rafmacalaba/datause-encoder-data huggingface dataset:
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 29 Productivity The results confirm the convenience of having a more integral definition of urban form that goes beyond the use of population density. Regarding the shape of the perimeter of the urban extent, the models show that, _ceteris paribus_ , a higher level of compactness is associated with higher productivity levels. The coefficients of _exchange_index_ and _perimeter_index_ are both positive and significant. These results provide support for hypotheses **H1** and **H2** regarding the shape of the perimeter of the urban extent.

Changing our focus from the periphery to the inner city, we covered four aspects: the level of urbanization ( _fullness_ ), the urban structure ( _circuity_ ), the level of connectivity ( _intersection density_ or _street density_ ) and the land use pattern ( _sprawl_ ):

- The models show a nonlinear relation between the level of fullness and productivity: the coefficient of _fullness_ is positive and significant, showing how less interrupted urban layout (i.e., low urban sprawl) increases the productivity levels. Yet, the coefficient of _fullness_<sup>_2_</sup> is negative and statistically significant, suggesting that an excessive fullness may reflect a lack of public space and/or a lack of planning, setting the conditions for the appearance of diseconomies of agglomeration. Thus, this finding supports hypothesis **H3** .

- Regarding the urban structure of the city, the results show no evidence in favor of hypothesis **H4** . The coefficient of _circuity_ is not significant in all specifications presented in Table 7. This implies that the level of productivity of the city does not depend on whether the city has a reticular or an organic urban structure.

- Connectivity, measured as either the _street_density_ or the _intersection_density_ , appears positive and highly significant at the 1 percent level, suggesting that, _ceteris paribus_ , a well-connected city is a more productive city. These results support hypothesis **H5** .

- We also find empirical evidence in favor of our hypothesis **H6** : the _sprawl_ variable, as measured by Fallah et al (2010), is negative and statistically significant, at the 10 percent level. Therefore, there is some evidence that the distribution of population density within Latin American cities can affect their level of productivity. It is important to note that the average level of sprawling in our sample is very similar to
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Generally, only about 20 percent of the gender gap in earnings can be explained by observed differences in worker and job characteristics, leaving an unexplained gender wage gap of about 0.20 (World Bank 2001, pp.55-57). We find that the average occupational gender wage gap is 0.11 across countries. Unfortunately these figures are not completely comparable, as we are looking at the average occupational gender wage gap across countries, without taking into account employment patterns. They still suggest however a significant bias in the labor market treatment (discrimination) of women even within narrowly defined occupations.

_Occupational gender wage gap by level of economic development_ Does the occupational gender wage gap become larger or smaller with economic development? Figure 1 shows the adjusted occupational gender wage gap for 54 countries by the level of economic development, measured as the logarithm of GDP per capita (in constant 1995 US $). For each country the year is selected for which most occupational gender wage gaps were reported (at least two occupational gaps). There is a positive cross-section relationship between the occupational gender wage gap and the level of economic development. A positive relationship is surprising, as we would expect a negative relationship between occupational gender wage gap and the level of economic development, given that gender disparities in human capital tend to fall with economic development (World Bank 2001).

Countries report different occupations and this may be a reason why there is a positive relationship between the occupational gender wage gap and the level of development. However, if we adjust the occupational gender wage gap for cross-country differences in occupations reported, we still find a positive relationship.<sup>15</sup> Also if we limit the analysis to the country/year pairs reporting at least five of the 20 most reported occupations, we also find a positive relationship between the level of economic development and the gender gap.

> 15 Specifically, we ran a regression of the occupational gender wage gap on occupation dummies and country by year dummies. The adjustment was done by subtracting the part of the occupational gender wage gap which could be explained by the occupation dummies.

13
16 per capita on average for 1990-2004 and US$163 PPP per capita in the last year). On average, government health spending accounted for almost 70% of a country’s total health spending over the period of analysis and 60% in the year 2003; Armenia and Tajikistan exhibited the smallest shares of government health spending in 2003—less than 21%—whilst in Czech Republic and Slovak Republic that share was higher than 88% in the same year.

