rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3.2

**Trends in East Asian Average Tariff Rates (as reported in APEC Individual Action Plans)**

<!-- Start of picture text --> 45.0 -<br>40.0 _-- - - - - - - - - - - - - - - _ _ __-_- - _ -__-_- - - -__- - - -_-__- _ - -_-__- _ - -_-_ _-_ - -_-__- _ - -_-<br>35 .0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -_ _ _ __ _<br>30.0 - - ---------------------------- ~~~~~~~--- - *-China<br>---------- _-u _Hong Kong<br>Indonesia<br>c 25.0 -- - - - - _-_-_-_-_-__-_ < _ _ _ _ _ - _ _ _ _ _ _ _ _ _ _ _ Korea<br>eS\ \ -uMalaysia<br>o. 20.0 ------ Philippines<br>--_- Singapore<br>I 5 - ' -w - --------- --------------- - Thailand<br>10.0 - - - - - - -<br>5.0 -- -- - -- -- __ - -- __ -- _ _ - -- _ -___ ------- _ __ --------_ _ __<br>0.0<br>1988 1993 1996 1998 2000<br><!-- End of picture text -->

**Note: Figures are simple average tariff rates.** Source: **Japan, METI (2001:** Fig. 1.2.1, 19)

This figure indicates that East Asian economies have liberalized trade. These tariff reductions, however, cannot be attributed solely to the effect of APEC's TILF program but also derive from the GATT/WTO framework as well from AFTA's CEPT process, as previously discussed.<sup>20</sup> ' A 1999 APEC Economic Committee study (an update of the original 1997 study) estimated that APEC's trade liberalization and facilitation measures, committed to date (i.e., 1998), would "expand the region's annual income (GDP) by US$75 billion (at 1997 prices), or 0.4% of the region's total GDP."<sup>202</sup> (This is in addition to annual income gains created from the remaining Uruguay Round commitments, which would be 0.7 percent of GDP or $114 billion.) APEC members were shown to benefit more than nonAPEC economies, and if dynamic effects are included, income gains would be $90-lOS billion.

The study also estimated greater gains in real income from trade facilitation measures than from the trade liberalization measures that were included in the model. Of the estimated 0.4 percent total increase in GDP, 0.25 percent was from trade facilitation and 0.16 percent from trade liberalization. Of the aforementioned $75 billion gain to the region's annual income from both trade liberalization and facilitation, $46 billion would be from facilitation alone. Trade facilitation refers to trade measures that reduce trade costs (except for trade liberalization measures such as tariff cuts). These include activities such as streamlining customs procedures, aligning domestic standards with international standards in certain sectors, establishing MRAs (mutual recognition arrangements) in the telecommunications sector, reducing paper document use, enhancing business mobility through use of a special travel card, and others.

62
# **Exposure of Belt and Road Economies to China Trade Shocks**<sup>*****</sup>

Paulo Bastos World Bank JEL: F1; F6

> * The author is grateful to Caroline Freund, Matilde Lebrand, Mauro Boffa, Francois de Soyres, Michele Ruta, Chunlin Zhang, and other participants at the BRI authors workshop for helpful comments and suggestions. Federico Merchan provided excellent research assistance. Funding from the China World Bank Partnership Facility is gratefully acknowledged. Research for this paper was also supported in part by the World Banks Multidonor Trust Fund for Trade and Development and through the Strategic Research Program on Economic Development. All remaining errors are my own.
Table 1: Change in monthly sales relative to 2019 and recovery

