rafmacalaba/datause-encoder-data huggingface dataset:
captured by two indicators. The first concerns intellectual property (IP) rights protection, which is proxied by the proportion of IP infringement cases won by the plaintiff in the local courts. Given the substantial monetary costs to initiate lawsuits in China, the probability of winning an IP infringement case by the IP owner is a reasonable proxy for the enforcement quality of IP rights (Ang et al., 2014). The second measure is on how well a firm’s property rights and legal contracts can be protected by the local legal system, based on firms’ responses to the following question from the World Bank Enterprise Survey (WBES): “Regarding commercial or other legal disputes, in what percent of cases were your company’s legal contracts or properties protected when a verdict was given and enforced?” The court supports the plaintiffs in most of the IP infringement cases, with over 80% of plaintiffs winning in the local court (see Panel A in Table 1). This percentage has risen from 81% in 2006 to 92% by 2013, signaling steady improvement in IP protection. Regional variations are only feasible from the WBES, which indicates that firms in coastal provinces report better protection for property rights and legal contracts than those in inland provinces (see Panel B).

Infrastructure quality is measured as follows. The first focuses on roads. By utilizing data from statistical yearbooks of China, we construct measures such as the lengths of highways and expressways for each province. Second, by using the WBES data, we construct measures of energy provision capacities (i.e., frequency of power outages) and the efficiency of telecommunication services (i.e., days needed to install a new telephone and days needed to gain access to internet services). Finally, we collect data on internet access, since internet access is found to significantly affect firms’ sales growth and productivity level around the world and is a general-purpose technology (Clarke et al. 2015).

Panel C in Table 1 indicates quantum leaps in infrastructure provision in China during the economic reforms, with the quantity of roads, railways, and expressways skyrocketing. For instance, the average length of highways at the provincial level rose from 3.6 (in 10,000 km) in 1993 to 14.8 in 2015. Within 10 years, the percentage of the population covered by internet service increased from 4.6 to over 50 percent. Infrastructure access exhibits some, but only _limited,_ regional variations. Compared with coastal provinces, firms in inland regions face 17
Table 7 displays the results when using mean village consumption instead of the mean of the bottom half as the village welfare measure. Three main findings are clearly apparent. First, the partial registry approach continues to perform vastly better than the other alternatives when attempting to predict mean village welfare. Second, both the partial registry approach and the RWI suffer moderately when predicting the mean over all households rather than the mean of the poorest half of households, particularly when it comes to rank correlations. This may be because of idiosyncratic positive outliers in the upper half of the household predicted welfare distribution which are more difficult to predict using both geospatial data and predictions trained on asset indices.

**Table 7.** <u>Metrics of all the methods when using all households in the villages</u>

|<br>**Rank correlations**|<br>**All districts-all HH**|
|---|---|
|Partial registry (10% of the census sample)|0.61|
|PMT scores|0.02|
|IHS training sample|0.19|
|RWI|0.14|
|**AUC**||
|Partial registry (10% of the census sample)|0.77|
|PMT scores|0.50|
|IHS training sample|0.59|
|RWI|0.55|
|**R-squared**||
|Partial registry (10% of the census sample)|0.35|
|PMT scores|0.00|
|IHS training sample|0.01|
|RWI|0.02|

Third, the method that combined survey and geospatial predictors without a partial registry (IHS plus geospatial predictors) performs much better when using the full sample of households than when only using the bottom half for each village. The rank correlation increases from 0.13 to 0.19 and the AUC increases from 0.53 to 0.59. This is because on average there are only approximately sixteen households interviewed in each village in the IHS, and average per capita consumption is much more accurately measured when all sample households in each EA are used to train the model rather than only the bottom half. The resulting predictions, when using the full IHS sample, also performs better than the Meta relative wealth index. In this context, when trying to predict the average predicted per capita consumption from a census extract, the fact that the RWI uses additional training data from many countries and proprietary indicators on connectivity does not fully compensate for the fact that it is trained to predict an asset index rather than a consumption-based welfare measure. Therefore, a model that predicts average village per capita consumption directly on the basis of publicly available geospatial characteristics is slightly superior for targeting in this context, though both are far worse than collecting additional partial registry data to train a better geospatial model.

