rafmacalaba/datause-encoder-data huggingface dataset:
# **VII.   MITIGATING THE REVENUE IMPACT OF INTEGRATION**

# **_The likelihood of product exclusions_**

First, using a partial equilibrium model, we assumed the abolishment of all tariffs on EUoriginating imports at the initial stage of the EPA. As discussed above, one should bear in mind that WTO consistency does not necessarily imply the complete and symmetric liberalization of all trade under the EPA. Certain products that may be sensitive from a fiscal or other perspective could be excluded from the agreement. As a result, the revenue loss would be lower than our estimates.

# **_The length of the implementation period_**

Second, our estimates report the ultimate effect of a fully phased-in EPA. However, tariff elimination is most likely to take place gradually over 10 years or more, from January 2008 (UNECA, 2004; Busse et al., 2004, Hinkle and Newfarmer, 2005) under the EPA timetable. Therefore, countries would have considerable time to introduce compensatory measures.

# **_Reform of exemptions regimes_**

Third, in view of the large gap in some countries between the official tariff rate and the collected (applied) tariff rate (Table 4), tax yield could be improved through a reduction of tariff exemptions<sup>23</sup> and a modernization of customs administration.

|Table4:Efficiency of ImportTariff Rev<br>|enue Collection,2001<br>|
|---|---|
|Applied tariff<br>rate|Import-weighted (official) tariff<br>rate|
|Cape Verde<br>12.1|15.4|
|Ghana<br>4.7|16.2|
|Nigeria<br>15.9|20.0|
|Senegal<br>8.5|9.4|
|ECOWAS (1)<br>8.0|12.2|

Source: Busse, Borrmann and Grossmann, 2004 (1) Unweighted averages for ECOWAS, excluding Liberia.

While it is true that a proportion of exemptions is associated with categories that appear difficult to eliminate (for example, government, diplomatic and donor imports), experience shows that some revenue gains and minimization of fraud can be achieved with a tighter monitoring and control mechanisms (Tsikata, 1999).

> 23 Countries that make the most use of exemptions tend to be those with the highest tariff protection (Hood and Radack, 1998).

14
frequent events and build up the response capacity of the state to an acceptable return period (e.g., 200 year event). The decision on this return period is arbitrary and depends on the scenario at play and financial capacity of the government considered.

A hypothetical risk financing strategy is illustrated in Table 5. It provides coverage up to the worst case scenario described in Table 4 (500 years return period). Parametric insurance, are used to cover shortterm budget outlays (mainly relief and early recovery), assuming that the building of financial reserve is not politically feasible beyond a limit of US$6 million. In the medium term, the country finances the expenditures through budget reallocation and domestic credit. Particular efforts can be put on broadening the state insurance program. Long-term expenditures (including mainly reconstruction costs) are financed with donor aid, external credit and tax increase.

**Table 5. Hypothetical Risk Financing Strategy**

|Time horizon|Liquidity gap<br>(US$million)|Risk financingstrategy|
|---|---|---|
|Short term|26|Building of reserve for US$6 million<br>Purchase of parametric insurance for 20 million<br>coverage|
|Medium term|39|Broadening and optimization of the state insurance<br>program|
|Long term|915|Conservative fiscal policies to expand potential access<br>to external capital|

The use of dynamic financial analysis also illustrates the need to differentiate between the concept of liquidity gap, which the difference between resources needed and available at specific time in the aftermath of a potential disaster event; and the overall resource gap faced by a country, which is the difference between the resources available post disaster overall compared to the needs that would be generated by a potential disaster event.

Financial strategies can easily be devised to limit the risk of liquidity gap post disaster. These strategies can be highly effective in ensuring that resources are available to finance post disaster operation when needed. A good understanding of the timing in the needs for resources can help optimize such strategy.

A resource gap tends to indicate a deficit in the resources needed to address disaster losses in the longrun. This is often the case for countries with high debt to GDP ratio, which would not be able to access external credit to the full extent their needs in case of disaster. These countries have literally lost the possibility of spreading risk over time. Recent analysis conducted by the IADB (2005) show that a number of countries in Latin America face resource gap for return period a frequent as 100 years.

