rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7947

# **Abstract**

This paper studies the impact of deep agreements on United Kingdom–European Union trade relations. A standard gravity model is applied to assess the effect that European Union membership had on the United Kingdom’s trade. The paper uses new information on the content of trade agreements to build a measure of “depth” based on the number of provisions the agreements cover. The analysis relies on information on goods, services, and value-added trade from the World Input Output Database. Deep trade agreements are found to increase goods and services trade by 42 percent, and value-added trade by 14 percent on average. European Union membership had a particularly strong effect on United Kingdom’s services and global value chain trade. Because of its membership, the United Kingdom’s services trade more than doubled, and the country’s backward and forward participation in global value chains increased by more than 30 percent each. The paper uses these estimates to evaluate the future of United Kingdom–European Union trade under different scenarios. The findings show that United Kingdom–European Union trade declines under all scenarios, ranging between 6 and 28 percent for trade in value added. This drop is sharper (particularly for services and global value chain trade) the lower is the depth of the future arrangement relative to the depth of the European Union agreement. As trade flows adjust slowly to changes in trade costs, these effects are expected to emerge over time. But the trade-off between the depth of trade agreements and trade intensity will delimit policy choices going forward.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mruta@worldbank.org.

_The Policy Research Working Paper Series disseminates
# **Figure 2 Average MSE of Poverty Measures for LAC Countries**

<!-- Start of picture text --> 10.00<br>8.00<br>6.00<br>4.00<br>2.00<br>0.00<br>2017 2018 2019 2020<br>Old Model New - Rescaling New - No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

The difference between actual and estimated poverty gap values is presented in Figure 3. The poverty gap measures the ratio between the shortfall of the household per capita income from the poverty line and the poverty line. In general, results suggest that in the pre-COVID-19 pandemic scenario, the different 'model's variations overestimate the poverty gap. However, the magnitude varies once again with the country. Countries such as Bolivia and El Salvador have higher levels of overestimation in the poverty gap, while the measure is underestimated in Brazil.

Like in the poverty incidence case, the MSE for the poverty gap indicates that all the proposed models' variations predict better in the short run with very little difference between the _old model_ and the _new model with rescaling_ (Figure 4). This means the variations succeed at identifying the poor and estimating the intensity of poverty in the most immediate years. Yet, the performance of the _new model without rescaling_ is poorer. However, in the medium run, the less flexible variations ( _old model_ and _new model with rescaling_ ) fall behind _the new model with rescaling_ since the trend of their error increases exponentially over time. This way, both poverty headcount and the poverty gap indices suggest that, in the short run, there is little difference among the analyzed model variations. Even the simplest one (the _old model_ ) brings reliable results. Still, in the middle run, it is necessary to incorporate more flexibility in the income structure to capture the changes in poverty and
-<br>Bonus "Ayuda Familiar"<br>-<br>Transfers from “Proteger”|
|Costa Rica|-<br>Cash transfers due to COVID-19 pandemic<br>-<br>Non-cash transfers due to the pandemic<br>-<br>Transfers from "Plan Quedate en Casa"|
|Dominican Republic|-<br>Transfers from “FASE – Fondo de Asistencia Solidaria al Empleado”<br>-<br>Transfers from “Asistencia al Trabajador Independiente”|
|Paraguay|-<br>Transfers from Tekopora<br>-<br>Transfers from Pytyvo<br>No conditional cash transfers programs, this includes:<br>-<br>Transfers from “Bono Yo me quedo en casa”|
|Peru|-<br>Transfers from "Bono Independiente"<br>-<br>Transfers from "Bono Rural"<br>-<br>Transfers from "Bono Familia"|

Source: Own elaboration based on SEDLAC.

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, and food preparation services (15%; Figure A3). The sampling frame in some countries excluded micro-firms and/or businesses in agriculture (see Table A2 and Table A3) and in some countries, when micro-businesses were included, the survey instrument offered simplified versions of some questions in order to facilitate data collection.<sup>8</sup> The sampling frame in most countries where the pulse survey was not a follow-up of the Enterprise Survey was based on censuses from Statistics Agencies, Ministries of Finance or Economy, or business listings from Business Associations, and typically only included registered businesses. In the case of the Enterprise Survey, by design the implementation covers only formal firms. Only Cambodia, Gabon, Ghana, Pakistan, the Philippines, Senegal, South Africa, Sudan, and Tunisia include informal firms in their sample.

