rafmacalaba/data-use-mentions-extended huggingface dataset:
% prevalence of any helminthic infection among preschool aged children at baseline in Peru, with only 1.8% of children exhibiting moderate or heavy infections, while Miguel and Kremer (2004) report hookworm, whipworm, and roundworm prevalences of 77%, 42%, and 55%, with 15%, 16%, and 10% prevalence of moderate to severe infection respectively. Moreover, Miguel and Kremer (2004) use thresholds that are lower than the WHO thresholds used by Joseph et al. (2015) for moderate to severe intensity infections, suggesting that the comparable prevalence of moderate-severe infections in the Miguel and Kremer (2004) sample is lower than the figures presented here.

> 7 According to WHO growth charts, a boy at the 25th percentile of the weight for age distribution grows 1.9 kg between age 2 and age 3, while a boy at the 50th percentile grows 2.1 kg over that year. Note that the difference in weight gain for boys at the 25th versus at the 50th percentile of the weight-for-age distribution between ages 3 and 4 is also 0.2 kg (World Health Organization Multicentre Growth Reference Study Group, 2006). In our full sample, the median duration of follow up at which weight gain is measured is 1 year.

6
two effects work in the opposite direction of each other, and if we find coefficients that are significantly different from zero, this will tell us which effect dominates in the data. In fact, the second channel usually dominates: the coefficients are usually positive, meaning that better performing firms face bigger constraints. The exception also fits with the priors expressed above—poorly performing firms report bigger financial constraints.

**_International evidence._** The main results from the 51 PICS and BEEPS surveys are summarized as follows:

- Privatized and especially new private firms perceive greater business constraints practically across the board compared to similar state-owned firms in the same country.

- Foreign-owned firms also generally perceive greater business constraints, except for constraints related to finance and crime, about which they complain less than domestically-owned firms.

- Exporters state they face greater constraints from infrastructure, skills shortages and regulation (especially, and not surprisingly, customs regulations), and complain less about crime, than do comparable non-exporting firms.

- Large firms complain more about infrastructure, regulation, and skills shortages, and less about financial constraints, than do smaller firms.

11
0.501)|(0.333)|
|DTRI DR||||0.408|0.828|0.480|
|||||(0.485)|(0.235)|(0.475)|
|WEF NRI||||1.126***|1.203***|1.399***|
|||||(0.000)|(0.000)|(0.000)|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp-yr + imp-yr|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|43846|35297|37823|32354|26760|28484|
|P-R2|0.994|0.994|0.990|0.995|0.995|0.992|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the ITPD-E database. Sector groupings in each column can be found in Annex Table A3. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter-year and importer-year are applied. CB refers to the cross-border transfer of data, whereas DR refers to the domestic data processing. The variables ECIPE CB and ECIPE DR are taken for the importer’s side and are interacted with intl as part of the C ∗intlo term. The WEF NRI variable is taken for the exporter’s side and interacted with intl. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A7.

would be again reduced to only a couple of years, namely 2014-2016, and therefore these results are omitted. However, results can be
�||[0.000]|[0.321]||
|Add’l Year of MTI||-0.081|-0.001||
|with Script Change<br><br> <br>||(0.024)|(0.011)||
|�<br>_I_<sup>_MT I_</sup><br>_zy_<br>�||[0.002]|[0.894]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I−T_</sup><br>_zy_||-0.003|0.005||
|||(0.003)|(0.001)||
|||[0.318]|[0.001]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I_</sup><br>_zy_||0.005<br>|-0.008<br>||
|||(0.003)<br>|(0.001)<br>||
|||[0.062]|[0.000]||
|�<br>Years of Schoolingizy||||-0.297<br>(0.144)<br>[0.039]|
|First Stage F-Statistic||9.72||9.72|
|Number of Clusters|60|60|60|60|
|N|180,243|180,243|180,243|180,243|

_Source_ : Author’s analysis based on data from the Ethiopian census of 2007. _Note_ : The dependent variable is either years of schooling in column 2 or
**|Yes|Amount of tuition fees|
|**Vietnam**|Yes|7.89%|

_Source: Living Life data. Notes: Cost expressed as percentage of GNI per capita using the Atlas Method in current (2016) U.S. dollars._

# **3.3.2. Appeals**

The credibility of a tax appeal system is determined by the independence of the appeal body. When tax authorities are dealing with appeals, the possibility of conflict of interest is higher (Crawford 2013). In all four economies, appeals should be first submitted to tax authorities.

The timely resolution of appeals should be ensured by making the appeals process time-bound. The typical timeframe for the relevant tax authority to put in effect a decision on tax appeals ranges from 21 to 90 days, according to an OECD survey of 44 countries, including Chile and India (OECD 2009). To file an appeal, a taxpayer must submit the required documents within 30 days after a tax notice is received in Ghana and India, and within 90 days in Chile and Vietnam (Table 3.3).

