rafmacalaba/data-use-mentions-extended huggingface dataset:
cultivated crops contribution to GDP at provincial level. The same holds for employment. It is thus assumed here that there are no significant differences in productivity or yield among different provinces. For rice, a slightly different methodology has been applied. Export value and jobs are disaggregated based on provincial share of area used for rice production. To arrive at provincial rice output (in ton), provincial rice area (in hectare) and provincial rice yield (ton/ha) are used. Then, jobs or monetary value at risk is calculated multiplying the provincial level estimates with the provincial level exposure for the different return periods. Detailed province-level results for each return period and type of flooding are summarized in the technical annex to the main report (De Vries Robbé et al. 2020).

## 5.2. Economic impacts of flooding in the aquaculture sector

The economic impact of flooding on aquaculture production is estimated based on the exposure calculated in section 4. For each coastal province in the Red River and the Mekong Deltas, the analysis of section 4 gives the share of aquaculture production affected by any flooding of a certain return period. Multiplying the affected share with the provincial aquaculture output in tons, aquaculture contribution to GDP in $ or number of aquaculture jobs at the province level gives an estimate of the economic impact of each flood event in each province. To arrive a provincial disaggregation of export, GDP and jobs, the national values are distributed among the provinces using provincial aquaculture production numbers. Here assumption is made of that factors as yield and productivity are uniform across provinces. Put differently, a province creating X percent of national aquaculture production is assumed to produce X percent of national aquaculture exports. Detailed province-level results for each return period and type of flooding are summarized in the technical annex to the main report (De Vries Robbé et al. 2020).

## 5.3. Economic impacts of flooding in the tourism sector

For tourism, economic estimations are conducted for flooding and coastal erosion. The share of
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29
# **5.3 Comparing poverty estimates for countries with actual household surveys**

At the time of this writing, household survey micro data for 2020 is available for only 20 countries of which 12 are in LAC with the remaining countries in EAP and ECA. For these countries, it is possible to compare the poverty estimates using the survey microdata with (i) poverty derived using per capita GDP growth-based distribution-neutral projections, and (ii) for a subset of 13 countries, poverty derived using HFPS. Comparing the estimates based on distribution-neutral and phone survey-based projections with the actual survey micro data-based poverty estimates will give us some sense how these estimates might differ. However, given the limited number of countries and the issues discussed above with the actual household surveys themselves, it is not clear how generalizable the results of the findings to other countries will be.

Panel a of figure 13 shows the correlation of the poverty rate calculated using the actual survey data and poverty derived using per capita GDP growth-based distribution-neutral projection. For 13 countries, panel b compares the change in poverty using survey data with poverty using phone surveys. Panel c shows the percentage points change in extreme poverty from 2019 to 2020 using the two sources in panel a, and panel d compares the change in poverty for the sources in panel b.

The figure shows that for most countries in the sample, the change in poverty derived from survey micro data is fairly close to estimates derived using per capita GDP growth and phone surveys. Poverty derived using survey data is on average 0.25 percentage point higher compared to projections based on per capita GDP growth and 0.72 percentage point higher compared to projections based on phone surveys. The correlation coefficient of poverty changes is 0.60 between survey data and per capita GDP growth-based projection, and 0.62 between survey data and phone survey-based projection. Yet there are clear outliers. The change in poverty is 4.5 percentage points higher in Colombia in survey data compared to per capita GDP growth-based projection and
. Ceriani, Olivieri and Ranzani (2019) test the accuracy of home-owners’ selfassessed rental value in an application to Peruvian data.

# **4. Results**

In this section, we analyze the distributional impact of including the flow of services from dwellings in the consumption aggregate using data from four developing countries: Albania, Bangladesh, Iraq and Peru. We measure the effect on different outcomes including inequality and poverty indices, profiling of the poor and shared prosperity by comparing consumption aggregates without the housing component with aggregates that include rental values imputed by means of one of the two approaches described above.

