rafmacalaba/datause-encoder-data huggingface dataset:
_(Table C.4 continued.)_

|Self-effcacy|standardized index following Anderson (2008) and O’Brien (1984)<br>of 10 self-effcacy items, higher score means higher self-effcacy,<br>based on Schwarzer and Jerusalem(1995)|
|---|---|
|Emotional competence|standardized index following Anderson (2008) and O’Brien (1984)<br>of 16 emotional competence items, higher score means higher com-<br>petence,based on Mikolajczak et al.(2014)|
|Multi-factor Leadership|standardized index following Anderson (2008) and O’Brien (1984)<br>of 3 multi-factor leadership items, higher score means higher leader-<br>ship,based on Avolio and Bass(2014)|
|Life satisfaction|Rung chosen on Cantril ladder from 0-10, higher rung means higher<br>satisfaction,based on Cantril(1965)|
|Self-assessment|Assessment of own performance as supervisor on a scale of 1-10<br>minus assessment of typical supervisor in factory|
|Ambition|Number of positions nominee would accept a promotion to (supervi-<br>sor,line chief,assistantproduction manager)|
|Muslim|Dummyvariable =1 if religion is Islam|
|Socioeconomic<br>back-<br>ground|standardized index following Anderson (2008) and O’Brien (1984)<br>of variables proxying nominee’s socioeconomic background during<br>youth:<br>1. Father’s years of education<br>2. Mother’s years of education<br>3. Type of house respondent lived in at 14 years of age<br>4. How often respondent went to bed hungry at 14 years of age|
|Marriage duration|Completedyears of marriage(if married)|
|Assets into marriage|Dummy variable =1 if respondent brought assets into marriage (if<br>married)|
|Age at marriage|Age - Marriage duration|
|Husband works in gar-<br>ment industry|Second respondent works in garment industry<br>**Panel C: Variables from diagnostic tests**|
|Numeracy score|Nominee’s score on 10 numeracy questions, score out of 1 which<br>represents aperfect score|
|Literacy score|Nominee’s score on 20 literacy questions, score out of 1 which rep-<br>resents aperfect score|
|Processing speed score|Nominee’s score on 193 processing speed questions, based on the<br>Wechsler Adult Intelligence Scale, score out of 1 which represents a<br>perfect score|

61
Denham, S. A. (2006). Social-emotional competence as support for school readiness: What is it and how do we assess it? _Early education and development_ , _17_ (1), 57-89.

<mark>Denham, S. A., Bassett, H. H., & Zinsser, K. (2012). Early childhood teachers as socializers of young children’s emotional competence.</mark> _<mark>Early Childhood Education Journal</mark>_ <mark>,</mark> _<mark>40</mark>_ <mark>(3), 137-143.</mark>

- Devine, R. T., White, N., Ensor, R., & Hughes, C. (2016). Theory of mind in middle childhood: Longitudinal associations with executive function and social competence. _Developmental Psychology_ , _52_ (5), 758.

- Dobbie, W., & Fryer Jr, R. G. (2013). Getting beneath the veil of effective schools: Evidence from New York City. _American Economic Journal: Applied Economics_ , _5_ (4), 28-60.

- Dotterer, A. M., & Lowe, K. (2011). Classroom context, school engagement, and academic achievement in early adolescence. _Journal of Youth and Adolescence_ , _40_ (12), 16491660.

- Drago-Severson, E. (2004). _Helping teachers learn: Principal leadership for adult growth and development_ . London: Sage Publications.

Duckworth, A., & Gross, J. J. (2014). Self-control and grit: Related but separable determinants of success. _Current Directions in Psychological Science, 23_ (5), 319325.

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. _Journal of Personality and Social Psychology, 92_ (6), 1087.

- Duflo, A., & Kiessel, J. (2017). Evaluating the Teacher Community Assistant Initiative in Ghana. Retrieved on 14 October 2018. <u>https://www.povertyaction.org/study/evaluating-teacher-community-assistant-initiative-ghana.</u>

- Duflo, E., Dupas, P., & Kremer, M. (2011). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in Kenya. _American Economic Review_ , _101_ (5), 1739-74.

- Duke, N. K., & Pearson, P. D. (2009). Effective practices for developing reading comprehension. _Journal of Education_ , _189_ (1-2), 107-122.

- Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. _Psychological Science in the Public Interest_ , _14_ (1), 4-58.

- Durkin, D. (1978). What classroom observations reveal about reading comprehension instruction. _Reading Research Quarterly, 14_ , 481–533.

Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions. _Child Development_ , _82_ (1), 405-432.