21
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 21. Zimbabwe’s pattern of capital investment in infrastructure differs from that of comparator countries**

Investment in infrastructure sectors as percentage of GDP, by source <!-- Start of picture text --> 3.0<br>Zimbabwe<br>2.0 LIC-fragile<br>1.0<br>0.0<br>Public ODA Non-OECD Private<br>% of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

_Note:_ Private investment includes self-financing by households. ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and sanitation; LIC = low-income countries.

# How much more can be done with existing resources?

As much as $752 million of additional resources could be recovered each year by improving efficiency (table 18). The most vexing problem is the underrecovery of costs in power, roads, and water. Every year up to $321 million is lost due to undercharging in these three infrastructure sectors. Raising power tariffs to cost-recovery levels could save $220 million annually, a better-balanced road fuel levy could bring in an additional $71 million, and more economically reasonable water tariffs could recover additional $48 million per year. Collection of bills also needs to be improved—as a result of poor collection discipline, Zimbabwe is losing $255 million per year in the power sector and $18 million per year in the water sector. Overstaffing in power and water parastastals is also quite significant. Cutting staff levels to economically viable benchmarks could save $71 million and $8 million in the power and water sectors, respectively; reducing distributional losses could save another $27 million and $10 million in the water and power sectors, respectively. Looking across sectors, the power and water sectors can benefit the most from tackling identified inefficiencies, followed by the roads sector.

**Table 18. Zimbabwe’s potential gains from greater operational efficiency (annualized)**

||ICT|Irrigation|Power|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Underrecovery of costs|—|n.a.|220|71|48|339|
|Overstaffing|n.a.|—|91|—|10|101|
|Distribution losses|—|—|10|—|27|37|
|Undercollection|—|n.a.|255|n.a.|18|273|
|Low budget execution|0|0|0|1|1|2|
|Total|0|0|576|72|104|752|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ WSS = water supply and sanitation; ICT = information and communication technology. — = Not applicable. n.a. = Not available.

Undercharging for power alone costs Zimbabwe about $220 million each year. Even compared with the rest of Africa, where underpricing of power is commonplace, Zimbabwe‘s power company ZESA stands out as a relatively poor performer. It is estimated that the average total cost of producing electricity 46
_Figure 10: Population estimates for Syria_ <!-- Start of picture text --> 30,000,000<br> 25,000,000<br>POP CENSUS<br> 20,000,000<br> 15,000,000<br>HNAP MNM<br> 10,000,000<br>CONFLICT STARTS<br> 5,000,000<br> -<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>CBS - HNAP Prospects 2010 Prospects 2015<br>Prospects 2019 Prospects 2022<br><!-- End of picture text --> _Source:_ WB staff calculations based on World Population Prospects (UNDESA), HNAP and CBS _Note:_ Population projections based on medium fertility scenario _Population data from the 2022 revision of the World Population Prospects (which is included in WDI) will be used for the poverty projections._<sup>_29_</sup>

- 4.2 Distribution neutral poverty projections: Sensitivity analysis

This section presents the results of distribution neutral poverty projections for Syria. As the country was re-classified as low-income country in 2018, estimates have been computed using both the lower-middleincome countries international poverty line ($3.65 in 2017 PPP) and low-income countries international poverty line ($2.15 in 2017 PPP).