This section summarizes the distributional impact of including rents in the consumption aggregates of the selected countries, differentiating between (i) the baseline scenario, with no rent, and two rent-imputation methods: (ii-a) self-assessment and (ii-b) log-linear hedonic model.<sup>12</sup> We start off by illustrating the relevance of the housing component as a share of the total consumption aggregate on average and along the distribution (by quintile of the original welfare indicator) (Table 4). On average, rents’ shares range from a minimum of 4.8 percent of total per capita household welfare (Bangladesh, 2010, self-assessment imputation method) to a maximum of 22.7 percent (Albania 2008, log-linear imputation method). In Albania and Iraq, two upper-middle income countries, housing contributes about 20 percent of total household consumption. Yet, we cannot generalize this result to upper-middle income countries since Peru exhibits a rent share of 14 percent maximum. Rent shares vary according to the rent imputation method. In line with the expectation associated with the type of question used to collect self-assessed rental values, Albania is the only country where on average rental shares based on self-assessment are higher than those obtained with a log-linear model. However, this does not hold all along the distribution: the log-linear approach produces higher rent shares in the bottom three deciles. A similar pattern is recognizable in Bangladesh (2005)
24 The figure shows that recent growth has brought the urban poverty rate approximately on track to achieve the MDG by 2015. By the interpretation used here, the MDG target urban poverty rate for 2001 was 22.9 percent, while the estimate of the actual value from the 2000/01 HBS survey was 23.3 percent.<sup>13</sup>

# **Figure 13**

<!-- Start of picture text --> Poverty incidence and the MDG goals<br>simulated poverty based on two-survey approach, stratum distribution<br>1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Year<br>All urban areas (including DSM) Rural areas<br>MDG, urban target MDG, rural target<br>.45<br>.4<br>.35<br>.3<br>.25<br>.2<br>Fraction below poverty line.15<br>.1<br><!-- End of picture text --> The rural poverty rate remains substantially above the path necessary to achieve the MDG target. The MDG target for rural areas is attainable, but it will require sustaining growth at or above the rates achieved in 2001 and 2002. The rural GDP per capita growth rate was 2.6 percent in 2001 and 2.1 percent in 2002. A simulation forward from the 2000/01 HBS data, assuming distributionneutral growth, implies that the rural poverty reduction target will be met if a rural GDP growth rate of 2.3 percent per capita is maintained through 2015. The estimate of 2.3 percent is a lower bound, assuming no increase in inequality. If future growth is accompanied by increases in inequality, a growth rate greater than 2.3 percent in GDP per capita will be needed to achieve the MDG target. Assuming rural population growth net of migration continues at the rate of 2.5 percent annually and inequality does
may include financial information, the assessment of business risks such as regulatory environment, competitive environment, and management quality.

When entering a guarantee/on-lending agreement risk managers should define information to be collected and assessed in the future. Risk managers may also want to explore third party and market information on beneficiaries and the sectors they operate. Third party information may include risk assessment by banks, rating agencies, or credit bureaus. Market information may include prices of traded debt and equity as well as credit derivatives (e.g. credit default swaps (CDS)).

However, governments should recognize that market conditions pertaining to beneficiaries and potential beneficiaries might reflect perceptions of implicit government support. Furthermore, the provision of a guarantee/on-lending may change the perceived implicit guarantee by market participants. In such cases, market prices would not accurately reflect risk, and risk managers may instead need to rely on market data from proxies with risk characteristics similar to the stand-alone<sup>13</sup> ~~r~~ isk of the beneficiary.

## **_Resources and capacity_**

Sovereign risk managers need to invest in capacity and resources to implement a sound risk management framework. Different approaches to risk analysis and measurement require different skills. For example, fundamental risk analysis (e.g. credit scoring) requires an understanding of industries and their respective risk drivers. Governments should model their risk management framework around available capabilities and financial resources, and adapt the framework over time as capacities improve through training, hiring of new staff, and support from external consultants.

# **2.2 Credit risk analysis**

Assessing the credit quality of entities benefitting from credit guarantees and on-lending is a core part of a risk management framework. First, risk managers need to understand what industry-specific factors are driving the risk of beneficiaries not servicing their debt obligations. Second, risk managers have to assess these risk drivers by beneficiary and on a regular basis.<sup>14</sup> Governments can choose from
# **V. Results for Tariffs at the Sector Level**

# **Distribution of Applied MFN Tariff Rates (Impact of WTO Accession)**

In figure 1 we show the distribution of applied MFN tariff rates in 2011 and 2020. Prior to the implementation of WTO commitments, the distribution of tariff rates is bi-model with the largest number of tariff lines falling in the 5-10 percent range and in the 15-20 percent range (with a mean of 11 percent as shown in table 1). After all WTO commitments are implemented, the large number of tariff lines in the 15-20 percent range is eliminated and the distribution has a single mode in the 5-10 percent range (where the mean also falls). Tariff peaks fall as well, as there is also a very sharp drop in the number of tariff lines in the 20-30 percent range.

