rafmacalaba/datause-encoder-data huggingface dataset:
# **Competitiveness Assessment of Tourism in Sierra Leone: A Cluster-Based Approach**

## **1. Introduction**

The tourism industry, defined broadly to include all travel, lodging, restaurants, supporting services and local attractions, represents about 10 percent of the world’s GDP (World Bank, 2006). Prior to the start of the recent global recession, tourism has been arguably the largest industry in the world for a considerable period of time, and one which has the potential of rapid growth post-recession. International tourism receipts grew over US$ 1 trillion in 2007, corresponding to an increase in real terms of 5.6 percent (UN/WTO, 2008). In 2006, the industry supplied over 230 million jobs and over US$800 billion in tax revenues internationally (World Bank, 2007). For several years, it has been a major generator of foreign exchange revenues and facilitates regional and international trade and investment linkages.

Tourism is an industry that is key to long-term growth and competitiveness in Sierra Leone. It is also one of the few industries which rapidly bounced back once the civil war was over in Sierra Leone, and it is still one of the few industries where it has a strong potential. The World Bank Diagnostic Trade and Integration Study (2006) has estimated that Sierra Leone has the potential of growing up to US$ 150-200 million<sup>i</sup> by 2015 provided that certain prerequisites are met. Despite have rich natural endowments such as climate, location, sea, sun, wildlife and interesting local history and hospitality, advanced factors for tourism such as skilled labor, physical infrastructure, and a national brand image are almost nonexistent in Sierra Leone. More importantly, because Sierra Leonean economy is not well integrated with the global economy, entrepreneurs find it hard to mobilize finance for private investment.

This analysis was prepared following the initial fieldwork done in January 2009 for convening a tourism cluster initiative in Sierra Leone. The approach employed for ascertaining the potential of this activity is derived from a cluster competitiveness handbook developed by the International Trade Department of the World Bank based on the work of Professor Michael Porter from Harvard Business School.

## **2. Tourism in Sierra Leone**

Despite being eliminated during the violent civil war of the 1990s, tourism was one of the few industries which rapidly bounced back in Sierra Leone once the turmoil was over. While tourism has been one of the hardest hit sectors during the global recession, it remains an important sector for Sierra Leone as for several other less developed countries. Sierra Leone has a strong potential on tourism development. It has pristine beaches and islands, mountains and rich biodiversity, interesting wildlife, friendliness and rich cultural capital among people and its special place in the world history of anti-slavery movement as ‘the land of the free.’ However, the violent conflict of the late 1990s has taken its toll, and tourism-related revenues declined from US$ 57 million in 1995 (or 7 percent of GDP) to a low of US$ 6 million in 1999 (or 1 percent of GDP) 2
newly created child districts. These district splits were more pronounced outside Java, in larger areas, as well as those that had significant resource endowments (Fitrani, et al., 2005).<sup>5</sup> Given that it is difficult to compare the spending structure and levels of districts affected by splits, our analysis focuses on those districts that are unaffected by splits.<sup>6</sup> By 2007, 304 out of a total of 434 districts – including ―undivided‖ and split districts -- had held direct elections. Among the 264 districts that remained undivided between 2005 and 2007, 182 districts had held direct elections for district heads by 2007, including 77 that held their direct elections in June 2005 (Figure 3). All districts had been subject to direct elections by the end of 2009. We define autocratic heads as those that were selected by the old DPRDs.

**Figure 2: Timing of direct elections of district heads in all 304 districts by 2007**

**Figure 3: Timing of direct elections of district heads in 182 undivided districts by 2007**

<!-- Start of picture text --> 180 90<br>155 77<br>160 80<br>140 70<br>120 60<br>100 50<br>80 40<br>60 36 30 26 21<br>4020 12 16 22 9 27 2 4 7 14 2010 9 14 5 11 2 4 4 9<br>0<br>0<br>Frequency Frequency<br>Note: 304 districts out of 434 had a directly elected district head by 2007 Note: 182 out of 264 undivided districts had a directly elected district<br>head by 2007<br>2005_Q2 2005_Q3 2005_Q4 2006_Q1 2006_Q2 2006_Q3 2006_Q4 2007_Q1 2007_Q2 2007_Q3 2007_Q4<br>2005_Q2 2005_Q3 2005_Q4 2006_Q1 2006_Q2 2006_Q3 2006_Q4 2007_Q1 2007_Q2 2007_Q3 2007_Q4<br><!-- End of picture text --> The timing of a direct election for undivided (and ―parent‖) local governments depended on when the five year term of the previous head had come to an end. For example, if a district head was last elected in January 1999 under the New Order ―autocratic‖ selection process, (s)he would have been subject to indirect election by the 1999 elected DPRD in January 2004. Empirical analysis

> 5 Among the original 336 districts in 2001, 264 districts did not split during 2001-07. Fifty two districts split once and 20 districts split twice or more during this six year period.