For an analysis of the sensitivity of poverty projections to underlying assumptions, we present results obtained using baseline poverty estimates from all years available (2003, 2007 and 2009); growth estimates based on per capita GDP in constant terms and per capita GDP in current LCU adjusted using CPI; and using either a unit pass-through or a “conflict-adjusted” pass through (Box 2).<sup>30</sup> Figure 11 show poverty projections results at the Lower income countries international poverty line (in 2017 PPP), using different poverty baselines and different NA aggregate growth with a unitary pass through. Figure 12 displays a comparable analysis at the Lower Middle Income countries international poverty line.<sup>31</sup>

> 29 Additional analysis was further conducted to contrast national poverty estimates provided in the 2022 revision of the Population Prospects with different geospatial population estimates. Results of this analysis are discussed in Annex 3. 30 Corresponding estimates obtained using private per capita consumption from NA are provided in Annex 4.

> 31 Corresponding estimates obtained using poverty lines in 2011 PPP  ($3.2  and $2.15) are provided in Annex 5.
# **Table 21 (Cont): Cross-country covariates associated with the loan penetration**

||(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|
|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|1.00***<br>(0.08)|0.98***<br>(0.12)|0.93***<br>(0.13)|0.96***<br>(0.15)|0.92***<br>(0.15)|0.92***<br>(0.15)|0.92***<br>(0.14)|1.04***<br>(0.09)|
|Concentration|-1.40***<br>(0.46)||||||||
|Government bank share||0.03<br>(0.91)|||||||
|Foreign bank share|||-0.28<br>(0.43)||||||
|Places to submit loan||||-0.05<br>(0.10)|||||
|Minimum loan|||||0.00<br>(0.00)||||
|Fee consumer loan||||||-0.06<br>(0.09)|||
|Days process loan|||||||0.01<br>(0.02)||
|Operations agents (credit)||||||||0.38<br>(0.39)|
|Constant|-2.1***|-2.7***|-2.1*|-2.4***|-2.2*|-2.1|-2.2*|-3.4***|
||(0.72)|(1.01)|(1.12)|(1.07)|(1.25)|(1.32)|(1.15)|(0.76)|
|N<br>|64|42|39|32|30|27|31|65|
|R<sup>2</sup>|0.76|0.73|0.74|0.76|0.75|0.73|0.76|0.74|

57
structural reforms that follow – which often entail difficult trade-offs between reestablishing confidence in the short term and containing moral hazard in long term.

Keeping in mind the importance of incentives and tensions between short term and longer term policy responses to crisis management, we address the following questions about the implications of this crisis for financial and macroeconomic policies going forward:

- Are blanket guarantees inevitable to halt a systemic crisis?

- Should governments bail-out and own financial institutions?

- Should governments regulate finance much more aggressively given the failures

in market discipline?

- Should monetary policy target asset prices?

- Should countries resort to capital controls to contain the crisis?

Crises recur in part because people forget the lessons from the previous ones. While every crisis is different, past crises also provide important lessons that need to be learned to prevent policy makers from reinventing the wheel every time a new crisis erupts. This paper draws on past research and country experiences to address the issues that are in the forefront of policy debate today.

# **II.  Use of blanket guarantees in containing a systemic crisis**

Crises go through different stages. The first stage is called the “containment” stage where crises often emerge unexpectedly and evolve very quickly.<sup>1</sup> This is the stage that attracts the most attention, with bank runs, emergency liquidity loans, and

> 1 Honohan and Laeven (2005) bring together research and first-hand crisis experience to catalog lessons on a variety of issues that regularly arise in crises.

