rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9511

# **Abstract**

This paper analyzes a prominent dimension of the initial policy response to the COVID-19 pandemic observed in many countries: the imposition of export restrictions and actions to facilitate imports. Weekly data on the use of trade policy instruments during the first seven months of the COVID-19 pandemic (January-July 2020) are used to assess the relationship between the use of trade policy instruments and attributes of pre-crisis public procurement regulation. Controlling for country size, government effectiveness and economic factors, the analysis finds that use of export restrictions targeting medical products is strongly positively correlated with the total number of steps and time required to complete procurement processes in the pre-crisis period. Membership in trade agreements encompassing public procurement disciplines is associated with actions to facilitate trade in medical products. These findings suggest that future empirical assessments of the drivers of trade policy during the pandemic should consider public procurement systems.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at varuneknath@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
approach. Finally, we estimate the distribution of road benefits by gender, examining whether road investment benefits men and women as well as boys and girls differently.

The paper is organized as follows. Section 2 presents an econometric framework to estimate the impact of road development. Section 3 discusses the panel data, spanning two project samples, and their respective control villages. Section 4 discusses the results and presents the estimates of economic rates of return to road investment. Section 5 discusses the distribution of road benefits accrued by households across different percentiles of per capita consumption. Section 6 shows how the effect of road

> 4 In previous studies, control areas have often been selected retroactively, contaminating the data selection with the econometrician’s knowledge of actual outcomes, as well as reducing much of the data to less-than-reliable, retrospective information.

> 5 The main intent of this paper, however, is to assess the fungibility of development aid between ex ante targeted and non-targeted areas, presenting road improvement as one type of aid initiative.

> 6 They find that the non-poor benefited more from roads through improved medical emergency assistance and opportunities for non-agricultural employment; in contrast, the poor benefited more from female off-farm employment opportunities.

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bottom half of households in the village. This was based on a conscious decision to match the UBR administrative data, which only contains PMT scores for the bottom 50 percent of households in each village. This section considers how the results change when we consider mean village consumption, taken across all households, as the main welfare measure.

Changing the welfare measure has three main implications for the methodology. First, the census model must now be retrained using all households, not just the bottom half, in the survey. This of course also changes the predicted values of household per capita consumption in the simulated partial registry, which is equal to the benchmark welfare model for villages included in the registry, which entails re-estimating the geospatial model. Finally, we re-estimated the IHS plus geospatial model to train it against average per capita consumption across all households in the survey, rather than just the bottom half.

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may not be. In the United States, RM were subject to somewhat incoherent regulations at the state level until recently. Moreover, elderly homeowners risk losing their access to need-based assistance like Medicaid, depending on the RM disbursement method and eligibility criteria of social services (Caplin, 2002; Chatterjee, 2016). In some countries (e.g. Australia) where pensions are asset or means tested, there could also be a risk of losing entitlement to social security benefits.

Many of the supply-side constraints mentioned above are particularly salient in developing countries. Lenders may face legal uncertainties regarding property ownership, which raises the transaction costs of mortgage origination, foreclosure, and adds to cross-over risk. Patchy housing registry and valuation data represents another impediment to RM market development. Moreover, actuarial information may be limited in many developing countries, making it harder for lenders to correctly assess and price longevity risk. Insurance markets are also less deep than in advanced economies, which exacerbates the risk-pooling constraints inherent in the RM market in rich countries. It is indicative that lenders in Singapore and South Africa have exited the market in the 2000s because they considered RM economically unviable.

Developing countries are also facing greater constraints on the demand side. Financial literacy and consumer protection are already major concerns in advanced economies, they are even more likely to operate against the development of a healthy RM market in middle and low-income countries. Creating an RM market in an environment of relatively weak consumer protection regulation and oversight may open a new door to fraudsters and financial criminals. In addition, non-interest fees for RM origination are likely to be even higher than in rich countries. Anecdotal evidence suggests that upfront fees for the closing of an RM in South Africa amount to about 15% of home equity, roughly twice as much as in the United States (Fisher-French, 2018). Moreover, bequest motives and the desire for inter-generational capital accumulation may be stronger in many developing countries, further constraining demand for RM.

High transaction costs and interest rates cast the value of
, we use temperature data (2010–21) from the Bangladesh Meteorological Department and the dates of holidays and Ramadan from timeanddate.<sup>2</sup> To measure the stringency of the COVID19 containment measures Bangladesh adopted and their impact on mobility, we rely on the Oxford COVID-19 Government Response Tracker (OxCGRT)<sup>3</sup> Stringency Index (2020–21) and Google mobility data from the Google Community Mobility Reports. Finally, data on daily COVID-19 cases and division-level data on the number of cases and deaths were retrieved from

> 1 Using the Tabulizer tool pack from the R statistical software, the data were scraped from PDF documents published on the Bangladesh Power Development Board website.