> 6 Combining the newly split districts with each other would make them comparable in terms of geography and population with the ―old‖ district. However, governments of split districts are unlikely to behave the same way as the government of the old district would have if it had not been split, given that political, institutional and other factors that influence fiscal performance are likely to change as a result of the split. Furthermore, in many cases the parent districts continued with their existing government while the newly formed districts held direct elections. Combining the split districts may therefore lead to a mixing of different electoral regimes for the same district in the same time period, which would distort or dilute those very effects this study intends to measure.

8
# **7. Cost-benefit analysis**

The final part of our study focuses on a basic cost-benefit analysis of the subsidy by monetizing the benefits of the subsidy, taking into account its potential to increase formal employment, and the cost of the subsidy for the government. Whereas such cost-benefit analyses are rarely conducted in ALMP evaluations (as asserted in Card et al. 2018), they provide important information for policy makers. We use the data from September 2018, the latest available month in our dataset. The analysis is straightforward in the sense that it includes the government's direct (opportunity) costs – i.e. the monetary benefits in terms of social security contributions and tax payments it sacrifices to implement the subsidy – and its direct benefits, i.e. the additional social security contributions and tax payments it would receive due to the subsidy’s effect in increasing formal employment.

The design does not take into account costs and benefits from the perspective of workers, for example, in the sense of an increase in income of informal workers due to formalization. Similarly, it does not account for any increase in tax revenues to the government caused by an increase in consumption as a result of income increase. Finally, costs that the government could bear, for example, to provide social assistance to individuals or households with no formal employment are not considered in this analysis.

The cost of the subsidy for the government is the amount of revenues sacrificed to cover part of the social security premiums for workers in eligible firms. As defined, the government would forgo 6% of the social security premium payments (based on the minimum wage) for eligible firms that the firm would otherwise have needed to pay to the government. The benefit of the subsidy for the government is the increase in social security premiums and tax payments due to the subsidy’s effect on increasing workers that are registered to the Social Security Institution by the firms. The net benefit of the subsidy for the government at time is thus calculated as: TT Where is the additional formal employment caused by the subsidy taking into account the average treatment effect on the treated, is the total tt NNTTNNAANNTTNNTTll payments by the firm to the government, is the minimum wage that the 6 tt percent subsidy would be calculated from, and PPTTPPNNTTLLT NNPPNNPPTT LLNNTTLLTT tt MMBBLLBBNNMMN TTPPTT population covered by the subsidy. We need to make several assumptions on the values of tt<sup>is the formal worker</sup> TTNNT llTTNNPPT these variables to complete this calculation, and we provide each of these assumptions in detail below.

18
**Table 7: Estimated wage elasticities with respect to local sugar** **<u>prices</u>**<sup>_a_</sup>

||Emp|loyee|Self-em|ployed|Emp|loyer|
|---|---|---|---|---|---|---|
||Low-ed.<sup>_b_</sup>|High-ed.<sup>_c_</sup>|Low-ed.<sup>_b_</sup>|High-ed.<sup>_c_</sup>|Low-ed.<sup>_b_</sup>|High-ed.<sup>_c_</sup>|
|In major sugar region|||||||
|Sugar grower|0.30|0.39|0.29|0.38|0.34|0.43|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|
|Sugar processing|0.39|0.48|0.38|0.48|0.43|0.52|
||(0.19)|(0.19)|(0.19)|(0.19)|(0.19)|(0.19)|
|Other agriculture|0.15|0.25|0.15|0.24|0.19|0.29|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)|
|Other industry|0.13|0.23|0.13|0.22|0.17|0.27|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)|
|Services|0.02|0.11|0.01|0.10|0.06|0.15|
||(0.02)|(0.02)|(0.03)|(0.03)|(0.04)|(0.04)|
|Not in sugar region|||||||
|Sugar grower|0.25|0.34|0.24|0.33|0.29|0.38|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|
|Sugar processing|0.34|0.43|0.33|0.42|0.38|0.47|
||(0.19)|(0.19)|(0.19)|(0.19)|(0.19)|(0.19)|
|Other agriculture|0.1|0.19|0.09|0.19|0.14|0.23|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)|
|Other industry|0.08|0.17|0.07|0.17|0.12|0.21|
||(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|(0.03)|
|Services|-0.03|0.06|-0.04|0.00|0.01|0.10|
||(0.02)|(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|

> _a_ Obtained using the estimates provided in Table 6 and taking averages over group of individuals (using sample weights). Numbers in parenthesis are standard errors. The standard errors were calculated as the square roots of the corresponding variance:

where _β_ 1 _...βn_ are the parameter estimates on the interaction variables in the wage equation of the Heckman model and _a_ 1 _...an_ are indicators that signal whether a person belongs to a particular group (all _ai_ s, except years of education, are either zero or one).

