rafmacalaba/datause-encoder-data huggingface dataset:
progress towards the goal of eliminating extreme poverty using the same yardstick as previously used.

# **_3.5 Alignment, aggregation and reporting of poverty estimates_**

For individual countries, the World Bank publishes poverty numbers based on the international poverty line only for years for which it has household survey data on income or consumption. However, the estimated total count of poor people in the world for 2012 is based on a poverty estimate for all countries in 2012 - that is, poverty in 2012 is estimated for a country even if no household survey data is available from 2012. For example, if a country has data from 2010, these data are used to project an estimated poverty rate to 2012 for the purposes of the global count. An alternative to projecting the 2010 data forward to 2012 would be to use the poverty count from the actual year the data was collected, but this is viewed as an undesirable option. If the economy of this country had been growing between 2010 and 2012, then the poverty headcount from 2010 would most likely overestimate the poverty headcount in 2012.<sup>47</sup> For countries for which there is no survey available in the specific reference year for the global estimate, the country-level poverty count is estimated by extrapolating consumption or income from the latest survey using growth rates from national accounts. This procedure assumes distribution-neutral growth, i.e. no change in inequality. In addition, because growth in survey means has historically been lower than growth observed in national accounts data, the growth rates used for extrapolating the survey data are adjusted for these observed differences.<sup>48</sup> If a survey is available after the reference year, that information is also used: An average of the two extrapolated estimates is then reported, weighted by their relative distance from the

> 47 Kraay (2006) shows that growth is one of the key sources of poverty reduction.

> 48 For most countries, extrapolation is done using the household final consumption expenditure (HFCE, also known as private consumption expenditures, PCE) component of national accounts. For the SubSaharan Africa region, overall real GDP growth rate is used due to poor or inconsistent measurement of HFCE component.  Historically, across developing countries, growth in household survey means has averaged approximately 87% of the consumption growth from national accounts (Ravallion, 2003). Therefore, when applying national accounts growth rates to modify survey means, an adjustment factor has been used: 0.87 for all countries, with two exceptions. Those exceptions are China and India, for which additional specific research has been conducted. For China, we use 0.72, and for India 0.51. Note that, for China and India, survey data is available in the majority of the reference years, including in 2012, and no such extrapolation takes place for these two countries.

27
# **Online Appendix**

# **A Interventions and Design**

**CWLP program** Between 2012 and 2016, 40 rural Imadas took part in a project of Community Works and Local Participation (CWLP) implemented by the Ministry of Vocational Training and Employment (MFPE) with the technical support of the World Bank (The World Bank 2017). The CWLP program was implemented as a cluster RCT: 40 Imadas were randomly selected to benefit from public works and 40 Imadas were placed in a pure control group. In treatment Imadas, 35 households were randomly selected to participate in the cash-for-work program and 15 households were chosen as control. Our baseline data are the endline data of the CWLP evaluation. We built our impact evaluation on the CWLP evaluation for three reasons: facilitate sampling, minimize costs, and allow for the evaluation of the long-term effects of the CWLP. Our interventions were cross-randomized without stratifying by the CWLP treatment status. 710 households of our study sample had been randomly selected to benefit from the CWLP cash-for-work program while 1290 were part of the control groups. We study heterogeneous treatment effects by treatment status in the CWLP evaluation in Appendix G.

**Cash grants** The World Bank worked closely with the Ministry of Vocational Training and Employment (MFPE) and Tunisia’s Center of Arab Women for Training and Research (CAWTAR) to distribute unconditional cash grants to 1,000 women. While the grant was unconditional, women were encouraged to either invest the money in physical capital (e.g., starting a micro-enterprise) or human capital (paying for vocational training of their choice).