**Table 3.3: Time Limits in Appeal Procedures**

|**Country**|**Time limit for filing appeals**<br>**after a tax notice is received by**<br>**the taxpayer (days)**|**Time limit for the relevant**<br>**authority to put in effect a**<br>**decision on the tax appeal (days)**|
|---|---|---|
|**Chile**|90|60|
|**Ghana**|30|60|
|**India**|30|365|
|**Vietnam**|90|30|

_Source: Living Life data_

> 15 According <u>to Ghana’
Policy Research Working Paper 8731

# **Abstract**

There are significant value chain linkages between India and Bangladesh, particularly in the textile and apparel sector. India specializes in the upstream segment, supplying such intermediate inputs as silk, cotton, yarn, and fabrics to Bangladesh. Bangladesh specializes in the downstream final apparel segment, exporting worldwide as well as to India. Tariffs and nontariff barriers in both countries inhibit the growth of value chain linkages. In addition, subsidies and other industrial policies in India distort incentives away from the natural pattern of specialization. The results of a new survey of textile and clothing firms in both countries corroborate these findings. Reforms in trade policy (including rules of origin), trade facilitation, trade-related standards, and institutions could help both countries better take advantage of value chain linkages.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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/research. The authors may be contacted at mferrantino@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
Haugh, D., A. Kopoin, E. Rusticelli, D. Turner and R. Dutu. 2016. “Cardiac Arrest or Dizzy Spell: Why is World Trade So Weak and What Can Policy Do About it?,” OECD Economic Policy Paper no. 18, September.

Hlatshwayo, S. (2018). “Unpacking Policy Uncertainty: Evidence from European Firms”, IMF Working Paper.

Hoekman, B. (2015). “The Global Trade Slowdown: A New Normal,” A VoxEU.org eBook, CEPR Press.

IMF. 2016. “World Economic Outlook: Subdued Demand: Symptoms and Remedies”, Washington, October.

Pastor, Lubos, and Pietro Veronesi (2013), “Political Uncertainty and Risk Premia,” Journal of Financial Economics, 110, 520-545.

Timmer, Marcel, Bart Los, Robert Stehrer, Gaaitzen De Vries. 2016. “An Anatomy of the Global Trade Slowdown based on the WIOD 2016 Release,” GGDC research memorandum number 162. World Bank. 2017. Global Economic Prospects, January 2017. Weak Investment in Uncertain Times. Washington, D.C.: World Bank.

17
scenario includes the implementation of trade facilitation measures that would reduce the trade transaction costs by 2% annually.

## **_US-Vietnam FTA_**

This scenario assumes that the United States signs a bilateral FTA with Vietnam in 2018. The tariff and NTM reductions in goods and services are consistent with the bilateral concessions under the TPP. Tariff commitments originate from the ITC database (MacMaps, 2016). NTMs in goods estimates are based on the ongoing work of DECTI of Kee et. al. (2016). These estimates are based on the surveys of NTMs conducted in 2015/2016. Estimates of NTMs in services are based on Jafari and Tarr (2015). We run a central scenario with the percentage NTM reductions similar to those as in Petri and Plummer (2016).

## **_Suspension of AGOA_**

This scenario studies the impact of the loss of duty-free access to the US market under AGOA in 2018. As a result, Lesotho starts paying the GSP rates on its exports to the United States and its trade weighted import duty on wearing apparel sector increases to 24 percent.

## **_Suspension of AGOA with trade facilitation_**

This scenario studies the impact of the loss of duty-free access to the US market under AGOA in 2018 as in the previous scenario, but it includes a reduction of bilateral trade costs between Lesotho and all its trading partners at 2% per year. Moïsé and Sorescu (2017) estimate that a full implementation of the WTO Trade Facilitation Agreement could reduce trade costs in Sub-Saharan Africa by 18.3%.

# 4.2. Results

Our results indicate that the negative impacts due to a sudden suspension of AGOA privileges in 2018 would have been much higher than losses resulting from the potential US-Vietnam FTA. The decline of income would reach 1% relative to the baseline in 2020, while exports of textiles and apparel would drop by 16% leading to a drop of output of textiles and apparel by 9%. Given that employment in textiles and apparel
origin-year-industry and destination-year-industry fixed effects. The results also hold in the extended sample, for which we calculated IME disaggregated at HS2 product level. As reported in the Online Appendix Table I6, this IME is also close to 0.52.<sup>12</sup>

# **IME by percentiles**

A positive IME could be due to the presence of export superstars that increase both average exports per firm and total exports for some country pairs, as discussed in Freund and Pierola (2015). We study this possibility by considering separate IME regressions for each exporter size percentile. For each origin-destination-year combination we distribute the exporting firms into percentiles based on the value of their exports. Denoting average exports per firm in percentile