> _b_ Low-educated individuals are defined37 as those with less than 5 years of education. Elasticities are computed at the average level of education within this group: 2.3 years.

> _c_ High-educated individuals are defined as those with more than 8 years of education. Elasticities are computed at the average level of education within this group: 11.8 years of education.
20 Table 3: Probabilities assigned to the reforms' achievement levels, and CV values, Round 2, Reforms 1 and 2

|**Reform**|||||**2**||
|---|---|---|---|---|---|---|
||Low|Mediurn|High|Low|Medium|High|
|Probability|1.285|2.857|1.571|1.428|2.428|1.571|
|CV|0.351|0.291|0.463|0.346|0.372|0.463|

Note: Probability values are 1 _ 0-25%; 2 - 25-50%; 3 = 50-75%; 4 _ 75-100% We present the consolidated values from the Delphi process in Table 4. The values should be read in the following way: For example, for reform 1, a low achievement level was assigned a (low +) probability, a medium achievement level was assigned a (high -) probability, and a high achievement level was assigned a (medium -) probability. Reform 3 was assigned the highest probability, and reform 4 was assigned the lowest probability for high achievement level. Reform 4 was also assigned the highest probability for the low achievement level.

Table 4: Final Delphi probability values of reforms' achievement levels

|Reform|1|2<br>3|4|5|
|---|---|---|---|---|
|Low achievement|1.3|1.4<br>1.4|2.4|1.6|
|Med Achievement|2.9|2.4<br>2.9|2.0|2.4|
|High Achievement|1.6|1.6<br>2.1|1.1|1.6|
|Note: Probability valu|es are 1 =_0-2_|_5%;_2=25-50%; 3 = 50-75%; 4|75-100%||

# **DISCUSSION**

As is the case in many reforns, information on the political parameters of the various interest groups is not available to policy makers so that they can evaluate the likelihood of success of the proposed reforms. A Delphi process, as suggested in this paper, may provide a sound mechanism to address such data needs. There are several questions, however, that should be addressed in generalizing the results of this study. First, is the policy maker better off when possessing the information provided by the Delphi approach? Second, to what extent does the
the 24 states in the country. Inferences for shorter periods (three-month intervals) can also be done at the national and rural/urban levels.

The survey has an extensive and detailed expenditure module, which collects information on all dimensions of household expenditure reported by a household informant. With respect to food consumption, we can separately identify food consumed at home and food consumed away, as well as different components of FAFH. Expenditures on food consumed at home are reported by the household informant, for a total of up to 650 items. Data on expenditures on food away from home are collected from different informants and in different modules depending on the place of consumption - take-out vs food consumed outside – and on the age of the household members - children versus adults.

Take-out food and food consumed outside the home by children younger than 15 years is reported by the household informant, within the same expenditure module used to collect at-home consumption. The questionnaire allows the respondent to list the names of the meals consumed, together with information on the mode of acquisition, frequency of consumption, source of the food, quantity and amount spent. By contrast, information on food consumed away by adults is collected through a different module applied to each adult household member, who reports expenditures by meal event – breakfast, lunch, dinner, or snacks. For each meal event, the respondent reports frequency, amount spent, and place of consumption, such as street food, restaurant, social program, or work.

Regarding the content in the questionnaire, the survey takes into consideration most of the elements reviewed by Smith et al. (2014), making it well suited for the analysis. It considers both food produced outside and consumed outside as well as food produced outside but consumed at home, i.e. take-out; it contemplates different modes of acquisition – paid versus in kind; it explicitly considers both meals and snacks; and specifies multiple sources, including street vendors, restaurants, market, social programs. Furthermore, this survey is among the few that collect FAFH at the individual level, though only for adults. According to the report by Smith et al. (2014), only 17 percent of the surveys reviewed in their analysis do so. Recent evidence suggests that relying on a household informant to elicit information on consumption away from home is likely to suffer from high measurement error 14
Table B7: Mechanism at play, controlling for Share of services in GDP