47
Attaran, Amir. 2004. How Do Patents And Economic Policies Affect Access To Essential Medicines In Developing Countries? _Health Affairs_ 23 (3).

- Barder, O., M. Kremer, R. Levine, A. Albright, Development Center for Global, and Group Advance Market Commitment Working. 2005. _Making Markets for Vaccines: From Ideas to Action_ : Center for Global Development.

- Bird, R. C. 2009. Developing nations and the compulsory license: maximizing access to essential medicines while minimizing investment side effects. _The Journal of law, medicine & ethics: a journal of the American Society of Law, Medicine & Ethics_ 37 (2):209.

- Crager, S. E., and M. Price. 2009. Prizes and Parasites: Incentive Models for Addressing Chagas Disease. _The Journal of Law, Medicine & Ethics_ 37 (2):292-304.

- Fink, C., and P. Reichenmiller. 2005. Tightening TRIPS: Intellectual Property Provisions of US Free Trade Agreements. Washington, D.C.: World Bank.

- Flynn, S., A. Hollis, and M. Palmedo. 2009. An Economic Justification for Open Access to Essential Medicine Patents in Developing Countries. _The Journal of Law, Medicine & Ethics_ 37 (2):184-208.

- Hollis, A. 2007. Drugs for Neglected Diseases: New Incentives for Innovation. In _Pharmaceutical Innovation: Incentives, Competition, and Cost-Benefit Analysis in International Perspective_ , edited by F. A. Sloan. Cambridge: Cambridge University Press.

- ———. 2008. The Health Impact Fund: A Useful Supplement to the Patent System? _Public Health Ethics_ 1 (2).

- Hollis, A., and T. Pogge. 2008. _The Health Impact Fund: Making New Medicines Accessible to All_ : Incentives for Global Health.

- Johnston, J., and A. A. Wasunna. 2007. Patents, biomedical research, and treatments: examining concerns, canvassing solutions. _The Hastings Center report_ 37 (1):1-36.

- Kanavos, Panagiotis G., and Joan Costa i Font. 2005. Pharmaceutical Parallel Trade in Europe: Stakeholder and Competition Effects. _Economic Policy_ 20 (44).

- Kesselheim, A. S. 2008. Drug Development for Neglected Diseases--The Trouble with FDA Review Vouchers. _New England Journal of Medicine_ 359 (19):1981.

- Knowledge@Wharton. 2009. _Selling Life-Saving Drugs to Poorer Countries: At What Cost?_ Wharton School of the University of Pennsylvania. 2002 [cited October 2 2009]. Available from

<u>http://knowledge.wharton.upenn.edu/articlepdf/649.pdf?CFID=10523235&CFTOKEN=5 7577734&jsessionid=a830e8209473235a68416a2a1c402f66b731.</u>

- Kremer, M. 1998. Patent Buyouts: A Mechanism for Encouraging Innovation. _Quarterly Journal of Economics_ 113 (4):1137-1167.

- ———. 2001a. Creating markets for new vaccines. Part I: rationale. In _Innovation Policy and the Economy_ , edited by A. B. Jaffe: MIT Press.

- ———. 2001b. Creating Markets for New Vaccines. Part II: Design Issues. In _Innovation Policy and the Economy_ , edited by A. B. Jaffe: MIT Press.

- Kremer, M., and R. Glennerster. 2004. _Strong Medicine: Creating Incentives for Pharmaceutical Research on Neglected Diseases_ : Princeton University Press.

- Lambsdorff, Johann Graf. _Corruption Perceptions Index (CPI)_ . Transparency International 2008 [cited December 13, 2008]. Available from:

44
- HENGEL, E. (2022): “Are Women Held to Higher Standards? Evidence from Peer Review,” _The Economic Journal_ .

- HOLZER, H. J. AND K. R. IHLANFELDT (1998): “Customer discrimination and employment outcomes for minority workers,” _The Quarterly Journal of Economics_ , 113, 835–867.

- IFC (2021): “Women and E-Commerce in Africa,” _International Finance Corporation_ , 74. ILO (2019): “Wages in Africa,” .

- JAYACHANDRAN, S. (2015): “The roots of gender inequality in developing countries,” _economics_ , 7, 63–88.

- KAHN, L. M. AND P. D. SHERER (1988): “Racial differences in professional basketball players’ compensation,” _Journal of Labor Economics_ , 6, 40–61.

- KLUGMAN, J. AND S. TWIGG (2016): “Gender at Work in Africa: Legal Constraints and Opportunities for Reform,” _African Journal of International and Comparative Law_ , 24, 518– 540.