Given some of the heterogeneity related to the differences in country samples, implementation strategy, and timing of the surveys, we introduce different controls in the analysis. To control for differences in the composition of the sample, we include in the analysis dummies for size and sub-sector (10 groups), in addition to country fixed-effects. The timing of implementation of the

> 6In Brazil, the standard pulse survey was implemented not at the national level but on two representative states; Ceara, one of the poorest states located in the North-East, and S _a_  ̃o Paulo, the largest and richest of the country, concentrating almost one third of Brazil’s GDP.

> 7Source: Enterprise Surveys, The World Bank, http://www.enterprisesurveys.org. The two instruments, the standard pulse survey and the Enterprise Survey follow-up, were implemented in Togo. In Bangladesh, the standard pulse survey was implemented on different samples and at different times of the shock. The survey instrument differed across countries but in most cases the Enterprise Survey COVID-19 follow-up excludes some questions on the adjustment to employment and the channels affecting the operations of the business, the module on expectations, and
displacement, such as lack of access to education, food insecurity, and inadequate housing. Forcibly displaced individuals may be more likely to live in overcrowded houses with poor infrastructure, leading to poor health outcomes (IOM 2013). They also might have difficulty accessing social services or schooling because of a lack of identification or other barriers. These challenges also differ based on the gendered experience of the individual. Labor market integration and social integration – two major considerations for forcibly displaced communities – feature long-standing gendered concerns (Blau, Kahn, & Souza 2003, Blau, Kahn, & Papps 2011, OECD 2018). Women may struggle more than their male counterparts to find opportunities for decent work (ILO 2009). Women and girl IDPs may also become more vulnerable to other forms of exploitation and marginalization as a result of their deprivations, such as human trafficking, child marriage, and sexual-based violence (IOM 2020, Kelly 2021, Kelly et al. 2021). Despite this, to date, there have been very few studies that have looked at multidimensional poverty among forcibly displaced populations (Temgoua Noumedem et al 2020, Loaiza Quintero et al 2018), and even fewer that then apply a gendered lens.

This paper adds to the emerging scholarship by developing a tailored Multidimensional Poverty Index (MPI) for a multi-country study of refugees and internally displaced populations in subnational regions of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. The aim is to better understand differences between the forcibly displaced communities and host communities with

> [†] World Bank, Washington, D.C

> [‡] Oxford Poverty & Human Development Initiative

> [§] Inter-American Development Bank, Washington, D.C, USA

> 1 Provisional estimates from UNHCR suggest it is likely that the 80 million figure has been surpassed as of mid2020.

> 2 Throughout the paper we refer to refugees as inclusive of refugees and people in refugee-like situation, as per the classification by UNHCR.

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the International Roughness Index (IRI): very good, good, fair, poor, and very poor.<sup>13</sup> The data shows that the road quality substantially differs between different regions (Marzes). For example, as Figure 8 shows, Yerevan - the region of the capital city has only 4% of its roads in poor or very poor quality, the reported road quality is better in in Kotayk and Ararat Marzes - surrounding Yerevan - more than 85% of primary and feeder roads are in good or very good condition, while Lori and Shirak regions have only around one-fifth of the primary and feeder roads in good condition.

# **5 Methodology**

## **5.1 Empirical strategy**

The impacts of infrastructure are often challenging to capture. This paper studies the questions using several datasets and estimation methods.

The first method studies the association between regional road quality and different employment outcomes. It uses the Integrated Living Conditions Survey - 10 years of repeated cross-sectional data - to answer the research questions. The survey has collected rich data on various employment outcomes, which is not usually the case in other surveys, making it possible to look at more variables of interest.<sup>14</sup> The first part of the analysis studies the association of reported road quality leading to towns and markets, and binary employment outcomes: whether a person is employed in non-agricultural sector, in seasonal employment, and whether at least one member of the household works outside the village. The second part of the analysis studies the relationship between hours worked, agricultural employment, and road quality.

The second method utilizes the opportunity of GPS location information collected by the surveys to answer the research questions. It combines the georeferenced data on quality of roads in Armenia collected by the World Bank, matching the geographic coordinates of household cluster locations collected by the DHS. The combined dataset with geographic locations provides an opportunity to calculate the proximity to good quality roads from

> 13Road
FDI) is generally regarded as highly effective mechanism for the transfer of technology, skills, and management practices. In addition, it is less prone to volatile swings in line with global business cycles, as the foreign-held assets are in general longterm. To examine the openness of Malaysia to FDI, we use the FDI Regulatory Restrictions Index published periodically by the OECD. This index, published at the sector level for a variety of countries, should provide a natural gauge for the openness of Malaysia to FDI across sectors and over time.

