rafmacalaba/datause-encoder-data huggingface dataset:
- Senhadji, A. (1998), ‘Time-series estimation of structural import demand equations: A cross-country analysis’, _IMF Staff Papers_ **45** (2), 236–268.

- Snow, A. & Warren, R. (1996), ‘The marginal welfare cost of public funds: Theory and estimates’, _Journal of Public Economics_ **61** , 289–305.

- Stuart, C. (1984), ‘Welfare costs per dollar of additional tax revenue in the United States’, _American Economic Review_ **74** (3), 352–362.

- Taliercio, R. (2004), Designing performance: The semi-autonomous revenue authority model in Africa and Latin America, Policy Research Working Paper 3423, World Bank.

- Terkper, S. E. (1995), Ghana’s tax administration reforms (1985-93), Development Discussion Paper 504, Harvard Institute for International Development.

- Triest, R. K. (1990), ‘The relationship between the marginal cost of public funds and the marginal excess burden’, _American Economic Review_ **80** (3), 557–566.

- Wildasin, D. E. (1984), ‘On public good provision with distortionary taxation’, _Economic Inquiry_ **XXII** , 227–243.

38
-31-

Annex Table A2

# Table A2: INDIA **COUNTRY and PUBLIC SECTOR SOLVENCY and SUSTAINABILITY**

||**1995/96**|**1996/97**|**1997/98**|
|---|---|---|---|
|**Country Solvency**||||
|Non-Interest Current Account (NICA) ($ millions)|-1,650|-588|-365|
|NICA/Exports|-3.5%|-1.1%|-0.7%|
|Real Export Growth|31.5%|7.0%|10.1%|
|Difference between 10% and Real Export Growth|-21.5%|3.0%|-0.1%|
|Country Solvency Index|-43.1%|_5.5%_|-0.1%|
|Country Solvency Adjustment (% of Exports)|-39.6%|6.7%|0.5%|
|**Public Sector Solvency**||||
|Primary Balance (LCU billions)|-115|-151|-266|
|Primary Balance/GDP|-0.9%|**-1.1%**|-1.7%|
|Real GDP Growth|8.0%|7.3%|_5.0%_|
|Difference between 10% and Real GDP Growth|2.0%|2.7%|_5.0%_|
|Public Sector Solvency Index|-3.5%|2.5%|3.1%|
|Public Sector Solvency Adjustment (% of GDP)|-2.5%|3.5%|4.8%|
|**Country Sustainability**||||
|Gross Disbursements (LT+IMF+ST)**($**millions)|7,140|6,682|9,044|
|Gross Disbursements/Exports|15.0%|12.8%|16.3%|
|Interest Payments (LT+IMF+ST) ($ millions)|4,605|4,441|5,514|
|Interest Payments/Exports|9.7%|8.5%|9.9%|
|Net Disbursements (LT+IMF+ST)**($**millions)|220|37|2,493|
|Net Disbursements/Exports|_0.5%_|0.1%|4.5%|
|DOD/ Exports|198.8%|179.7%|169.9%|
|Sustainable NICA Balance(- - deficit)|-32.4%|7.8%|-11.7%|
|Actual NICA (- = deficit)|_-3.5%_|-1.1%|-0.7%|
|Country Sustainability Adjustment (% of Exports)|-28.9%|9.0%|-11.0%|
|**Public Sector Sustainability**||||
|Real GDP Growth Rate|8.0%|7.3%|_5.0%_|
|Exports/GDP|15.5%|16.8%|17.9%|
|DOD/GDP|30.9%|30.3%|30.5%|
|Devaluation Rate|9.2%|1.0%|-6.4%|
|Grants/GDP|0.1%|0.1%|0.1%|
|Sustainable Primary Balance(-<br>deficit)|-2.2%|2.9%|1.6%|
|Actual Primary Balance (- = deficit)|-0.9%|-1.1%|-1.7%|
|Public Sector Sustainability Adjustment (% of GDP)|-1.2%|4.0%|3.3%|
|Conventional Fiscal Deficit|-7.9%|-7.8%|-8.5%|
|**_Memorandum Items:_**||||
|Domestic Debt/GDP|67.5%|64.9%|66.8%|
|Domestic Revenue/GDP|22.0%|21.0%|21.5%|
Figure 7 (a): Mongolia Skill Premiums – basic controls <!-- Start of picture text --> Mongolia: Skill premium and Skilled Workforce - Basic controls<br>only<br>0.9<br>0.8<br>0.7 Skilled1: At least<br>Upper Secondary<br>0.6<br>0.5 Skilled-2: Tertiary and<br>above<br>0.4<br>Skilled Premium-1<br>0.3<br>0.2 Skilled Premium-2<br>0.1<br>0<br>1998 2002 2007<br>Year<br>Figure 7 (b): Mongolia Skill Premiums – industry controls<br>Mongolia: Skill premium and Skilled Workforce - with industry<br>controls<br>0.9<br>0.8 Skilled1: At least<br>Upper Secondary<br>0.7<br>0.6<br>Skilled-2: Tertiary<br>0.5 and above<br>0.4<br>Skilled Premium-1<br>0.3<br>0.2<br>Skilled Premium-2<br>0.1<br>0<br>1998 2002 2007<br>Year<br>skilled workers<br>Skill premium and proportion of<br>skilled workers<br>Skill premium and proportion of<br><!-- End of picture text --> 28
|**Letsoalo****_et. al._. 2005**|ORANI-G<br>(Harrison<br>and<br>Pearson,1996)|to analyze the triple dividend of<br>water consumption charges in<br>South Africa: reduced water use,<br>more rapid economic growth,<br>and<br>a<br>more<br>equal<br>income<br>distribution.<br>Static CGE|Nested CES with an overall<br>Leontieff<br>structure<br>under<br>Armington assumption|<br> <br>linear expenditure system<br>under Armington assumption|
|---|---|---|---|---|
|**Velazquez, 2007**|Cardenete and<br>Sancho, 2003; André_et. al.,_<br>2005|to analyze the effects of an<br>increase in the price of the water<br>delivered<br>to<br>the<br>agriculture<br>sector on the efficiency of the<br>water<br>consumption<br>and<br>the<br>possible reallocation of water to<br>the remainingsectors.<br>Standard Static CGE|Nested Cobb-Douglas, under<br>Armington assumption|<br>Cobb-Douglas|