|||Change|in sales|||Reco|very||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Conditional|(2)<br>Cont. initial<br>productivity|(3)<br>Conditional|(4)<br>Cont. initial<br>productivity|(5)<br>Conditional|(6)<br>Cont. initial<br>productivity|(7)<br>Conditional|(8)<br>Cont. initial<br>productivity|
|Informality|-10.08**|-10.41**|||-5.317**|-6.058**|||
||(1.702)|(2.029)|||(2.349)|(2.924)|||
|Necessity|||-11.17**|-9.289**|||-6.397**|-7.103**|
||||(1.347)|(1.432)|||(1.580)|(1.703)|
|De Soto|||-8.770**|-7.929**|||-4.855**|-5.198**|
||||(1.151)|(1.231)|||(1.319)|(1.429)|
|Parasite|||-8.428**|-5.553**|||-6.577**|-2.056|
||||(1.208)|(1.691)|||(1.343)|(1.960)|
|Fully formal|||_Baseline_|_Baseline_|||_Baselne_|_Baselne_|
|log(Initial labor productivity)||0.430**||0.324**||0.0223||-0.100|
|||(0.110)||(0.114)||(0.110)||(0.111)|
|Lag change in sales|||||-0.724**|-0.754**|-0.724**|-0.756**|
||||||(0.0130)|(0.0152)|(0.0127)|(0.0150)|
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Lag change in sales|No|No|No|No|Yes|Yes|Yes|Yes|
|<br>Observations|19569|15190|19984|15365|10203|8100|10417|8191|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Recovery is calculated as the difference in the changes in sales across rounds of data collection.

Robust standard errors in parentheses * p<0.10, ** p<0.05, ** p<0.01 Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
only in one country—Nigeria—do we observe average absence below 30 percent. Being absent from school is about as common as being present in the school but absent from class. The rank correlation coefficient between the two measures is less than 0.5 at the country level, making the school absence rate at best a partial measure of teacher effort (Table 2). This is most starkly illustrated in the cases of Kenya and Tanzania (2014), both of which have relatively low school absence rates (15 percent) but relatively high classroom absence rates conditional on being in school (38–39 percent).

When a large share of teachers is not teaching, unsurprisingly, a large share of classrooms will be occupied by only students. Consistent with the absenteeism findings discussed above, we find, averaging across countries, that one-third of the classrooms were “orphaned” classrooms, where students are present but there is no teacher.

Over time in these countries, the absenteeism rates appear remarkably stable. Chaudhury et al. (2006) estimated a school absence rate of 27 percent in Uganda in 2002–03, which compares to our measure of 30 percent in 2013. Similarly, while absence from school fell by a third in Tanzania between 2010 and 2014, this was largely offset by an increase in absence from the classroom conditional on being in school; the net result being a small decline in absence from class between the two surveys.

What do these results imply for the amount of instruction time that students receive? To answer this, the surveys first recorded the scheduled time of a teaching day—after break times—according to school records. Averaged across schools and countries, this comes to 5 hours and 27 minutes. We then multiply this number by the proportion of teachers absent from class. If 10 teachers are supposed to teach 5 hours and 27 minutes per day, yet 4 teachers are absent from either the school or the classroom at any one time, then the scheduled teaching time is reduced to 3 hours and 16 minutes.

Moreover, even when in the classroom, teachers may not necessarily be teaching. We address this by using the results from the classroom observation carried out as a part of the survey – an observation schedule based on recording a minute-by-minute snapshot of what the teacher was doing, for a randomly selected fourth-grade mathematics or language class.<sup>9</sup> The percentage of the lesson lost to non-teaching activities varied from 18 percent in Nigeria, the country with the lowest classroom absence rate, to 3 percent in Uganda, the country

> 9 This approach likely provides an upper bound on the time devoted to teaching. In the two pilot countries, Tanzania and Senegal, data on teacher behavior were collected while observing the teacher both inside and from outside the classroom. We find that time spent teaching when in class is about 30 percent lower in Senegal and about 10 percent lower in Tanzania if the enumerator stands outside the classroom rather than inside.

