rafmacalaba/data-use-mentions-extended huggingface dataset:
**_<u>________________________________________________________</u>_**

The UNCTAD maintains a database of tariff and non-tariff measures. This database, the Trade Analysis and Information System (TRAINS) database, contains tariff-line information on Trade Control Measures (tariff and non-tariff measures) classified according to the UNCTAD Coding System of Trade Control Measures.

UNCTAD also produces <u>a</u> _<u>Handbook of International Trade and Development Statistics</u>_ . This Handbook, based on existing international and national data sources, is intended to provide a complete basic collection of statistical data relevant to the analysis of world trade and development for the use of the UNCTAD, government officials, university and research specialists, and others interested in these subjects.

A paper by _<u>Francis Ng and Alexander Yeats</u>_ has a statistical data appendix on trade barriers in 80 developing countries. The tables provides details as to how this information was used to compute country specific information on trade and non-tariff barriers, and also gives detailed statistics on these national trade barriers and the profiles of protection in the developing countries.

# _<u>SITPRO</u>_

SITPRO is the facilitation body of the U.K. SITPRO started a project in 2002 on measuring and comparing the regulatory cost of international trade on a consistent basis. This initiative called International Trade Regulatory Costs Survey (ITReCS, and recently changed to Interact) develops benchmarks (indicative costs for specific activities) by gathering regulatory costs data of international trade from individual companies in different sectors. Its methodology can allow companies to calculate their own costs of compliance and then compare them with benchmarks covering the core costs that impact the majority of cross-border movements of goods. Costs include direct costs of third parties, such as a forwarder’s or broker’s account for entry/clearance services, and costs incurred by companies in employing personnel and facilities to achieve customs/trade law compliance and planning. While the pilot surveys are
Access to finance is one of the most important assets for a business to be successful, especially for the rural poor, who require collateral. The limited availability of much-needed loans remains a principal constraint among rural entrepreneurs because they need loans to start and expand their businesses (Islam 1997).

Improving the access entrepreneurs have to finance can be accomplished in a number of ways. In Yemen, increasing the number of financial institutions and improving the financial literacy of entrepreneurs suggest themselves as ways forward. According to the survey data, 19
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _20_ <!-- Start of picture text --> Severe Food Insecurity in IDA + IBRD<br>(U.S.$ Needs, billions) 2005−01−01 / 2027−01−01<br>140 140<br>120 120<br>100 100<br> 80 80<br> 60 60<br> 40 40<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan<br>2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027<br><!-- End of picture text --> Figure 2. Predictions made with final model, aggregated across all 144 IDA, Blend and IBRD countries.

_Note:_ The total food insecure population is based on the predicted prevalence rates, combined with the country population totals taken from the WDI database, and the population growth rates projected by the WEO. The black solid line corresponds to the period with observed data (2015-2019), the historical part of the black dashed line are predictions generated based on historical covariate values from the World Bank WDI database and the future values are generated using the IMF’s WEO outlook of April 2022. The red dash line is a downside projection that considers the slowed growth rates and higher inflation rates identified in the WEO’s downside analysis. Projected population totals are converted to financing needs based on the assumption of a 25% replacement cost, roughly sufficient to offset a 30% price hike, and assumes a US $ 0.75 daily cost of a minimum calorie sufficient diet (2020), adjusted annually for food price inflation using the FAO FPI. _Source:_ Figure prepared by the author for this paper.

by 2027 compared to below US $ 60 billion prior to the pandemic. In the downside scenario, the cash value of feeding the entire severely food insecure population a full year (4 times the plotted values,
|$1 778|**$1 857**|$6 085|$1 463|**$7 548**|**$212 997**|
|**Malaysia**|$149|$1 638|**$1 781 **|$2 851|$1804|**$4 655**|**$254007 **|

aIncludes Franchises, Similar Rights, Other Royalties, and License Fees. b Includes Art Crafts, Audio Visuals, Design, New Media, Performing Arts, Publishing, and Visual Arts. So ‘goods’ here probably implies goods and services given the composition of the items included.<sup>c</sup> BPM6 = Balance of Payments and International Investment Position Manual Sixth Edition.

