rafmacalaba/data-use-mentions-extended huggingface dataset:
barriers and quantify their aggregate effects using the DBI as their source. On the measurement front, we contribute to this literature by providing a complementary metric of entry taxation that is based on equilibrium conditions and is disciplined by the cross-country distribution of average firm sizes. As we show below, this complementary metric depicts a different picture about the degree of entry barriers, albeit for a narrower set of countries. On the quantitative side, by studying entry barriers and idiosyncratic distortions jointly, we can establish the relative importance of these sources of inefficiency.<sup>10</sup> Our work builds on and contributes to the idiosyncratic distortions literature. From Hsieh and Klenow [2009], we inherit the inference strategy of these distortions. An important feature of this strategy is the independence of the distribution of wedges from aggregate equilibrium variables in the economy. This independence, which emerges from the definition of misallocation as dispersion in marginal revenue products relative to the average return in the industry, enables us to estimate the elasticity between TFPR and TFPQ directly from the data and to devote the model’s equilibrium condition to pin down the entry barrier.

Our work also contributes to Hsieh and Klenow [2009] in proposing a rich theory of the size distribution where average size is indeed responsive to idiosyncratic distortions. Recall that in its most basic form, Hsieh and Klenow [2009] reads idiosyncratic distortions taking as given the firm size distribution, but the mechanisms in the theory are such that the average firm size is unresponsive to the existence of idiosyncratic distortions.<sup>11</sup> Thus, to be able to connect distortions to cross-country differences in average firm size, we need to construct a richer theory of the firm size distribution. Our model enriches

> 10Boedo and Mukoyama [2012] is an exception in the sense that it studies entry barriers in the context of a particular type of allocative distortions: firing taxes. Poschke [2010] also considers an interaction between entry barriers and mark-ups through a reduced-form relationship between the
<!-- Start of picture text --> Singapore<br>United Arab Emirates<br>Chile<br>Malaysia<br>Colombia<br>Kenya<br>Rwanda<br>Tanzania<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>Mobile money account (%, age 15+) Credit card ownership (%, age 15+)<br><!-- End of picture text --> _Source:_ Findex 2017.

Mobile money services are playing a big role in promoting the digital economy in developing countries. Alibaba’s Alipay and Tencent’s WeChat Pay are underpinning the tremendous e-commerce market in China. M-Pesa, Kenya’s leading mobile payment service provider, has partnered with PayPal and Singapore’s TransferTo to increase access to the global marketplace for millions of Kenyan businesses. A vast majority of the unbanked population who were excluded from the traditional financial system are now able to access financial services, including payment. In Cambodia, through a network of 60,000 agents, including local franchises, street vendors, and cafés, Ascend Money is allowing more people to participate in digital transactions.

Firms benefit from a significantly reduced transaction cost through mobile money services. In contrast to traditional credit/debit payment model under which numerous players each peel off a layer of the processing fee, merchants now need only pay a low processing fee to the nonbank mobile money service providers. For example, merchants pay a transaction fee of 0.55 percent on the flip side for every purchase under Alipay, whereas they would be charged 2.75 percent under the credit/debit card model.<sup>74</sup> Reduced transaction costs, greater liquidity, and increased credit worthiness are associated with mobile money activities.<sup>75</sup> Regulations have not caught up with the rapid development of mobile money activities.<sup>76</sup>
|Cross-sectional:<br>demographic<br>and<br>reproductive health<br>surve|General population|1|1|1|1|1|1|1|1|11|
||||y|||||||||||
|**9**|Technical<br>report|Unpublished|Cross-sectional:<br>KAP survey|MSM|1|0|1|0|1|1|0|0|6|
|**10**|Technical<br>|Technical<br>|Cross-sectional:<br>KAP survey|Youth|1|1|1|1|1|1|0|0|9|
||report|publication|Case series|Female sex workers|1|0|0|0|1|1|0|0|4|

KAP = Knowledge, attitudes and practices 47
we have consistently employed thus far. However, the aggregation ρρ of scores from different modes is different from the aggregation of measure scores because in any sector, one mode of supply will invariably be more important than others so that the symmetric treatment of measure scores is no longer appropriate for this step of the aggregation.

Finally, to complete the aggregation of scores from the level of individual measures to the modes and the subsector, we then compute STRI at the sector and economy levels, respectively. We do this using the weights provided in Table A6.1 of Annex 6 in Borchert et al. (2019).

Overall, the new aggregation methodology offers conceptual as well as practical improvements. Its modular structure affords the flexibility to accommodate a range of different tasks in one framework, from the aggregation of measures that are nearly additive to those whose incremental impact on restrictiveness is almost negligible. At the same time, the approach is extremely parsimonious and fully transparent. In addition, STRI scores thus constructed are reproducible, implying that comparable scores for new economies or sectors can be obtained as the STPD is continuously being expanded.