3
# **Table A7: Determinants of Excess Interaction**

|**Dependent variable:**|**Ex**|**cess inter**|**action sh**|**are**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Tax rate|0.025**|0.020**|0.020**|0.011**|
||(0.011)|(0.008)|(0.007)|(0.005)|
|Risk score|0.001**|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Mixed shipment dummy|-0.000|-0.000|-0.000|0.001|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Diferentiated share|0.002|-0.000|-0.000|0.001|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Valuation advice dummy|0.014*|0.009**|0.009**|0.005**|
||(0.007)|(0.004)|(0.003)|(0.002)|
|Red channel dummy|0.000|-0.001|-0.001|-0.001|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Log initial value|||0.000<br>(0.001)|-0.000<br>(0.000)|
|Log initial unit price (rel. to internal prices)|||-0.002|-0.001|
||||(0.001)|(0.001)|
|Month-year fxed efects|Yes|Yes|Yes|Yes|
|Source country fxed efects|Yes|Yes|Yes|Yes|
|HS2-product fxed efects|Yes|Yes|Yes|Yes|
|Inspector fxed efects|No|Yes|Yes|Yes|
|Broker fxed efects|No|Yes|Yes|Yes|
|Importer fxed efects|No|No|No|Yes|
|Observations|44,522|44,522|44,497|43,669|
|R-squared|0.072|0.225|0.226|0.377|

_Notes:_ Standard errors clustered two-way by inspector and by broker are presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated using calibration methods (as explained in Section 4). "Observations" refers to the number of non-singleton observations. OLS estimation is used. The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

66
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co
In the next section we begin our discussion of the combined use of these trade remedies. For readers interested in tracking the trends in the underlying data on a policy-by-policy basis, appendix figures A through D present information on the initiations and measures imposed of each of the four distinctive trade remedy instruments over time.

# **2.1 The increase in trade remedy use during the crisis**

As Figure 1a indicates, WTO members initiated 35 new product-level investigations requesting imposition of new import restrictions under national trade remedy laws during the first quarter (1Q) of 2009, our most recently available data.<sup>7</sup> This is an increase of 22.3 per cent compared to the same period in 2008 and also continues an upward trend. The total number of new, product-level importrestricting investigations launched in 2008 was 34.0 per cent higher than the number of new investigations initiated during 2007. It is worth pointing out, however, that the 2007 year was a relative low point for new trade remedy initiations under instruments such as AD, SG, and CSG (see again appendix figures A through D).

The historical data on the use of these trade policies, especially in the case of the dominant AD instrument in use in many countries around the world, indicates that the vast majority of new investigations and requests for import protection ultimately result in the imposition of new “definitive” import restrictions in the form of tariffs, price undertakings, or quantitative restrictions. While the share of new investigations resulting in the imposition of final measures in developed economies like the U.S. and EC may have recently fallen to the range of 50-60 per cent, the share is much higher in many developing countries. This includes some of the countries that are the major new sources of the current rise in initiated investigations (e.g., India, Turkey), where it is not uncommon to find 80-95 per cent of the initiations resulting in the imposition of new measures. Overall, 79 per cent of the investigations initiated in 2007 that reached the stage a final determination by 1Q 2009 resulted in the imposition of a definitive import restriction.

Figure 1b tracks the imposition of new measures at the product level across the same sample of WTO members illustrated in figure 1a. WTO members imposed 20 new _product-level_ definitive import restrictions in 1Q 2009 under national trade remedy laws, an increase of 5.1 per cent compared to 1Q

> 7 Most of this activity is antidumping, however, the use of other instruments has been increasing recently. As described in the _Global Antidumping Database_ , these figures are based on original source, nationally provided data for AD and CVD. The data reported in the text and figures is based on that collected for 20 AD- (18 CVD-) using countries, and while this does not comprehensively cover the global use of the instrument, historically these countries represented 90 per cent of AD (93 per cent of CVD) initiations by all WTO members during 1995-2007. The data collected on countries’ use of SG and CSG is comprehensive and obtained from the WTO in addition to national government sources.