41
Three, and others, that comprise different groupings of Asian and Pacific nations in an attempt to find one that works best economically and politically for the region.

Emergent from the historical economic literature is a picture of a gradually expanding world economy that included not only trade but also the institutions and systems that supported it. Of considerable significance in this world system and, from some perspectives the central focus of it, is Asia. While many of the territories, countries, nation-states and cities that make up Asia have changed over time, this region's importance in, and contribution to, the world economy cannot be denied. This chapter will show that throughout history Asia has functioned not only as an integrated region but also as an active and, sometimes leading, participant in the global economy.

# **Size of the Asian Economy**

One indication of the prominence of a country within a region, or a region within **the** world, is the size of its economy. The size of the global economy historically, and Asia's position therein, is reflected in the population and GDP statistics.<sup>8</sup> Population growth and share for periods between 1000 and 1800 are compared for Europe and Asia in the tables below using data from three sources; i.e., Bennett (1954), Clark (1977) and Maddison (2001). (See Tables H.2 through H.5 in Historical Appendix for details of population levels, growth rates, and shares as estimated by the three historians.)<sup>9</sup> Table 1.1

|||**Compara**<br>1000-1500|**tivePopulation**|**Growth**|1500-1600||
|---|---|---|---|---|---|---|
|Region|Bennett|Clark|Maddison|Bennett|Clark|Maddison|
|Euopef|64.3%|74.4%|121.9%|29.0%|22.1%|28.1%|
|**AllAsi7**|51.2%|30.5%|55.2%|15.0%|31.2%|33.4%|
|**China**|**78.6%**|66.7%|74.6%|12.0%|50.0%|55.3%|
|**India**|12.5%|12.9%|46.7%|25.9%|26.6%|**22.7%k**|
|**Japan**|300.0%|60.0%|105.3%|25.0%|12.5%|20.1%|
|Wodd|62.2%|52.5%|63.2%|9.0%|16.6%|27.0%|
|||**1600-1700**<br>|||**1700-1800**<br>||
|Region|Bennett|Clark|Maddison|Bennett|Clark|Maddison'|
|**Europe**|29.2%|27.7%|**10.7%**|63.5%|63.2%|68.6%|
|**AlAsia**|37.7%|38.6%|6.2%|52.2%|40.5%|76.8%|
|**China**|46.4%|0.0%|-13.8%|68.3%|110.0%|176.1%|
|**India**|47.1%|100.0%|22.2%|57.0%|-5.0%|26.7%|
|**Japan**|35.0%|44.4%|45.9%|3.7%|**0.**%|14.8%|
|**Wodd**|27.0%|28.7%|8.6%|48.9%|38.8%|72.5%|
|**'Growth rate**|**covers**1700-1820|**forMaddison.**|||||
|**2 Includesbot**|**hEasternandWes**|**ternEurope.**|||||