We also include among our indicators health sector salaries as a percent of total health spending. Data on this—like the data on many of the remaining health sector outcome indicators—are taken from the World Health Organization’s _Health for All Database (WHOHFA)_ . This database, maintained by the European Office (Copenhagen) of the WHO, contains data for all European countries plus the former USSR republics in Central Asia on about 600 health indicators, including annual information on morbidity and disability; hospital discharges; and health care resources, utilization and expenditure. The original sources of information are mainly own WHO estimates, country statistical offices and other international organizations. Given the general scarcity of available health information for developing countries, the availability of this database is good news for researchers dealing with health-related issues specifically in the former communist countries of Europe and Central Asia. In our attempt of getting a comprehensive, general picture of the (potential) SHI impact on population health conditions, we include dependent variables related to life expectancy, group-specific mortality rates, disease-specific standardized death rates and incidence rates and measures of utilization of services such as caesarean sections and immunization. We used the same database for obtaining data on hospital indicators, which include measures of average length of stay, bed occupancy, number of hospital beds (from the WB-WDI database), admissions and disease-specific discharges. We also include in our analysis a few indicators of avoidable deaths—such as
we were able to gather the complete time series of the VAT rate since its introduction. However, since our study focuses on the output implications of tax changes, we only use those tax changes for which we have real GDP data collected on a quarterly basis (as opposed to interpolated data). As discussed in detail in Ilzetzki, Mendoza, and Vegh (2013), relying on interpolated quarterly data creates serious problems associated with measurement error. The coverage, which varies across countries, starts as early as 1970:Q1 and ends as late as 2014:Q4 (see column 1 in Table 1 for country-speci...c coverage). As sources for the narrative analysis, we use contemporaneous International Monetary Fund (IMF) documents, OECD Economic Surveys, and news articles to gather evidence on policymakers’intentions and primary motivations for VAT rate changes. IMF documents include Sta¤Reports and Background Material for Article IV Consultations, as well as additional IMF country reports and publications such as Recent Economic Developments, Selected Issues, and Public Information Notices. IMF documents published prior to 1997 are available in digitalized hard copies at the IMF Archives in Washington, D.C., whereas documents from 1997 and onward are available at the IMF website. News articles were obtained from global media including BBC, Bloomberg News, EU Business, Financial Times, International Herald Tribune, Los Angeles Times, New York Times, Reuters, The Daily Mirror, The Daily Telegraph, The Guardian, The Independent, The Times, Wall Street Journal, and Xinhua News Agency as well as from individual countries’media outlets such as Mmegi (Botswana), National Post (Canada), The Globe and Mail (Canada), Prague Daily Monitor (Czech Republic), Intellinews-Czech Republic Today (Czech Republic), Irish Times (Ireland), The Belfast News Letter (Ireland), Sunday Business Post-Cork (Ireland), Baltic News Service (Latvia), The Southland Times (New Zealand), Sunday Star-Times (New Zealand), and El Pais (Spain).

# 4 Tax changes: Identifying motivation

This section explains the actual identi...cation of tax changes. We ...rst discuss some general considerations and then classify all the tax changes in our sample into endogenous and exogenous. Then, within each category, we further classify each change into various subcategories that will prove critical for the remainder of the analysis.

## 4.1 Identi...cation strategy

We identify a total of 96 VAT rate changes in 35 countries (18 industrial and 17 developing). Given the time coverage of these 35 countries, there is, on average, a VAT rate change every 11 years with an average change (in absolute value) of 1.9 percentage points. The remaining 16 economies in our sample of 51 countries show no VAT rate change. Out of those 96 VAT rate changes, 60 occurred in industrial countries and 36 in developing ones.

10
hand, for sectors that are low in R&D intensity, their results suggest that for technology diffusion trade with developing countries can be as important as trade with industrialized countries.

Based on these considerations, we first classify the increasing returns to scale sectors of our model into low, medium and high technology sectors. The classification is defined by the share of R&D expenditures in total sales, based on U.S. data. For low R&D intensive sectors, we assume that the elasticity of firms with respect to price is the same for the Africa region as for the EU, but the elasticity is only one-third of Rest of the World elasticity (trade with the Africa or EU regions misses out on trade with China or the U.S.). For medium and high R&D intensive sectors, we assume that trade and FDI with the Africa region is only one-eigth as valuable as trade with the Rest of the World (as discussed above), while trade with the EU is two-thirds as valuable as trade with the Rest of the World. Finally, we allow the elasticity of the Rest of the World to vary depending on the R&D intensity of the sector, where we allow for more technology diffusion in more R&D intensive sectors. The results of these assumptions are in table 6b.

To determine the impact of this parameter on the results, we conduct three types of sensitivity analysis on these parameters: systematic sensitivity analysis, piecemeal sensitivity analysis and a third where we simulate the model 100 times. When we conduct sensitivity analysis, we scale all the elasticities from 0.5 times their central values to 1.5 times their central values.

