rafmacalaba/datause-encoder-data huggingface dataset:
while simulations in this paper suggest that the poor earn a far greater share of the total increase in incomes from tourism.

These findings strongly suggest that the tourism sector must be seen as an important sector in the efforts of Panamanian authorities to reduce poverty in this highly dual economy. But as suggested by the global experience, poverty benefits from the tourism sector are not automatic, and whether or not they receive a significant share of the benefits depends on the way the supply chains are structured and the way tourists spend their money.

Given the importance of the tourism sector and its potential to result in large benefits for the poor, devising a set of indicators and effective monitoring schemes is of paramount importance. These indicators fall into three categories: (1) accurately measuring the flows of tourist expenditures to provinces and to the indigenous areas _comarcas_ , which was so far not possible with the available data; (2) measuring the impact of tourism on local employment and wage levels at hotels, restaurants and other tourism-related enterprises; (3) measuring the extent to which tourism establishments affect local food and beverage production and devising complementary projects to enhance agricultural productivity and improve access of local, particularly smallholder and poor farmers, to the supply chains; (4) stimulating production and availability of quality handicrafts and artisan products in areas with high potential for these industries and measuring impacts of tourism on these sub-sectors; (5) measuring availability of infrastructure availability and services to assess the degree of connectivity to markets, and the ease of travel within the country’s more peripheral areas.

The household level data and data from tourism surveys (Satellite Tourism Accounts) that are needed for monitoring local impacts of tourism growth are already available in Panama. This paper has identified the data needs in order to carry out a more comprehensive and accurate assessment of local impacts of aggregate growth in tourism expenditures—survey data at the level of tourism destinations. No data are available to help determine which specific types of employment and specific subsectors of the economy the incremental income from the tourism sector accrues to. For example, it is not possible to distinguish how much of the increased demand for agricultural products and services is met through local providers, and how much is imported from outside the region. Availability of such data would greatly enhance the possibility of accurate monitoring of local impacts of tourism development.

17
# **Table A8: Effects of Workerʹs Own Future and Past Illness on Labor Supply**

|Dependent Variable|1(Work)|Days worked|Hours worked|
|---|---|---|---|
|_Panel A: The worker him/herself ill next week_||||
|Self ill next week|‐0.014|‐0.656|‐6.483|
||[0.110]|[0.678]|[5.718]|
|Self next week X Female|‐0.048|0.047|3.294|
||[0.132]|[0.902]|[7.434]|
|Other worker ill|0.114***|0.522*|5.180**|
||[0.042]|[0.275]|[2.588]|
|Other worker ill X Female|‐0.149***|‐0.610|‐6.251|
||[0.055]|[0.390]|[4.060]|
|Mean dependent variable|0.806|4.274|37.61|
|Observations|3,514|3,514|3,514|
|R‐squared|0.492|||
|_Panel B: Self ill last week_||||
|Self ill last week|‐0.009|0.086|0.830|
||[0.100]|[0.676]|[5.479]|
|Self ill last week X Female|‐0.032|‐0.326|‐2.396|
||[0.113]|[0.877]|[7.699]|
|Other worker ill|0.081*|0.402|5.043*|
||[0.045]|[0.269]|[2.600]|
|Other worker ill X Female|‐0.128**|‐0.583|‐6.739*|
||[0.057]|[0.388]|[4.004]|
|Mean dependent variable|0.820|4.345|38.170|
|Observations|3,496|3,493|3,493|
|R‐squared|0.529|||

_Notes: Other worker ill = 1 in a given week if another household member reported missing work that entire week (if s/he was planning to work that week) and the reason for missing work was injury/illness. Self ill next week/previous week = 1 if that worker reported missing work that entire next week (if s/he was planning to work that week) and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Regressions include individual fixed effects and controls for the number of household members planning to work in that round interacted with gender. Column 1 is estimated by OLS and columns 2 and 3 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
countries, with only a few being applied bilaterally.<sup>10</sup> The database is based on the WTO notifications available from the Integrated Trade Intelligence Portal (I-TIP) for more than 100 importers and over 5,000 products for the period 1995-2019, complemented with data from Bown (2016).<sup>11</sup> This dataset simplifies econometric analysis because it has been enriched by imputing missing product codes at HS 6-digit level. The final dataset provides information on (i) the concerned country and the country implementing the measure, (ii) the year the measure was introduced, (iii) the affected HS 6-digit product, (iv) the nature of the measure (e.g., SPS, TBT, quantitative restrictions<sup>12</sup> ), and (v) where applicable, the actual date the measure was removed. Using this information, we calculate the count of SPS and TBT measures imposed by a destination country to exports of HS6-digit products from an origin country. This count proxies regulatory intensity with regulatory burden increasing with increasing count of NTMs.