> 3 Includes East, West and South Asia Source: Compiled **from** Maddison (2001), **Frank** (1998), **Bennett** (1954), **and Clark** (1977)

2
Table 3c: Shares of plants with financial loans by urban/rural setting for ASI plants

|||||Urba|n share|||||Rur|al share|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||1989|1994|2000|2005|2010|Average|1989|1994|2000|2005|2010|Average|
|15|Food products and beverages|0.89|0.89|0.89|0.90|0.90|0.90|0.85|0.86|0.90|0.90|0.93|0.89|
|16|Tobacco products|0.24|0.54|0.55|0.56|0.57|0.49|0.27|0.29|0.44|0.43|0.38|0.36|
|17|Textiles|0.95|0.94|0.95|0.94|0.94|0.94|0.95|0.93|0.93|0.93|0.94|0.93|
|18|Wearing apparel; dressing and dyeing of fur|0.98|0.93|0.93|0.94|0.94|0.94|0.96|0.96|0.91|0.88|0.92|0.93|
|19|Leather tanning; luggage, handbags, footwear|0.92|0.91|0.90|0.87|0.92|0.90|0.90|0.89|0.92|0.89|0.96|0.91|
|20|Wood and wood products; straw and plating articles|0.81|0.80|0.80|0.87|0.84|0.83|0.75|0.73|0.68|0.82|0.86|0.77|
|21|Paper and paper products|0.96|0.95|0.97|0.96|0.94|0.96|0.94|0.95|0.94|0.94|0.95|0.94|
|22|Publishing, printing and media reproduction|0.85|0.85|0.91|0.90|0.92|0.89|0.85|0.85|0.85|0.87|0.95|0.87|
|23|Coke, refined petroleum and nuclear fuel|0.77|0.84|0.91|0.87|0.93|0.87|0.79|0.87|0.89|0.81|0.97|0.86|
|24|Chemicals and chemical products|0.93|0.94|0.92|0.92|0.89|0.92|0.89|0.89|0.90|0.85|0.87|0.88|
|25|Rubber and plastic products|0.99|0.96|0.96|0.96|0.93|0.96|0.96|0.93|0.95|0.93|0.91|0.93|
|26|Other non-metallic mineral products|0.86|0.87|0.87|0.84|0.85|0.86|0.78|0.77|0.74|0.74|0.68|0.74|
|27|Basic metals|0.97|0.97|0.96|0.98|0.96|0.97|0.97|0.97|0.96|0.96|0.96|0.97|
|28|Fabricated metal products, except machinery|0.94|0.95|0.93|0.93|0.94|0.94|0.96|0.94|0.94|0.95|0.93|0.94|
|29|Machinery and equipment, n.e.c.|0.95|0.94|0.93|0.94|0.94|0.94|0.95|0.96|0.92|0.94|0.95|0.94|
|30|Office, accounting and computing machinery|0.97|0.97|0.88|0.90|0.93|0.93|0.92|0.90|0.51|0.62|0.87|0.76|
|31|Electrical machinery and apparatus, n.e.c.|0.96|0.96|0.96|0.94|0.93|0.95|0.94|0.94|0.94|0.93|0.90|0.93|
|32|Radio, television, and comm. equipment|0.95|0.93|0.96|0.87|0.92|0.93|0.97|0.87|0.96|0.88|0.84|0.90|
|33|Medical, precision and optical instruments, watches|0.94|0.93|0.93|0.90|0.92|0.93|0.99|0.96|0.83|0.89|0.82|0.90|
|34|Motor vehicles, trailers and semi-trailers|0.96|0.94|0.95|0.94|0.95|0.95|0.96|0.96|0.94|0.94|0.91|0.94|
|35|Other transport equipment|0.94|0.96|0.95|0.95|0.94|0.95|0.97|0.94|0.95|0.95|0.95|0.95|
|36|Furniture, manufacturing n.e.c.|0.87|0.87|0.90|0.93|0.87|0.89|0.89|0.87|0.87|0.87|0.86|0.87|
||Nationwide|0.86|0.89|0.92|0.92|0.91|0.90|0.82|0.82|0.87|0.86|0.85|0.85|