Those randomized into treatment received the cash grant in their bank account without any additional conditions. However, more than 80% of women contacted to receive a grant reported not having a bank or postal account. These women were invited to open a bank or postal account as soon as possible and provide account information at the training session. An official invitation was provided to them by the CAWTAR to facilitate 32
|Table**S.**Sim<br>**_Region_**|ulationofgainsin<br>**_Baseline_**|netfarmincome<br>**_Threat_**_of_**_re_**<br>**_eliminated;co_**|duetoterracing<br>**_distribution_**<br>**_nflictsresolved_**|resulting from<br>**_Transfe_**<br>**_(mortgagin_**|more securela<br>**_rrights_**<br>**_gandsale)_**|ndtenure,differ<br>_No_**_redistri_**<br>**_Transf_**|entscenarios<br>**_butionand_**<br>**_errights_**|
|---|---|---|---|---|---|---|---|
||Netincome|Netincome|Gain**in**%|Netincome|Gain in%|Netincome|Gain in%|
|Tigray|1027.9|1040.8|1.3%|1053.5|2.5%|1064.7|3.6%|
|Amhara|1143.3|1161.8|1.6%|1194.7|4.5%|1210.3|5.9%|
|Oromia|1361.6|1382.6|1.5%|1426.1|4.7%|1440.9|5.8%|
|SNNPR|280.0|282.6|0.9%|290.3|3.7%|292.3|4 4%|
|_National_|_1093.9_|_11105 _|_1.5%_|_11426 _|_44%_|_11550 _|_5.6%_|

23
Table A3: Using Within-Region Variation for Interaction Results

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent|FOFM|FOFM|ln_L_|ln_L_|
|ln_L_|-0.133<sup>_∗∗∗_</sup><br>(0.0088)|-0.099<sup>_∗∗∗_</sup><br>(0.0039)|||
|Trust_×_ln_L_|0.089<sup>_∗∗∗_</sup><br>(0.0215)||||
|Complex_×_Trust||0.057<br>(0.0398)||-0.153<br>(0.0928)|
|FOFM|||-0.242<sup>_∗∗∗_</sup><br>(0.0769)|-0.028<br>(0.0905)|
|Trust_×_FOFM|||-0.425<sup>_∗_</sup><br>(0.2175)|-0.972<sup>_∗∗∗_</sup><br>(0.2691)|
|Complex_×_FOFM||||-0.086<br>(0.0553)|
|Trust_×_FOFM_×_Complex||||0.164<br>(0.1480)|
|Controls|X|X|X|X|
|RegionFE|X|X|X|X|
|Clustering|Region|Region|Region|Region|
|No. Clusters|133|133|133|133|
|_R_<sup>2</sup>|0.18|0.21|0.34|0.41|
|_N_|112945|41726|112943|41726|

Note: All specifications include 3 digit SIC effects. Controls include log firm age, log region gdp per capita and the region’s college share of residents. Complex is complexity measure defined in text. Regressions include full interactions; only coefficients with remaining within-region variation are reported.

3
**Table 29: Geographical concentration and clusters of innovative industries**

|High-tech|KIBS|Media/<br>Content|Overall|
|---|---|---|---|
|_Beijing_||||
|Are peer firms concentrated in a particular part of the city?||||
|No<br>33|65|33|46|
|Yes<br>67|25|67|50|
|No opinion<br>-|10|-|4|
|Total<br>100|100|100|100|
|Does the respondent consider the firm interviewed to be a par<br> <br>|t of a cluster?<br>|||
|No<br>40|40|20|34|
|Yes<br>60|55|80|64|
|Not clear<br>-|5|-|2|
|Total<br>100|100|100|100|
|_Shanghai_||||
|Are peer firms concentrated in a particular part of the city?||||
|No<br>29|56|30|34|
|Yes<br>67|33|55|56|
|No opinion<br>4|11|15|10|
|Total<br>100|100|100|100|
|Does the respondent consider the firm interviewed to be a par|t of a cluster?|||
|No<br>38|22|25|30|
|Yes<br>52|56|70|60|
|No opinion<br>10|22|5|10|
|Total<br>100|100|100|100|

**Table 30: Suppliers of high-tech manufacturing**

|Are any of the suppliers in this city part of<br>business clusters oftheirown?|High-tech|
|---|---|
|_Beijing_||
|No|67|
|Yes|13|
|No opinion|20|
|Total|100|
|_Shanghai_||
|No|43|
|Yes|33|
|No opinion|24|
|Total|100|

# _Intellectual Property Law_

Intellectual property rights are important to the knowledge-based economy for they endow the intangible assets of knowledge and innovations with measurable property ownership, which facilitate transactions of such assets. However, intellectual property law (IP law) can 40
the various groups studied have the lowest overall mortality rate in both high and low HIVprevalence countries. On the other hand, men with less than primary education have the largest increase in mortality. In high HIV-prevalence countries these men have mortality rates that are substantially higher than men with more education, and than women.

