rafmacalaba/data-use-mentions-extended huggingface dataset:
very difficult. Restraining auto ownership in urban areas requires very high tax rates, increasing the supply of roads in urban areas is very costly, and increasing the supply of transit alternatives is costly and has little effect on congestion in the few cases where it has been studied.

The paper outlines some basic economic hypotheses about motorization and road provision and then evaluates these hypotheses with some simple summary statistics compiled at the national and city level in high income and developing countries. Next, it surveys more sophisticated empirical work on motorization and on road provision and summarizes this knowledge as stylized facts. It then examines the production of motor vehicle transport services-passenger and freight transport that uses vehicles and roads as inputs-and ends with a prognosis and issues that need to be addressed.

# **2. Economic hypotheses concerning motorization and road provision**

Economic reasoning produces a number of hypotheses about the relationships among motor vehicle ownership, road provision, and the production of motor vehicle World Resource Institute, _World Resources 1996-97_ (New York: Oxford University Press, 1996).
upward shift in the incidence curve. The ”true” distribution of welfare costs of the 10 percent rise in cereal prices is likely to lie somewhere in between these pairs of incidence curves. Repeating this exercise using the better-fitting cubic Engel curve (see Figure 12) leads to practically identical conclusions.

# **5.6 Changes in poverty**

Since consumption changes do not map linearly into poverty changes, predicting the increase in the poverty rate attributable to climate change involves an additional step. On top of the assumption of distributionally neutral growth underlying the CCICs, we need to take an explicit stand on the growth-rate that will prevail between our initial consumption measurement (2005) and the endpoint of our horizon (2040). In particular, the predicted poverty rate in the population given climate change, as calculated from our sample, is where _g_ and _H_ are as in the previous subsection, _c_<sup>0</sup> _i_<sup>is household</sup><sup>_i_’s initial per-capita expen-</sup>

> 33The regression includes household demographics (size, proportion females, proportion old and young, head age and age squared), scheduled caste/tribe dummies, an urban dummy, and district fixed effects. Standard errors are adjusted for clustering on district. Separate regressions for urban and rural areas yield very similar estimates of _β_ 1 and _β_ 2.

26
rise in one trilemma variable is traded off against a linear weighted drop in the sum of the other two.

12 The analysis was applied to a panel of the trilemma indices of 50 countries (32 of which are developing countries) during 1970-2006 period for which there is a balanced data set (the requirement of a balanced panel reduced the number of countries in the sample substantially). The study applies a linear regression, testing the hypothesis that the linear sum of the three indices (without a constant term) adds up to a positive constant, where all the regression coefficients are positive. The regression analysis confirms this hypothesis, with R<sup>2</sup> well above 0.9, for three sub-groups: Industrial Countries, Non-Emerging Developing Countries, and Emerging Market Countries. The overall results are robust to the possibility of allowing structural, endogenously determined breaks in the data (the years of 1973, 1982, 1997-98, and 2001 were identified as candidates for structural breaks, and tested for the equality of the group mean of the indexes over the candidate break points for each of the subsample groups).

.

9
provide more details on each of the data sources.

We use the administrative longitudinal employer-employee database collected by the Brazilian Ministry of Labor Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS) from 2004 to 2017. The RAIS database tracks every Brazilian formal worker and contains information of all firms with employees, identifying each worker’s employing firm at any point in time. The RAIS database includes job records for each worker with worker and firm unique identifiers and all corresponding characteristics. We use information on worker demographic characteristics (gender, age, education), job characteristics (monthly earnings, date of hire and separation and motive for separation, occupation, hours worked), and on location, industry, and state ownership of the firm for which the individual works at each point in time.<sup>5</sup> In order to construct the worker panel database needed for our analysis of the impact of firm foreign shocks on worker outcomes we take several steps. First, we identify in the RAIS database the complete cohort of individuals employed in the tradables sector in 2004 or entering the sector after 2004.<sup>6</sup> For each of these individuals we keep all their work histories until 2017 (including their employment in the non-tradables sector). For each worker, we select the highest paid job in December of each year to identify her/his wage, employing firm, and sector.<sup>7</sup> We designate this dataset as the starting RAIS worker panel database. Second, we construct an auxiliary database that restricts the sample to workers in the 16-65 age range and importantly to workers employed at least once by an exporting firm.<sup>8</sup> Third, we select a 10% random sample of workers from this auxiliary database due to the computational impossibility of using the entire RAIS database.<sup>9</sup> If a worker’s unique identifier is selected for our random sample,
observed again in year tt<sup>∗∗</sup> setting is very attractive as it summarizes most of the estimation and representativeness tt and tt<sup>∗</sup> problems that can arise in actual large-scale longitudinal studies. The following subsections formalize, using specific notation, the aspects related to sampling design, movers, and direct estimation.

