rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Trade Impacts of Intellectual-Property-Related PTAs: Evidence from Using the World Bank Deep Trade Agreements Database**<sup>**1**</sup>

Keith E. Maskus William Ridley Department of Economics Department of Agricultural and Consumer Economics University of Colorado Boulder University of Illinois at Urbana-Champaign <u>Keith.Maskus@colorado.edu wridley@illinois.edu</u> When this paper was written, the researchers were working as short-term consultants for the World Bank under a project on deep trade agreements jointly implemented by the Development Research Group, Development Economics (project RA-P168366-RESETF0A8655 “International Trade Cooperation”, TTL: Ana Margarida Fernandes) and the Macroeconomics, Trade and Investment Global Practice (project AA-P172238-GADTF0B1129, TTLs: Nadia Rocha and Michele Ruta).

> 1 This paper has benefited from support from the World Bank’s Umbrella Facility for Trade trust fund financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom .
Another key aspect of the auction methodology is setting the reserve price. McAfee et al. (2002) generate a bidding model to illustrate how to optimize auction yields based on a reserve price. They find that, based on the model, the reserve price should be “at least as large as, and generally strictly larger than, the average value (to the seller) of good that fail to meet the reserve”. While this empirical study corroborates intuitive notions of what the reserve should be, it does not offer practical advice to local finance managers. A review of the literature suggests that reserve prices are typically set slightly below the true market value. In Singapore’s sealed-bid auctions, the reserve is set at 85 percent of the Chief Valuer’s assessed market value (Ooi et al. 2005: 13). Kaganova (2010) offers a “rule of thumb” of a starting or reserve price set to 80 percent of market value. While there are theoretical and practical examples of informed reserve price setting, countries who have turned to auctions in order to better grasp prices will struggle with setting a reserve price that relies on market value. Frequent auctions, however, provide dynamic data on revealed prices which allows for developing land pricing models based on historical prices.

When it comes to the acquisition of private land, the use of eminent domain sparks questions around what can be determined to be just compensation. “Just compensation,” a term used in the United States Constitution, holds no explicit meaning, but suggests that it reflects commonly understood values. Shapiro and Pincus (2008) propose a “Strong Pareto” auction for land assembly under eminent domain. In this model, each landowner sets her own reserve, and the total reserve amount aggregated into this assemblage and divided by the number of shares each landowner holds. Shapiro and Pincus’ model offers a view of the interaction between reserve setting and bidding on assembled plots. The resulting efficiency and fairness benefits demonstrate welfare maximization and provide a case
GTA reporting pipeline. Where missing in the first step, an official source is sought and, if found, saved for each lead. Once the official source has been located, the responsible GTA trade policy analyst processes the lead into a GTA database entry that combines a brief description of the state act along with identification of the most accurate policy instrument among a rich taxonomy plus the appropriate United Nations product and sectoral codes.<sup>3</sup> The final database on trade policy changes includes information on the sector affected (medical products and/or food); the type of information used to document the trade policy change (official source, report on the Global Trade Alert website, media sources, or consultancy or law firm reports); the jurisdiction implementing the trade policy change; an initial assessment whether the trade policy change is liberalizing or restrictive; where relevant/available the date the trade policy change was announced/was implemented and lapsed; and whether the trade policy change affects imports and/or exports.

The version of the data set used in this paper spans the period up to October 9, 2020. It includes 701 policy measures covering 135 customs territories (the European Union, the South African Custom Union, and the Eurasian Custom Union are considered as a single territory, independent from of their member states).<sup>4</sup> For the ensuing descriptive analysis of trade policy measures across countries and over time we consider the date of announcement as the starting date of each measure. We clean the data accordingly to remove inconsistencies in the reported measures. We also exclude measures with a 2019 announcement date, i.e., before the COVID-19 outbreak. This leaves 645 measures, of which 284 have no removal date indicated and 52 are to be removed in 2021 or later.

> 2 See https://globalgovernanceprogramme.eui.eu/wp-content/uploads/2020/05/Methodologynote050420.pdf.

