rafmacalaba/datause-encoder-data huggingface dataset:
# **4 Conclusion**

The severity of the increase in the global environmental problems alerts for the likely water security in the 21<sup>st</sup> century. International community officially accepted scarcity of water only recently. Governments all over the world state that they are well aware of necessity of implementing appropriate measures to cope with this scarcity. Since water is one of the main production factors in agriculture and agriculture demands a very high proportion of water through out the world this alert also has serious implications for food security.

Efficiency in the allocation of water is offered as a remedy to increasing. However efficiency may require transfer of water usage rights from less efficient crops and regions to more efficient ones. At this point, equity has become a central concern in allocation since water is crucial for human life. Most frequently used institutional settings for allocating irrigation water are public administration and user based administrations. Water markets, which are advocated by many economists as a solution to efficient allocation problem, face difficulties during implementation. The allocation of water as an economic good is more complicated than the allocation of other economic goods because water possesses unique characteristics which in turn bring about externalities and market failures. Although markets have been considered to the most efficient mechanism to use in allocating scarce resources, water allocation has proved to be different. Water pricing is considered as the main policy tool that can be used to mitigate both quantity and quality dimensions of water scarcity and thus enhance efficient water use. The economic solution to the issue of water allocation may need some support in the form of institutions without which they may not work well.

Recent developments in the literature have shown that the issue of equity in the allocation of irrigation water is linked to poverty alleviation. Irrigation has been proven to be a poverty reduction tool for the 21<sup>st</sup> century.

The modeling of water in partial equilibrium models can either be from the supply side or demand side. Demand is generally found by maximizing some of objective function, which could be either the profits or surplus of producers under certain constraints. Biophysical farm models are another alternative that can be used to determine crop and time specific water demand, although they require more detailed data about yields, water requirements, irrigation areas and crops produced.

Modeling the supply side is more complex due to the uncertainty because water supply is largely dependant on unpredictable weather conditions. Outputs from hydrological 50
Naturally, the rapid and widespread adoption of digital platforms in the tourism industry leads us to ask (i) what their impact on the demand for tourism services is and (ii) how countries can tap into the opportunities that the new technologies present, as well as cope with some of the accompanying challenges. While there is a growing literature looking at the “material economy” implications of the digital economy, there are relatively few studies focusing specifically on international trade and specifically on trade in services and tourism. Freund and Weinhold (2004) provide an early look at how the Internet shaped international merchandise trade and find that, on average, the Internet led to a 1 percentage point increase in export growth from 1997 to 1999. More recently, Lendle et al (2016) look at eBay international trade transactions and conclude that the digital platform reduces the impact of distance on bilateral trade flow by 65 percent, on average. The authors posit that the reduction in search costs is the main reason behind their finding, with seller‐rating information having an additional impact. With regards to services trade, Freund and Weinhold (2002) provide the earliest look at the topic, focusing on how the Internet influenced US bilateral services trade with 31 different countries across 14 industries. They find that as Internet penetration in a partner country increases by 10 percent, service exports growth increases by 1.1 percentage points and import growth by 1.1 percentage points. Eichengreen et al (2016) focus on the foreign exchange market and find that greater connectivity via undersea cables dampens the impact of spatial frictions, by up to 80 percent, between local markets and major financial centers and increases offshore trading by 21 percent.

Related to the present paper, Hoonsawat (2016) explores the question of the extent to which the Internet has promoted tourism flows and, as in the present paper, applies a gravity equation model to bilateral tourism flows. The author motivates his analysis by modeling how the Internet helps mitigate a traveler’s lack of information about a given destination, thus increasing her demand for tourism services. Empirically, the author uses internet penetration rates as the variable of interest and finds that they have a significant positive impact on tourism flows, particularly as internet usage increases in the origin country.