5
<!-- Start of picture text --> Total Regional Consumption<br>CES<br>Region1 ... Region r-1 Region r+1 ... Region31 Rest of World Import from Domestic produced<br>CES<br>CET<br>Transfers out to Rest Local Produced for Transfers in from Rest<br>of Mainland of China Local Consumption of Mainland of China<br>CES<br>CET<br>Export to Rest Domestic Region1 ... Region r-1 Region r+1 ... Region31<br>of World Supply<br>CET<br>Total Regional Output<br><!-- End of picture text -->

**Figure 2  Structure of total regional supply and demand**

# **2.2.1 Exports and transfers out**

The first tier, CET function describing the allocation of supply between domestic market and export: where _QDAi_ , _r_ and _QEi_ , _r_ are the supply of commodity produced in sector i in region r to domestic market and export respectively,  _i rt_ , and  _it_ , _r_ are the share parameter and efficiency _t_ parameter;  _i_ , _r_ is the transformation elasticity parameter between domestic market supply and export, and  _it_ , _r_  1 (  _it_ , _r_ -1) ,  _it_ , _r_ is the transformation elasticity between domestic market supply and export.

First order condition: 14
Figure 14: Equity profile, PISA science score, Georgia, 2015

# _Children’s access to basic utilities_

The inequality in the access to basic human capital inputs among children ages 0–16 measured using a standard HOI on access to water and sanitation is generally low and mostly derives from spatial disparities.<sup>9</sup> In this analysis, the life circumstances of children are based on the number of children in the household; the educational attainment, age, and gender of the household head; the household consumption quintile; the gender of the children; and the urban-rural and provincial location of the household.

Among the three countries, the share of children who have access to water appears to be highest in Armenia (figure 15). In 2015, 96 percent of Armenian children had access to water, and the HOI for access to running water was approximately the same, at 94 percent, highlighting the narrow between-group inequality in access to the service. However, these large shares may

> 9 Because the data are generated though separate household surveys across the countries of South Caucasus, the definitions of access to water and sanitation are also slightly different. In Azerbaijan, the definition revolves around whether children have access to water in their dwellings, though the survey does not specify the source of the water. In Armenia, children are considered to have access if their households obtain water from a central supply. In Georgia, access refers to whether the households have water systems within the home or outside in the vicinity. Likewise, in Azerbaijan, children are considered to have access to sanitation if there are toilets in their dwellings. In Armenia, access refers to whether the households have local or centralized sanitation utilities. In Georgia, where all households claim to have toilets, children are considered not to have access to sanitation if the household toilets are latrines emptying into a river, channel, or ravine, and so on.

27
**_Energy demand models for policy_** **_<u>formulation</u>_**

Ghanadan, R. and J. G. Koomey, 2005, Using energy scenarios to explore alternative energy pathways in California, Energy Policy, 33, pp. 1117-42.

Ghosh, S., 2006, Future demand of petroleum products in India, Energy Policy, 34, pp. 2032-37.

- GOI, 2006, Integrated Energy Policy – Report of the Expert Committee, Planning Commission, Government of India, New Delhi, 2006 (see http://planningcommission.nic.in/reports/genrep/rep_intengy.pdf, last visited on 28 March 2008).

Goodwin, P. B., J. Dargay and M. Hanly, 2004, Elasticities of road traffic and fuel consumption with respect to price and income: A review, Transport Review, 24(3), 275-292.

Graham, D. and S. Glaister, 2002a, The demand for automobile fuel: A survey of elasticities, Journal of Transport Economics and Policy, 36 (part 1), pp. 1-26.

Graham, D. and S. Glaister, 2002b, Review of income and price elasticities of demand for road traffic, Imperial College, London (see http://www.cts.cv.ic.ac.uk/documents/publications/iccts00267.pdf).

Graham, D. and S. Glaister, 2004, Road traffic demand elasticity estimates: a review, Transport Reviews, 24(3), pp. 261-74.

Griffin, J., 1993, Methodological advances in energy modelling: 1970-90, The Energy Journal, 14(1), pp. 111-24. This has also appeared in Lesourd et al. (1995) as Chapter 1.

Grover, R.B. and S. Chandra, 2006, Scenario for growth of electricity in India, Energy Policy, 34, pp. 2834-47.

107