> 2 https://www.timeanddate.com/.

> 3 OxCGRT collects publicly available information on 20 indicators (Containment and Closure Policies, Economic Policies and Health System Policies, and so forth) of government responses.

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were violated in the data, Hamilton and Flavin would conclude that the borrowing constraint need not be satisfied. By contrast, I regard the necessity of the presentvalue borrowing constraint in a dynamically efficient economy as established on theoretical grounds.. .This suggests a natural definition for the concept of sustainability: a sustainable fiscal policy is one that would be expected to generate a sequence of debt and deficits such that the present-value borrowing constraint would hold.... Moreover, an unsustainable policy would be expected to change...

If the present-value borrowing constraint does not hold, what will be the form of the violation?

Hakkio and Rush (1991, p.429) also interpret their PVC tests as tests of the sustainability of current fiscal policy: Is the [U.S.] budget deficit "too large?" Yes. Specifically, we find that recent spending and tax policies of the government -- if continued -- violate the government's intertemporal budget constraint. As a result government sending must be reduced and/or tax revenues must be increased.

The authors state explicitly that they are testing whether the NPG condition would be satisfied _if government revenue and expenditure continued to follow their past stochastic processes._ I conclude that the PVC tests are appropriately viewed as tests of the sustainability of the current fiscal policy stance, as reflected in the historical times series data on government spending, revenue, deficits, and/or debt, not as _solvency_ tests. An analysis of solvency would have to consider all conceivable government policies, and ask whether there is _any_ economically and politically feasible policy stance that would satisfy the PVC, given the value of current debt. If there is none, then the government is insolvent.

In the U.S. context, it is reasonable to assume that government expenditure or tax policies would ultimately have to change in order to bring the projected stream of discounted future primary surpluses into line with the PVC in situations where the current fiscal policy is unsustainable. In the LDC context
nonetheless consider this the best feasible way to examine the role of e-commerce participation on income growth in Taobao Villages.

# **_<u>Common support</u>_**

0 < P P P P P PP(PP= 1|XX) < 1

The assumption of common support states that It ensures that persons with the same X values have a positive probability of being both participants and nonparticipants (Heckman et al. 1999). In other words, X values will not be perfect predictors of e-commerce participation.<sup>12</sup> This assumption can be checked by looking at whether the matching variables are balanced across the treatment and control groups. Specifically, t-tests on household and village characteristics between the matched treatment and control observations are provided in the results section.

Critiques of PSM are usually based on violations of the unconfoundedness assumption in empirical settings. However, to the extent that observational surveys can capture most of the factors that influence treatment status, PSM remains one of the workhorses in impact evaluation literature. Several studies used PSM to analyze development issues in a cross-sectional and non-experimental

> 11 We observed a high share of returned migrants in Taobao Villages. In the absence of the opportunity to participate in e-commerce, some of the individuals/households that currently reside in Taobao Villages would have migrated in pursuit of better employment opportunities.

> 12 In plain English, common support means no attributes should perfectly predict participation. For example, if all participants are males, then being a male perfectly predicts participation, common support fails. Instead, if we observe both males and females as participants, common support is satisfied.

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# **Figure 2**

Trends in Domestic and Affiliate Employment among Multinational Firms <!-- Start of picture text --> 1980 1985 1990 1995 2000 2005<br>Year<br>US Employment Low Income Employment High Income Employment<br>12,000<br>2,500<br>11,000<br>2,000<br>10,000<br>9,000<br>1,500<br>Parent Employment (000s)<br>Affiliate Employment (000s)<br>8,000<br>1,000<br>7,000<br><!-- End of picture text --> _Notes_ : Author's calculations based on the most comprehensive available data and is based on firm-level surveys on US direct investment abroad, collected each year by the Bureau of Economic Analysis (BEA) of the US Department of Commerce. Using these data, we compute number of employees hired abroad by country and year, and then aggregate employment by Low (High) Income country according to World Bank income classifications.
Several existing studies analyze the effect of the COVID-19 pandemic on LGBTI people. These studies primarily focus on assessing the impact of the pandemic on LGBTI people’s health and access to health care services (for example, see: United Nations Human Rights 2020; Banerjee and Nair 2020; Gato et al. 2021; Jarrett et al. 2021; Santos et al. 2021). There are also a few studies on the economic impact of the pandemic on LGBTI people in the United States (US) (Movement Advancement Project 2020; Whittington, Hadfield, and Calderón 2020; Sears, Conron, and Flores 2021). The studies, which are based on US data, find that in addition to a greater risk of health complications during the pandemic, LGBTI people in the US are more likely than non-LGBTI people to live in poverty, lose jobs, experience financial constraints, and lack access to necessities. More widely, a report by OutRight Action International using qualitative in-depth interviews conducted in 38 countries concludes the same (Bishop 2020).