9
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7. Benchmarking water and sanitation indicators, 2006**

||Unit|Low-income countries|**Cape Verde**|Middle-income<br>countries|
|---|---|---|---|---|
|Access to piped water|% pop|10.1|**34.9**|56.4|
|Access to standposts|% pop|16.1|**36.5**|20.4|
|Access to wells/boreholes|% pop|38.3|**10.1**|10.1|
|Access to surface water|% pop|33.8|**14.3**|13.9|
|Access to flush toilets|% pop|5.3|**48.7**|44.0|
|Access to latrines|% pop|57.2|**8.3**|33.9|
|Open defecation|% pop|37.1|**45.4**|15.8|
|Domestic water consumption|liter/capita/day|72.4|**34.6**|n.a.|
|Revenue collection|% sales|96.0|**93.3**|99.2|
|Distribution losses|% production|33.0|**29.8**|23.1|
|Operating cost recovery|% total costs|56.0|**484.8**|80.6|
|Total hidden costs as % of revenue|%|130.0|**10.2**|84.9|
|||Countries with scarce<br>water resources|**Cape Verde**|Other developing<br>regions|
|Residential tariff|U.S. cents per m<sup>3</sup>|**309.30**|60.26|30–600|
|Nonresidential tariff|U.S. cents per m<sup>3</sup>|**453.30**|120.74|..|

_Source:_ Banerjee, Skilling, and others 2008; Morella, Foster, and Banerjee 2008. Derived from the AICD water and sanitation utilities database (http://www.infrastructureafrica.org/aicd/tools/data). Access data comes from DHS (2005) as presented by the WHO-UNICEF Joint Monitoring Programme. _Note:_ m<sup>3</sup> = cubic meter. n.a. = Not applicable.

**Table 8. Evolution of hidden costs associated with ELECTRA’s water operations**

||Volume<br>produced|Distribution<br>losses|Collection<br>ratio|Average<br>operating cost|Average effective<br>tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|
||(Mm<sup>3</sup>/year)|(%)|(%)|($/kWh)|($/kWh)|($ million/year)|(% revenue)|
|2000|3.0|25.9|91.0|0.86|1.70|0.6|16.4|
|2001|3.0|22.8|91.0|0.91|1.64|0.5|12.5|
|2002|3.6|27.6|95.9|0.73|1.72|0.6|13.7|
|2003|4.0|29.1|83.9|0.84|2.83|2.2|28.1|
|2004|4.1|29.7|94.4|1.13|3.13|1.6|17.8|
|2005|4.3|30.8|98.0|1.10|3.17|1.4|15.4|
|2006|4.3|29.8|93.3|0.66|3.20|1.8|18.1|
|2007|4.2|30.6|104.8|1.27|3.83|1.5|13.0|
|2008|4.2|31.5|98.1|0.55|4.31|2.0|16.4|
|2009|4.5|35.3|99.1|0.76|4.43|2.6|21.9|

_Source:_ Derived from ELECTRA Annual Reports 2001/09. _Note:_ Mm<sup>3</sup> = millions of cubic meters; kWh = kilowatt-hour.

16
Figure 16: EU imports by tariff regime and country group (in million USD) <!-- Start of picture text --> Exports under zero-MFN Eligible exports that used preferences<br>Eligible exports that did not use preferences Exports non-eligible for preferences<br>Developed* 391,025 90,528 22,779 174,751<br>Developing 592,627 174,042 33,333 298,071<br>LDC 11,760 26,913 1,607 5<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Source: Authors’ calculation using statistics from Eurostat, 2016.<br>Note: (*) Excluding EU countries<br><!-- End of picture text --> Preference utilization rates vary widely across countries. Countries such as Bangladesh represent more than 60 percent of preferential trade from LDC countries to the EU and have rates of utilization above 90 percent (see Figure 17). In contrast, countries such as Chad and Guinea‐ Bissau rarely use preferences provided through the EU’s EBA. Developing countries such as Sri Lanka used GSP preferences for only 55% of their eligible exports. A key explanation of the low utilization rates is restrictive rules of origin as well as the related administrative burden. In fact, 11 percent of Sri Lankan firms, interviewed in an ITC survey<sup>18</sup> on non‐tariff measures in 2011, considered rules of origin a recurrent problem.