- KRICHELI-KATZ, T. AND T. REGEV (2016): “How many cents on the dollar? Women and men in product markets,” _Science advances_ , 2, e1500599.

- LEONARD, J. S., D. I. LEVINE, AND L. GIULIANO (2010): “Customer discrimination,” _The Review of Economics and Statistics_ , 92, 670–678.

- LIST, J. A. (2004): “The nature and extent of discrimination in the marketplace: Evidence from the field,” _The Quarterly Journal of Economics_ , 119, 49–89.

- LIVEAGENT, W. (2022): “Live Agent,” https://www.liveagent.com/customer-supportglossary/agent-alias/.

- LOWE, M. AND M. MCKELWAY (2021): “Coupling Labor Supply Decisions: An Experiment in India,” _Working Paper_ .

- MACNELL, L., A. DRISCOLL, AND A. N. HUNT (2015): “What?s in a name: Exposing gender bias in student ratings of teaching,” _Innovative Higher Education_ , 40, 291–303.

- MCKELWAY, M. (2021a): “How Does Women’s Employment Affect Household DecisionMaking? Experimental Evidence from India,” _Working Paper_ .

- ——— (2021b): “Women’s Employment in India: Intra-Household and Intra-Personal Constraints,” _Working Paper_ .

- MENGEL, F., J. SAUERMANN, AND U. ZÖLITZ (2019): “Gender bias in teaching evaluations,” _Journal of the European economic association_ , 17, 535–566.

- NARDINELLI, C. AND C. SIMON (1990): “Customer racial discrimination in the market for memorabilia: The case of baseball,” _The Quarterly Journal of Economics_ , 105, 575–595.

19
Today, these assemblies constitute the most widely used deliberative institution in human history, affecting over 840 million people living in approximately one million villages in rural India. Deliberative democracy has deep historical roots in India where, for centuries, deliberative bodies were central to systems of local governance, and religious discourse and dialogue (Parthasarathy and Rao, 2017). In the period of colonial rule in the 19th century the interplay of ideas between Western liberal philosophers and Indian intellectuals led to India becoming a fertile ground for experiments in governance. Te idea of self-sustaining village democracy, in particular, appealed greatly to Mohandas Gandhi, who made it a central tenet of his philosophy. In 1993, 45 years afer independence, the Gandhian push for deliberative village democracy was given constitutional sanction with the passing of the 73rd amendment to the Indian constitution.

In general, Tamil Nadu has not been a front-runner in devolving much power to VPs, nor have recent improvements significantly improved policy devolution.<sup>3</sup> Tough Tamil Nadu VPs are not sufficiently well-financed to actually deliver public goods and services on their own, they do play a vital role in (a) implementing the last mile of various functions and programs, and (b) relaying information about local needs to the higher block tier of government, which has final authority on the provision of key services. For example, the VPs identify areas that need more drinking water; keep track of repair and construction needs; collect census data on household toilet access; provide information on local infrastructure needs (such as roads and drainage); and identify beneficiaries from the target population for several other federal and state anti-poverty programs. Te VP also provides information to higher levels of government on public service problems that range from the functioning of the public food distribution systems to glitches in the new electronic payments system for public works. Lastly, the VP is fully responsible for the full implementation (including payment of salaries) of the federal rural employment scheme (NREGA), which guarantees 100 days of work on public works for any individual who wants this work.<sup>4</sup>

> 3http://www.iipa.org.in/upload/panchayat ~~d~~ evolution ~~i~~ ndex ~~r~~ eport ~~2~~ 012-13. pdf

> 4Recently, payment of salaries has begun transitioning to an electronic system; as such, it is not directly controlled by the VP.

8
to 2007 as reported in Table 4.<sup>51</sup> Likewise, the predicted change in industry DVAR is also about 13%, according to (29), which explains fully the average increase in the industry DVAR during the sample period. This suggests that our simple translog model of using the relative price of materials (driven by upstream input tari¤, FDI, and exchange rates) to explain the ...rm’s and aggregate DVAR ...ts the data very well.

Let us summarize the main ...ndings of the paper. First, the DVAR of processing ...rms is increasing within ...rms across time in our sample and has led to an upward trend in both the industry and aggregate DVAR. Firm entry and exit do not explain the upward trend. Second, such an increase is mainly driven by ...rms substituting domestic for imported materials. Third, such a substitution is a response to the declining relative prices of domestic to imported materials caused by the expansion of domestic input variety. Fourth, the expansion of domestic input variety is induced by an increasing presence of foreign ...rms in processing exports and decreasing input tari¤s facing upstream suppliers. Fifth, based on the decrease in the relative price of domestic to imported materials, our model explains nearly all of the increase in the ...rm’s and aggregate DVAR from 2000 to 2007.