One group for which the higher mortality among men does not hold is among young women (ages 15-24) especially those with less education. For this group, mortality rates are higher than all other groups of the same age. This is consistent with the age profile of HIV/AIDS, but is also consistent with findings by others that younger more educated women appear to be engaging in more “protective” behaviors as knowledge about HIV and AIDS is disseminated.

Last, perhaps unsurprisingly, countries that have experienced conflict or episodes of internationally recognized genocide, display heterogeneous patterns in the time trends and socioeconomic patterns in mortality. The experience from several countries suggests that male mortality is typically more responsive to these events—and in several cases it is urban or more educated males for whom mortality increases most. At the same time, it is important to recognize that increased mortality during these episodes of conflict is not restricted to men—in all cases women’s mortality is affected as well.

# **References**

Bicego, George. 1997. “Estimating Adult Mortality Rates in the Context of the AIDS Epidemic in Sub-Saharan Africa: Analysis of DHS Sibling Histories.” _Health Transition Review_ 7(Suppl 2): 7-22.

- Case, Anne and Christina Paxson. 2010. “The Changing Role of Education in the AIDS Crisis”. Center for Health and Wellbeing. Princeton University. Mimeo. August 2010.

- Conley, Dalton, and Rebecca Glauber. 2005. “Sibling similarity and differences in socioeconomic status: life course and family resource effects.” NBER Working Paper 11320.

- Dahan, Momi, and Alejandro Gaviria. 2001. “Sibling Correlation and Intergenerational Mobility in Latin America.” _Economic Development and Cultural Change_ , 49 (3): 537-554.

- Deaton, Angus, and Christina Paxson. 2003. “Mortality, Income, and Income Inequality Over Time in Britain and the United States.” In David A. Wise, ed., _Perspectives on The Economics of Aging,_ vol. 8. Chicago, IL: University of Chicago Press for NBER.

24
for “risk-free” UK and US government bonds (marked in blue) using the full 200-year series for both countries (the average yearly real return is 3.4% for US bonds and 4.1% for UK bonds, with standard deviations of 12% and 10%, respectively).

Figure 9 focuses on country credit histories and plots the average yearly real ex-post bond returns on the vertical axis against the total number of external sovereign default events (on private external debt) since 1815 on the horizontal axis, using the updated Reinhart and Rogoff (2009) default data. Similar to Figure 7, we find that bond returns increase in the riskiness of a country, as measured by the number of past defaults. In Appendix B7, we add to these results with a capital asset pricing model (CAPM) analysis. Serial defaulters have higher betas, as bond returns for these countries are more sensitive to US/UK stock market conditions.

Figure 9: The role of credit history: Returns and default frequency, by country, 1815-2016 <!-- Start of picture text --> CIV<br>15 ECU<br>CHE PAK<br>EST<br>NGA<br>IRQ UKR<br>FIN MEX<br>10 DEU POL SRB CRI<br>VEN<br>FRA CZK<br>PHL<br>LBN JPNITA PAN ZWEMARHTICUB PRTGRC COLSLVBRA URYPRYNICDOMGTM CHLARG<br>5 NLDSWEMYSDNKBEL VNMHRV HUN BGR RUSIDNHNDAUT EGY ESP PER TUR<br>NORIRL THA ROUZAF<br>US bondsNZLAUS<br>CAN<br>UK bonds CHN BOL<br>t-statistic:1.87<br>p-value:0.07<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11<br>Number of external defaults<br>Serial defaulters All others<br>Mean annual real return in %<br><!-- End of picture text --> _Notes:_ This figure plots real, average total returns on country-level portfolios of sovereign external bonds against the total number of defaults since independence by that country on the horizontal axis. The data come from Table 6, covering countries with 10 years or more of bond price data. The default data from Reinhart and Rogoff (2009) and updated in Reinhart and Trebesch (2016).