# **3.1.1** **_Stylized facts on non-tariff measures_**

Over time, the use of NTMs to regulate trade flows has increased. The number of countries imposing an NTM, whether SPS and/or TBT measures, either unilaterally or bilaterally on exports from Burkina Faso, Malawi, and Senegal has been on a steady increase since 2000 (Figure 2a). The number of unique HS6-digit products that attract an NTM has also been rising since 2003. We observe a similar pattern for both SPS and TBT measures and in both the agriculture and manufacturing sectors (see Figure A2 ). This contrasts with the use of tariffs which have been on a downward trend over a similar time frame (see Figure 4).

When it comes to the use of NTMs to regulate trade, there is a clear divide between developed and developing countries. Often, the higher the income level of a country, the lower the level of tariffs and the higher the likelihood of making extensive use of standardlike NTMs (World Bank, 2020; Garcés and Vogt, 2024). We observe a similar pattern in our dataset: developed countries—measured in terms of their gross domestic products—are more likely to use standard-like NTMs as market access tools (see Figure 2b).

> 10Using NTM data from multiple sources, Garcés and Vogt (2024) review the intensity, prevalence and structure of NTMs globally and confirm that the majority of technical measures are formally applied in a non-discriminatory fashion across trading partners.

> 11This dataset contains information on antidumping, countervailing, and safeguard measures for about 30 countries. The data is available at `https://www.worldbank.org/en/data/interactive/2021/03/02/ temporary-trade-barriers-database`

> 12We limit our focus to standards and technical regulations, that is, SPS and TBT measures. At this stage, we only lose 1% of the observations when we do this. TBT measures make up 66% of the sample observations and SPS measures make up 32.74%. With the 1% we exclude made up of non-technical measures such as pre-shipment controls, quantity controls, and price controls.

7
Policy Research Working Paper 10374

# **Abstract**

A 2021–22 survey of trade promotion organizations conducted by the World Bank to understand how the COVID-19 pandemic affected their functioning suggests four main findings. First, trade promotion organizations in high-income countries are larger than those in low-income countries, suggesting a stronger capacity to adapt during the pandemic. Second, trade promotion organizations in high-income countries saw a significant jump in their median budget in 2021 indicating a strong response effort. In low-income countries, the budgets of trade promotion organizations declined. Third, most trade promotion organizations in high-income countries put in place a COVID-19 recovery plan, while none of the trade promotion organizations in low-income countries had one in place at the time of the survey. Fourth, trade promotion organizations used several virtual tools during the pandemic, such as business-to-business matching events and training of small firms on e-commerce, and are expecting to increase the use of all virtual tools post COVID-19. Trade promotion organizations’ use of virtual tools in low-income countries remained limited..

This paper is a product of the Development Research Group, Development Economics and the Finance, Competitiveness and Innovation 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 agrover1@ifc.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
finds that only 25% of the rise can be explained by such an intersectoral reassignment. Similarly, Goldberg and Pavcnik (2003) find that when decomposing the change in the share of informal workers in total employment between 1987 and 1998 into within and between industry shifts, eighty-eight percent of the increase in the informal employment in Brazil stems from movement of workers from formal to informal jobs _within_ industries.<sup>11</sup> Hence, the source of the documented trend is largely working through the composition of subsectors of workers, formal and informal, as opposed to the structure of the economy.

# **IV. Gross Flows of Workers**

The analysis of gross worker flows in Brazil is rendered complicated by the substantial macro volatility just documented that accompanied the secular tendencies that are our primary interest. In particular, it is difficult to know whether we are seeing a change in how the labor market adjusts to shocks due to micro economic reforms, or the secular adjustment to a new macro policy regime. In what follows, we will attempt to tease these apart. Generally speaking, we find patterns of overall gross flows of workers and their complement, duration within sectors, that are consistent with previous work in Mexico and, in many cases, the OECD, that suggest that the informal sectors behave much more like alternative modes of employment than unemployment. However, there is strong evidence of a sharp change in the early 1990s.

