rafmacalaba/datause-encoder-data huggingface dataset:
# **V. Results for Tariffs at the Sector Level**

# **Distribution of Applied MFN Tariff Rates (Impact of WTO Accession)**

In figure 1 we show the distribution of applied MFN tariff rates in 2011 and 2020. Prior to the implementation of WTO commitments, the distribution of tariff rates is bi-model with the largest number of tariff lines falling in the 5-10 percent range and in the 15-20 percent range (with a mean of 11 percent as shown in table 1). After all WTO commitments are implemented, the large number of tariff lines in the 15-20 percent range is eliminated and the distribution has a single mode in the 5-10 percent range (where the mean also falls). Tariff peaks fall as well, as there is also a very sharp drop in the number of tariff lines in the 20-30 percent range.

# **Tariffs at the Level of the Russian Input-Output Table**

**Tariff Rates.** In table 2, we show the evolution of tariff rates for the sectors of the Russian input-output table.  (See table 4 for the mapping from the 2-digit Harmonized Tariff system to the 17 sectors of the Russian input-output table.)  There sectors where the tariff rates will fall by at least three percentage points are the following (with the percentage points reduction in parentheses): timber, wood, pulp and paper industry (6.1); light industry (4.1); food industry (4.6);  other goods producing industries (3.5); other industries (3.0); mechanical engineering and metal working (2.8).

**Tariff Lines with a Specific Tariff Component.** In table 3, we show the evolution of tariff lines with a specific tariff component at the level of the Russian input-output table.  Light industry and the food industry are the sectors where specific tariffs are intensively used.  In 2011, 83 percent of the tariff lines with a specific tariff component were in these two sectors. By 2020, however, these two sectors will comprise 88 percent of the tariff lines with a specific tariff component. Thus, while the total number of specific tariffs in these sectors falls considerably by 2020,  the percentage reduction in specific tariffs in other sectors is larger. We can see that the Russian government does not use specific tariffs for and energy, mining or ferrous metal products.

# **Tariffs at the 2-Digit Harmonized System Level**

**Applied MFN Tariff Rates.** In table 4 we present the applied MFN tariff rates of the Russian Federation at the 2-digit Harmonized System level in 2011 and

20
- Stolper, W. F.; Samuelson, Paul A. (1941). "Protection and real wages". The Review of Economic Studies. 9 (1): 58–73.

- Topalova, Petia. 2010. “Factor Immobility and Regional Impacts of Trade Liberalization: Evidence on Poverty from India.” American Economic Journal of Applied Economics 2(4): 1– 41.

- <mark>Zovanga L Kone, Maggie Y Liu, Aaditya Mattoo, Caglar Ozden, Siddharth Sharma, Internal borders and migration in India. (2018). Journal of Economic Geography, Volume 18, Issue 4, July 2018, Pages 729–759, https://doi.org/10.1093/jeg/lbx045</mark>

# **Annex A: Two-Stage Least Squares (2SLS) Regression Analysis**

**Table A1. Change in the average annual real wages after a US$100 increase in exports per worker (Real Bangladeshi Takas of 2017 and percentage) using Two-Stage Least Squares (2SLS) Regression**

**Analysis**

||||1st Stage||2n|d Stage (Wage|s)|
|---|---|---|---|---|---|---|---|
|Type of worker||2005-2010|2005-2013|2005-2016|2005-2010|2005-2013|2005-2016|
|||(1)|(2)|(3)|(1)|(2)|(3)|
|All|Coefficient|13.16***|11.05***|8.828***|3,062**|658.2***|347.6|
||Standard<br>error|(1.576)|(1.102)|(0.681)|(1,475)|(246.5)|(252.5)|
||N|448|448|448|408|395|423|
|Manufacturing|Coefficient<br>|13.28***|11.60***|9.163***|1,810|448.3**|245.1|
||Standard<br>error|(2.013)|(1.321)|(0.904)|(1,446)|(216.0)|(162.4)|
||N|132|132|132|70|71|115|
|Textiles and<br>apparel|Coefficient<br>|12.61***|12.19***|9.497***|990.4|105.4|205.7|
||Standard<br>error|(2.645)|(1.538)|(1.103)|(1,485)|(404.5)|(176.9)|
||N|46|46|46|29|24|35|
|Services|Coefficient<br>|14.20***|9.267***|7.889***|4,810*|218.7|866.7|
||Standard<br>error|(2.169)|(1.673)|(0.819)|(2,729)|(808.5)|(854.0)|
||N|256|256|256|223|161|230|
|Males|Coefficient|13.07***|10.66***|8.575***|4,014**|638.8*|659.3|
||Standard<br>error|(1.422)|(1.000)|(0.552)|(1,878)|(363.7)|(460.2)|
||N|439|439|439|401|370|411|
|Females|Coefficient|13.62***|11.63***|9.241***|782.7|515.9**|-123.2|