For these countries, there is an urgent need to engage both in risk reduction activities to reduce their overall exposure and in conservative fiscal policies that would over time increase their capacity to mobilize resources should a disaster event occur.

16
# _Measuring skills requirements by occupation_

We calculate two measures of skills requirements. The first measure is the skills count, that is the average number of times a skill from a given skill category appears in job postings mapped to a given occupation. We use this measure to explore complementarities among the nine subsets of skills categories (advanced, intermediate, and basic digital skills; three subsets each of cognitive and socioemotional skills and their subsets) within occupations.

Our second skills measure is the average relative frequency with which a skill category appears in an occupation. The measure is calculated by averaging for each occupation the number of times a skill category appears in the job posting mapped to that occupation divided by the total count of skills in the job postings for that particular occupation (that is, expressed as a share of total skills rather than the raw skill count).

# **3.2. Creating occupational skills profiles and digital occupation levels**

# _Using the online job postings data to create occupational skills profiles_

We take two additional steps to create our final data set. First, we map each job posting classified with the 2013 Malaysia Standard Classification of Occupations (MASCO) and its related skills profile to the 2008 International Standard Classification of Occupations (ISCO-08; see ILO 2012), the international occupational classification system.<sup>15</sup> This gives us occupational skill profiles for the occupational codes in the employment data from other countries.

Second, we impute the occupational skills profiles of 27 occupations. Among the 127 3-digit ISCO-08 occupations, the online job postings data did not include job postings for 16 occupations — primarily agricultural workers and street vendors — and 11 did not have sufficient job vacancy postings to generate a skills profile for that occupation.<sup>16</sup> These 27 occupations roughly represent between 20 and 40 percent of employment in the four countries studied.<sup>17</sup> Omitting them would distort the distribution of digital skills in a country. Instead, we impute their skills profiles by identifying similar

> 15 Job vacancies are mapped to ISCO codes at the 4-digit level.

> 16 We defined a threshold of at least 45 job postings per occupation.

> 17 The imputed occupations with employment shares exceeding 1 percent in any of the four countries (and the ISCO code) in the sample are: street and market salesperson (521), market gardeners and crop growers (611), animal producers (612), fisheries workers/hunters/trappers (622), subsistence crop farmers (631), subsistence livestock farmers (632), subsistence fishers/hunters/trappers/gatherers (634), street vendors (excluding food) (952). The full list of imputed occupations, and the occupations used to impute their skills profiles, are provided in Annex 1.

14
**Figure 8: Rate of Housing Growth, in Sq. Meters**

<!-- Start of picture text --> Total Housing Stock<br>(Sq. Meters)<br>530,000 3,420<br>520,000<br>3,400<br>510,000<br>3,380<br>500,000<br>3,360<br>490,000<br>480,000 3,340<br>470,000<br>3,320<br>460,000<br>3,300<br>450,000<br>3,280<br>440,000<br>430,000 3,260<br>Jan-15 Jan-16 Jan-17 Jan-18 Jan-19<br>Private Public<br><!-- End of picture text --> <!-- Start of picture text --> Average Growth in Housing<br>(Sq. Meters: 2015-2019)<br>7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>Average Growth Rate National Average<br>Syrdarya Namangan Surkhandarya Tashkent Region Jizzakh Khorezm Fergana Andijan Tashkent city Samarkand Bukhara Navoi Karakalpakstan Kashkadarya<br><!-- End of picture text --> _Notes: State Statistics Committee and Author’s calculations._

# **How affordable is housing in Uzbekistan?**

There are several customary approaches to measure the affordability of housing. The L2CU data allow two of the most common: one based on monthly consumption/expenditure, and another based on the estimated value of an occupied dwelling. Both approaches suggest that in addition to being legally inaccessible, Tashkent and a few other cities are not affordable for most of Uzbekistan’s citizens.

The first approach to measuring affordability, based on monthly consumption or expenditure, is usually benchmarked to thresholds first calibrated on data from the United States. The first such definition was applied in the 1920s and set at monthly expenditure at 25 percent of income (see Pelletiere (2008) for a detailed history). In later years, the threshold rose to 30 percent of income, which remains a popular standard, and has been officially adopted by the U.S. Department of Housing and Urban Development.<sup>21</sup> This standard is commonly used in international analyses of housing affordability.