> 3 For products this is the Harmonized Commodity Description
to 0 and included a m<br>Gender equality index: gender equality, maternity and patern<br>Social policies index: all the gender equality policies plus sick<br>Subsidy policies index: house, food and transport subsidy.<br>Gender equality - 308 yes<br>Maternity and/or paternity leave - 195 yes<br>Subsidized childcare - 92 yes<br>Paid annual and/or sick leave - 392 yes<br>Subsidized housing - 315 yes|cation.The sample size w<br>h education, small and<br>isma and trade are all du<br>mpleted at least high sch<br>ooking after dependents<br>issingness dummy as an<br>ity leave and subsidized<br>and annual leave.|as reduced to 441 firm<br>medium firm, whether t<br>mmy variables.<br>ool<br>and doing hh chores - g<br>additional covariate in<br>childcare.|s as those with only 1 e<br>he firm has a permit to<br>iven these two variable<br>the analysis.|mployee were not<br>operate, conflict area<br>s have more than 10%|

34
in the last two rows of Table 2. We find that for the average high-income economy, the elasticity of domestic GDP to exports is very close across the two periods (0.5 and 0.6, respectively), both in gross terms and in value added terms. For the average developing economy, the change over time in the estimated elasticity is slightly larger: from 0.7 to 1.0 in gross terms, and from 0.5 to 0.7 in value added terms. T-tests confirmed that the differences in elasticities between 1990s and 2000s were statistically significant for estimates based on gross export data, but not for estimates based on value-added trade data, possibly due to the reduced sample size.<sup>6</sup>

> 6 A set of alternative methodologies validated the results reported in Table 2. First, averages were recalculated after removing elasticities from estimations with non-significant coefficients of adjustment. Second, ratios of 10-year (for gross) and 5-year (for value-added) moving averages of export and GDP percentage changes were computed and averaged by country group. Differences in the 2000s relative to 1990s were assessed by inspecting the monotonicity of the country-specific ratios. Finally, estimates of elasticities were derived using panel estimations with country fixed effects. Regressions were run only on the gross export data set – due to limitations in the value added exports data– and separately for high-income and emerging market economies. Elasticities for the 1990s and 2000s were found to be statistically different from each other.
**

The Bank‘s recent public sector portfolio suggests borrowing for public sector reforms has been spread across a large share of (potential) client countries. Of a total of 128 potential client countries or territories with populations of over 500,000 in 2000, only 41 (32 percent) did not borrow for some form of public sector reforms between fiscal years 2000 and 2010 (see Table 2).<sup>22</sup> They include countries where Bank operations are generally non-existent or limited (e.g. Islamic Republic of Iran) as well as those with significant other operations (Togo, Uzbekistan, Panama, or Egypt).<sup>23</sup>

> 20 In 2009, funding from the World Bank‘s _International Development Association (IDA) alone_ accounted for 40% of all donor funding on PFM reforms, nearly 14% of all donor spending on PS policy and administration (surpassed only by EU institutions and the United States), and nearly 26% of all donor funding on decentralization according to the OECD aid statistics database. No detailed sector breakdown is available for the International Bank for Reconstruction and Development (IBRD).

> 21 Documents reviewed include Project Appraisal Documents (PADs), Implementation Status Reports (ISRs) and Implementation Completion Reports (ICRs). Ratings refer to outcome ratings confirmed by the Independent Evaluation Group (IEG) unless noted otherwise. These ratings are composites of ratings for sometimes quite divergent project components and do not formally take the level of risk of an operation into account – either in terms of country context or in terms of the change being sought.

> 22 Based on board approval dates.

> 23 The count only includes Public Sector Governance Board (PSGB) mapped development policy and investment operations for the decade of fiscal year (FY) 2000-2010. If operations mapped to the Bank‘s Financial Management

23
# **Decrypting New Age International Capital Flows**

Clemens Graf von Luckner<sup>1</sup> , Carmen M. Reinhart<sup>2</sup> and Kenneth Rogoff<sup>3</sup> JEL E42, E51, E58, F21, F32, F38, F24 Keywords: Cryptocurrencies, Bitcoin, International Capital Flows, Transactions, Speculative Bubbles We thank Neil Shephard, Kathryn Holston, Juan Farah Yacoub, Matthew Ferranti and Pierfrancesco Mei for very helpful comments and to the Molly and Dominic Ferrante Fund for research support. The findings, interpretations, and conclusions expressed in this paper are those of the authors. They do not necessarily represent the views of the institutions they are affiliated with.

> 1 Sciences Po & World Bank

> 2 Harvard University, World Bank, CEPR & NBER

> 3 Harvard University & NBER
new era where manufacturing makes increasingly significant contribution to the economy. The government has formulated well-articulated goals geared towards making the country a manufacturing hub by 2025 and has put in place the appropriate legal, regulatory, and institutional frameworks. In addition, substantial investments are being channeled into developing industrial parks and relevant infrastructure. The sharp increase in FDI flow to the country is another reason to be optimistic about the future of Ethiopia’s nascent manufacturing sector.