As we explain below, the motivation, proxies for digital platform use, and econometric specification we choose differ from those in Hoonsawat’s paper. Indeed, we also adapt the rich literature on the use of gravity models in the international (goods) trade literature to the case in point, but we offer a more comprehensive discussion on how digital technologies impact the different cost elements of the tourism market, parsing through the channels through which digital technologies affect the demand and supply of international tourism services. Our econometric approach also uses population‐wide internet use in origin countries, but we focus on business‐to‐consumer internet use as the relevant variable of interest in Page **3** of **34**
**Table 7**

**Parameter Estimates for Panel Specification with Interaction Dummies, Nairobi**

||**Central**|**Coastal**|**Lake**|**Northern**|**Southern**|**National**|
|---|---|---|---|---|---|---|
||-0.04***|-0.03***|-0.02***|-0.04***|-0.05**|-0.04***|
|_μ_|(9.10)|(5.83)|(4.01)|(18.49)|(4.34)|(8.80)|
|E<br> <br>i<br>_*_|0.10***|0.18***|0.14***|0.12***|0.10**|0.13***|
|(pt−1<br><br>− pt−1<br><br>)IBAN|(6.46)|(5.40)|(7.09)|(12.58)|(4.11)|(7.98)|
|E<br> <br>i<br>_*_|0.17***|0.31***|0.25***|0.19***|0.15***|0.21***|
|(pt−1<br><br>− pt−1<br><br>)INO_BAN|(10.80)|(5.89)|(5.03)|(70.44)|(7.62)|(9.30)|
|E_*_|0.22***|0.15|0.26***|0.34***|0.23***|0.23***|
|Δpt<br>IBAN|(6.56)|(1.58)|(5.65)|(27.60)|(4.69)|(7.60)|
|E_*_|0.22***|0.14|0.26***|0.35***|0.22***|0.23***|
|Δpt<br>INO_BAN|(6.73)|(1.51)|(5.68)|(27.00)|(4.71)|(7.51)|
|F|0.36***|0.04|0.35***|0.29***|0.17**|0.23***|
|Δpt<br>|(9.60)|(0.29)|(4.70)|(15.11)|(2.27)|(4.82)|
|I|2.89***|0.40|1.75***|2.76***|0.65***|1.61***|
|Δpt<br>|(9.99)|(0.43)|(3.84)|(8.46)|(3.15)|(4.97)|
|2πt<br>_*_|0.04***|0.08***|0.03***|0.05***|0.06***|0.05***|
|Cos(<br>12<br>)IBAN|(13.09)|(3.43)|(3.84)|(2.72)|(5.95)|(7.45)|
|2πt<br>_*_|0.04***|0.09***|0.03***|0.04**|0.06***|0.05***|
|Cos(<br>12<br>)INO_BAN|(6.23)|(5.24)|(5.58)|(2.57)|(8.10)|(7.54)|
|2πt<br>_*_|0.00|-0.02|-0.02***|0.04***|-0.03**|-0.01|
|Sin(<br>12<br>~~)~~IBAN|(0.10)|(1.24)|(3.35)|(7.22)|(2.33)|(1.07)|
|2πt<br>_*_|-0.02***|0.00|-0.03***|0.00|-0.03**|-0.02**|
|Sin(<br>12<br>~~)~~INO_BAN|(3.66)|(0.36)|(5.56)|(1.14)|(2.58)|(3.47)|
|_*_|-0.28***|-0.37***|-0.27*|-0.59***|-0.86***|-0.38***|
|NDVItIBAN|(8.16)|(3.07)|(1.74)|(5.55)|(3.98)|(6.18)|
|_*_|-0.19***|-0.40**|-0.13**|-0.43***|-0.21|-0.27***|
|NDVItINO_BAN|(3.17)|(2.37)|(2.01)|(3.73)|(1.36)|(5.03)|
|_R-square_|0.27|0.23|0.24|0.30|0.21|0.20|
|**Chi-square tests**|||||||
|_Difference in adjustment_|0.07|0.14|0.11|0.07|0.05|0.08|
|_Diff in Adj-Chi_|29.56***|20.92***|11.16***|70.93***|21.05***|47.58***|
|_Difference in NDVI_|0.09|0.03|0.14|0.17|0.65|0.11|
|_Diff in NDVI-Chi_|1.31|0.05|0.95|6.39***|3.36**|3.30*|

**Notes:** The dependent variable is the change in the nominal price in market _i_ . All regressions employ a (market) fixed effects methodology with bootstrapped standard errors (1,000 replications). Robust absolute z-statistics in parentheses, significance level, * = 10 percent, ** = 5 percent, *** = 1 percent; significance levels are different than typical due to clustering adjustment of the standard errors. The bootstrapped standard errors are clustered at the market level. The _Diff in Adj-Chi_ and _Diff in NDVI-Chi_ provide the chi-squared statistics from a Wald test of the difference in the values taken by the adjustment coefficient and the NDVI anomaly, respectively, under Ban and No ban regimes.