The second approach measures the affordability of housing by comparing the median home value in each locality with the median income in the same locality. The resulting indicator is expressed as a

> 21 For instance, Feldman (2002).

25
# **2.1. Financial Development in Africa in International Comparison**

Despite progress by Africa’s banking systems over the past twenty years, they remain relatively small, costly, and focused on the short-term end of the yield curve, and thus neither efficient nor inclusive.

# **2.1.1. Size and Outreach**

To compare banking systems in Africa to a proper benchmark, the sample is limited to low- and lower-middle-income countries in Sub-Saharan Africa. The median for this group is then compared to the median country across low- and lower-middle-income countries outside Africa.<sup>6</sup> Several upper-middle-income African countries are therefore dropped in the statistical comparisons in this section, although they are included in the discussion on intra-regional variation below.<sup>7</sup> Figure 1 shows that the median African country has a markedly shallower financial system than the median non-African country. Three standard indicators of financial development are presented: liquid liabilities to GDP, Bank Deposits to GDP, and Private Credit to GDP, using data for 2011 and 2021. While the median non-African developing country had liquid liabilities of 65% of GDP in 2021, the median African country had only 31%. Moreover, the median liquid liabilities to GDP ratio increased by 16 percentage points from 2011 to 2021 for non-African developing countries, compared to a five-percentage point gain for African countries. Similarly, the median

> 6 Throughout, the text refers to these two groups of countries as African and non-African developing countries.

> 7 The countries not included in the statistical comparison are: Botswana, Equatorial Guinea, Gabon, Mauritius, Namibia, Seychelles, and South Africa.

7
26 . |

.

-

likely to be non-linear — with the impact of a second independent decision maker being much greater than the addition of a third, for example — providing one explanation for the : insignificant results in column 2, Table 3. Similarly, the reduced dispersion of the IPC inthe DPI would also explain the differences in the results across columns 1 and 2 of Table 2.

Table 3: Differences in Coding the Index of Political Cohesiveness: Database of Political Institutions and Roubini and Sachs (1989) (OECD nations, 1975-1985

|Source ofDifference|Observations Affected|
|---|---|
|DPIcountsCDUandCSUas2parties;RS89_<br>—<br>countsthem as 1.|-FederalRepublicofGermany,<br>1983-1985|
|DPI indicates 2-party government; RS89<br>indicates a 1-partygovernment.|UnitedKingdom, 1978<br>Norway, 1975-1980<br>Sweden, 1975|
|DPI indicates aminority government; RS89<br>indicates amajority governmentofoneparty<br>|<br>oramajority coalition.|Belgium 1982-1985<br>§Denmark 1978, 1984-1985<br>Japan 1977-1980, 1984-1985<br>Norway 1985<br>Sweden 1983-1985<br>UK 1977, 1979|
|DPI indicatesaminority government, RS89<br>indicates a majoritygovernment ofone party<br>oramajority coalition. It is also possible that<br>language- based “wings” ofparties are treated<br>differently -DPI often countsthem as separate<br>parties.|Belgium, 1976-1977|
|Foreachyear,DPI records the situation as of<br>|<br>January 1; RS89 seemtouse adifferent<br>convention.|Denmark, 1981<br>Ireland, 1981<br>Norway, 1981-1982<br>Sweden, 1976, 1978-1979, 1981|
|Possiblethat language-based “wings” ofparties<br>are treateddifferently-DPI oftencountsthem<br>asmultiple parties.|Netherlands, 1978-1985<br>Belgium, 1981|
|DPI classifiesthem asparliamentary; RS89<br>classifiesaspresidential.|Finland, 1975-1985<br>France,1975-1985|
coming decades. These authors combine data on the types of tasks involved in occupations in the United States with the opinions of machine-learning experts to determine the probability that an occupation can be automated. They use the Occupational Information Network data set (O*NET) to derive the types of tasks involved for the United States’ standard occupational classifications (SOC). For a subsample of 70 occupations, they examined the types of tasks and job descriptions and determined, with a team of machine learning experts, whether all the tasks of a job can “be sufficiently specified, conditional on the availability of big data, to be performed by state of the art computer-controlled equipment.” If so, the occupation was deemed automatable. Then, by modeling the association between key O*NET task variables and whether these 70 occupations are automatable, they predicted the probability that the remaining six-digit occupation classifications are automatable. Their estimated probability that an occupation can be automated is therefore based on the tasks involved in the occupation derived from the O*NET data set and the association of these tasks with whether the original 70 occupations are automatable. They publish the estimated probabilities for 702, six-digit SOC occupational codes. These data are used to estimate the proportion of United States’ workers in occupations that are at low, medium and high risk of automation by probability cut-offs of 30 and 70 percent.

