rafmacalaba/datause-encoder-data huggingface dataset:
## POLICY RESEARCH WORKING PAPER 1415

# Summary findings

China's gross domestic product per capita was only US$300 to $370 in 1980-91 in an estimate based on the World Bank Atlas approach used in the _World Development Report._ These estimates fail to capture the fact that in the 10 years since embarking on a program of economic reform aimed at rapid economic developmnent, China has been one of the fastest growing economies in the world. Knowing what its true standard of living and productive potential is important iaot only for measuring the size of China's economy but for assessing its growth performance.

Ruoen and Kai provide a detailed comparison of the GDP of China and the United States using the purchasing power parity approach formulated by the U.N.

International Comparison Program (ICP), with 1986 as a base. Using this approach, which establishes a conversion factor based on prices for comparable items rather than on exchange rates, they find that:

> * China's per capita GDP in 1986 international dollars is between $770 and $1,044, depending on assumptions made about comparison-resistant service sectors and quality adjustments made in a number of selected ones in calculating purchasing power parities.

* China's per capita GDP in 1991 international dollars is between $1,227 and $1,663, allowing for the impact of inflation in the United States on the purchasing power parity and growth rates in China computed from national currency GDP data in constant prices.

- - This paper a product of the Socio-Economic Data Division, International Economics Dcpartment is part of a larger effort in the departmcnt to cxpand coverage for purchasing power parity-based estimates to most countries of the world. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Elfrida O'RiellyCampbell, room S7-136, extension 33707 (46 pages). January 1995.

_The Policy Resare WorkiNg Paper Seris dissemina_ **_t_** _es he fdings of uork ai progress to ncoge the exuchng of deas about dc pment sses. An obective of the seriei_ **_s_** _to get the findmgs out quicy. even if the presentations are kss than lly polishae The papes cany the names of the autors andshould be used and sitd accordingly. The findings_ **_anterpretations._** _and conclusions are the autors' own and should not be atributed to the World Bank. it Executive Board oflDirectors_ or _any of its member countries._ Produced by the Policy Research Dissemination Center
**<u>Table 3.13.1 : Additional US Data Included in AD-USA-v1.0.xls</u>**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-U**|**SA-Master :**||
|**U.**|**CASE_ITA**|Case code associated with the dumping investigation administered by the<br>International Trade Administration (ITA) at the Department of Commerce|
|**V.**|**CASE_ITC**|Case code associated with the injury investigation administered by the US<br>International Trade Commission (ITC)|
|**W.**|**RELATED_CVD**|Case code of a related countervailing duty investigation against the same<br>product and country, see CVD-USA workbook|
|**X.**|**ITC_REPCODE**|First (chronological) ITC case code for multiple country/identical product<br>antidumping investigations|
|**Y.**|**INIT_DATE_ITA**|Date of initiation of the case at the ITA|
|**Z.**<br>|**INIT_DATE_ITC**<br>|Date of initiation of the case at the ITC<br>|
|**AA.**|**TERM_DATE**|Date of termination of investigation if terminated|
|**AB.**|**P_AD_DUTY**|Preliminary “all other firm” antidumping duty imposed|
|**AC.**|**F_AD_DUTY**|Final “all other firm” antidumping duty imposed|
|**AD.**|**INIT_ITA_GP**|Citation to the_Federal Register_government publication for initiation at the<br>ITA|
|**AE.**|**INIT_ITC_GP**|Citation to the_Federal Register_government publication for initiation at the<br>ITC|
|**AF.**|**P_DUMP_GP**|Citation to the_Federal Register_government publication for ITA<br>preliminary dumping determination|
|**AG.**|**P_INJ_GP**|<br>Citation to the_Federal Register_government publication for ITC<br>preliminary injury determination|
|**AH.**|**F_DUMP_GP**|<br>Citation to the_Federal Register_government publication for ITA final<br>dumping determination|
|**AI.**|**F_INJ_GP**|Citation to the_Federal Register_government publication for ITC final injury<br>determination|
|**AJ.**|**F_AD_MEASURE_GP**|Citation to the_Federal Register_government publication for imposition of<br>antidumping measure order|
|**AK.**|**REVOKE_GP**|<br>Citation to the_Federal Register_government publication for revocation of<br>antidumping order|
|**AL.**|**NOTES**|Comments|
|**In AD-U**|**SA-Products :**||
|**D.**|**TS_CODE**|Prior to 1989, the US did not subscribe to the HS system and instead used<br>the TSUSA system to classify imports, thus this column reports the pre-<br>1989 cases which referenced the investigated products’ 5-7 digit TSUSA<br>code|
|**E.**|**TS_DIGITS**|Number of digits of the TSUSA product code reported in TS_CODE (e.g.,<br>5,7, etc.)|
|**F.**|**CASE_ITC**|Case code associated with the injury investigation administered by the US<br>International Trade Commission (ITC)|
|**G.**|**NOTES**|Comments|
|**In AD-U**|**SA-Domestic-Firms :**||
|**C.**|**CASE_ITC**|Case code associated with the injury investigation administered by the US<br>|
|**In AD-U**|**SA-Foreign-Firms :**|International Trade Commission (ITC)|
|**D.**|**CASE_ITC**|Case code associated with the injury investigation administered by the US<br>International Trade Commission(ITC)|