# 4.5. Liquidity of the sovereign bond market over the past 200 years

This section gives a brief overview on sovereign bond market liquidity over 200 years, as proxied by bidask spreads (additional insights are provided in Appendix B6). To complement our main database of prices, haircuts and returns, we gathered a large additional dataset of bid and ask bond prices. The resulting bid-ask spread series cover all countries in our sample, but with some gaps, in particular in the first half of the 19th century (see Figure B3). In total, we could gather monthly, bond-level bid-ask spreads for 62% of our pricing sample (165,638 out of 266,134 observations). For historical bonds traded on the London 33
Table 14: Aid & bilateral TRADE-CASH - Robustness Test OECD vs. NON-OECD

|)<br>|1<br>=<br>(<br>_V_<br>_cash_<br>_Pr_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||OE|CD||
|_Ln(Fatalities)_|-0.065***|-0.064***|-0.064***|-0.060***|
||(0.018)|(0.018)|(0.018)|(0.018)|
|_Ln(Affected)_|0.016**|0.017**|0.013|0.013*|
||(0.008)|(0.008)|(0.008)|(0.008)|
|_Ln(GDP p.c.)_|-0.167***|-0.170***|-0.187***|-0.180***|
||(0.062)|(0.062)|(0.066)|(0.066)|
|_Trade with donor_|0.063***|0.068***|0.064***|0.105***|
||(0.023)|(0.024)|(0.023)|(0.027)|
|_Regulatory quality_|0.082|0.009|||
||(0.098)|(0.172)|||
|_Trade with donor_×||0.015|||
|_Regulatory quality_||(0.030)|||
|_Corruption control_|||0.143|-0.285|
||||(0.115)|(0.215)|
|_Trade with donor_×||||0.077**|
|_Corruption control_||||(0.032)|
|||NON-|OECD||
|_Ln(Fatalities)_|0.010|0.019|0.000|0.022|
||(0.042)|(0.043)|(0.044)|(0.048)|
|_Ln(Affected)_|-0.021|-0.019|-0.033|-0.047**|
||(0.019)|(0.020)|(0.022)|(0.024)|
|_Ln(GDP p.c.)_|0.129|0.081|-0.052|-0.065|
||(0.145)|(0.150)|(0.181)|(0.180)|
|_Trade with donor_|-0.050|0.010|-0.090|-0.080|
||(0.043)|(0.056)|(0.065)|(0.064)|
|_Regulatory quality_|-0.210|-0.554**|||
||(0.231)|(0.270)|||
|_Trade with donor_×||0.130*|||
|_Regulatory quality_||(0.073)|||
|_Corruption control_|||0.457|-0.161|
||||(0.372)|(0.504)|
|_Trade with donor_×||||0.196**|
|_Corruption control_||||(0.092)|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is cash, a dummy that switches to 1 if the bilateral aid contribution was cash. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

28
# Trading off the Income Gains and the Inequality Costs of Trade Policy<sup>_∗_</sup>

Erhan Artuc<sup>_†_</sup> Guido Porto<sup>_‡_</sup> Bob Rijkers<sup>_§_</sup> The World Bank Dept. of Economics The World Bank DECTI UNLP DECTI Key words: Trade Policy, Poverty, Inequality, Households, Social Welfare, Trade Liberalization JEL classifications: D1, D6, F1

> _∗_ We thank R. Adao, I. Brambilla, A. Deaton, L. Gasparini, A. Mattoo, B. McCaig, A. Nicita and M. Olarreaga for comments and discussion, and C. Arkolakis and two anonymous referees for constructive feedback. J. Angbazo, S. Fernandez, W. Kassa, H. Liu, A. Luo and M. Saleh provided excellent research assistance. This research was supported by the World Bank’s Research Support Budget, the ILO-World Bank Research Program on Job Creation and Shared Prosperity, the Knowledge for Change Program, and the World Bank’s Umbrella Facility for Trade. 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 Bank of Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the countries they represent. All errors are our responsibility.

> _†_ Development Economics Research Group, Trade and Integration, The World Bank. email: eartuc@worldbank.org

> _‡_ Universidad Nacional de La Plata, Departamento de Economia, Calle 6 e/ 47 y 48, 1900 La Plata, Argentina. email: guido.porto@depeco.econo.unlp.edu.ar

> _§_ Development Economics Research Group, Trade and Integration, The World Bank. email: brijkers@worldbank.org
demographic characteristics with units of analysis as individuals. For the purpose of this paper, we use the LFS for the years 2007, 2012, and 2018.

Our analysis includes the following labor force variables: real wages, informality status and female labor force participation. Over the period 2007 and 2018, several changes were introduced in the Egyptian LFS together with updates in concepts and definitions used which are standardized to make key labor market outcomes, administrative geographies as well as industry classifications comparable over time.