The real exchange rate, when managed carefully, can be a useful policy instrument to support the goals of structural change and export promotion. Several studies provide compelling evidence that a competitive or an undervalued real exchange rate (henceforth RER) plays a pivotal role in unleashing structural transformation, which in turn enhances overall productivity and fosters growth (Rodrik, 2008a; Johnson _et al_ ., 2010; Bhalla, 2008a). Analysis based on Ethiopian data finds that a competitive RER will yield beneficial effects by boosting exports, improving external balance, and strengthening economic growth. Specifically, a 10 percent depreciation of the real effective exchange rate increases exports by 5 percent and reduces imports by 6 percent, thereby improving the trade balance by roughly 11 percent. Similarly, a 10 percent lower RER increases real GDP growth by about 0.23 percentage points. In addition, a depreciation has a stronger impact on manufacturing exports: a 10 percent real devaluation increases manufacturing goods export by about 10 percent.

Against this backdrop, the present paper attempts to address the following main questions: What are the major macroeconomic challenges facing Ethiopia? Is devaluation of the birr a step in the right direction to boost exports and improve the country’s external balance? Why and how is the real exchange rate an important policy tool to bring about structural transformation and reinvigorate economic growth? What are the key macroeconomic trade-offs related to exchange rate devaluation? What complementary measures could policy makers introduce to come to grips with any negative

> 2Structural transformation is conventionally defined as the reallocation of labor from low-productivity sectors to more dynamic (
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis

# 1. Introduction and Literature Review

A large body of literature has examined the relationship between natural resources, economic growth, and income inequality, revealing a set of complex and often ambiguous associations. The relationship between natural resources and economic growth is especially controversial. On the one hand, general economic theory suggests that a booming natural resource sector will boost economic growth, and that it will facilitate— though not necessarily cause—improvements in poverty and shared-prosperity indicators. Ideally, rising natural resource output would increase public revenues, enabling greater public investment in physical and human capital, while the private returns to resource production would encourage greater private investment (both domestic and external), as well as higher rates of household savings. On the other hand, the substantial literature on the so-called “Dutch disease” finds that a resources boom can divert resources away from the non-resource tradable sectors (especially manufacturing), distort the growth of nontradables (especially services), and put upward pressure on the exchange rate. These effects erode the competitiveness of exports and give imports an advantage over domestic production, undermining long-term growth (Sachs and Warner 1999).

The World Bank (2012) analyzed the impact of natural capital<sup>4</sup> on income by examining the adjusted net savings of resource-rich countries. It found that the sustainable management of natural resources has a positive and significant correlation with economic resilience and welfare gains. The World Bank analysis underscored that the elements of natural capital cannot be managed in isolation, but require an integrated approach to “regulating” and “provisioning” natural resource exploitation in order to preserve the underlying stock of natural capital.

Other studies have focused on the depletion of natural resources. Dasgupta (2010) concludes that GDP per capita and the Human Development Index (HDI) should not be regarded as comprehensive indicators of economic and social welfare, as they do not reflect the depletion of the natural capital stock, and that more sophisticated
_Table 4: Distributions of propensity scores before and after matching_

|||# cases|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Before matching|Uninsured|14795|0.135738|0.124385|0.000453|0.988689|
||Insured|3014|0.332719|0.233047|0.004923|0.996576|
|After nearest neighbor matching|Uninsured|3011|0.331963|0.231969|0.004934|0.988689|
||Insured|3011|0.332058|0.232220|0.004923|0.988621|
|After matching on common support|Uninsured|2797|0.295473|0.194748|0.004934|0.988689|
|and with 0.001 caliper|Insured|2797|0.295474|0.194748|0.004923|0.988621|

# V. **HEALTH OUTCOMES**

We measure health outcomes through anthropometry, there being no data in the VLSS on activities of daily living or self-assessed health. The VLSS is unusual among living standards measurement surveys in recording the height and weight for _everyone_ in the sample, not just children under the age of five (Alderman 2000). Furthermore, anthropometric measurements were taken for the full sample in _both_ waves of the VLSS.

For children, we use z-scores for both height-for-age (HAZ) and weight-for age (WAZ).<sup>10</sup> Higher z-scores indicate improved nutritional status, and children with values below –2 (i.e. less than two standard deviations below the mean of the reference group) are by convention classified as “underweight” (WAZ) or “stunted” (HAZ). We present results for three age groups: children aged between zero and 4 inclusive in 1992
not contribute appreciably to longer-term potential growth in SAR.

