rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: International Tourist Arrivals, 1995‐2017**

|Region<br>1995 2000 2005 2010 2017<br>Tourist Arrivals (Millions)|
|---|
|East Asia & Pacific (excl hi income)<br>43.57 62.85 91.04 119.85 172.30|
|Europe & Central Asia (excl hi income)<br>32.28 50.36 80.95 105.42 144.45|
|Latin America & Carib (excl hi income)<br>32.66 39.76 46.37 51.35 83.30|
|Latin America & Caribbean<br>47.00 55.65 64.12 72.04 112.44|
|Middle East & North Afr (excl hi income)<br>13.83 22.30 33.28 55.26 47.74|
|Middle East & North Africa<br>24.18 37.37 53.99 87.83 89.19|
|South Asia<br>3.81 4.83 6.37 9.23 22.85|
|Sub-Saharan Africa<br>12.96 17.58 23.58 31.46 42.36|
|World<br>523.91 677.39 808.77 956.37 1341.46|

**Source** : World Bank, World Development Indicators.

# **2.3 Digital tools are increasingly used in tourism planning**

Digital platforms in the travel and tourism industry are increasingly used, both by travelers and by businesses engaged in the industry. Digital platforms and online travel agents are major players. For instance, Booking.com comprises 29 million accommodation listings in 154,000 destinations worldwide and operates in 190 countries. Similarly, TripAdvisor reports 8.4 accommodation listings in 156,000 destinations spread across 49 markets, with 490 unique monthly visitors that provide more than 250 reviews and opinions per minute.<sup>7</sup> Figure 3 shows that, in developing countries, tourism service providers’ use of digital platforms is growing, both across traditional (e.g., hotels) and non‐traditional (e.g., “bed and breakfast”) establishments.

At the same time, foreign travelers are increasingly relying on digital platforms to plan their travel plans. Figure 4 illustrates the use and potential of digital planning tools looking at the case of Jordan. The figure shows a growing number of “page views” in TripAdvisor of accommodations, attractions (e.g., museums, tours), and eateries in the country. It further helps gauge interest in Jordan vis‐à‐vis other competitor destinations and helps benchmark the quality of services in Jordan against other countries in the region. The data collected through such platforms shed light on how tourist services provided in Jordan compare to those offer by its peers in the region.

> 7 Data provided to WBG staff by Booking.com and TripAdvisor.

Page **7** of **34**
- 6 -

participate in more than one auction is ignored, in total 1,656 “firms” who solely or jointly participated are identified in these procurement auctions. With joint bidding practices accounted for, in total 862 “bidders” were involved in the competition process.

Importantly, some firms are repeatedly participating in a series of procurement auctions in developing countries. Some are domestic companies, and others are multinational enterprises. This phenomenon is related to a market concentration question. Our sample reveals that one firm obtained seven road contracts solely or jointly (Table 1). In the water sector, one firm was awarded six public contracts related to ODA projects, though the market concentration looks relatively moderate compared with the other sectors. In the electricity sector where our sample data are quite limited, six firms won three or more contracts each.

Table 1. Concentration of Contract Awards

|Num<br>No. of|ber of firms|
|---|---|
|Road<br>wins|Water<br>Power|
|1<br>74|79<br>4<br>3|
|2<br>17|21<br>1<br>1|
|3<br>11|5<br>4|
|4<br>2|1|
|5<br>3|1|
|6|1|
|7<br>1||
|Source: Autho|r’s calculation.|

Figure 1 shows the conventional concentration ratio of the top three or five firms in each sector (in terms of the number of contracts awarded). While the water sector is least concentrated, the electricity sector is most concentrated. The concentration ratios of the top five firms are 13 percent and 22 percent in water and power project procurements. The ratio is about 15 percent in the road sector. These figures may not be significantly high, though they risk being underestimated; the data are by no means comprehensive in terms of coverage of sample countries, periods and projects. Moreover, the identification of the same firm may not be perfect (see Estache and Iimi (2008a; 2008b) for details).

Nonetheless, it seems evident that there are a number of firms that can be characterized as fringe (weak) bidders in these markets. Notably, this contrasts strikingly with the concession-
other than financial services do established firms seem to have a productivity advantage over younger businesses.

The shift in employment from startups and younger enterprises to established ones observed between UBR 2001 and UBR 2010 must then have reduced the growth of labor productivity in manufacturing, commercial farming and tradable services very much in the same way that the employment shift from SMEs and larger firms to microenterprises was seen to have done.

