rafmacalaba/data-use-mentions-extended huggingface dataset:
The risks to commitment that the EIZ would face are three-fold. First, is the risk of the host country or countries reneging on their EIZ commitments – i.e. reverting on their commitment to give up sovereignty over the zone (fully or potentially over certain aspects like revenue collection, security, etc.) or putting up barriers to the interaction between the zone and the national economy. One can imagine a number of reasons why this might happen. Some of the most likely triggers would be political, social and economic issues within the host country, including fiscal crisis, economic or political nationalism, and elections and subsequent policy instability. In the recent research by Wellhausen (2010), 57% of the 412 arbitration and expropriation events in a global FDI database between 1970 and 2009 took place in or preceding an election year. One need only look at some of the situations that have impacted EAC countries in recent years, including the post-election violence in Kenya in 2007-2008 and to a far lesser degree the constitutional referendum in Uganda in 2005. One can also imagine a scenario where natural resources were found in or near the EIZ location and the implications this might have on commitment of the host government to share revenues from the zone. Finally, the success of the zone itself might raise (or equally reduce) the incentive of the host government to renege on the agreement in an effort to capture more rents or reduce liabilities.

The second risk is that other EAC members renege on their own commitments to the EIZ. Most likely this would occur with regard to commitments to support some infrastructure expenditure, to commit necessary funds to operational budget or to deliver effectively on their responsibilities for managing certain aspects of zone governance. One of the most likely reasons for this would be if member states (particularly those that did not host the zone) felt they were not getting sufficient benefit from it. The main concern here is the distribution of gains – i.e. the fact that the EIZ will contribute to a concentration of
after comparisons. In any given wave there are between 20,000 and 30,000 manufacturing plants, covering all states and districts.

This allows the creation of a 283 district-level pseudo panel tabulating seven outcome measures and extensive covariates for 90% of the country’s plants, employment and output. The reduction from a total of 630 districts arises from either the limited district presence of organized manufacturing, or incomplete series across the 2000 and 2009 period. Since we follow synthetic cohorts, plants born after the GQ upgrades began in 2000 are dropped<sup>8</sup> and all economic outcome variables are winsorized at the bottom 1 percentile to limit outliers and unavailable values in plant size and labor productivity coming from zeros in establishments counts, employment or output levels. Nine districts are categorized as _nodal_ (Delhi, Mumbai, Chennai and Kolkata, and the several contiguous suburbs Gurgaon, Faridabad, Ghaziabad and

> 5 For more details on data preparation, see Ghani et al. (2016a).

> 6 The sampling frame for the ASI is based on the lists of registered factories or units maintained by the Chief Inspector of Factories (CIF) in each state.

> 7 See Annual Survey of Industries Manual (2008, p. 12-13). A supplementary frame is prepared each year for new units, while closed factories only affect the sampling weights calculated for the respondent units. At the end of the cycle, when the data on all the units in the frame become available, the frame is updated for new factories, closed factories, and the composition of census and sample schemes.

> 8 In this sense, our analysis of young plants is quite different from that presented in Ghani et al. (2016a) who also compare plants entering after the GQ upgrades.

3
# **5. Discussion and Conclusions**

Egypt has struggled to create good, formal jobs for its young and growing labor force (World Bank, 2013; Gatti, Angel-Urdinola, Silva, & Bodor, 2014; Assaad, AlSharawy, & Salemi, 2019). Informal firms are a challenge not only for Egypt’s workers, but also the macroeconomy and tax base (AfDB, 2016; Ali & Najman, 2016). For some time, there have been recommendations to formalize the largely informal sector of micro and small firms in Egypt in order to expand the tax base and increase the availability of good jobs (Egyptian Center for Economic Studies, 2005; World Bank, 2014). Yet such calls assume that firms that are currently informal are formalizable, that they could afford and survive formalization, without negatively impacting job creation. This assumption has not been tested or substantiated in Egypt.

In an attempt to answer whether informal firms may be formalizable, in this paper we compared formal and informal non-agricultural firms with 1-24 workers. We used multiple data sources on micro and small firms in Egypt and analyzed which firm and owner characteristics predict formality. We used the predicted probability of formality to characterize informal firms that are like formal firms and may be amenable to formalization efforts, in contrast to those firms that are quite different and are thus highly unlikely to formalize. For the most part we used an expanded definition of formality that counts a firm as formal if it has a commercial registration or if it pays social insurance premiums or taxes. In the dynamic analyses, we used a more basic definition that just depends on the commercial registration status of the firm.