These improvements imply, however, that the new STRI scores are not directly comparable to the STRI scores published in the 2008-11 World Bank Database because of changes in methodology and sectoral coverage, respectively. A detailed comparison and a full decomposition of changes in STRI scores relative to the World Bank 2008-11 STRI is provided in Borchert et al. (2019). Both STRI data sets are publicly available from the I-TIP services portal.<sup>12</sup> We emphasize, however, that a comparison of policy restrictiveness over time using the World Bank STRI (as in Section 3 of the paper) requires a separate set of _comparable_ scores. Backward compatibility requires two steps: first, the 2008-11 STRI needs to be re-computed using the improved scoring and aggregation methodology. Second, the 2016 STRI needs to be purged of measures and subsectors, respectively, that were not part of the 2008-11 STRI.
donors for procurement of works, goods and services. Mean use of procurement systems in the main sample of observations is 37.7%.

The PDMS data form an unbalanced three-dimensional panel: surveys were conducted in 2006, 2008 and 2011, and were designed to obtain information on aid flows for the previous calendar year (2005, 2007 and 2010, respectively) for each donor-recipient pair. In each successive survey round, the comprehensiveness and quality of data were improved. The number of recipient countries choosing to participate in the survey increased from 34 in 2006, to 54 in 2008, and 78 in 2011. These 78 recipients account for about three-fourths of total aid worldwide (OECD, 2011a). Guidance on definitions was strengthened and comparability of data reported by donors and by recipients was enhanced in later rounds (OECD, 2011a; OECD 2008).

Coverage of aid within aid-recipient countries is comprehensive with respect to donors. Most of the “emerging” bilateral donors and a few of the smaller multilateral donors and vertical funds are dropped from the analysis, due to missing data on other donor variables from the DAC database. Dropped donors collectively account for a total of only 1.4% of aid reported in the survey. The 34 donors remaining include the 22 traditional DAC donors, Korea (a new DAC member in 2011), Turkey, 7 multilateral organizations, and 3 vertical funds.<sup>4</sup> The panel is unbalanced in multiple respects. Not all recipients are included in all three years. In addition, in each survey year where aid flows for a donor-recipient pair are 0, the dependent variable is undefined so the observation is treated as missing. These features of the data create potential selection problems, considered in more detail below.

_Independent variables_

> 4 Multilaterals in the sample include the African, Asian and Inter-American Development Banks, the World Bank, IMF, EU and UN.  Vertical funds include the GAVI Alliance, IFAD, and the Global Fund to Fight AIDS, Tuberculosis,
Policy Research Working Paper 10176

# **Abstract**

This paper investigate the consequences of forced displacement using a panel of households that were surveyed during the Republic of Yemen’s conflict both before and after they became displaced. It demonstrates that forced displacement resulted in an immediate but temporary decline in food access. Pre- and post-displacement food access outcomes were indistinguishable within four months of displacement and, for later months, there were no economically large declines in food access. The quick rebound is partially explained by an increase in assistance to displaced households that had worse food access prior to displacement. Households that were slightly better off prior to displacement did not receive an increase in assistance. These are the first estimates that directly address how non-security dimensions of well-being change immediately following forced displacement and demonstrate that, in some contexts, forcibly displaced households are more resilient than is typically assumed.

This paper is a product of the Poverty and Equity 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/prwp. The authors may be contacted at standon3@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
transfers (FODES) per capita received in t -1<sup>11</sup> The econometrics model includes municipality and year fixed effects. Thus, the empirical analysis explores the relationship between individuals’ attitudes and deviations of the municipality income from the national average. Since the treatment comes at the municipality*year level the error term, ε( _m_ , _t_ ), is allowed to be correlated within a municipality*year. I also perform a heterogeneity analysis by restricting the same across different groups based on rural-urban status and weak vs strong state capacity municipalities.<sup>12</sup>

# **4 Results**

I organize results in two parts. First, I analyze data from LAPOP for the time period 2004-2018. Second, and for the same time period, I analyze data from Latinobar ́ometro. As discussed above the main difference between the two surveys is that the latter is exclusively urban while the former interview individuals in both urban and rural areas. In spite of that, the two samples do not appear to substantially differ in terms of other observable demographic characteristics.

I use LAPOP to analyze the link between income at the municipality-year level and individuals’ attitudes towards the president of El Salvador and the municipal government. I also study how attitudes towards other institutions such as national legislature, judicial system, mass media, and the Catholic Church vary with the business cycle. Finally, I explore whether these empirical associations mask any heterogeneity based on location characteristics (i.e., rural vs urban and strong vs weak state capacity of the municipality).