Source: UNCTADstat Table 3.1 <u>|</u> **Royalties** **<u>and License Fees</u>** <u>, 2011| Total Trade Analysis in Millions (USD)</u>

|**Country**|**Thailand**|**Indonesia**|**Malaysia**|
|---|---|---|---|
|Royalties and license fees /Trade in all<br>Creative Goods|3 296÷10 953 =<br>~0.300922122|1 857÷7 548 =<br>~0.24602544|1 781÷4 655=<br>~0.38259936|
|Percentage ofRoyalties and license fees<br>in Total TradeinCreative Goods|~ 30.0922122%|~ 24.602544%|~ 38.259936%|
|Royalties and license fees /Total Trade in<br>allGoods and Services|3 296÷260 692<br>= ~0.012643273|1 857÷212 997 =<br>~0.0087184|1 781
# Motivation

Endeavors by multilateral agencies, donors, non-governmental organizations and academia to create a better understanding how institutions and policies determine economic and social outcomes have led to the considerable development of databases seeking to present meaningful global indicators of policies and policy outcomes across countries.

One of the most influential works that led to this boom has been the academic work on institutional economics (starting with Djankov, La Porta, Lopez de Silanes and Schleifer, 2002), itself inspired by the work of Hernan De Soto (1989). The research used cross-country variations in institutional quality (measured by indicators of performance) to demonstrate their importance for economic outcomes. This later led to the production of annual Doing Business indicators in 2003 and an earlier iteration of this in the World Development Report 2002 (Djankov, 2016). Doing Business has been hugely influential in informing development economics. Other influential indicators include the World Economic Forum’s competitiveness report, and the Logistics Performance Index. The business of indicators is now thriving: the World Bank’s TCdata360 on trade and competitiveness, an aggregator site, offers access to over 2,400 indicators.

The statistical relevance of many of these policy indicators has been amply demonstrated in the academic literature, and their relevance to economic policy through the widespread adoption by policy makers. However, the exact nature of what these indicators measure remains often subject to interpretation. They vary in degree of quality, and approaches to measurement: from ordinal measures such as perception of given issues, expert judgment of policy quality, to objective measurements of economic variables.<sup>5</sup> Amidst this variety, the sheer volume of indicators has become challenging to navigate, and thus to incorporate into coherent frameworks. For instance, we know that what affects trade or investment success is due to a very wide range of causal factors, but where are the best measures of these factors? Most analyses using indicators only look at selected subsets of indicators, and one can question how these indicators have been selected.
economy both directly and indirectly. Kucera et al. (2012) investigate the impact of the recent crisis through exports on employment in India and South Africa by computing the employment content of sectoral exports, including also employment via household expenditure due to export-related earnings.

> 2 For example, the boom in Vietnamese exports to the United States following the US–Vietnam Bilateral Trade Agreement of 2001 led to a large reduction in poverty (McCaig, 2011).

> 3 In the remainder of the paper we refer interchangeably to total wages, total labor value added, total compensation to employees, and total labor contained in exports.

2
benchmark when the only goal of fiscal policy is to smooth the consumption of hand-to-mouth households. Surprisingly, columns (1) and (2) of Table 2 show that the BBR outperforms the SSR. Moreover, the difference in welfare is significant: the welfare loss is 6% larger under the SSR. This result is in line with Mendes and Pennings (2017), where BBR outperforms SSR in RBC models (which are similar to the flexible price economy here). Still, it is at odds with the literature that finds substantial welfare gains from adopting structural rules over balanced budget rules for commodity exporters.

The OSR is shown in column (3). The 1q-MPS suggests that a 1 dollar increase in commodity revenues should lead to an increase of 54 cents in transfers, which is about halfway in between the BBR and SSR. The 1q-MPS is procyclical because shocks to commodity prices are highly persistent in SSA (6.7 years of half-life) and should be partly spent, according to the PIH. Acyclical rules such as the SSR oversave windfall revenues (and over-spend when commodity prices are low), which means that in the short term, consumption of the hand-to-mouth households underresponds to changes in commodity prices. The optimal prescribes that 2.5% of the sovereign wealth fund (SWF) should be spent each quarter, which is greater than the 1% quarterly interest rate.AA<sup>26</sup> This θθ implies that the _j_ -year MPS increases slowly as _j_ increases (saved revenues are spent at a 10% (4 x 2.5%) annual rate). A related problem is that the SSR leads to an excessively high standard deviation of public and private assets. In the model, the country spread responds to net public assets (SWF). The rate of return on the SWF will decrease as the SWF increases in size, or alternatively, the interest rate increases when the SWF is small. Therefore, a highly volatile SWF under the SSR makes it
of tax officials altered responses regarding businesses’ own tax-paying behavior, there was no influence on responses to any other questions.

Overall, survey results indicate businesses portray positive attitudes and beliefs toward tax administration in Tanzania. Several questions were asked to capture this element and there were high levels of agreement across a majority of these questions; ranging from an assessment of fairness in treatment by the TRA to how much the businesses trust the institution. However most businesses expressed a concern that tax laws were quite complex.