# 3.1. Sampling design

We introduce below some symbols to better illustrate the operations just described. Initial time , from the cross-sectional ,l tt<sup>∗</sup> tt<sup>∗</sup> . We select a fixed sample size of households, SS= ∑ ,l ,l individuals of sample tt<sup>∗</sup> tt<sup>∗</sup> tt<sup>∗</sup> tt<sup>∗</sup> population HH , where household l∈HH enters the sample with inclusion probability ππ . SS tt<sup>∗</sup> tt<sup>∗</sup> l∈HH,l) member of household l is included in the sample tt∗ tt<sup>∗</sup> tt<sup>∗</sup> includes mm households, being mm ππ . We observe all NN tt<sup>∗</sup> = ∑l=1 ,l denote the household l. Thus, the jj-th (jj= 1, ... , NN in terms of individuals. mmtt∗ tt<sup>∗</sup> tt<sup>∗</sup> with the same probability of inclusion in their household. Let nn NN Intermediate time tt<sup>∗</sup> , of households from the cross-sectional realized sample size of SS tt<sup>∗∗</sup> tt<sup>∗∗</sup> . We select a new sample, SS . We tt<sup>∗
report both sales and costs, the taxable income response to all events is driven by a similarly-sized increase in reported sales and reported costs, as the numbers is squared brackets demonstrate.

Reporting by state institutions or credit/debit card companies (panels A3-B4) has a relatively homogenous eect on the self-employed and corporations, leading to a 21-26% increase in reported taxable income in both samples.<sup>42</sup> Although most rms are already subject to reporting by other rms when being rst reported by a state institution or card company, these new reports enforce a dierent margin - sales to state institutions and nal consumers, as opposed to inter-rm transactions - and may thus have an additional eect on reporting.<sup>43</sup> However, the result could also be driven by the fact that information reporting mechanism that are associated with withholding have a stronger compliance impact than pure reporting mechanisms. The next section will show that withholding indeed increases reported taxable income even when information reporting requirements are held constant.

In all gures, the event and control group follow similar, almost identical trends until the event, and then diverge precisely at event time _k_ = 0<sup>44</sup> , until the dierence between the two groups stabilizes at approximately _k_ = 1 . In the monthly sales tax data, the event group has a slightly higher pre-event growth rate than the control group, but the sudden divergence in the event month still supports a causal interpretation of the eect. Although oering consumers the possibility to pay by credit/debit card may increase sales, this eect is unlikley to materialize immediately, as consumers need to acquire the new information and adjust their purchasing behavior. We thus conclude that the size and timing of the eect is hard to reconcile with a pure growth eect, and must be at least partly driven by a compliance response to information reporting.

# 6 Impact of
the average of the 20 responses and convert it to real prices of 2015 in US$/mt. For the trajectory of prices between 2032 and 2050 we assume copper prices are constant at their 2032 value.

> 7 N P × G /G 0 where P is the share of labor compensation in GDP in period , taken from Penn World Table 0 10.01 (PWT10), and PP G is non-resource GDP in period PP . 8 Following Loayza et al. (2022), the initial capital stock in activity ββtt = ββtt PPtt PPtt ββtt must satisfy the following tt equations, tt PP tt 2021 2021 = 0 )G jj 2jj021 f NN+ 1 jj iijj )G 2021 + ∑ii )G 2021 (1 −γγ II where G II2021jj ≡ ~~�~~ pp̅2015jjpp̅tjj �GKK PP2021ii /YY denotes real GDI in activity (1 −ββ<sup>N</sup> PP ii , year NNii=1(1 −γγtt ; γγ0 = ββii<sup>N</sup> IIii . ff ii= 0, ... , NN