Although the different data sources – the 2017 Establishment Census (EsC), the 2012-13 Economic Census (EcC), and the various waves of the Egypt Labor Market Panel Survey (ELMPS) – produce different rates of formality for our universe, they demonstrated similar relationships between formality and observable characteristics. Results on the relationship between formality and characteristics were comparable
of the total assets might be constant (or even increasing) even if the true (economic) value were declining. To adjust for this the team estimated the net value of fixed assets by subtracting accumulated depreciation, which will also allow easy comparison with similar ratios for other countries. In calculating total assets the team used the net-ofdepreciation values for fixed assets. The adjustment gives quite a different picture: for

> 3 There are total of 140 firms available for year 2001, while there are about 500 firms for year 2000 and about 600 for 1999. The firms that have the 2001 data available are mostly large firms—for example the size of the median firm in 2001 is over twice that of the median size in 2000.

3
prices for fixed annuities may exceed 20 percent of average prices (Rocha and Vittas, 2010), while data on sold annuities in Chile point to similar conclusions (Rocha and Thorburn, 2007).

Fourth, VPAs that share longevity risk among annuitants allow for a more balanced treatment of longevity risk because they do not have to rely on overly conservative assumptions about projected future mortality improvements. Longevity risk-sharing VPAs provide full protection against diversifiable longevity risk, i.e., the risk that any one annuitant may live much longer than the average, but share among surviving annuitants the non-diversifiable risk, i.e., any increase in average longevity.

Fifth, to the extent that providers do not assume the longevity, investment and inflation risks, VPAs imply smaller capital requirements for provider solvency and thus lower the cost of annuities.

# **3.2 Disadvantages of VPAs**

But VPAs also suffer from several shortcomings. Unlike holders of fixed real annuities, who are protected against longevity, investment and inflation risks, buyers of VPAs are exposed to investment and inflation risks and share the non-diversifiable component of longevity risk.

Second, depending on how the initial calculation of annual payments is made, they may start with relatively low payments. This depends on the level of the AIR relative to the level of market returns and the extent of projected mortality improvements that have been retained in the longevity assumptions.

Third, depending on the volatility of annual returns on investment funds, annuity payments may fluctuate widely from year to year. These fluctuations may cause large changes in the consumption patterns of pensioners.

Fourth, VPAs presuppose access to well functioning and liquid securities markets. If they offer extensive guarantees, they also require access to efficient hedging facilities.

Fifth, buyers of VPAs avoid the price dispersion of fixed annuities but they are exposed to the potential dispersion of investment performance, expense levels and bonus smoothing mechanisms.

Sixth, VPAs suffer from more complex charges that are both less transparent and more difficult to understand. Consumers often rely on
In many contexts this would suggest a favored relationship between these two leaders. However, in the villages we study 50 percent of all households were related to the village head.

The bottom panel of table 4 further considers whether civil society leaders can realistically be viewed as a mechanism for checking the power of village heads by comparing the attitudes and legal knowledge of village heads and civil society leaders prior to the intervention (by looking at the statistics for control villages only). Importantly, a slight majority of civil society leaders favored checks on the power of the village head’s court. In addition, civil society leaders express significantly higher levels of support for individual rights than village heads.<sup>15</sup> Knowledge of the laws and procedures regulating village governance is approximately equal between village heads and civil society leaders.

This evidence suggests that the average civil society leader in these villages has both the incentive and the ability to check the power of village heads. However, it is noteworthy that the NGO depended on the village head to identify civil society leaders living in his or her village, and to select which one to invite. Our expectation was that this would reduce the probably of finding differential effects across the two training variants insofar as village heads should choose to invite civil society leaders over whom they have more influence. In table 6 in the appendix, we compare the traits of the trained civil society leaders to our random sample. We find village heads were more likely to invite female leaders and NGO officers to accompany them and less likely to invite religious leaders; these patterns are interesting insofar as they suggest village heads chose people who likely had an interest in reform but perhaps had weaker independent power bases.

# **5 Data Collection and Measurement of Key Variables**

We conducted surveys of village heads, a randomly selected civil society leader, and a random sample of eight households in each of the villages in the study three months following the completion of the training sessions, in late August and
East Asian issuers, including firms listed in stock exchanges and unlisted firms. To explore which investors purchased the issued securities, we analyze the portfolios of mutual, pension, and insurance funds using data at the security level. To examine how firms used the funds raised in domestic markets, we merge the transaction-level data with firm-level balance sheet and income statement data.

The evidence shows that the most significant part of the expansion in bond issuance activity between 1990 and 2016 occurred in domestic bond markets, especially after 2008, surpassing the activity in international markets. The annual amount raised in domestic (international) bonds increased by about 381 (58) percent between 1990-98 and 2008-16. As a result, the share of bond financing obtained domestically increased over time, becoming firms’ main source of bond financing since 2008. Between 1990-98 and 2008-16, the average share of domestic bonds relative to the total annual amount raised (domestic plus international bonds) increased from 21 to 69 percent. Most of the bonds raised since 2008 were denominated in domestic currencies. Moreover, this growth in domestic markets comprised the participation of more and smaller issuing firms. The annual number of issuing firms in domestic bond markets increased 125 percent and the size of the typical issuer of domestic bonds declined by 30 percent when comparing 1990-98 and 2008-16. Instead, the number of issuing firms and the size of the typical issuer in international markets barely changed between these two periods.