# **Tariffs at the Level of the Russian Input-Output Table**

**Tariff Rates.** In table 2, we show the evolution of tariff rates for the sectors of the Russian input-output table.  (See table 4 for the mapping from the 2-digit Harmonized Tariff system to the 17 sectors of the Russian input-output table.)  There sectors where the tariff rates will fall by at least three percentage points are the following (with the percentage points reduction in parentheses): timber, wood, pulp and paper industry (6.1); light industry (4.1); food industry (4.6);  other goods producing industries (3.5); other industries (3.0); mechanical engineering and metal working (2.8).

**Tariff Lines with a Specific Tariff Component.** In table 3, we show the evolution of tariff lines with a specific tariff component at the level of the Russian input-output table.  Light industry and the food industry are the sectors where specific tariffs are intensively used.  In 2011, 83 percent of the tariff lines with a specific tariff component were in these two
from a quadratic specification confirm the overall negative relationship between recall length and plots listed (Table A.4). The BIC’ goodness-of-fit comparison suggests that the linear model is preferred in Tanzania NPS 2012/13 and Malawi IHS4 data and that the quadratic model provides a better fit in Malawi IHPS (Figure 4).

We have no way to observe the ‘true’ number of plots household-farms in our data operate, so we cannot ultimately determine the direction of the reporting bias. However, it seems plausible that farmers forget some of their less important plots as recall decays over time, as was observed previously for example in Gaddis et al. (2019) and Arthi et al. (2018).<sup>7</sup> Turning now to farmer self-reported land area, we assess the effect of recall length on the magnitude of error in self-reported plot area, comparing it to the objective GPS-based measure of plot. We split the sample in plots whose area was over-stated and plots whose areas was under-stated (positive and negative

> 7 The analysis in this paper relies on a different setup than Arthi et al. (2018) and Gaddis et al. (2019), who use a methodological experiment in which farmers are randomly assigned to report on labor inputs either through end-of-season recall modules or in weekly visits during the agricultural season. In contrast, we rely on variation in recall length stemming from variation in interview timing. The findings in Arthi et al. (2018) and Gaddis et al. (2019) are therefore a combination of a recall effect (end-of-season modules imply longer periods of time to recall) and a data collection effect (weekly visits in which respondents are prompted to report in detail on their plots). Our findings isolate and underscore the former.

12
22

# **Appendix: Comparability issues, poverty lines, and regional panel**

The various rounds of the HES from 1983 to 1996 provide comparable data, at least much more so than many surveys available in other countries. Yet, the 1995-96 survey differs in some respects from previous surveys. Hence there are a few comparability issues, mainly in terms of the sampling frame and the expansion factors, the diary for food consumption, and the standard errors of poverty measures.

The first comparability issue relates to the expansion factors. The sampling frame for the 1995-96 HES consists of 14 strata corresponding to the Standard Metropolitan Areas (SMAs), other urban areas, and rural areas of the five administrative divisions of Bangladesh, as described in Table A2.

Accordingly, 14 expansion factors were computed by the BBS for the 1995-96 survey (last column of Table A2). In previous years, there were strata for the SMAs, urban municipalities, other urban areas, and rural areas for each of four divisions. While a corresponding number of expansion factors should have been provided, the BBS used only two expansion factors for these years, one urban and one rural.

Because welfare measures and probabilities of being selected vary between geographical areas, using two expansion factors only generatesd bias in the estimates of poverty for previous years. The problem is not too serious for rural areas. As can be seen from Table A2, the probability that a household will be selected in the various rural strata are similar. In 1995-96, four out of five rural expansion factors belong to the interval [3702, 3916]. But this is not true for urban areas. Highly populated urban areas such as the Dhaka and Chittagong SMAs, which are under-represented in the sample, have higher standards of living. Hence using aggregate urban expansion factors would increase urban (and national) poverty measures since the population share of dense and well-off areas would be under-estimated.

To provide a consistent set of expansion factors matching the HES sampling frame for the survey years prior
|**Table 5B: Im**|**pact of SO**<br>|**FES on jobs on**<br>|**the side, Maste**<br>|**r students, SOF**|**ES data**<br>||
|---|---|---|---|---|---|---|
||N|<RD|>RD||PROBIT||
|DS=0.08|(1)|(2)|(3)|(4)|(5)|(6)|
|±0.01|86|0.333|0.522|0.188|0.089|0.077|
|||||(0.120)|(0.170)|(0.213)|
|±0.02|154|0.344|0.623|<br>0.279<sup>***</sup>|<br>0.289<sup>***</sup>|<br>0.312<sup>***</sup>|
|||||(0.079)|(0.097)|(0.101)|
|±0.03|211|0.326|0.618|<br>0.292<sup>***</sup>|<br>0.332<sup>***</sup>|<br>0.332<sup>***</sup>|
|||||(0.069)|(0.084)|(0.088)|
|±0.04|297|0.356|0.
Policy Research Working Paper 10068