> 11When we allow the IME to differ across origin countries depending on their GDP per capita, we find that IME estimates are close to or larger than 0.4 for any group of countries, as shown in Online Appendix Table I3. 12The presence of large trading firms could lead to both high exports per firm and high total exports and explain our IME estimates. While we are unable to identify large trading firms in the EDD data, we estimate the IME based on a sample including only HS 2-digit industries with low shares of firms exporting via intermediaries, as defined in Chan (2017). The results in Online Appendix Table I7 show an almost unchanged IME at 0.53.
rural towns, and peri-urban areas, excluding the primary production of crops, fish, trees, and livestock. The more organized part of the RNF sector consists of micro, small, and medium enterprises (simply termed microenterprises).<sup>2</sup> There were some 4 million rural microenterprises in Bangladesh in 2003 (a number that has certainly increased) accounting for 30 percent of overall manufacturing value-added and 70 percent of the nonagricultural labor force (World Bank, 2007). Given the scope of this sector both in terms of employment and income, growth in the nonfarm sector, especially in microenterprises, can play a significant role in furthering Bangladesh’s overall growth and poverty reduction. Growth in microenterprises seems to suffer from a variety of factors, of which lack of access to finance, infrastructure, and markets, and poor quality technology and regulatory barriers appear to be most common (World Bank, 2004; World Bank, 2007). Income from microenterprise sources, however, seems to have a greater effect on poverty reduction. For instance, using cross-sectional data from Sri Lanka, Deininger et al. (2007) find that self-employment in the RNF sector has a significant impact on poverty reduction and is not simply used by those unable to find gainful employment elsewhere. They also find that perceived constraints related to infrastructure and access to finance hinder micro-enterprise adoption.

This paper addresses the role of finance as a barrier to microfinance growth in Bangladesh. Microenterprise investment is financed largely by informal sources such as individual savings and informal loans from friends and relatives. Institutional credit can play a role, but it has until recently been marginal, as found in our analysis. With the advent of microfinance institutions, microcredit is expected to play a bigger role in supporting microenterprise development in rural areas. However, scaling-up microcredit to support progressive microenterprises with diversified loan and competitive

> 2 By microenterprise, we mean manufacturing and processing industries, transport, trade, services and other miscellaneous activities.  This does not include farm-related activities such
The backcasting exercise relies on two particularly strong assumptions.

First, the analysis assumes that inflation is fully captured by the GDP deflator and does not account for differential effects of inflation on the poor. Accelerating inflation in Nigeria in recent years has been driven disproportionately by food prices, and even poor households – many of whom are concentrated in subsistence agriculture – purchase food, so this could have uneven effects on consumption and hence alter the progress of poverty reduction (Joseph-Raji et al., 2021). Second, it assumes that there are no switches between sectors over time. While this is an important assumption, evidence on Nigeria’s labor market shows that structural transformation was slow over this period (Jenq et al., 2021) suggesting that this may not be too big of a concern for the purposes of this analysis.

While we do not address these limitations directly in the backcasting exercise, the fact that the survey-tosurvey imputations produce such similar results offsets some of these concerns. Distributional effects of macroeconomic growth are likely to be picked up – at least partly – by the non-monetary indicators collected in different waves of the GHS, which are used to impute household consumption. Similarly, some of the variables included in the imputation model – such as frequency of consumption of certain food items – would be affected by inflation and reflect the larger effect of changes to food prices among poorer households.

The main drawback of the survey-to-survey imputation exercise is the difference in data collection schedules between the GHS and NLSS. The GHS data are only available during the post-planting and postharvest seasons, which might bias our results if the indicators used in the consumption model vary significantly throughout the year and are hence different from those collected over a 12-month period in the 2018/19 NLSS. This may explain the difference between the imputed 2018/19 GHS estimate and the 2018/19 NLSS estimate. Yet, if the GHS systematically overestimate poverty rates – as also seems to be the case when comparing estimates in other waves to the backcasted series – this would
.100523 Aldoseri, A., Al-Khalifa, N., & Hamouda, A. (2023). Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Applied Science, 13(12), 7082. doi:10.3390/app13127082 Andrée, B.P.J. (2021a). Estimating food price inflationfrom partial surveys. World Bank, Development Data Group. The World Bank Group. Washington D.C. Retrieved 01 05, 2024, from https://openknowledge.worldbank.org/server/api/core/bitstreams/7bb07f51-6a93- <u>5207-a43f-eb563b24d87f/content</u> Andrée, B.P.J. (2021b). Monthly food price inflation estimates by country (Version 2023-1211). Washington, D.C., USA. Retrieved 10 11, 2023, from World Bank Microdata Library: <u>https://microdata.worldbank.org/index.php/catalog/4509</u> Andrée, B.P.J. (2022). Machine Learning Guided Outlook of Global Food Insecurity Consistent with Macroeconomic Forecasts. Policy Research Working Papers. Washington, D.C., Washington, USA. doi:10.1596/1813-9450-10202 Andrée, B.P.J., & Pape, U. (2023). Machine Learning Imputation of High Frequency Price Surveys in Papua New Guinea. 10559. Washington D.C., Washington D.C.: World Bank. Retrieved 02 01, 2024, from http://hdl.handle.net/10986/40410 the main text and all data sets can be retrieved by querying RTP (Real-Time Prices): <u>https://microdata.worldbank.org/index.php/catalog/?page=1&sk=RTP&ps=15</u> 16
Collecting information on FAFH through household surveys raises several methodological challenges, and scalable best practices have not been yet defined. A recent study that looks at the most recent nationally representative survey across many developing countries finds great variation in practices and quality of information collected (Smith et al., 2014). For example, 10 percent of the surveys analyzed do not have any reference to FAFH. Among those that do, 24 percent dedicate only one line to FAFH, and only 35 percent account for snacks. The aim of this study is to build systematic evidence by testing alternative modules and protocols, and ultimately identifying best practices for the collection of FAFH consumption in household surveys.