Their published data on the automation probabilities of the 702 SOC occupations are used by other researchers to estimate proportions of workers at high risk to automation. Several studies have used mappings between the United States’ SOC and International Standard Classification of Occupations (ISCO) to estimate the percent of workers in high risk occupations in other countries. Our methodology most closely resembles that of Chang and Huynh (2016), Asian Development Bank (2015) and Deloitte (2014). We use the publicly available mapping tables of the United States Bureau of Labor Statistics to map the six-digit SOC codes reported Frey and Osborne (2013) with the ISCO-08 coding system used in many census and survey data. We also use publicly available ILO mappings between ISCO-08 and ISCO-88 and the Hendrickx (2004) mapping between ISCO-88 and ISCO-68 to apply the automation probabilities to data sets using these earlier coding systems. In order to calculate the probability of automation for a particular ISCO-08 occupational classification, we take the average of the probabilities of the SOC occupations with a probability of automation that map to it. This same procedure is used to generate the probabilities of automation for the ISCO-88 occupation classifications from the 19
# **3.2 Market Activity**

- _3.2.1 Aggregate trading activity: The turnover ratio and real value traded have been increasing and reached expected levels in 2006, but dropped in 2007_

Like capitalization, value traded exhibited strong growth, resulting in a substantial increase of the stock market turnover ratio, which in 2007 stood at 47 percent, despite a decrease since 2006 (Exhibit 3.3). This places Egypt well ahead of some emerging countries such as Argentina, Chile, Colombia and Morocco. Egypt’s turnover ratio seems to have been catching up since 2001 and in 2006 reached a level that is in line with expectations once country characteristics are accounted for. The 2006 drop seems to have affected both the most active and least active companies alike: the turnover ratio of the 50 most active companies as a percentage of their market capitalization was 45.5 percent in 2007. This is still higher than other lower-middle income countries, but slightly lower than Jordan’s.

# **Exhibit 3.3 – Stock market turnover ratio and free float**

_% of GDP for selected countries, 2003-07 (upper left graph)_ _% of GDP for Egypt actual versus benchmarked values, 1990-07 (upper right graph) Free float distribution for private firms reporting to EGX, latest available information (lower graph)_ <!-- Start of picture text --> 100 Egypt Argentina Chile Colombia Hungary Israel Jordan Mexico Morocco Pakistan Peru Philippines Turkey<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007<br><!-- End of picture text --> <!-- Start of picture text --> %<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> 23
nationally representative – does not cover all of the upazilas in our upazila level panel data sample used for columns (1) and (2). The results in column (3) show a statistically significant and positive impact of rainfall on agricultural wage in both samples. Interestingly, the wage response is larger in magnitude in the rural sample, consistent with the theoretical prediction that the wage response to productivity shocks, under plausible conditions, is a positive function of the endowment of agricultural land (i.e., a higher _A_ ) (proposition 1). This evidence is reassuring as it confirms that the central channel through which agricultural productivity works in the theoretical model is in fact operative in the data.