# **Abstract**

How does trade policy affect exporters’ ability to respond to foreign demand shocks? Faced with a sudden change in the demand for their goods, exporting firms must optimally change their inputs and/or input sources. This paper tests whether a country’s own trade policy makes such adjustments harder for firms that rely on imported inputs. The analysis exploits new time-varying data on tariffs and non-tariff measures faced by Indonesian firms and focuses on the impact of exchange rate shocks on exports to Japan. In response to a depreciation of the yuan, which makes Chinese exports more competitive, the findings show that firms that face non-tariff measures on their inputs see a much larger drop in their export values compared to firms that do not face any non-tariff measures. That is not the case for import tariffs on inputs, which do not affect the export response to the shock. This difference is consistent with the (partial) fixed costs imposed by non-tariff measures on imports in contrast to the pure variable costs of tariffs. The magnitude of this effect depends on the type of non-tariff measure and on firms’ characteristics, such as their participation in global value chains, size, and product quality.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at amontfaucon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper
**Macedonia.** ( _Deposit Insurance Fund_ ) The scheme in Macedonia was first established in 1996 under the name Saving Deposit Insurance Fund Inc. as a joint shareholders’ company by the banks and savings houses. In July 2000, a single Deposit Insurance Fund came into force. The scheme is administered and funded jointly. The fund can borrow from the central bank or domestic and foreign sources if necessary. Membership to the system is mandatory. Coverage is extended to current accounts and savings deposits of natural persons that are denominated in domestic and foreign currencies. The coverage limits have changed according to the following over time: In 1996, the lesser of 75% of total savings per depositor or denar equivalent of DM 10,000; in 2000, 100% of deposits up to EUR 1,500 and 90% of EUR 1,500 to EUR 7,500; in 2002, 100% of deposits up to EUR 3,000 and 90% of EUR 3,000 to EUR 10,000; and since May 2003, 100% of deposits up to EUR 10,000 and 90% of EUR 10,000 to EUR 20,000. The coverage is calculated per depositor per institution and banks were charged a flat premium of 0.7% as of 2003. _Sources_ : IADI Survey: Macedonia (2002), IMF Country Report: Macedonia (2003), Deposit Insurance Fund Skopje (2004).

**Malaysia.** Malaysia introduced a blanket guarantee for depositors in December 1997 which came into force in 1998. The scheme was introduced in response to the financial crisis. _Source_ : Garcia (2000).

**Malta.** ( _Deposit Guarantee and Investor Compensation Scheme, Financial Services Authority_ ) The regulation approving the creation of a deposit insurance scheme in Malta became effective on January 3<sup>rd</sup> , 2003. The participants of the scheme were required to initially contribute LM 10,000 each and over five years were expected to
integrated in regional value chains. For instance, the share of intermediates value added on total domestic value added in UK exports (the majority of which goes to the EU) is close to 70 percent.

We first investigate the extent to which the depth of the EU contributed to boost trade between the UK and other EU members. We use data from the World Input Output Database (WIOD) on goods, services and value added trade and the World Bank data on the content of deep agreements to estimate a gravity equation augmented with a measure of depth for the period 1995‐2011. By interacting the depth of PTAs with dummies identifying the UK, we can quantify the effect of the depth of trade agreements on UK imports and exports of goods, services and value added. Deep trade agreements are found to increase goods and services trade by 42 percent on average. The depth of the UK’s trade agreements strongly increased trade in services: as a result of its EU membership and its participation in deep PTAs signed by the EU with third countries, UK services trade more than doubled. Deep PTAs also increased domestic value added in gross exports of the UK. This effect is mainly driven by stronger GVC relationships of the UK with its EU partners: the UK’s intermediates value added in gross exports (forward linkages) increased by 31 percent thanks to deep PTAs. In addition, foreign value added in UK exports (backward linkages) was boosted by EU membership by 37 percent. Finally, breaking down the EU into “new” and “founding” members reveals that EU membership has been particularly important to increase UK exports of services directed to the latter.

We then analyze the impact that changes in the UK‐EU trade agreement can have on UK‐EU trade relations going forward. This is a difficult task, as the only certainty on the future institutional setting is its uncertainty. We address this problem by considering three distinct scenarios, with decreasing depth of the future agreement between the UK and the