Nevertheless, due to the lack of available quantitative data, little is known about the magnitude of the economic impact of the COVID-19 pandemic on LGBTI people globally. Using multi-national crosssectional data from the first round of the 2020 COVID-19 Disparities Survey, this paper examines the impact of the COVID-19 pandemic and subsequent pandemic-control measures on the income, consumption, and mental well-being of individuals while accounting for their self-reported gender identity (cismen, ciswomen, transgender, and non-binary). More precisely, the paper investigates the differential economic impacts faced by transgender and non-binary populations due to existing systemic vulnerabilities combined with the COVID-19 imposed public health crisis.

The paper is organized as follows: Section 2 describes the data source and the methodology. Section 3 discusses the main findings under two headings; the first focusing on the impacts of the pandemic on income and consumption and the second focusing on the mental well-being of individuals. Finally, section 4 concludes with a brief summary and discussion of policy implications.

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Where ten years of historical data is not available at the current insurance unit level, Pure Premium Rates must be set with reference to aggregated data from the old insurance unit. If the yield distribution varies across the old insurance unit, use of data from the old insurance unit may lead to an underestimate of the expected cost of providing the product in some current insurance units. To protect against adverse selection the Pure Premium Rate therefore includes a Heterogeneity Load whenever a sufficient history of appropriate data is not available at the current insurance unit level.

Fourth the Commercial Premium Rate is calculated, giving the premium and premium subsidy rates payable by farmers and government (Section 0). The Commercial Premium Rate is higher than the Pure Premium Rate both to account for costs such as administrative and reinsurance costs, and to allow a margin against future contingencies.

# **2. Data Cleaning and De-trending**

An actuarial approach to design and ratemaking requires stability in the process for collecting data. Before the actuarial calculations described in future sections, data must be checked for any obvious errors, and trends or structural breaks caused by changes to farming practices or the recording of data. The de-trending step, in particular, requires actuarial judgment in addition to statistical analysis.

This section focuses on yield data, but the principles equally apply to other datasets. A related procedure for cleaning and de-trending weather data was suggested in Syroka (2007).

Sections 2, 3 and 4 of this paper illustrate the suggested procedure using worked examples of groundnut products to be sold across India in 2010 and cotton products to be sold in Gujarat in 2008. These examples use real yield data but district and subdistrict names are not disclosed for reasons of confidentiality.

# _Data Cleaning_

# <u>Description of step</u>

All historical yield data to be used for design and ratemaking should be collated into a standard format for use in actuarial calculations. Ten years of historical yield data is used in the procedure suggested in this paper.

Missing data should be
unknown<br>or incorrect.|Verified. When the dwell time perceived as<br>―normal‖ is higher than the actual dwell time<br>(Kenya, Nigeria), shippers are likely to exert<br>pressure to shorten it; if the perceived dwell time<br>is higher than the actual dwell time (South Africa,<br>Uganda, Zambia), there might be no pressure to<br>shorten it.<br>Communicating reliable information about dwell<br>time is key to avoid ill-adapted strategies and<br>stimulate timeperformance of customs brokers.|

_Source:_ Authors based on firm surveys.

This exercise is, to our knowledge, the first of its type and does not answer all of the questions raised in this field. However, it demonstrates the crucial importance of studying private sector practices and incentives before designing any program aiming to reduce dwell time. The assumption that ―importers are the victims of long container dwell time‖ is likely to be wrong in the case of many ports in SSA, which probably explains the multiple failures of many initiatives in this area.

This kind of study should be expanded to other countries, and some issues, such as the impact of market structure on pricing strategies or the impact of information asymmetry should be investigated further.

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)|(0.0197)|(0.0348)|
|Inflation Rate (t)|0.0226|-0.0174|-0.00993|
||(0.175)|(0.127)|(0.123)|
|Trade Openness (t)|0.107<sup>**</sup>|0.106<sup>**</sup>|0.106<sup>*</sup>|
||(0.0413)|(0.0331)|(0.0443)|
|Institutional Quality (t)|0.724|-1.534|-0.407|
||(3.495)|(2.454)|(2.291)|
|Country FE||||
|Year FE||||
|R2|0.605|0.622|0.655|
|Mean of Dependent Variable|2.858|2.899|2.899|
|Number of Countries|6|6|6|
|Observations|103|100|100|