> 18 <u>Sri Lanka: Company perspectives – An ITC series on non‐tariff measures, 13 Dec. 2011.</u>

19
Figure 2: SMEs have Shorter Survival Times than Large Firms to Sales Disruptions <!-- Start of picture text --> Survival if sales stopped (weeks) Coverage of fixed costs through retained<br>earnings (weeks)<br>30 30<br>25 25<br>20 20<br>15 15Large<br>SME SME<br>10 10<br>Large<br>5 5<br>0 0<br>Weeks<br>Zimbabwe Chad Albania Italy Russia Georgia Moldova Guinea Cyprus Greece Zambia Togo Niger Colombia Greece Italy Jordan Kazakhstan Kenya Morocco Peru Portugal Russia Turkey Ukraine<br><!-- End of picture text --> Source: World Bank Enterprise Follow-Up Surveys and Bosio et al. (2020) _Note_ : Mean estimates using the World Bank Enterprise Surveys on the left panel use stratification weights adjusted for the follow-up enterprise surveys according to median eligibility.were used to produce the estimates. The weights account for non-response, assuming that businesses that could not be re-contacted (unobtainable) have exited the market.

Without any sales, the survival time they self-report is on average 4-5 weeks across countries, which is not very different to large firms. An analysis of the distribution across countries shows that, without sales, half of them will have shut down at the end of the first month, and 3 out of 4 by the end of 10 weeks (see Annex Fig A1). A protracted downturn can therefore result in an exodus of firms at these turning points.

# 2.1 Prevalence of SMEs in affected countries and sectors

The vulnerability of SMEs to the crisis depends in part on their prevalence in more crisis-exposed countries and sectors. Measuring exposure to the crisis is difficult because countries and sectors are interconnected through trade linkages, which can magnify the effects of demand and supply shocks. For instance, in February, Chinese lockdowns led to shortages of key inputs and disrupted production worldwide. European consumers cutting back on clothing demand led to order cancellations and delayed payments for apparel suppliers across Asia (Wall Street Journal, 2020). Recently published measures of economic risk reflecting the impact of household demand shocks explicitly allow for these trade linkages.<sup>5</sup>

> 5 Osotimehin and Popov (2020) propose sectoral measures of both health and economic risk by countrysector using the World Input-Output tables to account for demand interlinkages. The economic risk is measured as the decline in employment induced by a 90% decline in the household demand for social

5
-6-

role also in the promotion and regulation of economic activities. like agriculture, mining, manufacturing, commerce which accolnt for a minor share of local expenditures.VA

1.07 A,gsirment of Public Service Functions Among Levels of Government. Legal statutes generally assign the performance of specific se.vice functions to different tiers of government. In general, the logic of these assignments is that functions with larger externalities or economies of scale should be performed by higher level authorities. However, the real situations are seldom clear cut because the definitions used are often too broad and because higher level authorities are frequently called upon to supply a given public good which the lower authorities are either financially or techhically unable to provide. As a result, the Drovision of many public services is shared by different levels of government and the very proportion of this sharing may vary considerably for different services from place to place even within the same country. Since the analysis of statutory responsibilities is often of little help, a better grasp of the factual situation can be derived from examining the expenditures effectively made by each level of government for specific functions. Table 1.7 provides a fair illustration of such expenditure breakdown in the case of Brazil for fiscal year 1984. As can be seen, apart from expenditures on external affairs and defense, which are obvious concerns of the central government, general services remain an important expenditure item for the subnational governments, with the bulk of public outlays for the legislative and the judiciary being carried out at the regional (state) level, while both the regional and thxe local budgets are heavily burdened by the costs of public administration. Subnational governments, especially at the municipal level appears principally involved also in the provision of social services, mostly