To understand both cyclical and secular movements, we study the transition of workers among the distinct various sectors of work. The transition probabilities among sectors are generated, as in Bosch and Maloney (2006) by assuming an underlying continuous time Markov process can be estimated from the discrete transition data. The details on the estimation process are in Annex 1.

> 11 Similar results are reported by Bosch and Maloney (2006) for the Mexican case.

10
**<u>Table 2</u>** <u>: Heterogeneity in Effects of Life Expectancy at Birth on Schooling</u>

|||Ma|les|||Fem|ales||
|---|---|---|---|---|---|---|---|---|
||I|II|III|IV|I|II|III|IV|
|Life Expectancy at Birth|**_0.098***_**<br>**_[0.015]_**|**_0.082***_**<br>**_[0.013]_**|**_0.084***_**<br>**_[0.009]_**|**_0.107***_**<br>**_[0.028]_**|**_0.157***_**<br>**_[0.018]_**|**_0.141***_**<br>**_[0.018]_**|**_0.135***_**<br>**_[0.014]_**|**_0.157***_**<br>**_[0.032]_**|
|% Urban||5.679***|5.766***|5.958***||4.249***|4.351***|4.737***|
|||[1.860]|[1.911]|[1.155]||[0.845]|[0.927]|[0.645]|
|% Male|||||||||
|Parents Life Expectancy|||-0.005|0.02|||0.007|0.019|
||||[0.022]|[0.017]|||[0.018]|[0.015]|
|Birth Year FE||||YES||||YES|
|Constant|4.420***|1.801|1.888|-1.172|1.622|-0.419|-0.52|-3.001|
||[0.905]|[1.713]|[1.949]|[2.288]|[1.148]|[1.491]|[1.755]|[2.623]|
|N|4690|4204|3878|3878|4622|4149|3822|3822|
|Adjusted R-square|0.984|0.98|0.98|0.983|0.983|0.981|0.982|0.982|
|||<br>Ur|<br>ban|||<br>Ru|<br>ral||
|Life Expectancy at Birth|**_0.112***_**<br>**_[0.018]_**|**_0.103***_**<br>**_[0.018]_**|**_0.097***_**<br>**_[0.011]_**|**_0.108***_**<br>**_[0.028]_**|**_0.129***_**<br>**_[0.017]_**|**_0.119***_**<br>**_[0.011]_**|**_0.114***_**<br>**_[0.010]_**|**_0.118***_**<br>**_[0.029]_**|
|% Urban|||||||||
|% Male||-0.25|-0.291|-0.106||-0.56|-0.68|-0.448|
|||[0.807]|[0.707]|[0.441]||[0.548]|[0.576]|[0.467]|
|Parents Life Expectancy|||0.007<br>[0.016]|0.02<br>[0.013]|||0.01<br>[0.019]|0.012<br>[0.018]|
|Birth Year FE||||YES||||YES|
|Constant|4.539***|5.461***|5.492***|3.861*|2.195**|2.567***|2.444**|1.785|
||[1.104]|[1.433]|[1.513]|[2.167]|[0.996]|[0.884]|[1.202]|[2.510]|
|N|4684|4200|3874|3874|4446|3959|3657|3657|
|Adjusted R-square|0.982|0.963|0.963|0.965|0.992|0.987|0.988|0.988|
|Note: Standard errors in|brackets. S|ignificance|levels: * p|<0.05, ** p|<0.01, ***|p<0.001.|||

40
# **Table A - 5 : Traders' Responses to Requirements for Inspection of Export Consignments**

||**Yes**<br>**(Count)**|**No**<br>**(Count)**|**If Yes, What Percent Of**<br>**Export subject to**<br>**inspection ?(Range)**<sup>**(*)**</sup>|**(Average)**<sup>**(*)**</sup>|
|---|---|---|---|---|
|**Egypt**|37|10|6.0-25.0|20.0|
|**Jordan**|23|1|1.0-2.0|1.5|
|**Lebanon**|16|11|1.0-2.5|1.0|
|**Morocco**|6|3|5.0-10.0|5.0|
|**Saudi Arabia**|20|9|1.0-2.0|2.0|
|**Syria**|19|10|1.0-2.5|1.0|
|**Tunisia**|33|15|3.0-20.0|10.0|
|**UAE**|7|6|0.5-5.0|1.0|
|**Yemen**|18|5|1.0-2.5|1.0|
|**Total**|179|70|1 - 15|3.0|

(*) See Footnote in Table A-1.