33
|Agricultural<br>assets|Yes|List of basic agricultural assets likely possible<br>over the phone. Unlikely to change over the<br>season so no specific comparative<br>advantage for phone surveys. However,<br>information on certain assets may be<br>sensitive complicating data collection over<br>thephone.||
|---|---|---|---|
|Agricultural labor<br>inputs|Yes|Evidence suggests phone surveys can<br>improve data quality. Auxiliary diaries may<br>be beneficial for data collection.|Arthi et al. (2018); Gaddis<br>et al. (2021)|
|Non-labor inputs|Potentially|Phone surveys can be used to reduce recall<br>length which may improve quality of the<br>data. Auxiliary diaries may be beneficial for<br>data collection.||
|Livestock<br>production|Yes|Phone survey was tested successfully in<br>Niger. Higher frequency data collection<br>considered advantageous for extended<br>production livestock products (milk, dairy).<br>Auxiliary diaries may be beneficial for data<br>collection. Phones may also be used to track<br>or contact semi-nomadic herders as they<br>move, provided sufficient penetration,<br>signal,energyaccess is available.|Bako (2018);<br>Zezza et al. (2016);<br>Hiraga, Uochi and Doyle<br>(2020); Karamba and<br>Salcher (2020); Teickner<br>et al. (2020)|
|Harvest of<br>extended harvest<br>crops|Yes|Evidence suggests phone surveys can<br>improve data quality. Auxiliary diaries<br>preferable.|Ceballos et al. (2020);<br>Kilic et al. (2021)|
|Crop harvest|No|Standard crop harvest module at crop-plot<br>level likely too heavy for phone surveys in<br>most countries. Crop-level harvest module<br>implemented in e.g. Georgia.<br>Harvest diaries have also been tested but<br>phone surveys unlikely to be suitable to<br>administer such diaries.<br>Cropcuttingrequires in-person visit.|Deininger et al. (2012)|
|Market prices|Yes|Market price data collection appropriate at<br>the community or market level but farm-<br>gate prices at household/farm level likely<br>difficult.|Hermosilla (2017);<br>Hoogeveen et al. (2014)|
|Shocks and<br>damage caused<br>by shocks|Potentially|Phone surveys can be used to reduce recall<br>length which may improve quality of the<br>data. Auxiliary diaries may be beneficial for<br>data collection.|Knippenberg et al. (2019)|

# 4.2. Respondent behavior and effects

The characteristics of respondents in agricultural surveys may differ from national populations in ways that are material for designing and implementing phone surveys for agricultural data collection.

18
van der Marel, Erik. 2011. “Determinants of Comparative Advantage in Services.” GEM Working Paper, Groupe d’Economie Mondiale, Paris.

<mark>van der Marel, E. (2012) "Trade in Services and TFP: The Role of Regulation",</mark> _<mark>The World Economy</mark>_ <mark>, Vol. 35, Issue 11, pages 1387‐1429.</mark> 25
recipient developing countries. One way of overcoming the information problem (and many other inefficiencies arising from the project approach<sup>5</sup> ) is for donors to pool their funds within a sector in a recipient developing country. Alternatively the donors can channel the aid as budget support to the recipient country so that the government makes the allocation decisions. There are, however, strong motives for the donor agencies to maintain the project approach, e.g., to demonstrate visible results to the taxpayers in the donor country and to accomplish the type of projects the donor country prefers. Another way to overcome the coordination failure is to simply improve information about the donors’ planned activities and budgets. According to the paper’s results, improving information is particularly important when the parties have similar priorities. A joint database on planned projects and budget allocations in each recipient country would provide such information. Such databases should not have just information on the current projects, but also forward-looking information on the planned activities to improve aid coordination.