We then study whether the acceleration in domestic bond issuances in 2008-16 was more likely driven by an increased appetite for domestic bonds by investors (supply-side expansion) or by a higher demand of capital by firms (demand-side expansion). If a supply-side expansion by investors seeking to buy bonds was the main force behind the acceleration in domestic bond financing, we could observe a decline in bond issuance yields, higher leverage growth for new (smaller) issuers relative to recurrent (larger) issuers, and an accumulation of issuance proceeds in cash as firms took advantage of favorable liquidity conditions to raise more debt than
(6.25)|(5.37)|(3.41)|(2.92)|(2.86)|(6.02)|(6.37)|(5.79)|
|Subdistrict FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Month and year FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|District controls||✓|✓||✓|✓||✓|✓|
|Subdistrict controls|||✓|||✓|||✓|
|Observations|1498|1498|1498|791|791|791|1338|1338|1338|
|Adj. R-squared|0.189|0.188|0.187|0.451|0.455|0.461|0.151|0.149|0.148|
|Baseline mean incidence|20.89|20.89|20.89|20.89|20.89|20.89|20.89|20.89|20.89|

Notes: Each observation is a subdistrict-month-year. The panel spans from April 2020 to May 2021. T takes value 1 if the subdistrict belongs to a treated district. Post takes value 1 after August 2020, i.e. when the Facebook campaign started. Subdistrict controls include: subdistrict area, share of urban population, elevation, terrain ruggedness and distance to the state capital, all interacted with Post. District controls include total population from the Census as well as the shares of baseline survey respondents living in kutcha dwellings, with university degree, sleeping under mosquito nets, and reporting any malaria cases in the last 5 years and 2 weeks, all interacted with Post. All regressions include state fixed-effects interacted with Post. Standard errors are clustered at the district level. Data come from the Indian Health Management
Policy Research Working Paper 10169

# **Abstract**

Somalia, one of the poorest countries in Sub-Saharan Africa, still faces many challenges as it remains fragile. Terrorist groups and their attacks are threatening the government and limiting its capacity to implement effective development policies. Using difference-in-difference and instrumental variables approaches with micro-data from two waves of the Somali High Frequency Survey, this paper estimates the immediate (within a week) impact of terrorist attacks on households. The consumption of households exposed to terrorist incidents decreases by 33 percent, mainly on food items. As a result, poverty and the depth of poverty among the poor increases. The decline in consumption seems to be explained by a smaller share of household members working and earning income after an attack. In addition, the effect on consumption is restricted to a 4-kilometer radius from incidents and has a heterogeneous impact, not affecting households in the top 20 percent of the consumption distribution. The paper also finds a deterioration in people’s perception of police competence. Achieving peace is a fundamental first step to increase welfare conditions that will also bring other wider long-term benefits in Somalia.

This paper is a product of the Poverty and Equity 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 gnunez1@worldbank.org and upape@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 are entirely those of the authors. They do not necessarily represent the views of the International
analysis (2014-2018). Each institution has its own mandate. This makes monitoring NTMs difficult, since there is no dedicated government institution to carry out NTM regulatory review and stocktaking (Munadi, 2019).

To overcome these issues, we use a novel comprehensive NTM data-base built by the World Bank on the basis of data collected by the Economic Research Institute for ASEAN and East Asia (ERIA) in collaboration with UNCTAD (Cal`ı et al., 2021). The UNCTAD/ERIA data identifies the stock of NTMs applied by Indonesia as of 2015. The data is cleaned extensively to address coding and interpretation errors and made time-varying by tracking the individual NTMs applied to each product before and after 2015.<sup>10</sup> The data uses the most disaggregated NTM classification available, i.e. 3-digit MAST level. This classification is the most appropriate for policy analysis as it identifies individual measures introduced or modified by each agency. A total of over 60 3-digit NTMs are available in the data set, each with values coded between 0 and 1 to signify when they were in effect, that is, the data varies at the month-product pair.

In addition, we complete the trade policy data with import tariff data collected by the UNCTAD Trade Analysis Information System (TRAINS) data set. For Indonesia this data includes yearly Most Favored Nations (MFN) import tariff at the HS 10-digit product level before 2017 and HS 8-digit level from 2017 onwards. We then convert all of the tariff data into the 10-digit used for the NTMs as well.