Note: This table presents the relationship between GCC reform chatter and reform sentiment in non-GCC international media and FDI net inflows to the GCC. Coverage includes the six GCC countries between 2000 to 2020. Reform discussion and reform sentiment are in both English and Arabic and normalized to have unit variance. Trade openness is measured as the value of exports plus imports as a percentage of GDP. Institutional quality is the average of six measures from the World Governance Indicators database. Standard errors are clustered at the country level and papered in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 47
of energy type is necessary since the subsidies vary across energy sources. Furthermore, electricity subsidies are netted out of carbon subsidies. kk To calculate the fossil fuel subsidy (or negative carbon tax) per energy source, we use IEA data (for the period 2010 to 2019) and IMF data (for 2007 to 2009) on the total amount of subsidies paid for each of the three energy sources and electricity. Subsidies for electricity are described in more detail below. We use data on the total amount of emissions from each fuel type. Fossil fuel subsidies for energy type divided by total emissions from energy type provides the subsidies for energy type per ton of CO2. The kk data for 1980 to 2006 are calculated using the average share of subsidies in the world market price. kk ii The taxed quantities are the energy sources in ktoe. In order to obtain the tax per ktoe we need a conversion coefficient, denoted as . It will be in units of tons of CO2 per kiloton of oil equivalent (t/ktoe). Following (Carrara and Marangoni 2017), we obtain the following values: = 2.26 , = αα<sup>kk</sup> tt 1.75 , = 1.34 and = 0 . αα<sup>EECCbbEE</sup> kkttCCEE αα<sup>CCiiEE</sup> tt tt tt kkttCCEE αα<sup>EEbbff</sup> kkttCCEE αα<sup>rrEECC</sup> kkttCCEE

# _3.2.1.2 Constructing the energy variables used in PAKMod_

Because we want to track the carbon and other GHG emissions associated with energy production from different sources, the model needs to account for energy volumes both in terms of physical quantities as well as the more traditional national accounting volumes derived from base-year values and market or factor-price deflators. We construct the national account equivalent variables in the model using the data above.

Energy prices and the valuation of energy value added is complicated by the mechanisms by which fuels are subsidized in
# Table A1.1 - Sumary Basic **Figures** for Total Grain Mill Products. United States (1977 and 1975) Brazil and Mexico (1975). in national currency

-- _--------------------------------------------------------__--------.-------__------------------------

||Grore<br>Value<br>ofOutput|Value<br>Added<br>US Census<br>Concept|Value Added<br>National<br>Accounts<br>Concept|Value Added<br>Former<br>National<br>Accounts<br>Concept|<br>Number<br>of<br>Employeex|
|---|---|---|---|---|---|
|UNITED STATES,1977<br> <br> <br> <br> <br>||(mi<br>|llion US doll|ars)||
|2041 Flour<br>and Other<br>Grain<br>Mill<br>Products<br>|3,683.3<br>|824.5<br>|---|---|15,600|
|2046 WetCorn Milling|2,014.8|666.7|---|---|10.300|
|TOTAL|5,698.1|<br>1,491 .2|---||26.500|
|UNITED STATES. 19i5<
one-size-fits-all policies (Sturgeon and Memedovic, 2012).

# **PART 2 – MEASURING A COUNTRY’S PERFORMANCE WITH REGARD TO GVCs**

# **_The participation of countries in GVCs_**

_The input-output structure of GVCs and trade in value-added_ GVCs have an input-output structure, which describes the process that brings a product or service from initial conception to consumption and beyond. Therefore, input-output tables are the basic statistical element for the measurement of a country’s participation in GVCs.

To date, and using different methodologies, several initiatives have sought to compile inter-country input-output tables. The pioneering work in this field has been carried out by the Institute of Developing Economies (IDE-JETRO), which covered linkages between 10 Asian countries and 76 industries in intermittent years between 1975 and 2005. Similar initiatives followed. A selection of the main recent initiatives is listed below. These datasets differ in country coverage, data source (official statistics vs. other sources) and methodology:

- The World Input-Output Database (WIOD) is compiled by a consortium of 11 institutions and funded by the EU.  Based on supply-use tables from official national statistics, it identifies the input-output linkages between 40 countries (plus the “rest of the world”) and 35 industries, providing time-series over the period 1995-2009. This dataset was released in April 2013.

- The OECD Inter-Country Input-Output tables represent a similar effort. Building on the OECD harmonized input-output tables, the objective is to mainstream the activity and extend the country coverage. The tables include 57 economies accounting for 95% of world output (plus the “rest of the world”), with a breakdown into 37 industries. Five years are covered so far (1995, 2000, 2005, 2008 and 2009) but time-series will be produced. The OECD ICIO

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