Finally, I use Latinobar ́ometro to explore the link between income (proxied by NTL density) and perceptions of fairness in income distribution, generalized trust, views on country’s economic situation, and attitudes towards democracy. I also perform a heterogeneity analysis based on the strength of municipal state capacity.

## _4.1 LAPOP Analysis (2004-2018)_

Table 3 presents the main results on the relationship between income
Promotion, Centers for Disease Control and Prevention, DHHS.

- ———. 2010. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for SmokingAttributable Disease; A Report of the Surgeon General. Atlanta: Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, DHSS. https://www.ncbi.nlm.nih.gov/books/NBK53012/.

- Doll, R., and A. B. Hill. 1956. “Lung Cancer and Other Causes of Death in Relation to Smoking.” _British Medical Journal_ 22 (5001): 1071–81.

- Fuchs, A., and F. J. Meneses. 2017a. “Regressive or Progressive? The Effect of Tobacco Taxes in Ukraine.” December 27, World Bank, Washington, DC.

- ———. 2017b. “Are Tobacco Taxes Really Regressive? Evidence from Chile.” World Bank, Washington, Gerasimenko N, Demin A. Tobacco Policy and Politics in Russia. Moscow, Russia: Public Health Association, 2001.

- Goodchild, M., N. Nargis, and E. Tursan d’Espaignet. 2018. “Global Economic Cost of SmokingAttributable Diseases.” _Tobacco Control_ 27 (1): 58–64.

- Global Adult Tobacco Survey (GATS) 2016, Russian Federation Factsheet:

<u>http://www.who.int/tobacco/surveillance/survey/gats/rus_factsheet_2016.pdf?ua=1</u> GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool Guindon, G. E., P. Tobin, M. Lim, and D. Boisclair. 2007. “The Cost Attributable to Tobacco Use: A Critical Review of the Literature.” Tobacco Free Initiative, World Health Organization,
exports market for the first time. For instance, it may be necessary for the exporting firm to set up a marketing department to investigate marketing channels, meet export orders etc. It also seems likely that the quality of the investment climate has a bearing on the magnitude of entry costs, but we have no rigorous empirical evidence to support this assertion. While entry costs are typically not observed, Roberts and Tybout argue that indirect evidence of costly entry can be obtained by testing for an effect of previous exports status on current status. The idea is that, in the absence of entry costs, firms will switch in and out of the exports market independently of whether they have exported in the past. If there are significant entry costs, however, then firms that have incurred these costs in the past (and thus will not have to incur them again) will be more likely to export in subsequent periods than firms that have not, simply because exporting is less costly for the insiders than for the outsiders. In their empirical application, which is based on data on manufacturing plants in Colombia, Roberts and Tybout find strong evidence that insiders are more likely to export than outsiders. This indicates that entering the exports market is associated with significant fixed costs.

Bigsten et al. (2004) carry out a similar analysis based on firm-level RPED data from Ghana, Cameroon, Kenya and Zimbabwe from the early 1990s. Controlling for a number of other factors including firm specific time invariant heterogeneity, they find that past export status has a significant effect on the propensity to export. The magnitude of this effect is large: for the “average” firm that did some exporting in the previous period, the likelihood of exporting in the current period is about 0.57 while the likelihood of exporting for an otherwise identical firm that did not export in the previous period is 0.18. Thus, for a non-exporting firm with the average characteristics, entering the export market raises the probability that this firm will export in the next period
citing different sources, refers to the quite critical deficiencies in Eyptian physical facilities, including inadequate laboratory equipment, problems of maintenance, and underutilization of sophisticated equipment because faculty are inadequately trained. In the poorer countries, supplies of imported equipment, arparatus, and spare parts depend on the government authorities in charge of allocating foreign-exchange.

A review of available figures on Arab university library collections in Annex I reveals a wide range of acquisitions. This varies from an impressive collection of 1.1 million in King Saud University, I million in Cairo University, and 920,000 in University of Helwan to 92,000 in Ain Shams University (which has the highest enrollment of all universities). The universities of Basrah, Jordan, Kuwait, King Abdul Aziz, and the American University of Beirut have collections exceeding 300,000 volumes. Some of the provincial universities like Al-Mansoura in Egypt, Al-Fateh in Libya, and Al-Baath in Syria do not seem to have adequate libraries. A number of the universities did not provide figures for their library collections.

Special mention has to be made of efforts to modernize certain libraries in the oil-rich countries -- particularly in Saudi Arabia and Kuwait. Safi (1986) argues that the twelve libraries of Kuwait University should be integrated into one central library for better service of the University community. But, as they are, Kuwait University libraries have an online access for databases through the Kuwait University computer services system. In addition, the National Scientific and Technical Information Center (NSllC) in Kuwait had nine regional universities and many research centers among its participating libraries in 1983. With online catalog and a machine-readable database of book collections. It utilizes an information storage and retrieval IBM system called srAiRs and was, in 1983, developing a version that allows user retrieval of Araoic documents using Arabic commands. In 1983 a program was launched in NSlC to catalog and store online all Arabic material pertf,ining to Kuwait (Khalid, 1983).