Notes: See Table 3a.
socioeconomic resilience to disasters 21

||**So**<br>|**cioeconomic**<br>||
|---|---|---|---|
|**District**|**Asset risk**<br>[,000US$/year]|**resilience**<br>[%]|**Well-being risk**<br>[,000US$/year]|
|Colombo|40,235|105|38,260|
|Rathnapura|8,194|32|25,487|
|Kalutara|7,434|62|12,050|
|Gampaha|6,931|73|9,449|
|Puttalam|3,321|46|7,252|
|Trincomalee<br>|1,257|29|4,315|
|Kurunegala|2,418|56|4,315|
|Polonnaruwa|2,099|49|4,312|
|Hambantota|1,552|61|2,540|
|Kegalle|1,006|41|2,442|
|Batticaloa|592|26|2,240|
|Mannar|528|38|1,396|
|Galle|673|53|1,280|
|Badulla|293|31|948|
|Kandy|430|49|880|
|Anuradhapura|473|55|860|
|Ampara|304|42|722|
|Matale|170|44|384|
|Moneragala|40|30|136|
|Mullaitivu|27|26|104|
|Vavuniya|44|54|82|
|Nuwara Eliya|1|34|4|
|Matara|–|40|–|
|Jaffna|–|31|–|
|Kilinochchi|–|18|–|
|Total|78,021|65|119,462|

**Table 2:** Risk to assets, risk to wellbeing, and socioeconomic resilience for the entire population of each district of Sri Lanka. Note: though our hazard dataset does not report asset loss exceedance curves for Matara, Jaffna, or Kilinochchi, still we are able to assess their respective levels of socioeconomic resilience using household survey data, and the fact that resilience is the ratio of asset to wellbeing losses.

# 3.1 District-level risk summary

The annual risk to assets, risk to wellbeing, and socioeconomic resilience for each district of Sri Lanka are summarized in Table 2. According to the SSBN model, annual asset losses to precipitation flooding are highly concentrated in Colombo district, totaling over US$40 million per year (1.0% of AHE). Rathnapura district suffers the second highest losses, valued at US$8.2 million per year (0.9% of AHE).
learn how to improve their product quality, use standardization, improve marketing skills, and enhance the reliability of their shipments (see, for instance, Haskel et al. (2007), Keller and Yeaple (2009), Poole (2013), Balsvik (2011)). FDI inflows also increase competition in the domestic market, which may force local firms either to improve or to exit.

Domestic firms may learn about the profitability of various export opportunities by observing their foreign peers’ exports, and this could make them invest in quality upgrading, or developing new products. Thus, the presence of foreign affiliates may lead to reduction of export costs (Aitken et al., 1997), an increase in the amount and unit value of trade transactions (Chen and Swenson, 2007), help develop new export connections (Swenson, 2008), and expand export varieties (Sheng and Yang, 2016).