# **3.3 Japan: The main regional destination for Indonesian and Chinese exports**

We base the analysis on a single individual export market in order to make the empirical analysis manageable and to focus on a similar set of exporters facing the same shock. Several

> 10ERIA has updated the data and the latest one is 2018, where some of these coding errors may have been addressed. Cal`ı and Montfaucon (2021) details the
# **1. Introduction**

To establish trust in the measurement of poverty and inequality, statistical offices must regularly assess and continuously improve the quality of their processes as well as the accuracy of their data. This should be one of the prerequisites to prevent misguided policies, especially when such policies are based on data from household budget surveys.

Over the past decades, national statistical offices worldwide have started to develop reports describing the quality criteria adopted in the surveys and explaining any instances in which these criteria could not be met. One common method for reporting the precision of poverty measures is to include sampling variability or standard errors and, consequently, the confidence intervals of such estimates.

This practice is needed much more when poverty measures are disaggregated at smaller territorial levels or for some sub-groups of the population. Recently, the United Nations Economic Commission for Europe (UNECE) has addressed these issues in a series of conferences resulting in a useful guide for data disaggregation (UNECE, 2020).

While in the past many national sample surveys were conducted with the aim of getting estimates that were significant at national level, only nowadays it is becoming more and more important to first define the exact type of _domains_ for which national statistical offices intend to calculate, and then publish poverty measures. This is necessary because when such measures were disaggregated at lower territorial levels or for other _domains_ , often such estimates ended up being non-significant according to standard errors calculated _ex-post_ . The ex-ante definition of sub-sample sizes in particular and sample designs in general, therefore, becomes crucial to have statistically significant estimates.

There is obviously a trade-off between more precise _domain_ estimates and the total cost of a sample survey. For this reason, several statistical offices have a strong incentive to plan and implement highly efficient sampling designs. Indeed, an efficient sample design is a prerequisite for any sample survey, and it plays a fundamental role in household budget surveys. As a matter of fact, improvements 2
***|[0.00267]***|
|Number of children 4-6|-0.0201|-0.0202|
||[0.00181]***<br>|[0.00180]***<br>|
|Number of children 7-12|-0.00763|-0.00782|
||[0.00106]***|[0.00103]***|
|Number of people > 12|-0.0109|-0.011|
||[0.000440]***|[0.000422]***|
|Presence of grandmother|0.0297|0.0292|
||[0.00212]***|[0.00208]***|
|Household head|0.105<br>|0.104<br>|
||[0.00384]***|[0.00397]***|
|Spouse works|-0.00621<br>[0.00396]|-0.0088<br>[0.00367]**|
|Income stability|0.156|0.156|
||[0.00445]***|[0.00444]***|
|At least one person receives remittances regularly|-0.0663|-0.066|
||[0.00265]***<br>|[0.00250]***<br>|
|Household Home & WASH index|0.00781|0.00818|
||[0.00169]***<br>|[0.00162]***<br>|
|Urban|0.0754|0.0724|
||[0.00564]***|[0.00551]***|
|Use internet in
# **Figure 10. Extreme weather event risks and macro‐financial risks**

## A. Physical (disaster) risk vs. macroeconomic risk

## B. Weather event & macroeconomic risks by region

<!-- Start of picture text -->
1.8<br>100%<br>1.6 BIH 90% 3 2<br>1.4 80% 4 6 1 5<br>1.2 MMR 70% 0 6 0<br>THA 60% 9<br>1<br>0.8 OMN 50% 6 4 3<br>0.6 CHN BGD VNMSRBNIC KHM 40%30% 0 2 8<br>0.40.2 SWECHELUXAUSISR BOLMWI MOZJAM 20%10% 3 8 5 6<br>0<br>0 NOR 0% 1 0 1<br>0 20 40 60 80 100 EAP ECA LAC MENA SA SSA<br>EIU economic risk index (100=High), 2017 low both high climate high macro high both<br>C. Physical (disaster) risk vs. banking risk D. Weather event & banking risks by region<br>1.8 100%<br>1 1<br>1.6 BIH 90% 3 3<br>1.4 80% 5 6<br>2<br>1.2 MMR 70% 2<br>THA 60% 0 11<br>1<br>0.8 OMN NIC 50% 6 8<br>SRB KHM 40% 5 8<br>0.6 CHE BGD VNM 30% 1 0<br>0.4 SWE LUXAUS ISR JAMCHN MOZ 20% 8 1 2<br>0.2 NOR 10% 3 2 4<br>ZAF 1<br>0 0% 0<br>10 30 50 70 90 EAP ECA LAC MENA SA SSA<br>EIU banking risk index (
] O’loughlin, J. _et al._ Climate variability and conflict risk in East Africa, 1990– 2009. _Proceedings of the National Academy of Sciences_ **109** , 18344–18349 (2012).

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