- _5/_ In its publication of government finance statistics, the IMF follows COFOG (the United Nations Classification of the Functions of Government) which considers four basic categories: (a) General Government Services, including all activities indispensible to the existence of an organized state, which cannot be allocated to particular groups of beneficiaries; (b) Community and Sonial Services, including all services supplied to the community or the household directly; (c) Economic Services: including those associated with the efficient operation of business, and (d) Other Functions: including interest payments or underwriting of public debt as well as transfers to other levels of government, which disappear in the consolidation of accounts of the levels and bodies of government. Thus, Housing, Community Amenities, Water Supply, Sewerage, Garbage Collection, Street Lighting, and Recreational, Cultural and Religious Facilities are classified as social services, while Street Construction and Maintenance, Urban Transport, Town Gas, Electricity and Telephone Connections are classified as economic services.
Consistent with diminishing marginal returns and the convergence hypothesis of the neoclassical growth theory, all the statistically significant coefficients on the ―convergence factor‖ (3-lag value added per worker) are negative, ranging from -0.038 for the mining sector to -0.018 for wholesale and retail sector. The coefficient on the ―convergence factor‖ for the construction sector is -0.015, but not significant at the 10 percent level, indicating that construction industry does not exhibit statistically significant diminishing returns.

The coefficients on banking and currency crises are negative in all the specifications; however, the sectors exhibit different degrees of sensitivity to such episodes. Currency crises have the most adverse impact on construction sector growth (coefficient of -0.04), while public utilities sectors are most affected by banking crises followed by the manufacturing sectors (coefficients of approximately -0.03). The coefficients on remaining controls are generally consistent with theory. Inflation and government spending exhibit a negative association with sectoral value added growth, while trade openness has a positive association.<sup>5</sup> Interestingly, the coefficient on de-facto financial openness is only significant for the construction sector regression, and is negative and significant at the 1 percent level.

# Table 6 [about here]

Table 6 reports analogous regression results with the additional set of controls for agricultural versus industrial shares, and institutional quality of each economy. The results discussed above are robust to the inclusion of additional controls, with the coefficient on lagged financial growth in the construction sector regression exhibiting a higher magnitude than in the baseline specification (0.22 compared to 0.17).

# **ii. Financial shocks**

Tables 7 and 8 report panel regression results based on specification (6) with lagged financial value added growth replaced by the financial sector contraction and expansion dummy

> 5 The data on government spending obtained from Heston, Summers and Aten (2009), which includes collective consumption of government for public good type activities, like police (at constant prices).

11
Table A.14: Eect of a direct SLM training intervention on other farmers' SLM adoption

|Adoption|Ctrl. Mean|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Mulching|0.336|-0.022<br>[0.033]|10,955|0.056|
|Strip-tillage|0.181|-0.035<br>[0.028]|10,955|0.031|
|Pit planting|0.071|0.028<br>[0.018]|10,955|0.016|
|Contour farming|0.005|-0.003<br>[0.003]|10,955|0.006|
|Crop rotation|0.153|0.017<br>[0.018]|10,955|0.005|
|Row planting|0.079|-0.012<br>[0.015]|10,955|0.018|
|Improved fallowing|0.020|-0.007<br>[0.006]|10,955|0.003|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013. Notes: Regressions include the same explanatory variables as models in Table 10. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

57
_Table 6: Household food security fixed-effect model_

|Variables|(1) Ate<br>fewer<br>meals|(2) No food<br>of any kind<br>to eat|(3) Went to<br>sleep at night<br>hungry|(4) Went whole<br>day and night<br>without eating|(5) Household<br>hunger score<br>(2 + 3 + 4)|
|---|---|---|---|---|---|
|Displaced with PDS|-0.111***|-0.077***|-0.046**|-0.034**|-0.157***|
||(0.027)|(0.022)|(0.018)|(0.016)|(0.046)|
|Observations|1,849|1,849|1,849|1,849|1,849|
|Adjusted R-squared|0.241|0.229|0.178|0.268|0.267|
|Qhada fixed effects|Yes|Yes|Yes|Yes|Yes|
|Number of clusters|597|597|597|597|597|
|Control mean(Displaced without PDS)|0.382|0.0922|0.145|0.0459|0.283|