8
migrants were younger than 5 years of age at the time of migration. Households also tend to be bigger as several members of the extended family tend to live together to save on housing costs (Unidad para la Atenci ́on y Reparaci ́on de V ́ıctimas, 2013). Previous studies, using surveys given to migrants who were forcefully displaced, also report that this population has low education

> 2005. Many of the combatants that had been part of the AUC, however, later fused into new criminal groups that are known today as _Bandas Criminales_ (BACRIM), illegal armed groups that obtained financial resources, mainly through extortion and drug trafficking, to carry on their activities.

> 7 The data is publicly available at: _http_ : _//rni.unidadvictimas.gov.co/RUV_

> 8The problem is still ongoing, however. At the time of writing, in mid-2017, the year’s forceful displacement rate has already surpassed 800.

8
# _<u>6.2. Survey-to-survey imputation</u>_

We test the sensitivity of the survey-to-survey imputations to different specifications of the regression model used to impute consumption into the different rounds of the GHS. These additional checks aim to address possible issues with seasonality in the non-monetary indicators used in the consumption model, such as (1) the share of household heads employed in non-farm activities and in wage-employment and (2) consumption dummies for imported rice, beef, and fresh fish. Employment and consumption of specific items might vary significantly during the year. Since the GHS is only collected during the postplanting and post-harvest seasons, the value of these indicators might be different from the average value of the same indicators collected over a 12-month period (as in the 2018/19 NLSS). For example, if a shock on imports were to hit the country during months not covered by the GHS, this could impact the comparability of indicators measuring consumption of imported rice.

One way to check if this is the case is to compare the summary statistics of the non-monetary indicators in the post-planting and post-harvesting visit of the GHS for the same survey year. Table _13_ shows the average value of each indicator for each visit of the 2018/19 GHS. We find that, while many of the variables included in the regression model show little seasonal difference, others show significant differences between the two periods. The largest differences are registered for variables measuring household head’s type of employment – such as employment in non-farming activities decreases between the post-planting and post-harvesting visit – and the consumption frequency dummies.

Fully testing the sensitivity of the survey-to-survey imputation approach therefore relies on excluding these potentially-seasonal variables from the estimation of the consumption model and seeing how the results change. The alternative models exclude wage-employment (Model 1); wage-employment and a dummy variable for there being no toilet facility in the household (Model 2); wage-employment, no toilet facility, and the imported rice consumption dummy (Model 3); non-farm employment (Model
4 of Table 10). Empowerment index in column 2: input into household decisions (on own income, household savings, major household purchases, childbearing, and children’s education) autonomy over household decisions, (-) prevented by husband or household member from visiting relatives or working outside the home, (-) believes that a husband is justified to beats his wife if she burns food or neglects children, partner contributes to food preparation, housekeeping, laundry, and childcare (outcomes in Table 8, 9, and columns 1 and 2 of Table 10). Mental health index in column 3: (outcomes in Table 11). Finance index in column 4: saved money during the past 12 months, saved money in a formal institution, saved money in an informal institution, able to pay 20,000 FCFA in case of an emergency (outcomes in Table 12). Child development index in column 5: gross motor score, fine motor score, and language score (outcomes in Table 13).

