for which taxpayers were or were not sent a message due to this error.

Finally, the randomization was conducted at the taxpayer identification level, but a small subset of taxpayer IDs shared identical phone numbers. This is likely because some taxpayers were issued multiple taxpayer IDs, or households sharing a single number contained multiple taxpayers. This leads to spillovers in actual treatment between the various treatment arms and the control group.

These spillovers complicate our ability to discern the effects of each treatment in isolation. To account for this, for our main analyses we restrict our sample to the roughly 60% of taxpayers that have a unique phone number (one that is not shared with any other taxpayer in the sample). As we show in Appendix section A1.3, those who only have unique phone numbers are no more or less likely to receive any particular treatment, so this restriction does not undermine the randomization.

The actual frequencies of treatment across each group are summarized in Table 2. Because we know the actual distribution of messages that were sent, we can instrument for actual message delivery with assignment to treatment.

# **3.4 Outcome data**

Our data on outcomes was retrieved from the TRA at the beginning of August 2018. We merged the complete record of all property tax payments made for a given taxpayer ID between June and the beginning of August to our original experimental sample. For each taxpayer ID we record, for each date during this period, whether any payments associated with that ID had been made up to that date as well as the cumulative amount of payments made so far. For a 6
<!-- Start of picture text --> PRECIO DE NUDO MEDIC<br>(Valores reales)<br>~_<br>— .390ee<br>=<br>[wee= 3 =<br>5030ee*. ee<br>10RR.<br>OCTUBRE1932 = ABRL.198d ARAL1586 ABRL4988 ABRIL1990 ABRIL12 ABRL1994 ABRIL1056 ABAL1908 AEAL2000 INDEX.PHA NDEXOCT-02 A<br><!-- End of picture text -->
which are the huge success cases of that period and the two most populous countries in the world, do not enter into calculation of Inequality 1 with greater weights than any other country.

Figure 2. International and global inequality, 1952-2011: “The mother of all inequality disputes” <!-- Start of picture text --> Concept 3<br>Concept 2<br>Concept 1<br>1950 1960 1970 1980 1990 2000 2010<br>year<br>.75<br>.65<br>Gini coefficient<br>.55<br>.45<br><!-- End of picture text --> Let us now consider further Figure 2. Why is it called “the mother of all inequality disputes”? To see what the dispute is about, consider the difference in the movements of Inequality 1 and Inequality 2. While the first, as we just saw, rose during the globalization era, the second declined, at times even dramatically. Measured by Inequality 2, the world has certainly become a much better (“more convergent” or more equal) place precisely during the same period. Thus, those who desire to emphasize the unevenness of globalization tend to focus on growing inter-country gaps, not taking into account sizes of population, and prefer Inequality 1. Those who, on the contrary, wish to focus on positive aspects of globalization tend to favor Concept 2, and to point to the indubitable successes of China and India. In effect, to grasp intuitively why and how Concept 2 inequality declined, we need just to recall that in these calculations, China counts a lot because of its large population size. And China, starting in the 1980s from an extremely low level of income, 6
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55
# **6.4 No rise in consumption inequality since 2011, but indications of a rise in 2019**

**The unreleased NSS-2017 shows a moderation in inequality but the magnitude of the reduction is comparatively large.** Based on leaked NSS-2017 results, Subramanian (2019) estimates rural and urban consumption inequality to have reduced by 0.0291 and 0.0387 Gini points since 2011 (based on modified mixed reference period in both NSS rounds). The direction of changes to inequality between NSS-2011 and NSS-2017 agrees with our findings. Our results differ, however, on the magnitude of the inequality reduction. Based on our estimates, average inequality reduction since 2015 in rural- and urban-India are 0.0007 and 0.007 Gini points (using the uniform recall periods of NSS-2011).

In Figure 26, we put the inequality estimates in a global context. Data on inequality is obtained from World Development Indicators. Countries that report at least one estimate of the Gini coefficient between 2009-2013 (two years before and after NSS2011) and 2015-2019 (two years before and after NSS-2017) are included. We average the Gini coefficients for each of the two time-periods and evaluate the difference in mean values to observe how much inequality has changed between the two points in time across countries. The MMRP-2011 level of Gini and the change in inequality based on NSS-2017 data is highlighted in blue; whereas the URP-2011 level of Gini and the change in inequality from our analysis is highlighted in red. It follows that there are only a handful of countries that report inequality reductions that are comparable to what is reported between the NSS-2011 and NSS-2017. By comparison, the rate of reduction based on URP-2011 and our analysis is found to sit well with global trends.

**Quintile consumption growth estimates in IHDS-3 show higher consumption growth in the bottom parts of the distribution** . Figure 27 compares quintile consumption growth rates from the IHDS-3 to our estimates. Average consumption growth in the bottom quintile of the distribution is higher than the growth rates observed for households at the top end of the distribution in both sources. These patterns are consistent with the observed moderation in consumption inequality. Desai (2020) finds that the Gini measure of inequality has fallen by 0.023 points between 2011-12 and 2017. Over the same period, inequality based on our estimates fell by 0.07 Gini points.

**NSS’ All-India Debt and Investment Surveys (AIDIS) show that wealth inequality too has fallen** . Using past rounds of NSS’ All-India Debt and Investment Surveys (AIDIS), Himanshu (2019) shows that gross wealth inequality increased by 0.01 and 0.08 Gini points between 1991-2002 and 2002-2012. The direction of changes

57
46 are more likely to rely on commercial sources. Countries vith long standing government programs, particularly those in Asia, tend to have big public programs that reach a large segment of the population. This reflects some combination of sound programs and established clients, vho have no desire or incentive to pay market prices for contraceptives. The Egypt program is also a long standing, vell supported endeavor, but serves only about 28 percent of users, and couples rely much more heavily on commercial sources.

NGOs include religious groups, many of vhich are active in family planning delivery in Africa, family planning associations (such as Colombia's Profamilia), and other charitable organizations. While NGOs vere included as a separate category in some countries' surveys, it vas not separated in a number of them. Moreover in some countries like Zaire, there is likely to be confusion among respondents between public and NGO sources since government and private voluntary groups vork in tandem, vith government financing some of the services and commodities and the NGOs delivering services. Thus although some countries differentiate, comparisons are limited because of the confusion in definition. The limitations not vithstanding, NGOs appear to play an important role in much of Africa, in NepaJ, Lebanon and a fev Latin American countries. Clinical vs. Nonclinical Methods. Government is more Important as a provider (or, less frequently, a financer) of clinical contraception, that is male and female sterilization and IUDs. Table III.2 summarizes the clinical/ nonclinical breakdowns for the countries for vhich data are avallable. Government is far more responsible for clinical as opposed to nonclinical methods.

**ul.tp:Faly** Plawd" **Papg** ({whole)