Note: Standard errors are clustered at census blocks . Significance levels are denoted as follow: * p<0.10 ** p<0.05 *** p<0.01. Sample is limited to displaced households from the full "short-form" sample.

_Table 7: Poverty and consumption fixed-effect model_

||(1) Below<br>|<br>Monthly|<br> per capita e|<br>xpenditure 000|<br>IQD(log)|
|---|---|---|---|---|---|
|Variables|poverty line|(2) Total|(3) Food|(4) Non-Food|(5) Education|
|Displaced with PDS|-0.146***|0.162***|0.128**|0.188***|0.017|
||(0.047)|(0.049)|(0.057)|(0.060)|(0.113)|
|Observations|327|327|327|327|327|
|Adjusted R-squared|0.241|0.534|0.383|0.500|0.059|
|Governorate fixed effects|Yes|Yes|Yes|Yes|Yes|
|Number of clusters|91|91|91|91|91|
|Control mean(Displaced without PDS)|0.415|4.910|4.009|4.337|0.150|

Note: Standard errors are clustered at Nahiya (sub-district) level. Significance levels are denoted as follow: * p<0.10 ** p<0.05 *** p<0.01. Sample is limited to displaced households from the "long-form" sub-sample.

34
|8|Martinez (2022)|_Journal of Political_<br>_Economy_|137 countries|Autocracies overstate yearly GDP<br>growth|Limitations in country statistical capacity do not<br>significantly affect autocracies' exaggeration of<br>GDP growth.|
|---|---|---|---|---|---|
|9|Oechslin & Steiner (2022)|_Review of_<br>_International_<br>_Organization_|146 countries|Statistical capacity and corrupt<br>bureaucracies|A positive relationship between the<br>growth rate of real GDP per capita and statistical<br>capacity exists for countries with low corruption,<br>but not for countries with high corruption.|
|10|Sanderfur & Glassman<br>(2015)|_Journal of_<br>_Development Studies_|Sub-Saharan<br>African countries|Political economy of bad data|Sub-Saharan African countries as a whole have a<br>lower SCI score (i.e., 58) than the global average<br>(i.e., 64), but much heterogeneity exists with<br>country scores ranging from the bottom to more<br>than the 75th global percentile.|
|11|Sanga et al. (2011)|_International_<br>_Statistical Review_|43 African<br>countries|Proposing an index to measure<br>statistical capacity for African<br>countries|The SCI does not cover certain aspects of an<br>NSO such as organization, human development,<br>and funding. There is a weak correlation<br>between the SCI and the proposed index.|
|12|Tapsoba et al. (2017)|_Journal of_<br>_International_<br>_Development_|62 developing<br>countries|Statistical capacity building<br>impacts on reducing procyclical<br>fiscal policy|IMF-supported technical analysis to countries<br>improves their statistical capacity during 1990-<br>2012.|

**Note** : SCI stands for statistical capacity index.

38
<!-- Start of picture text --> Comparison of Peak, Non-peak, Multiple, and Diversifying Peril Transactions (1997-2008)<br>16<br>5 a12 Pe<br>a<br>a<br>© 10 rs<br>a 3 oe" -<br>= 6 a<br>2] Ute<br>2 _. -<br>0 50 100 150 200 250 300 350 400 450 500<br>Expected Loss (basis points)<br>= Peak peril @ Non-Peak Peril 4 Multiple Peril TM Diversifying Peril<br>Source: Cummins and Mahul (2008)<br><!-- End of picture text -->