# **4. Summary and conclusion**

An analysis of UBR 2001 and UBR 2010 census data shows that Uganda’s non‐subsistence economy underwent significant structural transformation in the 2000s involving a 7 percentage point shift in aggregate employment from the production of tradable goods in the primary and secondary sectors to non‐tradable services. This is a major episode of sectorial reallocation of labor given the relatively short time interval in which it occurred, not necessarily in the sense of people moving from occupation in one sector to employment in another, but in large part as the redirection of new entrants to the labor market away from traditional ports of entry to new destination sectors.

The central theme of the paper is that the transformation could be a key factor in whether the economy will sustain or surpass the high growth rates of recent years over the coming decade and beyond depending on whether and how far it raised or depressed aggregate labor productivity over the long term. The effect of the transformation on aggregate labor productivity in turn hinges partly on how far the transformation altered the allocation of labor across the size and age distributions of business firms.

On the first of these, the paper has reported that the transformation involved a shift in employment from large scale business enterprises in commercial farming and manufacturing activities in the traded sector to microenterprises in retail trade and the hotels and restaurants industry in the non‐traded sector. On the second, UBR data also show large scale reallocation of labor across the age distribution of business firms between 2001 and 2010, in which the employment shares of established businesses (meaning those that had been in operation for at least a decade) rose sharply at the expense of younger firms including start‐ ups. On aggregate, the employment share of microenterprises rose by 12 percentage points over the decade at the expense of the share of SMEs and larger firms. The accompanying employment shift across business age groups was on an even more pronounced scale.

31
<!-- Start of picture text --> CONGO, DEM REP a TANZANIA ” "2J<br>y<br>a ft comgros<br>MALAWI. ‘.; a ye| ae aXMAYDTTE<br>N@ cS-<br>ZAMBIA ‘ j fas LA ‘<br>ryote ye ;Nam8. ~ . |<br>BIA 83 >,A hn)¥ CO) .<br>‘ 7<br>f”<br>ZIMBABWE GMagea _ Sofala<br>BOTSWANA ¥ MADAGASCAR.<br>Swe Inhambanei|<br>SOUTH Mw we><br>AFRICA anes he ><br>BB 3X de Maputo<br>. SWAZILA io<br><!-- End of picture text -->

<!-- Start of picture text --> Demeity peper Kma<br>24<br>110<br>io. 86<br>@ s- 10<br>@ ico.500<br>@ >50<br><!-- End of picture text --> <!-- Start of picture text --> CONGO, DEM REP i TANZANIA<br>Congo oundh é My 3 conftnos<br>Pa 9) ap! au<br>yr _ Re hy; Degado w=<br>;- / iE MAYSPITE<br>MALAW' ae<br>ZAMBIA z<br>« —_—<br>7 bn a")<br>ae Zambezia<br>“ ZIMBABWE ~ Sofala<br>.‘ee. - i<br>‘SouthInInterior indian Ocegp/@oast@ MADAGASCARMadasgacar<br>TSWANA ae .<br>cs<br>Limpopo Basin \ Gaza\ Inharfbane<br>‘eo<br>. SOUTH AFRICA Maputo Prov<br>ror e indian ofan de de Maputo<br>range Basin = OT? SflAZILAND<br><!-- End of picture text --> <!-- Start of picture text --> EBlevation{mi<br>=<br>100<br>100 - 200<br>@ «oer<br>@ 750-1000<br>e jeoe- i820<br>@®<br>@ +2001400-2000<br>gee River Gieein<br><!-- End of picture text --> <!-- Start of picture text --> CONGO, DEM REP é \ TANZANIA imeaBA<br>f @aN a comfiros<br>eeFiteDlgeyeS aXay<br>MALAW x © ateZOE<br>ZAMBIA ; Ce<br>“GGB MEL D*<br>, sir4| ie Say‘ ay, ;~*\e\) 4Z<br>BLA i = R<br>ZIMBABWE % :<br>BOTSWANA i aaa q }‘ MADAGASCAR‘<br>i<br>| “So"<br>SHUTRARRICK Map 5 _— f<br>SO ic asusas<br>rAZWA wd<br><!-- End of picture text -->

<!-- Start of picture text -->
[meidence in%<br>240<br>) +-50<br>® s-50<br>@ mn<br>® -7<br><!-- End of picture text --> <!-- Start of picture text --> CONGO, DEM REP é { TANZANIA 4<br>¢ Me re. of com@iros<br>ites we Ss a<br>roll Ee" Cabo Delgadp oe<br>a en Ae<br>MALAWI FialaSer ee fee iC<br>Zane ae ee } fee Lg Ae<br>LN ati oe Pe a a<br>Yi. Vests e..<br>meri: C J<br>:<br>- onRY yee<br>BOTSWANA TMOeEve Lee&Sty MADAGASCAR.<br>OO<br>\. 2 \Inhambane<br>Wazapa) xsgt A<br>SOUTH AFRICA Map uloigpyinciP .<br>yf ade de Maputo<br>swWazicanpy?<br><!-- End of picture text --> <!-- Start of picture text --> TetSoe Peto bolPrecious metas» Diarronats<br>wa ww<br>r<br>@ ail Fela<br>Cropland Extent<br>VO but High Subang<br>@ tox-so%<br>@ =s%<br><!-- End of picture text -->
ciently low; thus, policy changes that impact the profitability of exporting can affect the rate of export market entry, which in a differentiated goods context is expressed through changes in the number of varieties exported.