The rest of the paper is organized as follows. Section 2 sets up the model of two donors and a passive recipient. Section 3 analyzes coordination failure among donors. Section 4 introduces an active recipient with different preferences from the donor and analyzes how coordination failure can add to the aid fungibility problem. Section 5 concludes.

The literature on voluntary provision of public goods and the free-rider problem is related. (e.g., Olson 1965; Chamberlin 1974, 1976; McGuire 1974). See Kemp (1984) on an application to foreign aid. In this literature the level of contributions is endogenous, while the focus here is on allocating a fi xed budget to a two-dimensional public good.

The structure of the model has similarities with networks, in particular Farrell and Saloner (1985). There are network externalities when the utility a consumer derives from a product depends on the number of other consumers with the same product. Consumers have a fi xed budget (as in our model) and choose the product from two possible ones. There is incomplete information about the other consumers’ preferences and coordination failure can occur: in equilibrium the consumers can choose an inferior good. In this paper’s framework, there are network externalities as the donor’s utility depends on the number of projects funded in each sector by the other donor. However, the incomplete information is not about the other donor’s preferences but

> 5For other inefficiencies, see Kanbur, Sandler, and Morrison (1999).

4
the extreme context, surveyed consumption levels indicate an unusually high share below subsistence levels.

For this purpose, this study assesses the effectiveness of a bundle of light‐touch measures. In line with our hypothesis we find significant treatment effects, which cluster in lower (potentially underreported) consumption quintiles. Moreover, effects are stronger for the number of consumption items than for monetary consumption quantities, where former are more susceptible to deliberate misreporting. Furthermore, the significant treatment effects are driven mainly by the vulnerable IDP subpopulation, which are more likely to be in need for foreign aid. Primes can, hence, help to improve data accuracy and inform policy to develop durable solutions. However, results should be taken with a grain of salt as it is not possible to compare the reported consumption outcomes to more objective consumption data. Although the mortality rates among IDPs suggest that starvation is not happening systematically across the country, the precarious situation calls for further scrutiny.<sup>19</sup> Before adjusting poverty estimates a thorough comparison with more “objective” data from administrative, anthropometric or observational sources is needed. While this type of data was not available in IDP camps due to the fragile context, future research could validate this finding in other settings.

Moreover, unbundling the primes in different treatment arms could help to shed light on the underlying causal mechanisms. The underlying design of one treatment and control arm does not allow for further disentangling the results. However, if classical measurement error would be affected only, treatment effects of the primes should be uniform. In contrast, heterogenous effects across quantiles suggest that the targeting of intentional misreporting via the appeal to honesty and prime to report more accurately would be the driver of our results. In order to design more effective primes, disentangling the pathways and trying different combinations could be a beneficial way forward. Our research can be considered as an early step to employ priming for better targeted policy responses in challenging contexts, which might not only be applicable in South Sudan, but also in other contexts facing humanitarian crises.

19 FEWS NET (2018).

20
ESS data have some features important for the analysis of informality which are, to our best knowledge, not available in other multi-country datasets (in particular, in EU LFS). First, ESS questionnaires for rounds 2, 3, and 4 (implemented in years 2004-2005, 2006-2007, and 2008-2009, respectively), allow users to identify employees working without a contract. By contrast, LFS data (both the anonymized data sets disseminated by _Eurostat_ and, for most countries, also the original datasets) allow users only to distinguish between permanent and temporary contracts, while answer ̳no contract‘ is not offered to respondents (like it was in round 1 of ESS)<sup>2</sup> . Comparison of ESS data of rounds 2-4 with those of round 1 suggests strongly that if the answer ̳no contract‘ is not included, the proportion of employees who do not answer the question about type of contract (or choose answer ―Don‘t know‖) cannot, in general, be used as a proxy for proportion of informally employed dependent workers (see Table A2 in the Annex).

Second, in ESS data, a distinction can be made between self-employed persons with and without employees, and in the former case the number of employees is reported as well. This is important because in many studies which use data without direct information on contract, employees are ̳assigned‘ to informal sector if they work in enterprises with 5 or fewer workers. It would then make sense to apply the same criterion to employers, i.e. to consider an employer with 5 or fewer employees to be working in informal sector. However, LFS and most other internationally comparable datasets provide, at best, only information on ―number of persons working in the local unit‖ of respondent‘s main job; in case of employers this of course cannot be considered as a proxy for the total number of employees working for him. Third, ESS data are available not only for all EU countries, but also for Norway, Switzerland, Russia, Ukraine, and Israel (for various data-related reasons our analysis omits Luxembourg, Malta, Turkey, and Croatia).