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|
|---|---|---|---|---|
|Uncertainty (standardized)|-0.404|1.430***|1.274***|1.750***|
||(0.605)|(0.501)|(0.432)|(0.608)|
|Uncertainty _×_ Developing|1.805***<br>(0.688)||||
|Share of services in GDP (standardized)|0.749|0.507|0.397|1.385|
||(1.215)|(1.181)|(1.019)|(1.543)|
|Observations|2,283|1,460|1,789|1,158|
|R-squared|0.835|0.826|0.847|0.803|
|Data Coverage|All countries||Developing||
|Minimum # of|||||
|Foreign Investors|3||1|10|

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include logarithms of population, GDP and the average exchange rate. Standard errors are clustered at the destination country. Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

32
- ii -

modify extension systems to better reach women farmers, to modify the foeus of research to address women's activities and constraints, and to monitor and evaluate programs. Ways to collect such data are also suggested. Chapter 3 deals with the transmission of the extension message to women farmers -- the role of the extension agents and the importar e of gender, the use of home economists and subject matter specialists, and the use of contact farmers and groups. The final Chapter examines the formulation of the message to be delivered, and the linkage between extension and agricultural research and technology.

6. While the recommendations and analysis contained in this paper are relevant for different extension systems, particular attention is given to the Training and Visit (T&V) system. Much of the World Bank's investment in extension has been in the T&V system, originally in Asia and now in many African countries. T&V extension is a management system which has the flexibility to be adapted to local conditions. These guidelines are intended for use by Bank operational staff and government extension personnel. They supplement and can be used in conjunction with previous World Bank publications on T&V extension.

# Whv Women Farmers Need Help.

a) Women farmers operate under greater constraints than men, and hence need special help. They have less access to information, technology, land, inputs and credit. Because women have primary responsibility for the home and child care, they also have less available time and mobility. While there is much regional variation, women in rural Africa are frequently illiterate, engaged in subsistence agriculture and unfamiliar with current technology, and are often perceived by male extension agents as being "non-adopters".

b) There is a lack of awareness of these constraints. Most policy makers, managers, agents and participants in agricultural support servict ire males who are not directly affected by the problems and needs oQ women farmers, and hence are not sufficiently aware of them. A number of initiatives are needed to improve awareness. These include collecting necessary information, redesigning surveys and questionnaires, improving staff knowledge and understanding of the subject, and generating feedback on progress.

# General Guidelines for Proiect Design.

# (1) Situation specific

Given the wide variation in circumstances between and within countries, a single methodology or approach for improving extension services for women farmers is not appropriate. The approach chosen for any particular situation must be suited to that specific situation, and should be based on the socio-cultural and religious precepts of the farming community and on the institutional organization and resources of the country.
# **Box 1: Russia’s Export Sophistication**

Though the sophistication of Russia’s exports seems to have plateaued in recent years, it is still the highest among peer countries. Intuitively, measures of export sophistication capture whether a given country’s export basket consists primarily of services typically exported by high‐income economies (relatively sophisticated) or by low‐income economies (relatively less sophisticated). Figure B1.1 illustrates the relationship between development and export sophistication. All of the BRICS increased the sophistication of their services between 2005 and 2012, Brazil the most and India the least. Interestingly, although Brazil is less developed in terms of GDP per capita and has a less diversified basket of services exports, those seem to become more sophisticated than Russia’s. The sophistication of Russia’s services exports has taken a dynamic path that much resembles China’s. Finally, there is room for Russia to expand its export portfolio with services that have more value added, especially compared to India and Brazil.

**Figure B1.1. Export Sophistication and Development, 2005 and 2012**

<!-- Start of picture text --> 2005 2012<br>3.5 3.5<br>3.25 3.25<br>BRA<br>3 3 IND<br>ZAF<br>BRA IDN RUS<br>IND RUS CHN<br>IDN<br>2.75 2.75<br>ZAF<br>CHN<br>2.5 2.5<br>6 8 10 12 6 8 10 12<br>log GDP per capita PPP log GDP per capita PPP<br>Services sophistication Services sophistication<br><!-- End of picture text --> Source: IMF Balance of Payment Statistics and WDI. Note: Export sophistication/EXPY is a measure of the income content of a country’s export basket. Captures whether a country’s export basket consists primarily of services typically exported by high‐income or low‐income economies. GDP = Gross Domestic Product; PPP = Purchasing Power Parity; USD = United States Dollars; BRA = Brazil, CHN = China, IND = India, RUS = Russia, ZAF = South Africa.

Observations below the 45‐degree line show that the levels predicted by the specification with fixed effects are lower than those predicted by the specification with no fixed effects. Such a result would suggest that national barriers to trade are lowering a country’s trade potential. Since many observations for Russia lie below the 45‐degree line, the levels predicted by the specification with fixed effects are lower than without. That potential trade is lower after properly controlling for country‐specific obstacles 8