Before entering into the detail of our approach, it is useful to have a first look at the data using non-parametric methods. Like Imbs and Wacziarg (2003), we use the locally weighted scatterplot smoother (Lowess) for this purpose.° We use /ines as the dependent variable in all cases, resulting in a single observation per country. It is important to be clear that this analysis is intended to be exploratory only, since it is conducted one variable at a time, and does not control for the impact of other factors.

Results are presented in Figure 1. The first two plots verify the analysis of Hummels and Klenow (2005) to the effect that larger (GDP), richer (GDP per capita) countries tend to have a more diverse export bundle.° We do not observe the inverse-U relationship between /ines and GDP per capita that would be expected on the basis of the results in Imbs and Wacziarg (2003), undoubtedly because our sample includes only developing countries, which may not yet have reached the relatively high level of income (approximately that of Ireland) at which specialization effects come to dominate. The remainder of Figure | discloses generally negative relationships between export variety and entry costs (upper right corner), great circle distance from the EU (as a proxy for transport costs), and export costs (lower right). In the case of distance, however, the relationship is a complex one involving a mid-sample "hump".

The data therefore provide support for an important stylized fact: higher trade costs and barriers to domestic market entry are associated with a less diversified export bundle. In the next section, we develop a comprehensive theoretical framework that allows us to examine the mechanisms giving rise to these observations, before conducting more rigorous, parametric tests in section 4. <u>~ 5The Lowess smoother runs separate regressions</u> of Jines on trade or entry costs at each data point, using 80% of the total sample. For each regression, observations are downweighted according to their distance from the central data point around which the regression takes place. All calculations are performed in Stata 9.2SE. °The outlier in the upper right corner of the first plot is China. Results do not change if it is dropped.

10
Areas Act (1950), the Pass Laws Act (1952) and the Population Registration Act (1950). The latter assigned a population group to each citizen, which largely defined an individual’s political and social rights. The Group Areas Act assigned a native reserve to each black population group and enabled the government to remove people that were not living in the area assigned to their population group. To control population flows and black urbanization in particular, the government relied on a pass system. The Pass Laws Act forced every black African to carry an internal passport at all times.<sup>4</sup> If a black African could not present their passport demonstrating their right to be in a particular region, they were subject to arrest.

These strictly enforced laws significantly constrained the distribution of population in space as well as the process of urbanization. According to the Surplus People Project (1985),<sup>5</sup> the South African government forcefully relocated at least 3.5 million people between 1960 and 1983. Additionally, several hundred thousand arrests were made every year under the pass laws (Beinart, 2001, p.158f). Table 1 displays the share of the black population living in urban and rural areas within South Africa and the homelands from 1950 to 1980. While the proportion living in urban areas in South Africa stayed roughly constant over the three decades, the proportion living in rural areas decreased by around 15%, while the homelands experienced a commensurate increase. These movements resulted in densely populated areas in the homelands that can be defined as urban in terms of population densities, but not in terms of public service delivery or industrial development. This ‘dislocated urbanization’ (Beinart,

> 4 It built on pre-apartheid legislation including the Natives Urban Areas Act from 1923 and Natives Urban Areas Consolidation Act from 1945, which forced every black man in urban areas to carry passes at all times.

> 5The Surplus People Project was a non-governmental organization that documented forced removals through the apartheid government.

7
are relatively wealthier than other households, or have systematically different preferences, all else being equal, then their asset purchases may differ from other families—even in the absence of migrating members. To tackle these confounding factors, I added controls for initial levels of wealth and preference proxies by including , which serves as a vector of baseline (pre-migration) household and location characteristics as of 1998. The indicators encompassed within this vector are wide-ranging: whether the household has had at least one member who has ever migrated outside Kerala, the number of all adults in the household (age 25 or older, whose education decisions would mostly all have been made by that age) with their various educational attainments, overall household size, the number of male-headed households, the age of the head of household, the highest educational level of the head of household, the inverse hyperbolic sine transformation (IHST) of landholdings, and district-level dummies.<sup>26</sup> My second reason for adding these controls is that, to the extent that unobserved ex-post shocks are correlated with observed pre-migration characteristics of the household or community, incorporating a set of pre-migration controls would further help to mitigate the influence of time-varying unobservables. Robust (Huber–White) standard errors are reported in all cases.