# 6 Concluding Remarks

This paper provides micro-level evidence of China’s rising ratio of domestic value added in exports to gross exports (DVAR). We use China’s customs transaction data over the 20002007 period to measure a ...rm’s DVAR and show how the increase in ...rm DVAR might explain the aggregate trend. We ...nd that the drastic increase in the DVAR of Chinese processing exports is observed across all industries and trading partners, and accounts for almost the entire rise in the DVAR of the country’s aggregate exports during the period.

> 51The 95% con...dence interval of �DV AR\ it is (11.2%, 13.6%), which overlaps with (11.3%, 18.0%), the 95% con...dence interval of the 2007 ...xed e¤ect in Table 4.

37
11 Figure 5 Annual Growth Rates, 1985-90 and 2000 for World and Bank Regions <!-- Start of picture text --> 3.4<br>3.2<br>3.0<br>c 2.8<br>* 2.6<br>2.4<br>2.2<br>2.0<br>Li 1.8<br>1.6<br>1.4-<br>z 1.2-<br>1.0-<br>0.6-<br>0.4-<br>0.2-<br>World Africa EMN LAC Asia<br>Za 1985-90 2000<br><!-- End of picture text -->

# Figure 6 Contribution to World Population Growth by Bank Region. 1985-2150

<!-- Start of picture text --> 60<br>50<br>0<br>40-<br>o 30 -<br>1<br>Li 20 -[MN<br>0<br>(L<br>0-<br>1 980 2 000 2 020 2 040 2 060 2 080 2 100 2 120 2 140<br><!-- End of picture text -->
working hours were lost in 2020 compared with the fourth quarter of 2019, a loss equivalent to 255 million full-time jobs. About half of the losses are estimated to have come from working hour reductions within employment. In fact, many more households reported losing incomes than households with a respondent stopping work, especially in lower income countries. This is consistent with the nature of the labor market in these countries where many workers are self-employed or hold otherwise informal jobs. Within countries, income losses were strongly correlated with work stoppages, job changes to self-employment, and sectoral transitions to agriculture (Figure A6.4).

During 2021, the share of households reporting that total incomes are below prepandemic levels remains very high – almost half of the households globally, and 70 percent of households in LICs are in this category. This may also be because the mitigating measures put forth by governments were largely limited in scope and duration (World Bank 2022b). While we do not have sufficient data to disaggregate trends by region and time window, it appears that apart from the ECA region, a relatively high share of households has still not recovered their lost incomes. The setback in 2021 could have been due to the emergence of new variants and re-imposition of partial lockdowns in several countries.

Evidence of “long COVID” effects on particular groups can be further seen by comparing characteristics associated with income loss and recovery using multivariate regressions. Similar to analysis of employment recovery, we draw on panel surveys in about 20 countries that collected information on income changes between survey waves during the pandemic, as well as recording initial income losses relative to before the pandemic. We can then identify which households reporting income loss relative to prepandemic were more likely to recover at least some income, as observed in a subsequent HFPS wave. Figure 9a shows that households were significantly more likely to lose income in each country if they were larger in size, less educated, the respondent stopped working, and during a period of higher policy stringency.<sup>18</sup> Figure 9b shows that some of the same households were less likely to gain income during the pandemic. Those who returned to work (or did not stop working) were more likely to recover income, implying that the same groups less likely to recover work (Figure 4b) were also less likely to recover income. Moreover, results are consistent with changes in income occurring at the intensive margin of employment, especially for households where respondents have lower education levels. Households with less access to education were significantly less likely to gain or recover income during the pandemic, whereas urban households were better off than rural households in terms of income recovery. Controlling for household characteristics, policy stringency remains a significant determinant of initial income losses and was

> 18Regression results are reported in Table A6.19. Results are robust to alternate definitions of the outcomes or specifications of the model (logit, probit).

24
Table E11: Share of Significant Temperature Coefficients in Tanzania

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.50|0.42|0.33|
|Median Daily Temperature|0.33|0.58|0.50|
|Variance of Daily Temperature|0.67|0.67|0.58|
|Skew of Daily Temperature|0.42|0.33|0.33|
|Growing Degree Days (GDD)|0.00|0.00|0.67|
|Deviation in GDD|0.25|0.00|0.50|
|Z-Score of GDD|0.42|0.00|0.50|
|Maximum Daily Temperature|0.33|0.50|0.42|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Tanzania.