The contribution of the paper to the literature on informal employment is three-fold. First, we use direct survey evidence (rather than proxies) to provide a multi-country longitudinal analysis of the levels, dynamics and profile of dependent employment without contract, as well as informal self-employment, in Europe. Importance of using direct evidence is highlighted in Henley et al. (2006), who find that ―definitions of informality based on occupation and employer size seem the most arbitrary in practice‖, and in Perry et al. (2007), who report (based on a survey conducted in 9 countries in Latin America) that ―large firms... have a significant number of employees without social security contributions‖. On the other hand, we are able to draw the line between informal and formal self-employment more

> 2 Moreover, in cases when the original questionnaire includes the „no contract‖ option, _Eurostat_ groups these responses together with „temporary‖, making it very difficult to distinguish informally employed from who is legally employed fixed-term workers.

3
# **4.5 The impact on household poverty**

Given the positive and significant effect of the CTA adoption on the household outcomes (namely spending and income), the change in household poverty could be another spillover effect of the changes in trade outcomes. We evaluate it in this section by checking the extent to which CTA adoption affects the household poverty.<sup>18</sup> As previously in the section 4.4, we exclude from the sample the single SSCBTs with dependency ratio equal to zero and we report the result in the Table 5. We assess the effect of CTA adoption on: the income of the poor (people falling below the poverty line) (column 2); the individual poverty gap (column 3); the income of SSCBTs classed within the poverty depth (column 4); the individual poverty severity (column 5) and the income of SSCBTs classed within the poverty severity (column 5).<sup>19</sup> Table 5: LATE estimates of the Effects of CTA adoption on Poverty

|Status|Poverty|Depth|of Poverty|Svererit|y of the Poverty|
|---|---|---|---|---|---|
|Outcome|(2)<br>Income<br>of the poor|(3)<br>Poverty<br>depth|(4)<br>income within<br>poverty depth|(5)<br>Poverty<br>Severity|(6)<br>income within<br>poverty severity|
|CTA Adoption|0.180*<br>(0.095)|-0.027<br>(0.017)|0.163*<br>(0.098)|-0.062*<br>(0.025)|0.219**<br>(0.101)|
|Observations|900|507|507|443|443|
|F Statistics (excluded instruments)|668.74|396.40|396.40|325.44|325.44|
|Under identifcation test p value|0.0000|0.0000|0.0000|0.0000|0.0000|
|Wald test (joint signifcance)|3.58*|2.48|2.75*|6.04**|4.56**|
|Cragg-Donald Wald F|307.52|200.10|200.10|168.51|168.51|
|Stock-Yogo : critical values|16.38|16.38|16.38|16.38|16.38|

Notes: outputs are log-transformed. ***,**,* Denote significance at 1%, 5% and 10% levels, respectively. Standard errors are in parenthesis. Socio-demographic Controls includes variable such as Head of Household, gender, Age, Education, Living in urban, Experience as SSCBT, Dependency ratio, whether the partner has a Job, the logarithm of purchased volume amount (a proxy of SSCBT’s capital) and the dummy for Province The result shows that CTA adoption is negatively correlated with the ratios of poverty depth (-0.027 but not significant) and severity of poverty (-0.062 and significant). Consequently, CTA adoption increases significantly; by 18% the income of SSCBTs classed as poor, by 16,3% the income of SSCBTs classed within the depth of poverty and by 21.9% the income of SSCBTs classed within the severity of the poverty. Therefore, although the CTA might exclude SSCBTs with meagre capital (Keyser et al., 2022; Murhi and Mesa, 2021), their level of capital does not hinder the increase in their income once they are participants. In that regard, CTA can be taken as a pro poor policy as it does not affect only the SSBTs initially endowed with significant income but also those classified as poor

> 18Currently measured as people living on less than 1 _._ 90 a day.

> 19Individual poverty depth and severity are obtained by the FGT poverty formulas to decompose poverty which are respectively; ( _z − yi_ ) _/z_ and (( _z − yi_ ) _/z_ )<sup>2</sup> , see Section 3.2 for more explanation.