# **Ward-level tax payment data from presumptive taxpayers**

While the impacts identified by the survey data are meaningful, it is useful to understand how declaration behavior changes as a result of this exogenous shock in the TRA’s local presence.

Our main outcome measure comes from ward-level tax payment data. Since we are not able to link administrative tax records to survey respondents, the next best solution is to aggregate administrative data within the surveyed wards. In the presence of some spillover effects -that is, taxpayers in treated wards that were not surveyed but nonetheless indirectly treated through their own observation and word-of-mouth -we would expect to be able to identify changes in payment behavior that can be attributed to the local presence of the tax officials.

The ward-level data signals that, in the aggregate, there was no impact of local presence of tax officials on taxpayer behavior in the presumptive tax regime on the extensive (registration or any payment) or the intensive (payment amount) level. That is, considering all treated wards, presumptive taxpayers registered in these areas did not declare more often or higher tax liabilities.

However, given the heterogeneity we found in the first round survey in December 2022, we also explore heterogeneous impacts of local presence on aggregate presumptive tax filing behavior. Our main findings are summarized in Table 5. As can be observed, if we divide the targeted wards into geographic zones (at a higher level than Tanzania’s administrative 18
will be reversed, and smoke will have an even higher yield-boosting effect.

The reduction in yields due to future smoke exposure may impose significant costs on farmers. Using 2020 production and price data from the USDA, combined with our estimated yield losses, we can conduct a back-of-the-envelope calculation of lost revenue. Based on the total tonnage of corn and soybeans produced and the price of corn and soybeans in 2020, we estimate that the combined yield losses due to increased smoke exposure in 2050 would reduce farm revenue by roughly $1.5 billion annually in 2020 dollars. This is comparable to the reduction in farm revenue due to decreased sulfur fertilization after the implementation of the Acid Rain Program.<sup>28</sup> This calculation is a rough approximation of the costs and ignores how prices and production will evolve over time—although, as we discuss below, changes in the location of production may increase these damages. The point is not to provide a precise dollar estimate, but rather to highlight that, while the percent change in yields may be small, the lost revenue could be substantial.

Over our sample period, crop production has shifted towards counties that are more impacted 13
2 on skyscrapers and high-rise buildings only. Since 2003 Emporis has branched out, widening its database by collecting building information on all construction types.” As such, Emporis obtains data from its extensive member network of land developers, architectural firms, builders, city administrations, and banks to maintain the database. Emporis was recently bought by the CoStar group, a global leader in commercial real estate. As such, the data is confidential.

# **B2. First-step selection of the tall buildings**

We only consider “existing” or “demolished” buildings, thus ignoring “unbuilt” and “planned” buildings. We also exclude “under construction” buildings (as of 09-07-2022) since it means they did not exist as of 2019. This leaves us with 781,136 buildings. Next, we exclude buildings without geographical coordinates. This reduces the sample size to 764,343 buildings. Lastly, we exclude buildings for which we do not have any height information, which further reduces sample size to 748,651 tall buildings.

The main types of tall buildings are “high-rise buildings,” “low-rise buildings,” “skyscrapers,” and “building with towers.” Altogether, they account for 96% of buildings and almost 100% of total heights in the data. Other types of buildings include, for example, airport towers, openair structures, reactor buildings, and telecommunications towers. We do not exclude these as the satellite data that we also use cannot distinguish a building’s use. To be consistent with this other data set, we thus keep all buildings, noting that it should not be too consequential for the analysis given how little they contribute to overall heights globally.

**B3. Height variables used**

The main height variable used by Emporis is “architectural height” (meters), which “is the elevation from its base to its highest architectural element.” For some buildings where the information is missing, Emporis reports “architectural height (estimated),
Industrial GDP. Based on shares in aggregate sectoral output, the sample was largely drawn from eight manufacturing sectors: garments, textiles, leather, drugs and pharmaceutical, electronic goods and equipment, electrical white goods, auto parts, and food processing.

To complement the ICS data, we use employment statistics provided in the 199899 sampling frame of the ASI, which provides employment data on the universe of registered industrial establishments in India. We aggregate plant level employment to create measures of agglomeration economies. Table 1 lists the variables used in our empirical estimation along with their sources and summary statistics.