# <u>B.2. Asset Indices</u>

When one examines changes to a household’s welfare, the primary outcomes of interest are measures of its assets. The KMS data sets do not contain income and consumption information, but they do include asset indicators. While this could be interpreted as a limitation of the data, the use of asset holdings may better reflect a household’s long-term economic status and is also less likely to be subject to recall bias or measurement error.<sup>27</sup> To create the asset indices, I followed Filmer and

> 26 The inverse hyperbolic sine transformation of is . Interpretation is analogous to that of a logarithmic dependent variable, without suffering the problem of being undefined at zero.

> 27 A household is more likely to report a tangible asset accurately than to be able to recall precisely its consumption in the previous week, month, or year.

15
|5 = Highly competitive|||(0.03)|(0.03)|
|---|---|---|---|---|
|State of personal finances|||-0.007|-0.002|
|5 = Very good|||(0.02)|(0.02)|
|Cognitive Reflection Test||||-0.037**|
|3 = High||||(0.02)|
|Clarity of instructions||||-0.019|
|5 = Always clear||||(0.02)|
|Gender||||0.027|
|1 = Female||||(0.04)|
|Age (in years)||||0.016***<br>(0.00)|
|Constant|0.617***|0.666***|0.637***|0.394**|
||(0.04)|(0.04)|(0.13)|(0.17)|
|R-squared|0.015|0.031|0.063|0.088|
|P|0.051|0.004|0.000|0.000|
|Observations|650|650|650|650|

Notes: OLS regressions. The dependent variable is the proportion of total endowment that was spent. * 10%, ** 5%, *** 1% significance level. Clustered standard errors (by group) in parentheses.

Model 1 in Table 3 is the simplest model, controlling for treatment effects, and broadly confirms what we observe in Figure 1 above. When expenditure is observable, but endowments are equal (and hence, expenditure carries no signal of ability), expenditure is significantly reduced by our subjects ( _p<0.05_ in model I, _p<0.10_ in model IV). This suggests that subjects understand that consumption is “wasteful” and hence, reduce their consumption levels when it endowments are unearned, and expenditure is observable by others. However, when endowments are unequal (and expenditure signals ability), the effect of observability is significantly higher, relative to when endowments are equal ( _p<0.01_ in model I, _p<0.05_ in model IV). As seen in the figure, the models confirm the result that the observability of consumption has an additional effect when ability signals are present, relative to when they are not. When consumption can be conspicuous (i.e. can signal ability), expenditure increases when others can observe purchasing behavior, implying that the urge to signal ability outweighs the concern that “wasteful” consumption can be viewed by others. When consumption is inconspicuous (carries no signal of ability), expenditure reduces when others can observe purchasing because the urge to signal ability is no longer there.

In addition, our measure of (self-reported) real-world consumption<sup>14</sup> has a positive and significant relationship with expenditure in the game ( _p<0.05_ in model IV), indicating that (to some degree) real world consumption patterns are being captured by our game. We also find

> 14 This is simply a sum of responses to our four consumption questions described earlier.

14
# INCENTIVES AND PROTECTION IN INDIAN INDUSTRY

1. India's industrial policy environment has gone through major cycles of tightening and relaxation over the last few decades. These policy shifts have been caused primarily by balance of payments crises. The adjustments to the BOP crises have to a large extent been made (a) on the import side by increasing tariffs and restricting imports via various QRs and (b) by increasing regulations on industrial investment, output adjustment and placing other restrictions on the ability of firms to adjust to changing economi **c** onditions. The policy apparatus that has been created as a result of these crises have not been dismantled when the policy regimes have been relaxed. Instead, ad hoc adjustments and exemptions have been introduced as special schemes to ease the restrictions and resulting bottlenecks. As a result, a very complicated policy environment has been created with overlapping layers of control, riddled with special exemptions and schemes. In the 1980s significant reforms were implemented in areas of industrial regulation and export policies that eased the magnitude of bottlenecks that firms and exporters face. However, the reforms in the import regime have been more modest and the complex and restrictive structure of the regime has continued to date.