Table E12: Share of Significant Temperature Coefficients in Uganda

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.58|0.58|0.42|
|Median Daily Temperature|0.58|0.58|0.58|
|Variance of Daily Temperature|0.42|0.50|0.17|
|Skew of Daily Temperature|0.50|0.50|0.33|
|Growing Degree Days (GDD)|0.25|0.50|0.50|
|Deviation in GDD|0.33|0.67|0.17|
|Z-Score of GDD|0.33|0.33|0.42|
|Maximum Daily Temperature|0.50|0.25|0.67|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Uganda.

136
# Table 15: Poverty Equations (Probit): FSU Countries

||_Esto_|_nia_|_Kyrgyz R_|_epublic_|_Rus_|_sia_|
|---|---|---|---|---|---|---|
||_Probability_<br>_Derivatives_|_Standard_<br>_Error_|_Probability_<br>_Derivatives_|_Standard_<br>_Error_|_Probability_<br>_Derivatives_|_Standard_<br>_Error_|
|Number of children|0.056*|0.012|0.020*|0.008|0.065*|0.010|
|Number of male adults|0.050*|0.021|0.008|0.014|0.011|0.014|
|Numberoffemaleadults|0.013|0.018|-0.010|0.014|-0.006|0.013|
|Number of elderly|0.037|0.022|0.003|0.026|-0.005|0.016|
|<br>Education of head: primary|0.045|0.023|0.001|0.037|0.032|0.023|
|Education of head: vocational/technical|...|...|-0.027|0.036|-0.028|0.019|
|Education of head: university|-0.149*|0.029|-0.059|0.036|-0.094*|0.023|
|<br>Age of head|0.003|0.003|-0.010*|0.005|-0.006*|0.003|
|Age of head squared|-0.000|0.000|0.000*|0.000|0.000|0.000|
|Female head|0.077*|0.028|0.119*|0.036|0.066*|0.020|
|Household owns enterprise|-0.135*|0.022|-0.094*|0.025|-0.127*|0.023|
|Household owns land|-0.159*|0.022|-0.092*|0.025|0.010|0.018|
|Household is renter|0.088*|0.019|-0.032|0.043|-0.046*|0.016|
|Share of wages in household income|-0.277*|0.033|-0.104*|0.041|-0.184*|0.025|
|Number of unemployed inhousehold|0.115*|0.025|...|...|0.137*|0.024|
|Head is unemployed|...|...|0.036|0.044|...|...|
|Head is inactive|0.046|0.030|0.014|0.039|0.074*|0.024|
|Location: non-capital city|0.154*|0.024|0.195*|0.039|0.187*|0.025|
|Location: village|0.273*|0.032|0.266*|0.035|0.222*|0.030|
|Log-likelihood|-1504.8||-1241.5||-3201.8||
|Chi-squared|494.53*||136.75*||478.38||
|Prob>chi-squared|0.000||0.000||0.000||
|% correct predictions|72.6||61.5||65.0||

_Note:_ * Indicates significance of the underlying coefficient at 90% level probability. Derivatives are taken at the mean values of continuous variables or for discrete change of dummy variables from 0 to I.

78
TABLE 6 Regression results for total factor productivity

|**Dependent variable: TFP growth**|**HP-trend**|**HP-trend**|**HP-trend**|**HP-trend**|
|---|---|---|---|---|
|GDP per capita relative to advanced economies|-0.06***|-0.06***|-0.06***|-0.05***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Working-age population|5.96**|4.70|6.54**|6.13**|
||(0.024)|(0.115)|(0.038)|(0.047)|
|Secondary completion rate|-0.002|-0.001|0.013|0.000|
||(0.770)|(0.847)|(0.139)|(0.968)|
|Secondary completion rate<br>|0.007|0.011**|0.012**|0.006|
|(bottom two-thirds)|(0.125)|(0.028)|(0.013)|(0.255)|
|Investment growth<br>(three-year moving average)|0.009<br>(0.672)||||
|Investment growth<br>|||0.084***|0.111***|
|(five-year moving average)|||(0.000)|(0.000)|
|Investment growth<br>(seven-year moving average)||0.007<br>(0.763)|||
|Commodity exporters credit boom dummy|0.953***|0.924***|0.557***|0.902***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Urban population share|||-0.066**<br>(0.031)||
|R&D spending as percent of GDP||||-0.092<br>(0.752)|
|Number of observations|778|698|706|497|
|Number of countries|125|125|125|109|
|Within R-square|0.15|0.15|0.28|0.34|

# Source: World Bank.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. Estimations are based on standard errors clustered around countries. Sample includes unbalanced panel of 33 advanced economies and 92 EMDEs for 1983-2020. p-statistics are shown in parentheses.

50