Table 1 in annex 1 provides an overview of the main labor force variables’ summary during the study periods. The data reveals a notable rise of 8.2 percentage points in informal employment within Egypt from 2007 and 2018, reaching a substantial 59 percent in 2018. While variables like real wages and female labor force participation demonstrated positive growth from 2007 to 2012, they experienced a decline by 2018.

Measurement and Construction of Export Exposure Using Input-Output Linkages Any changes in the foreign export demand for products of a particular sector will have dual effects. First, it will lead to a direct increase in demand for output in that sector. Secondly, it indirectly affects the upstream sectors that supply inputs to the directly impacted sector. Not accounting for these linkages will lead to an underestimation of the export exposure at the district level, as some districts may not have a concentration of industries directly exporting but still be supplying to exporting sectors. The literature represents this chain of responses using Leontief inverse of an input-output production matrix for an economy, which clearly tracks the use of intermediate inputs by each sector (Goutam et al. 2017; Acemoglu et al. 2016; Acemoglu et al. 2012).

To explore potential effects of exports through domestic inputs, we employ the 2008 Egypt Input-Output table to calculate the input shares of each industry. These shares are determined by dividing the input usage by the gross output (which includes the value added in the own sector with own sector inputs). The resulting shares are then multiplied by the exports of the final sector and aggregated over the input industry to obtain the total value of exports for each input sector (representing the cumulative effect of servicing multiple exporting sectors). In this sense, non-traded sectors that are assigned a value zero for exports will also have an implied value and will be used to estimate the _total export exposure index_ at the district level using the following index.

The _total export exposure_ (accounting for supply chain linkages) is measured as the growth in exports in industry i between time periods, _t_ and _t+1_ captured by the term . This change is allocated to each district in Egypt by dividing this expression with ii which is ii tt+1 tt employment in areas (kizm and markaz) in Egypt in the initial period. This index is further WW −WWaa tt <u>t</u> LL weighted by share of area _a_ in total employment in Egypt in industry _i_ ( ). LLii,aa aa LLtt i,a(Wt+1i −Wti) = �<sup>Lt</sup> LaLi,egypt aa t t tt,tt+1 xx
(rather than large N) asymptotics. We believe these approaches are useful for addressing sample concerns in our paper, and also potentially for other field experiments where the data has a small cross-section but long time series.

This paper relates to several strands of literature. First, there is the long literature showing large productivity differences across plants in dozens of countries. From the outset this literature has attributed much of these spreads to differences in management practices (Mundlak, 1961), but problems in measurement and identification have made this hard to confirm (Syverson, 2010). This productivity dispersion appears even larger in developing countries (Banerjee and Duflo, 2005, Hsieh and Klenow, 2009). Despite this, there are still few experiments on productivity in firms (McKenzie, 2010a) and none involving large multi-plant firms.

Second, our paper builds on the literature on the management practices of firms. There has been a long debate between the “best-practice” view that some management practices are universally good so that all firms would benefit from adopting these (Taylor, 1911) and the “contingency view” that every firm is already adopting optimal practices but these differ firm by firm (e.g. Woodward, 1958). Much of the empirical literature trying to distinguish between these views has traditionally been case-study or survey based, making it hard to distinguish between different explanations and resulting in little consensus in the management literature.<sup>4</sup> This paper provides experimental evidence that a core set of best practices do exist, at least in one industry.

Third, the paper links to the large theoretical literature on the organization of firms. These papers generally emphasize optimal decentralization as driven either by minimizing learning and information processing costs or by optimizing incentives.<sup>5</sup> But the empirical evidence on decentralization is limited, focusing primarily on de-layering in large publicly traded US firms (Rajan and Wulf, 2006).

Fourth, the paper contributes to the literature on Information Technology (IT) and productivity. A growing body of work has examined the relationship between technology and productivity, emphasizing both the direct productivity impact of IT and also its complementarity with modern management and organizational practices (e.g. Bresnahan et al. 2002 and Bartel et al. 2007). But again the evidence has focused on survey data rather than experimental data. Our experimental evidence suggests one route for computers to affect productivity is by facilitating

> 4 See, for example, the surveys in Delery and Doty (1996) and Bloom and Van Reenen (2010).

> 5 See the recent reviews in Garicano and Van Zandt (2010), Mookherjee (2010) and Gibbons and Roberts (2010).

3