This study examines the impact of FDI on the quality of products exported by local firms in a country entered by FDI. The analysis focuses on the Russian Federation. I use detailed customs data recording Russia’s exports at the level of the firm, 10-digit HS (Harmonized System) product classification, destination country and year. Due to the data I have at hand, the time period I examine is 2012-2016.<sup>5</sup> The export data are combined with firmlevel balance sheets and ownership information. Although the balance sheet information covers 2007-2016, the ownership information is available to me only for 2012 and 2015. Thus, my analysis focuses on a long difference. I measure quality upgrading, defined as within-product improvements in export quality, in two ways. First, I focus on unit values of exports.<sup>6</sup> Second, I use the approach in Khandelwal et al. (2013), which builds on the work of Khandelwal (2010).<sup>7</sup>

> 5This time period includes trade sanctions imposed primarily by the United States and the European Union in 2014 against Russia, mainly affecting military and dual use exports. In contrast to the United States, existing export contracts in the European Union were exempted; the sanctions thus concerned only new contracts. Given that the sanctions affected only a handful of products and that this work focuses on export flows that existed prior to the sanctions being imposed, it is unlikely to have affected the conclusions of the study. Moreover, Russia prohibited imports of certain agri-food goods from these countries. Again, given the narrow focus of Russian countersanctions, there should be no concerns about their impact.

> 6Despite their shortcomings, export unit values have been extensively used as a proxy for quality, see, for example, Schott (2004), Hallak (2006), Bas and Strauss-Kahn (2015).

> 7The basic insight of this method is that, controlling for price, products that enjoy a higher market share are of higher quality.

3
Selective fertility and endogenous migration are other major concerns regarding the identication strategy. As discussed in the data section, I limit the analytical sample to those who were already born or in utero during the start of the conict in 1996. Purely on identication prospective, it helps limit the potential confoundedness between the explained variables of interest and selective fertility and migration. For instance, the strategy helps mitigate the scenario in which after grasping the seriousness of the war, couples that are highly concerned about their children's health in high conict areas may choose to delay having children or migrate to low conict areas to start a family. However, it could be true for the periods prior to the start of the conict that in anticipation of the war concerned couples may have delayed having children or may have migrated. Detailed robustness checks are presented in the empirical results section.

# 4.2 Intergenerational Health Impacts

The gap between the start of the Nepal's civil conict and the time of the NDHS 2016 survey is sucient enough that I can explore the impacts the conict had on the children of women who were exposed to the war in their childhood. Anthropometric measures were collected for children under the age of 5 at the time of the survey and hence I limit the second-generation sample to children under 5 in 2016. I employ the same strategy as in Equation (7) and add child specic controls to estimate. Following is the estimation equation: where _Y_ is a health outcome of a child _j_ whose anthropometrics were measured in month _n_ , was born in month _k_ and year _l_ to a woman _i_ who was born in month _m_ and year _t_ , and resides in village _v_ , district _d_ and development region _r_ . Child health endowment is dened as function of all mother's controls and exposure to war as dened in section 4.1 and child specic characteristics. _θl_ are child's birth-year xed eects. As with mothers, child month of birth xed eects, _μk_ , are included to account for season of birth. Similarly, child anthropometric measures are sensitive to the timing of the measurement; in particular child weight, hence, Equation (8) also includes month 16
However, it is also important to note that many of the techniques designed to capture sensitive information have been used in more traditional settings to address issues not able to adequately be covered by a wide range of traditional data collection. However, as mentioned in the Introduction, fragile and conflict settings both increase the potential need to capture sensitive information and pose significant additional challenges beyond those experienced in traditional settings. For example, embedding individuals in communities to ask about personal involvement in the conflict is potentially dangerous for enumerators and limits the ability of any amount of trust enabling individuals to admit opinions and actions that could place them in trouble with authorities (e.g., Human Rights Watch 2021). Additionally, many of the indirect methods have primarily been utilized in relatively stable environments, and some have suggested that sudden shocks and crises might limit the ability of these methods to precisely infer sensitive viewpoints (e.g., Kramon and Weghorst 2019; etc.).

Given the difficulty in validating and eliciting sensitive opinions and the added difficulties of collecting such information in fragile and conflict settings, it is important to rely on a number of different methods. In the remaining sections of the paper, we try and validate anonymous internet-based surveys that, if they prove valuable, offer advantages in conflict settings over other approaches and can be used alongside these other techniques to provide policy makers and stakeholders a more complete characterization of important, sensitive issues.

# **Section 3. The Increased Need to Collect Sensitive Information in the Republic of Yemen’s Conflict**

We analyze the validity of anonymous internet surveys in the midst of the Republic of Yemen’s conflict. Importantly, this is a setting that illustrates how traditionally sensitive behaviors become more prevalent and how a wide range of routine data can become sensitive in a conflict setting. These sensitivities arise from the complex nature of the conflict, the resulting humanitarian crisis, and the large humanitarian response.