# B. <u>Factors Influencing Firm Location Decisions</u>

We first estimate Equation (5) to recover industry specific city premiums and also the estimates of differences in valuation between branch plants and single establishment firms. We do not report the industry-city specific premiums here, but these are available on request. Let us first examine if multi establishment firms value location-based attributes any differently from single establishment firms. These results are reported in Table 2. These coefficients are difficult to interpret as they indicate the preference of multi establishment firms for characteristics of a city relative to single establishment firms. Interactions between multi establishment firms and city characteristics for all industry types are jointly significant. However, only few of the estimated coefficients are individually significant. There are two variables where multi-establishment firms across industry types are more sensitive to the local business environment. These are proximity to an international port and effective stamp duty rates.

25
# C. The geographic composition of the sample

The benchmark measure of welfare is crucial for evaluating different prediction methods. However, because the census extract is only available for ten districts, it is not immediately clear whether it would be best to use only the survey data from those ten districts, or the full set of survey data to train the census model. The latter takes advantage of a wider set of training data, but the former may better capture the specific relationships between welfare and household characteristics in those poor districts.

Table 8 shows the results when varying the household survey sample used to estimate benchmark welfare. Specifically, we experiment with using only the UBR districts in the household survey data to train the census model, rather than the full sample. While the partial registry method remains the most accurate method by far, it doesn’t do nearly as well when the benchmark welfare measure is derived from a model trained on data from only the UBR districts. This is because the sample size used to train the models declines significantly from 6,000 to 2,000 poorest households when limiting the training sample to households in UBR districts, leading to a less informative benchmark welfare model and measure. The partial registry method is particularly sensitive to the weakening of explanatory power in the census model, due to limiting the training data to UBR districts. This is because the predictions from the census model are also used as the dependent variable to train the second stage geospatial model that generates estimates for non-registry villages. Interestingly, however, the predictive performance of the RWI also declines substantially, due to the increase in noise in the benchmark measure of welfare. Nonetheless, the predictive performance of the UBR improves, suggesting that the PMT may have picked up some of the heterogeneity in welfare patterns within the 10 districts.

**<u>Table 8.</u>** <u>Results when training models on sample data from UBR districts instead of all  districts</u>

||<br>
stand, however, because the sensitive lists of SAFTA members are long.

Empirical quantitative studies on regional integration in South Asia differ significantly in terms of the methodologies employed. Broadly, three types of models have been used: (i) gravity models; (ii) partial equilibrium models; and (iii) CGE models.

Gravity models have been widely used to predict the impact of RTAs on bilateral trade flows,<sup>9</sup> and their findings have been mixed.<sup>10</sup> Srinivasan and Canonero predicted that the impact of SAFTA on trade flows would be small for India but much larger for the smaller countries. Coulibaly found net export creation, whereas Tumbarello and Hirantha found net trade creation from the South Asian Preferential Trading Arrangement (SAPTA). On the other hand, Hassan observed net trade diversion effects from SAPTA, and Rahman (2003) found that regional integration is unlikely to generate significant trade expansion in this region. Using an augmented gravity model, Rodríguez-Delgado found that trade liberalization under SAFTA might influence regional trade flows mainly by increasing India‘s exports and both Bangladesh‘s and Nepal‘s imports.

The advantage in partial equilibrium models<sup>11</sup> is that they are generally based on disaggregated data and are also flexible, which facilitates sector-specific study. However, they ignore general equilibrium interactions and thus cannot capture inter-sectoral effects on the economy.

> 8 See Pursell (2004) and World Bank (2004).

> 9 The gravity models basically try to explain bilateral trade flows with a set of explanatory variables, which in turn try to predict the impact of the arrangement on bilateral trade flows. Typically, the exercise involves estimating a bilateral trade-flow equation with bilateral trade (imports, exports, or total trade at the aggregate or sector level) as the dependent variable and country characteristics, such as the gross domestic products, population, distance, commonality of language or cultural ties and the existence of preferential trade arrangements, as independent
is also evident that _Formosa_ did not just affect saltwater fishery in the four provinces located within the contaminated zone, but also the nearby regions and other industries as well. These findings should be factored into any top-down incentives to compensate and subsidize individuals and households.

# **References**

> Al-Majed, Abdul Aziz, Abdulrauf Rasheed Adebayo, and M. Enamul Hossain, “A sustainable approach to controlling oil spills,” _Journal of Environmental Management_ , 2012, _113_ , 213 – 227.

> Belasen, Ariel R. and Solomon W. Polachek, “How Disasters Affect Local Labor Markets: The Effects of Hurricanes in Florida,” _Journal of Human Resources_ , 2009, _44_ (1).

> Burkhauser, Richard V., Kenneth A. Couch, and David C. Wittenburg, “Who Minimum Wage Increases Bite: An Analysis Using Monthly Data from the SIPP and the CPS,” _Southern Economic Journal_ , 2000,

23