Another bright library spot is in the University of Petroleum and Minerals (
## **Figure 6: STRI by subsector and mode of supply, 2016**

<!-- Start of picture text --> Mode 1 Mode 3 Mode 4<br>Notes: Mode 1 is not computed for Road, Telecom, Maritime cargo-handling, storage, etc., Legal host country advisory and<br>representation services. Mode 4 primarily reflects entry barriers such as quantity restrictions and labour market tests for the<br>supply of services by natural persons - especially with respect to intra-corporate transferees (ICT).<br>70<br>60<br>50<br>40<br>STRI 2016<br>30<br>20<br>10<br>0<br>Wholesale Retail Telecom Insurance Commercial banking Road Maritime Air Rail Accounting Auditing Legal<br><!-- End of picture text -->

# **3.2  Evolution of services trade policy since 2008-11 (55 economies)**

The purpose of this section is to chart the evolution of services trade policies in recent years. This analysis is conducted on the basis of a similar set of subsectors and modes (i.e. excluding maritime services, internet and wholesale distribution) and for the same economies (data for 55 economies are available for both 2008-11 and 2016). The information used for 2008-11 is drawn from the World Bank Services Trade Restrictions Database, with some adjustments as explained in Borchert et al. (2019).<sup>17</sup> Regulatory information in the 2008-11 series pertains to 2008 for all economies covered except for Brazil, China and Mexico, respectively, for which data were updated in 2011. Annex 3 describes the difference between the 2016 scores for all the subsectors and modes available in the new STPD (analyzed in the previous subsection) and the 2016 scores for the subset of sectors and modes for which data were collected in 2008-11 (i.e. those described in this subsection). In
Table 2: Change in selected aggregate variables due to Trade reform and water reform, Economy-wide CGE results, in millions of Dirhams (MD)

|Item||% change|from base<sup>a</sup>|
|---|---|---|---|
||Base (mil-<br>lion Dh)|Trade<br>Reform|Water<br>Reform|
|Real GDP|323,781|1.54|0.17|
|Real exchange rate|1.00|11.65|-0.12|
|Consumerprice index|1.00|-5.62|-0.08|
|Total exports|90,603|12.01|0.32|
|Total exports with EU|58,333|11.26|0.43|
|Total exports with rest of the world|32,270|13.35|0.13|
|Agricultural exports|14,963|35.93|2.73|
|Agricultural exports with EU|9,498|38.53|3.30|
|Agricultural exports with rest of the world|5,465|31.40|1.73|
|Nonagricultural exports|75,640|7.28|-0.15|
|Nonagricultural exports with EU|48,835|5.96|-0.13|
|Nonagricultural exports with rest of the world|26,806|9.68|-0.20|
|Total cropoutput from allperimeters|6,471|2.28|7.54|
|Total cropoutput from all non-perimeters|27,160|-1.04|-0.27|
|Total agricultural output from all non-perimeters|60,711|-2.63|-0.06|

aBase is normalized to 1.

22
Head and Mayer (2014) explain that the micro-foundation of gravity equations in trade contributed to the extensive use of these models for a range of other bilateral flows and interactions.

We proceed by specifying our gravity-type relationships between (backward and forward) GVC participation, skills, migration and other control variables such as average distance to global GVC hubs. These gravity-type relationships will then be estimated using fixed-effect estimation techniques to understand the determinants of manufacturing GVC participation in Sub-Saharan Africa. Following Kowalski et al. (2015), we define the backward and forward GVC participation equations for country c in sector s at time t as: Here, we decompose our explanatory variables into two. The first set of variables in matrix X are country-sector specific factors. These factors are skilled and unskilled-labor content in exports all at sector level by country. The second set of variables are country specific factors such as average distance to global GVC hubs, GDP per capita, natural resource endowment, population, net migration as a share of total population, human capital index, fragile and conflict situation (FCS), and net remittances as a share of GDP. Although we are interested in estimating the role of skills and migration in GVC participation in Africa, we include two important controls to increase the robustness of our estimates. The first is natural resource endowment. In the African context, natural resource endowment is an important determinant of a country’s comparative advantage in trade and can potentially stimulate or crowd-out country participation in manufacturing GVCs. We obtain data on natural resource endowment from World Bank’s WDI and use it to classify SSA countries into three groups base on their natural resource endowment.<sup>4</sup> The second important control variable is location/remoteness given the “gravity nature” of global trade. According to

> 4 Using the classification in Allard et al. (2016), SSA countries are first divided into 18 countries that are natural resource-rich and 22 countries that are not. The natural resource-rich countries are subsequently divided