# 4. Prospects for Potential Growth: What Could Happen?

Factors weighing on potential growth over the past five years are likely to persist over the next decade. Demographic trends are expected to become less favorable. This will weigh on potential growth even if trend improvements in human capital and female labor force participation (and, for advanced economies, migration) continue. Although investment growth is expected to recover from its recent weakness, it is unlikely to return to elevated pre-crisis levels. Short of unexpected surges in productivity growth—perhaps as a result of dissipating crisis legacies or unanticipated technological breakthroughs— these trends imply an outlook for mediocre potential growth. This is also reflected in repeated downgrades to global growth forecasts (Kose, Ohnsorge, and Sugawara forthcoming).

The forward-looking scenario presented here applies the production function approach to assumed paths for capital, population statistics, and education and health outcomes (Kilic Celik, Kose and Ohnsorge 2020). The baseline scenario is one of “follow the same path” in that it assumes that all policy variables follow their long-term average trends.

The scenario assumes that all population-related variables (including age and gender structure of the population, fertility, and life expectancy) evolve as in the UN Population Projections under the assumption of median fertility, normal mortality, and “normal” (i.e. trend) migration.<sup>14</sup> Past trend improvements in each economy’s education and health outcomes are expected to continue. Secondary and tertiary enrollment rates by gender are assumed to grow through the forecast horizon at their average growth during 1998-2017 but are capped at 100 percent. Economy-wide averages are calculated as the population-weighted (2000-16) average of these gender-specific rates.

Secondary and tertiary education completion rates by gender and age group are assumed to grow at their average rate during 1998-2017. Economy-wide averages are calculated as the population-weighted (2000-16) average of these gender and group-specific rates.

In line with historical experience, investment growth is
missing elements to obtain a complete price matrix. The imputation process filled in missing values using the first-feasible methodology according to the following order: 1) median of the 20 nearest neighboring districts; 2) province median of that month; 3) national median of that month; 4) median price of 20 neighboring districts of the quarter; 5) province median of that quarter; and 6) national median of that quarter.

In order to account for differences in price and quality between domestic and imported wheat and rice, we calculate price averages for domestic and imported varieties separately. (The survey includes questions on the percentages of imported wheat and rice that a household consumes; these percentages are used to calculate total expenditure for these items.) Finally, the value of expenditure on food away from home is included in the calculation of total food consumption, but not included in the calculation of calorie intake since quantity data on such food was not collected.<sup>26</sup> We adjust consumption estimates using both NRVA price data and the official consumer prices index data to compare consumption across regions and over time. For food prices, we use an NRVA-based, Laspeyres price index estimated by quarter for each region.<sup>27</sup> For non-food items, we use a non-food price index developed by the Central Statistics Organization that accounts for temporal, but not spatial, differences in prices. Real consumption is relative to the chosen base: urban areas in the Central region in quarter one; the capital, Kabul, is located in the Central region.

To sharpen the focus on food security, we also examine: (i) per capita daily calorie intake; (ii) the food consumption score – a measure of household dietary diversity; and (iii) per capita daily protein intake.<sup>28</sup> The first measure is related to United Nations Millennium Development Goal 1.9 – to reduce by half the proportion of population below a minimum level of dietary
subsidies in addition to the value of subsidies. This may be the case for example of spill-over effects due to improved market functioning of the subsidized product. The total envelope of subsidies in local currency for the product of interest represents the benchmark for assessing changes in budget revenues further in the process. It is also the monetary value that is used to allocate subsidies to households in the incidence analysis that follows.

# Step 3 – Distributional incidence analysis

This step implies a standard welfare distributional analysis. This is a static incidence analysis. There is no “change” to consider but only the existing distribution of subsidies across households. The purpose is to understand whether subsidies are pro-poor or pro-rich and whether subsidies affect the level of poverty and inequality or not. This is an analysis that existing packages like DASP<sup>5</sup> or ADePT<sup>6</sup> are perfectly capable of handling but that will be useful to carry out with the tools provided in this manual so that this step will represent the benchmark scenario for the shock and impact analysis. The output of this step will typically be tables and graphs illustrating the distribution of subsidies across households and welfare statistics such as poverty and inequality.

The only data requirement for an incidence analysis is a cross-section household survey that contains information on household expenditure on the subsidized product and the total amount of estimated subsidies on the product or, alternatively, the amount of subsidies as a share of the subsidized price. With this information, the analyst can spread the total amount of subsidies on households according to the share of household expenditure on the overall expenditure on the subsidized product. Or, equivalently, household expenditure on the subsidized good can be multiplied by the amount of subsidies as a share of the subsidized price.

Note that this step is often mistaken for a policy simulation reform that amounts to the elimination of subsidies where the impact of the reform is measured in terms of the difference between the distribution of income with and without subsidies. This is not the