The OECD separately examines four distinct measures of restrictions: i) foreign equity restrictions; ii) screening and prior approval requirements; iii) rules for key personnel; and iv) other restrictions on foreign enterprises.<sup>17</sup> We report the total index score, summing over these four measures, which is scaled between 0 (no restrictions) and 1 (foreign ownership forbidden). Figure 18 summarizes our findings over the period 1997-2012. Panel A compares the index score between the manufacturing sector and the Tertiary sector for Malaysia.<sup>18</sup> As is clear from the

> 17 Rules on state ownership are not scored, provided they are not discriminatory toward foreign enterprises in particular. For more information on the methodology, see Kalinova et al (2010).

> 18 The Tertiary sector consists of Distribution, Transport, Hotels and Restaurants, Media, Communications, Financial Services, Business Services, and Real Estate Investment.

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. The size of the bubbles is relative to the size of each group as measured with total market income plus pensions.

The Kakwani index, however, cannot tell if these and other fiscal interventions make the whole fiscal system more (un)equal,<sup>22</sup> because the effect of a tax or transfer is not independent from the effect of

> 20 The Kakawani index for all fiscal interventions analyzed is shown in the appendix Table A1.

> 21 If the concentration of direct taxes is analyzed by income deciles instead of income groups, the results are strongly consistent with the findings by Engel, Galetovic and Raddatz (1999), and Castelletti (2013).

> 22 When taxes or transfers are seen as single, independent interventions the Kakwani index is sufficient to unambiguously establish that a progressive (regressive) tax or transfer is equalizing (unequalizing). In a multi tax/transfer setting, however, this direct relationship does not necessarily hold (Lambert, 2001). The Kakwani (1977) index for taxes is defined as the

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that even for a developed country like the United States, consumption is a better measure especially for those at the lower end of the distribution.

In this paper we use expenditure as a basis for poverty and inequality measurement but also examine the income data and compare trends using both income and consumption measures.

Whatever welfare indicator is chosen, it is necessary to aggregate information provided at the household or individual level. The main guiding principle for the welfare aggregate is to make it as comprehensive as possible, but minimizing measurement errors through inclusion of components which may be noisy. For example, food consumption, typically, includes own production, transfers and food eaten outside home which pose challenges during the data collection phase. Nonfood consumption usually excludes lumpy infrequent expenditures (e.g. funerals, weddings), investment and items which are hard to estimate accurately (public goods). Durables require a special treatment because they are bought at a particular point in time, but consumed over a period of several years. Therefore, consumption should include only the annual use value of a durable rather than purchase value. The same principle should be applied to measure the value of housing services (Deaton and Zaidi 2002, Haughton and Khandker 2014).

There is no consensus in the literature on whether health and education expenditures should be included. One argument to exclude health expenditures is because they may reflect a regrettable necessity and do not contribute to welfare (e.g. expenditure to treat cancer). In contrast, preventive care expenditure clearly contributes to higher utility and welfare, but practically it is often difficult to distinguish between them. Education expenditures are another controversial item because they are associated with investment in the future rather than current consumption. Nevertheless, this component is often included in the welfare aggregate following standard national income accounting practice (Deaton and Zaidi 2002).

Having constructed the welfare aggregate, there is a need to make additional adjustments to facilitate ranking of individuals or households. One of the most important adjustments is spatial and inter-temporal deflation to
In terms of our variable of interest (age), older individuals are more likely to believe that combating climate change should be prioritized over economic growth, but they are also less willing to pay higher taxes or give up a part of their income to achieve that objective. The positive correlation between age and beliefs that climate action should be prioritized at the expense of economic growth is robust to the inclusion of other determinants of climate beliefs. In fact, when all the additional confounding variables are included in the model, the marginal effect of age increases relative to the baseline specification. In the case of willingness to pay for climate action, the negative association with age is robust to the inclusion of most confounding variables but becomes insignificant once we account for differences in risk aversion in the specification that includes all the confounding variables.

# **6. Sensitivity analysis**

This section examines the sensitivity of our results to the potential bias introduced by the endogeneity of climate beliefs in our climate action regressions, as well as the sensitivity of the age gradients to alternative framing of the climate action questions.