31
that this method gives the true cost of living for an unknown household whose expenditure gives zero utility at base period prices. As Nakamura et al. (2015) note, the Engel curve method recovers the change in the cost-of-living for a household that may be anywhere in the income distribution; thus it is hard to see what crucial experiment could compare the Engel curve deflator with another deflator. Even a fixed-weight cost of goods index that does not imply homothetic preferences, like a Laspeyres index, may be a poor benchmark for such a test since one would not know whether to weight it democratically, plutocratically, or at some other point in the income distribution so as to best match the unknown reference household of the Engel curve method.<sup>6</sup> Our strategy for testing the Engel method is more pragmatic. The issue of the preference framework that gives a price index consistent with the cost-of-living changes recovered by the Engel curve method is hard to resolve since the reference household is unidentified. But we note that researchers are not using the Engel curve method as a spatial deflator because they are guided by hypothesis tests that this is a more preference-compatible deflator.<sup>7</sup> Instead, this method is used for time-space deflation because spatially disaggregated prices are unavailable. For example, Almås and Johnsen (2012, 2) motivate their food Engel curve deflator study by stating: Why is it necessary to produce new price indices? First, data on prices in China are scarce. To our knowledge, there are no official and available price indices that allow for cross-province comparisons, and price data on specific goods are extremely limited.

> 6. The plutocratic weights for the CPI treat dollars equally and thus treat people unequally since some people have many more dollars than others. Deaton (1988) shows that in the United States the CPI weights are representative for a household above the 75<sup>th</sup> percentile of the expenditure distribution, while Ley (2005) shows that higher inequality, differences in consumption patterns by income groups, and greater variance in individual price behavior all contribute to the gap between plutocratic and democratic price indexes.

7. This contrasts with cross-country studies of PPPs for measuring global poverty where, for example, Ackland et al. (2013) test the hypothesis of common homothetic preferences and find that in samples from the 1996 and 2005 ICP they cannot reject homothetic preferences for about 70% of countries.

5
literature in economics. Autor et al. [2003], Costinot et al. [2011] and Acemoglu and Autor [2011], have argued that globalization and advances in technology and computerization have increased the comparative advantage of individuals who perform non-routine tasks requiring problem solving, intuition, persuasion, and creativity.

To obtain measures of human capital, following Ayyagari and Maksimovic [2017] we use O*NET, a database maintained by the U.S. Department of Labor that provides data on occupation-specific descriptors that define the key features of an occupation such as worker abilities, technical skills, job output, work activities, etc. We focus on the following three measures of human capital: _CPS_ (Complex Problem Solving) which is identifying complex problems and reviewing related information to develop and evaluate options and implement solutions; _NRCOG_ (Non-routine Cognitive Analytical skills) from Keller and Utar [2016] which is the sum of Mathematical Reasoning, Inductive Reasoning, Developing Objectives and Strategies, and Making Decisions and Solving Problems; and _RMAN_ (Routine Manual) from Keller and Utar [2016] which is the sum of Spend time making repetitive motions, Pace Determined by Speed of Equipment, Manual Dexterity, and Finger Dexterity. We merge the occupation-level scores with the Occupational Employment Statistics (OES), a US establishment level dataset from the Bureau of Labor Statistics, where workers are classified into occupations on the basis of the work they perform and skills required in each occupation. We compute a weighted average across occupations in each firm weighting by the number of employees in each occupation to obtain a score for each establishment. We then take weighted averages across all establishments in an industry to compute industry-level skill scores.