# **VIII. Results for Preferential Reduction of All Services Barriers—Central Elasticity Case**

We execute several scenarios to assess the impacts of Tanzania entering into a bilateral free trade agreement that includes services with the European Union, and similarly with the Africa region. In these scenarios we assume that Tanzanian ad valorem equivalents of the barriers against foreign investors in services are reduced by 50 percent with respect to the region with which Tanzania has an agreement. We assume that Tanzania already offers tariff free access to goods originating from its African trade partners, so in the scenario where we evaluate the agreement with the Africa region we include only liberalization of discriminatory barriers against foreign investors in services. Insofar as combining preferential trade agreements could 39
# **3.2  Changes over Time in Governance Using the WGI**

We now turn to the changes over time in our estimates of governance in individual countries. Figure 2 illustrates these changes for two selected governance indicators over the decade 1998-2007, Voice and Accountability and Rule of Law. In both panels, we plot the 1998 score on the horizontal axis, and the 2007 score on the vertical axis. We also plot the 45-degree line, so that countries above this line correspond to improvements in governance, while countries below the line correspond to deteriorations in governance. The first feature of this graph is that most countries are clustered quite close to the 45-degree line, indicating that changes in our estimates of governance in most countries are relatively small over the ten-year period covered by the graph. A similar pattern emerges for the other four dimensions of governance (not shown in Figure 2), and, not surprisingly, the correlation between current and lagged estimates of governance is even higher when we consider shorter time periods than the decade shown here.

Nevertheless, a substantial number of countries do show significant changes in governance. Over this period, we find that for each of our six indicators, on average 9 percent of countries experience changes that are significant at the 90 percent confidence level. Looking across all six indicators, 31 percent of countries experience a significant change in at least one of the six dimensions of governance over this period, roughly equally divided between improvements and deteriorations. We also note that the 90 percent confidence level is quite high, and for some purposes a lower confidence level, say 75 percent, would be appropriate for identifying changes in governance that are likely to be practically important. Not surprisingly this lower confidence level identifies substantially more cases of significant changes: 20 percent of countries experience a significant change on each indicator on average, and 59 percent of countries experience a significant change on at least one dimension of governance.

In Figure 2 we have labeled those countries for which the change in estimated governance over the 1998-2007 period is sufficiently large that the 90% confidence intervals for governance in the two periods do not overlap.<sup>17</sup> Examples of such 17 While this is not a formal test of the statistical significance of changes over time in governance, it is a very simple and transparent rule of thumb for identifying changes in governance that are 20
**xi**

# Exhibit A. Health effects of pollutants from motor vehicles

|Pollutant|Health Effects|
|---|---|
|Carbon Monoxide|Interferes with absorption of oxygen by hemoglobin (red blood cells);<br>impairs perception and thinking, slows reflexes, causes drowsiness,<br>brings on angina, and can cause unconsciousness and death; it affects<br>fetal growth in pregnant women and tissue development of young<br>children. It has a synergistic action with other pollutants to promote<br>morbidity in people with respiratory or circulatory problems; it is<br>associated with less worker productivity and general discomfort.|
|Nitrogen Oxides|Can increase susceptibility to viral infections such as influenza; irritate<br>the lungs and cause oedema, bronchitis and pneumonia; and result in<br>increased sensitivity to dust and pollen in asthmatics. Most serious<br>health effects are in combination with other air pollutants.|
|Hydrocarbons and other<br>Volatile Organic Compounds|Low-molecular weight compounds cause unpleasant effects such as eye<br>irritation, coughing and sneezing, drowsiness and symptoms akin to<br>drunkenness; heavy-molecularweightcompoundsmay<br>have carcinogenic<br>or mutagenic effects. Some hydrocarbons have a close affinity for diesel<br>particulates and may contribute to lung disease.|
|Ozone<br>(Precursors: HC and Nox)|Irritates mucous membranes of respiratory system causing<br>coughing, choking, and impaired lung function; causes headaches and<br>physical discomfort; reduces resistance to colds and pneumonia; can<br>aggravate chronic heart disease, asthma, bronchitis, and emphysema.|
|Lead|Affects circulatory, reproductive, nervous, and kidney systems; suspected<br>of causing hyperactivity and lowered learning ability in children;<br>hazardous even after exposure ends. Lead is ingested through the lungs<br>and the gastrointestinal tract.|
|Sulfur Dioxide|A harsh irritant, exacerbates asthma, bronchitis and emphysema; causes<br>coughing and impaired lung functions.|
|Particulate Matter|Irritates mucous membranes and may initiate a variety of respiratory<br>diseases; fine particles may cause cancer and exacerbate morbidity and<br>mortality from respiratory dysfunctions. A strong correlation exists<br>between suspended particulates and infant mortality in urban areas.<br>Suspended particulates have the ability to adhere to carcinogens emitted<br>by motor vehicles.|
|Toxic Substances|Suspected of causing cancer, reproductive problems, and birth defects.<br>Benzene and asbestos are known carcinogens; aldehydes and ketones<br>irritate the eyes, cause short-term respiratory and skin irritation and<br>may be carcinogenic.|