# _Addressing the endogeneity of climate beliefs_

One concern with the results presented in <u>Table 5 has to do with the possibility that perceived risks</u> of climate change for one’s self or children could be correlated with unobserved characteristics of respondents that affect their willingness to pay for climate action or their beliefs about the tradeoff between climate action and economic growth. For example, political conservatism, which we do not capture with LiTS data, could be correlated with less pessimistic views about the risk of future climate impacts and, at the same time, a lower willingness to support climate action, either politically or financially.

To account for such unobservable factors, we instrument the perceived risks to one’s self or children with past exposure to climate shocks based on the question “ _Have you personally experienced disruption or damage due to flooding, drought, or other natural disasters?_ ” Regressing the perceived risk for
Rogoff, K. 2021b. “Risks to Central Bank Independence.” in Independence, Credibility, and Communication of Central Banking, edited by Pasten, E. and R. Reis, Central Bank of Chile.

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Wheeler, C.M., J. Baffes, A. Kabundi, G. Kindberg-Hanlon, P. S. Nagle, and F. Ohnsorge. 2020. “Adding Fuel to the Fire: Cheap Oil During the COVID-19 Pandemic.” Policy Research Working Paper 9320, World Bank, Washington, DC.

Wong, B. 2015. “Do Inflation Expectations Propagate the Inflationary Impact of Real Oil Price Shocks? Evidence from the Michigan Survey.” Journal of Money, Credit and Banking 47(8): 1673-1689.

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sup>16</sup> and the disposable income constructed from the ECH microdata. Finally, the scaled-down costs are divided between the users of the in-kind benefits.

In addition, administrative data and indicators from public programs were publicly available and used as sources to model how each fiscal policy would be affecting individuals and households. This information includes tax brackets, tax refunds, rules on contributions to Social Security, and the functioning of direct and in-kind transfers. As for the aggregate data, it mostly comes from _Rendicion de Cuentas_ (Ministry of Economy and Finance), although some specific information was drawn from each individual source (INEFOP, BPS and MIDES).

Before turning to the results, a few points are worth highlighting. The methodology allows to make very detailed assignments of different policies. For instance, it provides a framework to map the income, type of occupation and family structure in calculating an individual’s personal income tax and deductions. However, the results only reflect a static effect of adding (subtracting) taxes

> 14 Nevertheless, this is still an estimation, and might differ from other calculations. Please refer to Appendix II.

> 15 The estimation of taxes and contributions made by workers are based on the procedure followed by OPP (Simulador de Políticas Públicas versión 2016.0.0, Oficina de Planeamiento y Presupuesto, Presidencia de la República) for 2016. The estimation was adapted to 2017, and this was made with invaluable support from the OPP team.

> 16 Income aggregates for 2017 were estimated by the authors using an extrapolation from macro indicators published by the Uruguayan Central Bank.

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about 1-1.5 hours (per person) to administer. The household questionnaire included modules on (i) basic demographic characteristics, (ii) economic participation, (iii) housing conditions, (iv) asset ownership, and (v) participation in the 4P program. The individual questionnaire includes modules on (i) decision making, (ii) time use, (iii) self-efficacy, (iv) well-being, (v) social capital, (vi) attitudes towards gender norms, violence against women and violence against children, (vii) pregnancy history, (viii) exposure to genderbased violence, and (ix) exposure to transactional sex. The last three modules are only administered to the women in the sample. The sub-sample of female respondents was randomly split in two groups for modules (viii) and (ix): half of the sample was interviewed using Face-to-Face Direct Questioning (FTFDQ) technique, another half responded to the same question via Audio Computer Assisted Self-Interview mode (ACASI). This was done to account for any social-desirability biases that may creep in while answering questions on socially sensitive topics like GBV and transactional sex in-person or face-to-face. Alternatively, the ACASI method, where respondents provide answers through a phone/tablet and not to the enumerator directly, helps in minimizing human interaction; thereby, increasing the respondents’ perception of privacy protection and inducing self-disclosure. However, we do not find differences in the prevalence rate of violence as reported via FTFDQ versus ACASI in our sample; therefore, we pool the responses in the current analysis, but control for the type of the interview conducted.<sup>10</sup> While collecting GBV data, we closely followed data collection protocols prescribed by the World Health Organization (WHO 2007), such as working with same-sex enumerators, providing specialized training to enumerators, developing protocol for potential interruptions, and providing information on support/referral services for GBV survivors. We received research ethics review approval from Health Media Labs IRB.

Further, we