36
to an additional fatality during the last pregnancy trimester reduced birthweight by 2.1 gr<sup>6</sup> and that the odds of a child being born with low birthweight increase if the mother was exposed to violence in the first trimester. Valente’s (2015) was the first study to explicitly address the concerns about survival selection and gender-specific effects in the context of violence shocks. Using DHS data for Nepal, she finds that violence exposure in utero increases fetal deaths<sup>7</sup> and that this is more pronounced for boys, as evidenced by a fall in the gender ratio at birth. However, there was not an increase in the probability that a baby was considered small at birth nor a reduction in neonatal mortality,<sup>8</sup> as would be consistent with a selection effect.<sup>9</sup> Still, the results contradict the earlier studies in Colombia and the West Bank and Gaza, as babies exposed to conflict during gestation were not systematically in worse health at birth. A suggested explanation for this difference is that the selection effect may be weaker (or the scarring effect stronger) in relatively more developed countries. The recent study by Dagnelie, Luca, and Maystadt (2018) also shows evidence of a stronger selection effect in a poor country, the DRC. This is seen in a reduction in the gender ratio at birth, a decrease in aggregate district-level number of male births and a lack of change in boys’ health, as captured by a fall in infant mortality of girls but not of boys. In contrast, QuintanaDomeque and Ródenas-Serrano (2017) find that in Spain, exposure to ETA bomb casualties in the first trimester of pregnancy had a small negative effect on birthweight despite an increase in fetal deaths (stillbirths).<sup>10</sup> This work in a developed country still leaves a gap in the evidence with respect to the strength of the selection effect in middle-income developing countries
them were used by firms located outside SEZs and other development areas in ‘open’ cities, and the remaining 17% were used by firms located anywhere in the rest of China (Figure 2). In sum, on the eve of WTO accession, duty exemptions benefited virtually all firms engaged in export processing in China, not just those located in SEZs and other development areas. This ability for enterprises outside the zones to engage in processing trade was critical for ensuring that this trade grew rapidly, rather than just benefitting a limited number of firms and workers in export processing enclaves.

**Table 1. Average tariff rates in China (percent)**

||All|products|Primary|products|Manufactur|ing products|
|---|---|---|---|---|---|---|
||Simple|Weighted|Simple|Weighted|Simple|Weighted|
|1992|42.9|40.6|36.2|22.3|44.9|46.5|
|1997|17.6|18.2|17.9|20.0|17.5|17.8|
|2001|16.6|12.0|21.6|17.7|16.2|13.0|
|After accession|9.8|6.8|13.2|3.6|9.5|6.9|

Source: Ianchovichina and Martin (2001). Authors’ calculations for tariff lines with imports in 1999 from COMTRADE; CDS Consulting for protection data for 1999-2001; and after accession data from China’s WTO final offer.

# _2.3 Reducing tariff and non-tariff barriers_

In addition to liberalizing processing trade, China made substantial progress in reducing both its own non-tariff and tariff barriers. The number of products subject to quotas and licenses fell from 1,247 tariff lines in 1992 to
government and citizens. Such “CivicTech”<sup>6</sup> tools are based on the use of digital technology to improve governance and the interactions between government and citizens (World Bank, 2022). Importantly, evidence shows that such technologies can promote citizens’ trust and help governments achieve improved development results (World Bank, 2022; Waddington et al., 2019; Bae et al., 2019; Diergarten and Krieger, 2015; Lindstedt and Naurin, 2010; Aidt, 2009; Shim and Eom, 2008).

However, while the existing studies have demonstrated linkages between digital tools and citizen engagement, there has been no empirical assessment of the link between these variables using econometric modeling at a cross-country level. **_In this lacuna, the present research paper seeks to estimate the extent to which and under which conditions GovTech platforms support improvements to citizen engagement._** In doing so, this study attempts to fill a gap in the literature by using original data for a large sample of 176 countries (for which data are available) in order to analyze the importance of GovTech platforms in improving citizen engagement. As such, this is a cross-sectional study using primarily: (i) the World Bank’s 2020 GovTech Maturity Index data (extracted from the 2022 GTMI version) and (ii) the 2021 Varieties of Democracy (V-dem) citizen engagement data to assess the impact of the implementation of national platforms that allow citizens to participate in policy decision-making as well as to provide feedback on service delivery. A time lag has been introduced to allow for consideration of the uptake and diffusion, as the effect of introducing digital platforms to support citizen engagement may take sufficient time to appear. Importantly, this research considers the effects of contextual and demographic factors on this relationship, including, _inter alia_ : political regime type; level of development; perceptions of public sector corruption; coverage of broadband subscriptions; distribution of political power among social groups; population distribution between 15 and 44; human capital
for every RTA that enters into force during the period of observation, while controlling for tariffs, thereby allowing every RTA to have a distinct NTB effect.