**Table A - 6 :  Bureaucratic Red Tape on Imports: Number of Man-days per Year Spent in Resolving Problems with Customs and Tax Administration**

||**2**<br>|**008 Survey**<br><sup>**(*)**</sup>|<sup>**(*)**</sup>|**2001 Survey**|
|---|---|---|---|---|
|**Country**|**% of total**<br>**respondents**|**Range** <br>**(days)**|**Average** <br>**(days)**|**Average (days)**|
|**Egypt**|84.6|25 - 60|45|100|
|**Jordan**|46.8|25 - 42|30|200|
|**Lebanon**|88.5|20 - 50|29||
|**Morocco**|81.8|20 - 56|30||
|**Saudi Arabia**|89.5|14 - 24|20||
|**Syria**|96.2|20 - 42|32|209|
|**Tunisia**|57.7|18 - 30|25||
|**UAE**|75|13 - 27|20||
|**Yemen**|92.6|15 - 30|20||
|**Total**|89.7|20 - 40|26|95|

(*) See Footnote in Table A-1.

**Note:** Number of man-days is total time spent on resolving problems with Customs and Tax Departments about the application and interpretation of import laws and regulations in the country (e.g., customs formalities, product classification in the tariff schedule and other regulations directly related to import process).

25
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# ICT

## **Achievements**

Tanzania has introduced key institutional reforms. In 2003, the country established the Tanzania Communications Regulatory Authority (TCRA), which merged the Tanzania Communications Commission and the Tanzania Broadcasting Commission. The licensing framework has been streamlined and Tanzania has intensified competition in the mobile sector. From four active operators in 2001, Tanzania had seven mobile operators by 2010 making its wireless sector one of the most competitive in the region. Penetration reached half the population by 2010. In common with other countries in eastern and southern Africa that have lacked access to fiber optic submarine cable, Tanzania had high costs of internet service. Two undersea fiber optic cables now land in Tanzania (SEACOM in 2009 and EASSy in 2010). This has led to a huge increase in international bandwidth and a 50% fall in fixed broadband retail prices between 2008 and 2010. Nonetheless fixed broadband prices remain high relative to Tanzanian incomes.

**Table 8. Benchmarking ICT indicators, 2008**

||Unit|Tanzania 20|08|Low-income<br>countries 2008|<br>Sub-Saharan Africa<br>2008|Tanzania 2010|
|---|---|---|---|---|---|---|
|GSM coverage|% population|67||63|72|75.8|
|International bandwidth|bits/capita|7.6||25|39|83.7|
|Internet|users/100 people|2.3||3.6|5.5|3.9|
|Landline|subscriptions/100<br>people|0.3||0.8|1.4|0.4|
|Mobile phone|subscriptions/100<br>people|32.7||24.4|33.1|50.6|
||Tanzania 2008|Low-inc|ome cou|ntries 2008|Sub-Saharan Africa<br>2008|Tanzania 2010|
|Price of monthly mobile basket|13.7||11.0||12.1|11.3|
|Price of monthly fixed line<br>basket|11.7||10.4||12.4|9.1|
|Price of 20-hour fixed broadband<br>Internet package|<br>66||287||209|32|
|Price of a 1 minute call to US|0.25||0.70||0.7|0.28|
|Price of inter-Africa tel. calls,<br>mean|0.34||0.94||1.0|0.39|

_Source:_ TCRA, TTCL, Vodacom and AICD database. Note: Low-income countries refer to those in Sub-Saharan Africa.

## **Challenges**

As of 2010, just over three quarters of Tanzania’s population lived within range of a GSM signal (table 8), falling somewhat short of the performance of east African neighbors such as Kenya and Uganda, where more than 90 percent of their population is covered by mobile networks. Detailed spatial analysis of potential costs and revenues for GSM service across the country suggest that around 95 percent of Tanzania’s population could be served on a commercially viable basis. There is thus a market 23
20 In al four country studies, private mechanisms of export marketing support turn out to be more important than collective ones. Yet, at least some firns, subsectors and countries benefit quite substantially from the presence of collective marketing support. So, given a policy decision to support SMEs, the question arises as to how to provide such support. We answer this question in two steps. First, we examine what specific kinds of collective support have proven most useful to SMEs. Thereafter, we examine what kinds of institutions have proven most effective in delivering this supportCollective support: what works? The Colombian, Idonesian and Korean field surveys collected detailed information from firms as to the relative usefulness of different types of collective marketing support for their export efforts. Table 10 reports the results for each specific type of support that was used by at least 20% of the firms sampled in an individual eubsector. The results are strikiingly similar across countries.