We separate our sample into high and low skill industries based on the _CPS_ , _NRCOG_ , and _RMAN_ scores where high skill is defined as greater than or equal to the median value for each of the skill measures and low skill is defined as less than the median value for each of the skill measures. In Figure 2, we identify star firms in each of these sub-samples as firms in the top 10% of ROIC in that sample in a particular year. We again focus on large firms to be consistent with the sample in Figure 1. Figure 2 shows that the ROIC and the run-up for star firms is higher in industries with high skill as measured by low RMAN. We find similar increases in high skill industries as measured by high CPS and high NRCOG as seen in Figure A1 of the Internet Appendix. If this finding is correct, it would imply that firms employing a high skill labor force are also more likely to earn 10
507 524 Note: ** significant at 5%; *** significant at 1%. Robust standard errors in parentheses clustered at school level. P- values are in brackets in the third column. Controls: (i) students characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his home), (ii) teacher characteristics (gender, age, years of experience, if she/he has post-graduate certificates), and (iii) school characteristics (school size, 7<sup>th</sup> size, 7<sup>th</sup> repetition rate, FEPBA score in Language, school district –or location). All the regressions exclude the classrooms where the Human Body Unit was not taught. Author’s analysis based on experimental data generated for this study **.**

# **Table A8.2. Results on Science learning according to student gender**

||Sequence vs.<br>Control|Coaches vs.<br>Control|Wald Test|
|---|---|---|---|
|_Dependent variable_|(1)|(2)|(3)|
||_Panel A: Female_|||
|Science score|0.519***|0.733***|1.93|
||(0.146)|(0.138)|[0.165]|
||544|545||
|Percent of correct answers|0.089***|0.121***|1.06|
||(0.030)|(0.025)|[0.304]|
||544|545||
|Percent of incorrect answers|-0.035|-0.049***|0.49|
||(0.021)|(0.016)|[0.486]|
||544|545||
|Percent of omitted answers|-0.062***|-0.092***|2.14|
||(0.022)|(0.019)|[0.143]|
||544|545||

_Panel B: Male_ 25
Learning from Developing Country Power Market Experiences: The Case of Philippines Table 5‐5 Technology‐wise gross power generation in Philippines (in GWh).

|**Technology**|**2005**|**2010**|**2017**|
|---|---|---|---|
|**Coal**|15,257|23,301|46,847|
|**Oil‐based**|6,141|7,101|3,793|
|**Natural Gas**|16,861|19,518|20,547|
|**Renewable Energy (RE)**|18,308|17,823|23,183|
|_Geothermal_|9,902|9,929|10,270|
|_Hydro_|8,387|7,803|9,605|
|_Biomass_|0|27|1,335|
|_Solar_|2|1|1,283|
|_Wind Generation_|17|62|690|
|**Total**|**56,568**|**67,743**|**94,370**|

Source: Department of Energy In contrast with the low contribution of renewable generation, coal‐based generation soared over the past decade (see Figure 5‐9). Coal‐fired power generation grew at a CAGR of 10%, reaching 50% of total power output in 2017 (with 47 TWh), up from 27% in 2005 (with 15 TWh). The power sector has thus become more reliant on imported fossil fuels (especially coal imported primarily from Indonesia).

<!-- Start of picture text --> GWh<br>100,000<br>90,000<br>80,000<br>70,000<br>60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Coal Oil‐based Natural Gas Hydro Geothermal Other renewables<br><!-- End of picture text --> Figure 5‐9 Evolution of technology‐wise power generation in Philippines.

Source: Department of Energy

# 6 Conclusions on the Philippine Experience with Power Market Reforms

Overall, the Philippine power market has partially delivered the objectives of competitive outcomes and sustainability. On the other hand, supply has been secure at the generation level, but generation adequacy 41
these with the RTA indicator. Figure 7 compares the overall RTA coverage for all country pairs (“Total RTA”) and the coverage of Egypt’s trading relationships as measured as the percent of Egypt’s trading relationships covered by an RTA as reported in the CEPII database. The figure shows that more than half of Egypt’s trading partners have an RTA with Egypt, and this share has remained steady from 2010 to 2019. The fraction increased in the mid-2000s when Egypt expanded its trade agreements but fell somewhat as Egypt expanded trade with countries that were not covered by a trade agreement.