2. Net effect of the import regime has been a gradual shrinkage of international trade as a proportion of domestic output (Aksoy and Tang, 1991). High protection given by QRs and increasing tariffs have led to indiscriminate import substitutioa **n** d very high product prices in India compared to international prices. On the other hand, specific exemptions, administered pricing of key inputs and uneven domesti **c** ompetition have led to certain product groups enjoying very high effective protection while other product groups having low or negative effective protection. Furthermore, very high tariffs and taxes levied on investment goods to protect the domestic producers have eroded the competitiveness of even otherwise efficient industries and require modifications in the interpretation of effective rates of protection.

3. This paper tries to estimate the structure of incentives and effective protection and its implication for Indian industry. First, the rates of nominal and effective protection collected from various studies are analyzed. These includ **e** ffective protection estimates made by Pursell (1988), various
Table 2.1

||SurfaceArea<br>1000Km<sup>2</sup>|Population<br>(millions)|Sizeof**the**Asia<br>Population<br>Density<br>(perKi <sup>2</sup>)|nEconomy**-1999**<br>GNI<br>(USSbillions)|<br>GMPerCapita<br>(USS)|PPP GNI<br>(USSbillions)|PPPGMPer<br>Capita<br>**(USS)**|
|---|---|---|---|---|---|---|---|
|BruneiDarussalam|6|0.3|61|7.8|24,630|||
|Camtodia|181|12.0|67|3.0|260|16|1,350|
|indonesia|1,905|207.0|114|125.0|600|550|2,660|
|LaoPDR|237|5.0|22|1.5|290|7|1,430|
|Mataysia|330|23.0|69|76.9|3,390|173|7,640|
|Myanma|677|45.0|68|_||||
|PhiNppines|300|74.0|249|78.0|1,050|296|3,990|
|Singapore|1|4.0|6,384|95.4|24,150|88|22,310|
|Thailand|513|60.0|118|121.1|2,010|358|5.950|
|Vietnam|332|78.0|238|28.7|370|144|1,860|
|China|9.598|1,254.0|134|979.9|_<br>780|4,452|3,550|
|HongKongSA|_|7.0||165.1|24.570|152|22,570|
|Taiwan|36|22.0|611|290.5|13,235|||
|Japan|378|127.0|336|4,054.5|32.030|3,186|25,170|
|Korea|99|47.0|475|397.9|8,490|728|15.530|
|Australia|7,741|19.0|2|397.3|20,950|452|23,850|
|NewZealand|271|4.0|14|53.3|13,990|67|17.630|
|Canada|9,971|30.0|3|614.0|20,140|776|25,440|
|Chile|757|15.0|20|69.6|4,630|126|8,410|
|Mexdco|1,958|97.0|51|428.9|4,440|780|8,070|
|PapuaNew Guinea|463|5.0|10|3.8|810|11|2,260|
|Peru|1,285|25.0|20|53.7|2.130|113|4,480|
|Russia|17,075|146.0|9|329.0|2,250|<br>1,022|6,990|
|United States|9,364|278.0|30|8,879.5|31,910|8,878|31,910|
|ASEAN|4,476|508.0|113|529.6|1,043|1.632|3,213|
|ASEAN+3|14,551|1,936.0|133|5,961.9|3,079|<br>9,998|5.164|
|APEC|62,377|2,522.01|40|17,242.1|6,837|22,352|8,863|
|NAFTA|21,293|405.0|19|9,922.4|24,500|10,434|25,763|
|EU|3,247|376.4|116|8,434.4|22,408|8,296|22,040|

GNI = gross nationai income; GNI per capita is calculated using the World Bank Atlas method; PPP is purchasing power parity. Brunei Darussalam - GNI data is 1998.

Taiwan data not precisely comparable to others. Taken from Taiwan Government Natb. Stats. GNI data is 'GNP at Current Prices'. Source: Wold Bank's World Developmfent Indicators 2001 The first 10 countries listed in the above table are the member economies of the Association of Southeast Asian Nations (ASEAN).1<sup>38</sup> This group comprises countries that range in size from only 1,000 sq. km. (Singapore, a city-state) to nearly 2 million sq. km. (Indonesia, a group of more than 17,000 islands). Population size ranges from the smallest of around 300 thousand (Brunei Darussalam) to 207 million (Indonesia).

The GNI per capita reveals wide differences in income levels for ASEAN members with Brunei Darussalam and Singapore reporting the highest and Cambodia, the lowest. The World Bank ranks Singapore 22"" worldwide for this measure'<sup>3 9</sup> and Cambodia, 187th. The newest members of ASEAN (Cambodia, Lao PDR and Vietnam) have GNIs well below those of the original members. (Data for Myanmar are not available.) The original ASEAN-5 (Indonesia, Malaysia, the Philippines, Singapore and Thailand) was a fairly homogeneous group. Its enlargement with the addition of new 32