> their responses are not anonymous to everybody. Although these approaches have led to increases in the reporting of sensitive responses in some instances, these increases are potentially spurious and have been questioned (e.g., Park et al. 2022).

7
10 _Employment-to-population is particularly low for youth._ It stands at 21.6 percent compared to respectively 43 percent and 60 percent for the 25 to 34 and the 35 to 54 age groups (Table 2). Young women in particular display the lowest employment-to-population ratio of all categories, standing at 12.9 percent. The employment gap with older women is, however, less marked than it is for men, mostly because female labor force participation is low throughout all age groups. Although 31.3 percent of all young men are employed, the employment-to-population ratio goes up to 74.6 percent and 91 percent for males aged 25 to 34 and 35 to 54. This differential appears to be due to both substantially higher labor force participation and lower unemployment rates among the latter groups.

_Employment-to-population increases with education for prime-age workers._ The employment-topopulation ratio is lowest among youth, whose levels of education are higher than the overall population’s. For the 15-to-24 year-old group, employment is highest among those with no education and lowest for those with secondary and higher education, which is consistent with the fact that some are still in school. As expected, employment for prime-age workers increases with education. Differences between levels of education seem to become more marked as workers gain experience. For instance, differences in employment ratios between people with secondary schooling and with primary schooling are strongest for the 35-to-54 age group. The benefits of having some schooling, as opposed having no education, is starker for elder workers (55 to 64 years of age, Figure 3).

**Figure 3. Employment-to-Population by Age Group and Level of Education (in %)**

<!-- Start of picture text --> 15-24<br>25-34<br>None<br>Primary<br>Secondary<br>35-54<br>Higher<br>55-64<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> Source: EPCV 2004.

_Self-employment is predominant in Mauritania, gathering over half of the employed population_ (Figure 4). As shown in Figure 5, three-quarters of the self-employed have received no education. 14 percent went to primary school, 9 percent to secondary school and virtually none have a higher
they from? What draws them to Africa? What impact do they bring to the countries where they invest?

Answers to these questions are difficult as long as data on _private_ Chinese investment overseas, both quantitative and qualitative, are lacking. As it stands, consistent OFDI statistics tracking the role of the private sector, from either China’s official sources or international organizations, have not been available, while information from African host countries are typically anecdotal and scattered.

As a first attempt to fill this information gap, a pilot research project sponsored by the World Bank’s research department was conducted in the spring of 2012, with a view toward exploring the extent and impact of Chinese private investment in Africa. The study took three steps to accomplish its goal: a) gathering and synthesizing the data available from China, the home country; b) gathering official data from selected African host countries; and c) conducting company interviews at selected sites in both home and host countries to gain insight into their respective perceptions and experience. The data unearthed through these efforts were crosschecked and analyzed, leading to a number of findings that shed light on this important but still largely unknown phenomenon.

# I. <u>Findings based on data from China, the home country</u>

The research started with China’s Ministry of Commerce (MOC), the authentic official source of data on China’s foreign trade and investment. Other sources, including China’s Council for Promoting International Trade (CCPIT), some coastal provincial departments of foreign trade and investment and business associations, were also consulted with for additional information.

The most useful data come from a comprehensive database containing 1,586 Chinese investment projects active in Sub-Saharan Africa by the end of 2011, made available by MOC based upon its approvals for Chinese outbound investment projects.<sup>8</sup> The database contains the following information: name of the investing company, origin province of the company, destination countries, activities to be invested in, and time of MOC approval. Ownership information is not readily available in the database. However, by following the companies’ names, their websites and in some cases phone calls, one is able to identify the company's controlling shareholders, thus determining its ownership.

> 8 Under the existing regulations, Chinese firms need to obtain government approvals when making outbound investment.  Investment projects that exceed 100 million US dollars are required to have central government approval; projects under US$100 but above US$10 million need to obtain approvals of Provincial Department of Commerce.  Other investment projects, under $10 million and individuals are not subject to approvals, but are required to receive a “formality review” by MOC, by providing information through the ministry’s website, <www.mofcom.gov.cn>.

5