Source: OECD [1988a], French [1990], ECMT [1990], Walsh [1989c] Note: There is growing evidence that the synergistic effects of these pollutants in combination may be far more serious than the adverse effects of individual pollutants. This is particularly the case where NOx and SOx coexist or occur in association with particulate matter.
20 economic growth that took place during the last decade may have caused a generalized improvement in economic conditions that improved child survival.

The rapid decline in infant and under-5 mortality that has taken place in recent years in many countries in Sub-Saharan Africa is an important and largely unrecognized trend that merits further analysis to understand its underlying causes. This study presents some modest findings suggesting that it is likely that the rise in usage and ownership of ITNs played a role in the decline in infant mortality in Kenya, which constitutes a small part of the much broader phenomenon of the reduction in under-5 mortality across many countries. The broader story, and in particular the role of economic growth, could be better analyzed in a study similar to this one, but using data across many countries. That analysis remains as a project for future work.

# **Annex**

**Data description and imputation procedures – DHS microdata**

The data used in the analysis is from the Demographic and Health Surveys (DHS) conducted in Kenya in 2003 and 2008-2009 (hereafter 2008). The DHS surveys collect information from women aged 15-49 years on their full birth histories, children's health status, and household characteristics, among other things. We focus on individual mortality outcomes of children born 12-35 months before the interview. An attempt was made to reach the highest possible precision when restricting the sample. Thus, the sample was created using data on the day of birth and day of interview. For observations with unknown day of birth, data on the month of birth were used. Our full sample comprises 4827 observations. Due to missing values on explanatory variables, a total of 159 observations was excluded from the analysis, yielding the final sample of 4668 observations. The 2295 observations from the DHS 2003 and 2373 observations from the DHS 2008 correspond to actual live births that took place in Kenya in April 2000-August 2002 and November 2005-February 2008, respectively.

Child mortality is measured at individual level by a dummy variable which takes the value 1 if a child died in a given period and value 0 if it did not die. Depending on the type of mortality, the relevant time period changes. Postneonatal mortality covers the time period after 28 days of age (inclusive) and before the age of 12 months (exclusive). Infant mortality takes into account the
# **1. Introduction**

The COVID-19 pandemic has resulted in substantial disruptions to international trade. The enforcement of lockdown policies and mobility restrictions suppressed economic activity and upset international supply chains (Bas et el. 2022; Bonadio et al. 2021; Espitia et al. 2022; Lachitew and Socrates, 2020). Global trade volumes experienced double-digit declines during the early phase of the pandemic but subsequently stabilized and recovered since mid-2020 (World Bank, 2022).

One economic activity that has been particularly vulnerable to the COVID-19-related health measures and mobility restrictions has been small-scale cross-border trade (SSCBT). It is widely recognized that SSCBT is of substantial importance in many African countries (Lesser & Moisé-Leeman, 2009; Golub, 2012 & 2015). Small consignments of goods are exempt from Customs duties and other border formalities, resulting in lively exchanges between border communities. At some border crossings, 20,000 to 30,000 traders per day ship merchandise into the neighboring country (Fruman & Soprano, 2015). The aggregate value of these unrecorded transactions has been estimated to often reach or exceed the value of Customs recorded, truckbased trade between countries (Nkendah, 2013; Bouët et al., 2018; World Bank, 2018).

SSCBT has been affected by the COVID-19 pandemic in several ways (Bouët et al., 2020). The most direct impacts have resulted from the closing of border crossings for pedestrian traffic, as well as the closure of border markets. In addition, health inspections and testing requirements have led to delays and burdened traders with new fees. Also, curfews prevented traders from moving products to the border during the cooler hours at night, which led to higher wastage of perishable produce. The empirical information on the extent of the impact of COVID2