The effects of rainfall on agricultural income cannot be estimated directly, as reliable data on income are unavailable. We can, however, estimate the elasticity of wage with respect to productivity (rice yield) and exploit the theoretical insights to understand if the data supports a positive income effect (a higher _Y_<sup>_v_</sup> in the model). Using the reduced form estimates from Table 2 (columns (1) and (4)), the implied wage elasticities with respect to productivity are: _Ewθ_ = 1 _._ 47 in small town sample, and _Ewθ_ = 1 _._ 82 in the rural sample. Although we do not have a precise estimate of _α_ , the available evidence in the context of Bangladesh suggests that it is likely to belong to the interval _α ∈_ [0 _._ 60 _,_ 0 _._ 70] (Hossain and Bayes (2009)). Thus (1 _− α_ )<sup>_−_1</sup> _∈_ [2 _._ 5 _,_ 3 _._ 33], satisfying the condition specified in proposition 1 [ _Ewθ <_ (1 _− α_ )<sup>_−_1</sup> ] _._ Even if we assume a conservative estimate of 2 _._ 33, the estimates for _Ewθ_ in both the rural and STE samples imply that a positive productivity shock to rice yield would increase rural income, providing a basis for structural change in employment driven by the Engel curve effect.

While a positive rainfall shock increases rice yield, for appropriate interpretation of the results, it is useful to understand whether this reflects only the impact of transitory weather shocks on farming. Though the rainfall variations across upazilas and over time are expected to affect the rice yield directly, they are likely to affect long-term productivity differences by influencing investment in irrigation. In a cross-section regression, irrigation investment is negatively correlated with rainfall, as they are substitutes.<sup>22</sup> But over time, in the same upazila, irrigation expansion is positively correlated with rainfall, as irrigation investment

> 22It is, however, important to appreciate that rainfall affects productivity even with irrigation. This is because of the simple fact that the marginal cost of irrigation water is positive, but it is zero for rainfall.

22
back into poverty on the other hand, what may be required is strengthening the coverage, reach and if needed also the generosity of social protection and safety net systems.

**Figure 4: Episodes of movements in and out of poverty (% of population)**

(a) Episode between 1995/96-2003/04 (b) Episode between 2003/04-2010/11 <!-- Start of picture text --> 40 40<br>36<br>35 33 35<br>29<br>30 30<br>26<br>24<br>25 25<br>20<br>20 17 20<br>14<br>15 15<br>10 10<br>5 5<br>0 0<br>Poor, poor Poor, non- Non-poor, Non-poor, Poor, poor Poor, non- Non-poor, Non-poor,<br>poor poor non-poor poor poor non-poor<br><!-- End of picture text --> _Source_ : World Bank staff calculations using data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11. The methodology used is called the synthetic panel methodology and is described in Annex 2.

What is the relative size of these groups in Nepal? We answer this question for two poverty reduction episodes between 1995/96 and 2003/04 using data from three rounds of the NLSS data. Recall that the headcount poverty rate using the comparable 2010/11 poverty line declined by 14 and 19 percentage points respectively during these two episodes. From what is presented in Figure 4, what we see is that if 26 percent of the population escaped poverty between 1995/1996 and 2010/11, 17 percent actually fell into poverty during the process. Likewise, between 2003/04 and 2011, 29 percent moved out of poverty while 14 percent fell back in. This essentially implies that that for every 2 Nepalis that got out of poverty in 2010/11, one slid back.

The figure also shows that there is a sizeable persistence of poverty status over the years. In both episodes, roughly three out of five Nepalese who were found to be poor in the latter period were actually already poor in the beginning period. There are different ways of thinking about chronic poverty, but if we consider households to be chronically poor if they are found to be poor in two consecutive survey rounds separated roughly by seven years in the case of Nepal, then we may conclude that roughly 60 percent of those that are found poor for any given year actually suffer from chronic poverty.

This definition is admittedly very simplistic. Among others, it does not take into account, for example, the duration or chronicity of poverty. For instance, a household that is poor in the first period, escapes poverty immediately the subsequent year and remains out of poverty only to fall right back in exactly the year of the follow-up survey would be identified as chronically poor. Conversely, another household that may have toiled for a majority of the intervening years in poverty, escaping only in the year of the follow-up survey may be characterized at non-chronic poor. There may be several other desirable characteristics we may want our measure of chronic poverty to pick up on, but all of them are likely to place heavy demands on data. The spirit of this analysis and the presentation of these numbers is to begin to ask the question about 9