20
- **, , , M Steinbacher, EA Celarier, E Bucsela, EJ Dunlea, and JP Pinto** , “Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument,” _Journal of Geophysical Research: Atmospheres_ , 2008, _113_ (D16).

- **Lamsal, Lok N, Nickolay A Krotkov, Alexander Vasilkov, Sergey Marchenko, Wenhan Qin, Eun-Su Yang, Zachary Fasnacht, Joanna Joiner, Sungyeon Choi, David Haffner et al.** , “Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments,” _Atmospheric Measurement Techniques_ , 2021, _14_ (1), 455–479.

- **Lelieveld, Jos, Steffen Beirle, Christoph H ̈ormann, Georgiy Stenchikov, and Thomas Wagner** , “Abrupt recent trend changes in atmospheric nitrogen dioxide over the Middle East,” _Science advances_ , 2015, _1_ (7), e1500498.

- **Leue, C., Mark Wenig, T. Wagner, Oliver Klimm, Ulrich Platt, and Bernd J ̈ahne** , “Quantitative Analysis of NOx Emissions from GOME-Satellite Image Sequences,” _Journal of Geophysical Research_ , 03 2001, _106_ , 5493–5506.

- **Levin, Noam, Christopher CM Kyba, Qingling Zhang, Alejandro S ́anchez de Miguel, Miguel O Rom ́an, Xi Li, Boris A Portnov, Andrew L Molthan, Andreas Jechow, Steven D Miller et al.** , “Remote sensing of night lights: A review and an outlook for the future,” _Remote Sensing of Environment_ , 2020, _237_ , 111443.

- **Lin, J-T and Michael Brendon McElroy** , “Detection from space of a reduction in anthropogenic emissions of nitrogen oxides during the Chinese economic downturn,” _Atmospheric Chemistry and Physics_ , 2011, _11_ (15), 8171–8188.

- **Liu, Fei, Aaron Page, Sarah A Strode, Yasuko Yoshida, Sungyeon Choi, Bo Zheng, Lok N Lamsal, Can Li, Nickolay A Krotkov, Henk Eskes et al.** , “Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19,” _Science Advances_ , 2020, _6_ (28), eabc2992.

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40
- 84 -

# Table 27: MEANS AND STANDARD DEVIATIONS OF REGIONAL EARNINGS FUNCTION VARIABLES

||Abid|jan|Other|Cities|Rural|Areas|
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD|
|ln(W2)|11.40|1.04|11.33|1.10|<br>10.41|1.35|
|Years of Primary Schooling|4.38|<br>2.59|<br>4.41|<br>2.61|<br>3.00|2.91|
|Years of Secondary Schooling|<br>2.49|<br>2.71|<br>2.43|<br>2.75|<br>1.18|2.05|
|Years of Higher Schooling|0.60|<br>1.73|<br>0.23|<br>0.94|<br>0.00|0.00|
|Years of Work Experience-|9.22|7.93|<br>9.69|8.03|<br>7.36|7.29|
|Current Occupation|||||||
|Years of Work Experience-<br>|1.44|3.19|<br>1.49|3.46|<br>1.99|4.54|
|Previous Occupation|||||||
|Years of Secondary VTE|1.02|<br>1.85|<br>0.79|<br>1.27|<br>0.22|<br>1.16|
|Years of Other VTE|0.15|0.60|<br>0.27|0.80|<br>0.22|0.57|
|Years of Apprenticesh5p|0.79|<br>1.85|<br>0.67|<br>1.73|<br>0.41|<br>1.49|
|Training|||||||
|CEPE|0.21|<br>0.41|<br>0.24|0.43|<br>0.18|0.39|
|BEPC|0.03|0.16|<br>0.07|0.26|<br>0.06|0.23|
|Secondary VTE Diploma|0.27|0.44|<br>0.21|0.41|<br>0.11|0.32|
|Other VTE Diploma|0.06|0.24|0.11|0.31|-|-|
|Other Diploma|0.09|0.28|<br>nn.0|0.22|<br>0.02|0.14|
|Sex (1-<br>female)|0.21|0.41|<br>0.18|0.39|<br>0.07|0.26|
|Non-Ivorian|0.16|0.37|<br>0.14|0.34|<br>0.30|0.46|
|Seasonal Job|0.00|0.00|<br>0.03|0.17|<br>0.18|0.39|
|Number of Cases|270|-|176|<br>-|54|-|