First, finms ascnbe a high value to participation in trade fairs at home and abroad as a means of penetrating export markets. Trade fairs emerged as the leading or second most valued coliective source of export marketing support in seven of the nine subsectors for which data are reported in Table 10. Moreover, as we shall see in the analysis of tehnical support systems, trade fairs also turn out to be an important source of technologica **l** earning.

Second, only in the garment and textile industry (but within that industry systematically in all three countries) do frms identify infornation on export opporunities, rather than participation in trade fairs, as themost important source of marketing support only in the garment and textile industies. This result reminds us that international trade is conducted in different ways for different industries: for some, high profile fairs are the high points of the trading season, for others an ongoing search by individual buyers and sellers for trading partners matters more.

Third, SMEs ascnbe very limited value to direct intoduction of buyers by official export agencies. Efforts along these lines played virtually no role in either Colombia or Indonesia. In Korea, the electroics parts subsector provides a partial exception to the general pattern even though it is noteworthy that recipients of this kind of support (every single one
<!-- Start of picture text --> FIGURE 4: PRODUCTIVITY GAINS FROM A 20 PERCENT IMPROVEMENT IN SELECTED<br>INVESTMENT CLIMATE VARIABLES IN VARIOUS LATIN AMERICAN COUNTRIES AND<br>INDONESIA<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Brazil Ecuador El Salvador Guatemala Honduras Nicaragua Indonesia<br>Loss in sales due to transport interruptions Average time to clear customes<br>Average duration of pow er outages<br>Percent<br><!-- End of picture text --> _Source: Escribano et al. 2005 and Fay and Morrison, 2007._ In the following section, we will expand the analysis of the three areas that have the largest impact on logistics costs: (i) infrastructure and transport services, (ii) company logistics organization, and (iii) the organization of the public sector in trade facilitation in the flow of foreign trade. Correspondingly, for each of the areas, we will present empirical evaluations of the impact of (i) infrastructure quantity and quality on growth, (ii) the impact of inventory levels on logistics costs, and (iii) improvements in trade facilitation and their impact on growth.

# **4. Analysis (i): The Impact of Infrastructure on Growth**

This section, relies heavily on Calderon and Serven (2004b), who assess the impact of infrastructure on growth. They use a large panel data set comprising 121 countries and spanning the years 1960- 2000. Using this data set, they estimate empirical growth equations including a standard set of control variables augmented by infrastructure quantity measures, and controlling for the potential endogeneity of infrastructure indicators.

12
- Comin, Diego, and Bart Hobijn. 2010. “An exploration of technology diffusion.” _American Economic Review_ 100 (5): 2031–59.

- Comin, Diego, and Martí Mestieri. 2018. “If technology has arrived everywhere, why has income diverged?” _American Economic Journal: Macroeconomics_ 10 (3): 137–78.

- Cusolito, Ana Paula, and William F Maloney. 2018. _Productivity revisited: shifting paradigms in analysis and policy._ Edited by License: Creative Commons Attribution CC BY 3.0 IGO. World Bank Group.

- De Loecker, Jan. 2013. “Detecting learning by exporting.” _American Economic Journal: Microeconomics_ 5 (3): 1–21.

- ~~.~~ 2019. “Digital platforms: a technical note.” _World Bank Group._

- DeStefano, Timothy, Richard Kneller, and Jonathan Timmis. 2018. “Broadband infrastructure, ICT use and firm performance: evidence for UK firms.” _Journal of Economic Behavior & Organization_ 155:110–139.

- Draca, Mirko, Raffaella Sadun, and John Van Reenen. 2006. “Productivity and ICT: a review of the evidence.” _Centre for Economic Performance_ Discussion Papers dp0749.

- Dutz, Mark A, Rita K Almeida, and Truman G Packard. 2018. _The jobs of tomorrow: technology, productivity, and prosperity in Latin America and the Caribbean._ Edited by License: Creative Commons Attribution CC BY 3.0 IGO. World Bank Group.

- Dutz, Mark A, Lucas Ferreira Mation, Stephen D. O’Connell, and Robert D Willig. 2017. “Economywide and sectoral impacts on workers of Brazil’s internet rollout.” _World Bank Group Policy Research_ Working Paper No. 8042.

35