**Figure 7. More than half of Egypt’s trading partners have an RTA with Egypt, 1996-2019**

Source: Authors’ elaboration using CEPPI gravity database

# • **Gravity Results**

The main results are shown in Table 1. Three estimation approaches are shown. In the first column, the Ordinary Least Squares (OLS) results include year fixed effects, importer fixed effects, and exporter fixed effects. The OLS results suggest that RTAs increase trade by over 50%, which is much larger than the 10-20% range found in most studies. Part of the reason for the large result is that the sample is restricted to country pairs with positive trade. The results in column (1) also suggest that the RTA effects for Egypt’s trade are much larger than that of the “average” trade agreement. Both imports and exports are much larger with Egypt’s RTAs. Note that the other gravity variables, including WTO, distance, and GDP have the expected signs. The magnitude of the importer and exporter GDP coefficients are smaller than expected.

The second column employs the high-dimensional fixed effects PPML estimator. This estimator includes importer and exporter time trends, year fixed effects, and country-pair fixed effects (and standard errors clustered on country pairs). The main results are similar but smaller in magnitude. The RTA estimate is now much lower than the 10-20% range found in the literature. Egypt seems to have exports and imports expand more through RTAs than the “typical” agreement, but the increase in imports is not statistically significant. As such, the main implication is that Egypt’s trade agreements are not generating a flood of new imports that might adversely affecting labor market outcomes. We explore this hypothesis in more detail in the next section. The observed drop in the number of observations between columns 1 and 2 represents 8
**Figure 3:  Evolution of sourcing patterns**

<!-- Start of picture text --> 2002 2006 2010 2014 2018<br>domestic sourcing by MNEs Fitted (MNEs)<br>foreign sourcing by small, domestic manufacturers Fitted (small)<br>60<br>40<br>Percentage<br>20<br>0<br><!-- End of picture text --> Note: The circles show the average values for the two GVC measures averaging over the firms in all countries that were surveyed in a given year. The magnitude of the circles show reflect the number of firms surveyed. The fitted line is obtained from a firm-level regression of each GVC measure on year and year-squared, controlling for country-fixed effects.

However, we should be careful not to infer too much from country averages. In Figure 3 we plot the average values for the two GVC measures—domestic sourcing by MNEs and foreign sourcing by small, domestic manufacturers—by year, but now averaged over all countries surveyed in each year. The size of the circles indicate how many firm-level observations were used to calculate each annual average. The fitted lines are calculated by a regression at the firm level on year and year-squared, controlling for country-fixed effects. The linear coefficients show the predicted signs for both measures, positive for the first measure and negative for the second, but it is clear that the non-linear component in the evolution should not be overlooked. In both cases, the coefficient on the year-squared variable has the opposite sign.

Domestic sourcing by MNEs (in blue) increases over most of the sample, but the negative square term implies that this effect gets weaker towards the end of the sample and by 2014 the square term even dominates. Once the average share of domestic sourcing reached 50% it stopped growing. The decline in foreign sourcing by small manufacturers (in red) shows an almost imperceptible negative trend in the first few years. The positive square terms starts dominating already from the middle of the sample period and overturns the negative linear trend. In the second part of the sample period, small firms even increase their foreign sourcing slightly.

With the third GVC measure, we turn our attention to the export orientation of these subSaharan African firms. Taglioni and Winkler (2014) propose to calculate this measure only for the subsample of supplier firms which sell their output to other firms and not to final consumers directly. Calculated that way the measure is supposed to capture to what extent 16
2

# **Acknowledgements**

We are grateful to Martin Rama, Elisa Gamberoni, Ana Luisa Gouvea Abras, and Cornelia Staritz and to participants of the Second International Conference on Global Value Chains in Geneva (2016) for useful discussions and comments. This paper is a byproduct of the South Asia Report “Stitches to Riches?: Apparel Employment, Trade, and Economic Development,” https://openknowledge.worldbank.org/handle/10986/23961. These are views of the authors, and do not necessarily reflect those of the World Bank, its Executive Directors, or countries they represent.