Our analysis uses the newly available International Trade and Production Database for Estimation (ITPDE, Release 2) put together by Borchert et al. (2020). Those data include domestic trade flows—this is crucial to identify trade regulations that do not vary across trade partners (Heid et al. 2021); usefully, the data also include services trade. The database has almost universal country coverage and a longer time span than alternative sources such as the WIOD provided by Timmer et al. (2015). To measure the depth of RTAs—and hence their ambition regarding NTBs—we build on two sources that are standard in the literature: first, the World Bank’s Deep Trade Agreement Dataset (DTA data) put together by Hofmann et al. (2019), second, the DESTA database provided by Dür et al. (2014).

We estimate a state-of-the-art structural gravity model following all recommendations by Yotov (2022). In essence, the strategy consists in running a Poisson Pseudo Maximum Likelihood (PPML) model on panel data of sectoral bilateral trade flows (including domestic transactions) with the largest possible array of fixed effects to control for other determinants of trade.

In contrast to the existing literature, we cannot confirm a robust trade-creating role for non-tariff provisions in trade agreements (TAs) using gravity, while tariffs—once properly measured—are robustly shown to be potent inhibitors of trade. We present some evidence that, in deep TAs, non-tariff provisions create trade in a non-discriminatory fashion. This is welcome from a global welfare-theoretic point of view. However, if policy makers want to use trade agreements to grant preferences between geopolitically aligned countries, tariffs are the better choice. The result is robust to alternative empirical specifications and is not driven by treatment heterogeneity. Our finding does not necessarily mean that RTAs do not reduce NTBs, instead, the
.95|0.96|0.95|0.93|0.95|0.96|
|Turkey<sup>o</sup>|-|-|0.91|0.92|0.92|0.94|0.88|0.93|0.93|
|Uganda|0.67|0.44|0.52|0.50|0.48|0.71|0.41|0.55|0.63|
|Ukraine|0.95|0.82|-|-|-|-|-|-|-|
|Uzbekistan|0.97|0.93|0.95|0.94|0.93|0.96|-|-|-|
|Vietnam|0.94|0.85|0.89|0.87|0.88|0.86|0.81|0.91|0.93|
|Zambia|0.71|0.37|0.50|0.52|0.48|0.59|0.36|0.67|0.71|

1 All differences are significant (at least) at 95 percent level except for countries marked as follows: l Location dimension; e Education dimension, g Gender dimension; o Occupation dimension (industry / services)

**Note** : _Adequate Housing Index_ disaggregated along several dimensions (Source: authors’ calculation based on countries’ available household consumption and expenditure survey). *Low education refers to household heads with at most secondary education completed, while high education refers to household heads with more than secondary education. – refers to respective disaggregation unavailable in the countries’ latest HCES survey.
political cycle effect is thus not driven by omitted political connections.

# **5.2.2 Alternative ways of predicting GDP growth using nighttime lights**

We conduct several robustness checks regarding alternative ways of predicting true GDP growth from the nighttime lights in Eq. (1) in Table A.1. In column 1, cities in Northwest China are excluded from the regression: in this region, the association between ln(GDP) and ln(Lights) is insignificant, as shown in Panel G in Figure 3. In column 2, we calibrate the raw nighttime lights data following the three procedures outlined in Liu et al. (2012), namely, inter-calibration, intra-annual composition, and interannual series correction. The calibrated lights data are then used to predict GDP growth. In columns 3 to 5, we try different specifications of the elasticities of GDP with respect to nighttime lights. We impose a common elasticity for all the cities in column 3, and allow for province-specific elasticities in column 4. In column 5, we allow the elasticity of each region to vary between the pre- and post2012 periods. The year 2012 is chosen since we find the biggest change in the lights-GDP relationship around 2012. When conducting the joint test of the equality of elasticities between two consecutive periods for each region, the cutoff year of 2012 yields the largest _F_ statistics. In the remaining columns, we use more controls to predict GDP in Eq. (1). We add a quadratic term of lights in column 6, and add city-level electricity consumption, total bank loans granted and total freight volume as additional controls in columns 7, 8 and 9, respectively. These three variables, used by Premier Li Keqiang to objectively measure economic performance (“Keqiang Index”), are highly correlated with real economic activity but less manipulatable, and thus suitable as additional predictors of GDP. Column 10 controls all these three variables. In column 11, following Clark, Pinkovskiy, and Sala-i-Martin (2020), we