To test different methods of collecting this consumption, we conducted a randomized experiment in Hanoi, Vietnam, within the framework of the Vietnam Household Living Standards Survey (VHLSS). Important considerations for the design of the experiment were not only potential improvements in accuracy, but also cost and scalability. The randomized control trial (RCT) consisted of five different experimental arms. Each arm tested alternative modules and protocols addressing methodological issues not only specific to the collection of food away from home (such as _what_ to report and _who_ should report), but also from traditional sources of measurement error (such as lack of knowledge or memory). The treatment arms draw from the survey methodology and behavioral sciences literatures to encourage more accurate reporting.

Drawing on the comparison across treatment arms the following lessons emerge. First, asking about household-level FAFH in a one-line question within a larger module significantly underestimates households’ FAFH consumption (by about 33 percent). Second, the introduction of behaviorally-informed changes to survey protocols can significantly improve FAFH measurement, to the point of making it indistinguishable from our first-best benchmark measure (i.e. a heavily supervised individual-level diary) at a significantly reduced cost. In particular, we introduced an initial visit prior to the interview to mark the beginning of the recall period and make the recall period more salient. Furthermore, we introduced a tool
2000. If educational attainment data on a specific country is contained in both data sets, then we follow Bosworth and Collins (2003) and take the simple average of two data values to obtain our final measure of the average years of schooling.

Even though average years of schooling or average educational attainment is by far the most commonly used proxy of the human capital stock, there are still deficiencies with the unweighted sum of schooling years as a measure of the economically productive human capital stock. First, average educational attainment assumes that each additional year of schooling increases the human capital stock by the same magnitude, regardless of the amount of education already attained. This implies that a year of primary education tends to increase the human capital stock by the same amount as a year of tertiary education. Second, average educational attainment does not account for quality differences in the different education systems in the world. As such, it assumes that a year of education in a less developed country increases the human capital stock as much as a year of education in a developed country. Therefore, the following section focuses
Not only are health professionals not the majority of high-skilled emigrants, but they appear to have lower emigration rates on average than other skilled professionals. We compare the overall brain drain rates for tertiary educated migrants who emigrated at age 22 or higher (Beine et al, 2007) to the medical brain drain rates in Bhargava et al. (2010). Across 161 countries, the median medical brain drain rate is 5.4 percent, compared to a median skilled brain drain rate of 8.4 percent. The skilled brain drain rate exceeds the medical brain drain rate for 69 percent of the countries in this sample.

Mattoo et al. (2010) note that there is often a concern that not all educated migrants end up working in skilled occupations after they have migrated— a phenomenon which they call ―brain waste.‖ However, Table 1 shows that the most common occupations for educated migrants are skilled occupations, particularly those in the so-called STEM fields (science, technology, engineering and mathematics). Moreover, using the same 2008 sample we calculate that 79 percent of working migrants from developing countries with a bachelors‘ degree or more are working in occupations in the United States in which the majority of workers have postsecondary education, as are 90 percent of those with a masters degree or more, and 96 percent of those with a Ph.D. The stereotype of foreign workers with Ph.D.s driving taxis is certainly the exception; only 2 out of 1,936 developing country migrants with Ph.D.s in the American Community Survey sample are taxi drivers. This is in line with Mattoo et al.‘s (2010) work using the 2000 census, where they find it is mainly skilled migrants from non-English speaking countries with poor quality education systems who struggle to find skilled work—a finding which might mean that the actual skill level of these migrants is lower than their education would suggest.

# **Question 2: Why Should Economists Care About Brain Drain?**