35
health centers (rather than at home, where institutional assistance for delivery is unavailable). Incentive payments are also supported by a growing body of research which uses randomized control trials (RCTs) to assess the impact of performance-based financing in the health sector (Ahmed et al, 2019, provide a review). These RCTs have found consistent evidence of positive impact on health facility quality indicators such as cleanliness and availability of medicines and equipment, but mixed evidence on the medical content of care, utilization and health outcomes. However, most RCTs are unable to disentangle how much of the impact is owing to greater financing becoming available, in poor resource contexts, versus the incentives-based design of providing that financing. Kandpal et al (2019) provide some of the first evidence on this question through the evaluation of a World Bank project in Nigeria that enabled lessons to be drawn about how to design the financing provided to health facilities. The findings suggest no substantial difference in impact between the performance-based arm, which had specific features of high-powered incentives, such as bonus payments to staff, and the general provision of financing to be used at the discretion of autonomous, decentralized health facilities.

The push in economics research to examine incentives in public service delivery came from the 2004 World Development Report, Making Services Work for Poor People (World Bank, 2004), which documented widespread absenteeism, a clear and measurable indicator of poor performance, among public sector health workers and teachers in public schools, along with more qualitative evidence which suggests that public service providers in poor countries are not motivated to do their jobs well even when they are present at their facilities. Given this status quo situation of poor performance and weak incentives (for example, in one study, Banerjee, Duflo and Glennerster, 2008, found that health workers face no sanctions when they are absent from work), it is perhaps not surprising that when incentives are credibly strengthened, such as within an RCT, outcomes are better than the status quo of weak incentives. These research studies also show that scaling-up what "worked" within the context of the study into actual policy (such as, for example, routine application of sanctions for unexcused absences) is difficult and often runs into political problems (Muralidharan and Sundararaman, 2011; Banerjee, Duflo and Glennerster, 2008; Dhaliwal and Hanna, 2014).

2
Appendix Table 6: Labor hours per week per cocoa plots

||Total hrs per<br>week per<br>cocoa plot<br>hectare|Planter hrs per<br>week per<br>cocoa plot<br>hectare|<br>Spouse hrs per<br>week per<br>cocoa plot<br>hectare|HH hrs per<br>week per<br>cocoa plot<br>hectare|Hired labor hrs<br>per week per<br>cocoa plot<br>hectare|
|---|---|---|---|---|---|
|Seedling Subsidy|-0.04|-0.15|-0.11|-0.23|0.28|
||(1.64)|(0.43)|(0.30)|(0.32)|(0.83)|
|Couples' Training|-1.96|0.22|0.08|-0.41|-1.44*|
||(1.85)|(0.50)|(0.35)|(0.33)|(0.84)|
|Seedling Subsidy + Couples'<br>Training (beta)|-2.00|0.07|-0.03|-0.64**|-1.16|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.17|0.87|0.91|0.05|0.15|
|Observations|1435|1435|1435|1435|1435|
|Mean for control group|11.81|3.24|1.85|1.78|4.41|
|Mean for individual training|10.93|2.95|1.59|1.33|4.46|

Robust and clustered standard errors in parentheses. All values are winsorized at 99%. * p<0.1 ** p<0.05 *** p<0.01 Note: Household (HH) hours exclude planter and spouse work hours. Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

Appendix Table 7: Labor hours per week on non-rubber and non-cocoa plots

||Total hrs per<br>week per not-<br>rubber/cocoa<br>hectare|HH hrs per<br>week per not-<br>rubber/cocoa<br>hectare|Planter hrs per<br>week per not-<br>rubber/cocoa<br>hectare|Spouse hrs per<br>week per not-<br>rubber/cocoa<br>hectare|Hired labor hrs<br>per week per not-<br>rubber/cocoa<br>hectare|
|---|---|---|---|---|---|
|Seedling Subsidy|-1.26|-0.23|-0.13|-0.24|-0.35|
||(1.41)|(0.24)|(0.37)|(0.26)|(0.75)|
|Couples' Training|0.35|0.14|0.00|-0.08|0.38|
||(1.38)|(0.24)|(0.36)|(0.27)|(0.71)|
|Seedling Subsidy + Couples'|-0.91|-0.09|-0.13|-0.32|0.03|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.50|0.73|0.75|0.29|0.96|
|Observations|1435|1435|1435|1435|1435|
|Mean for control group|7.60|1.05|2.16|1.25|2.46|
|Mean for individual training|6.01|0.72|1.96|1.00|2.00|

Robust and clustered standard errors in parentheses. All values are winsorized at 99%. * p<0.1 ** p<0.05 *** p<0.01 Note: Household (HH) hours exclude planter and spouse work hours. Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

4
# **Appendix: A Regression-Based Approach**

**The** bivariate relations examined via equations **(1)-(7)** draw from, and therefore allow direct comparison with, past empirical work on momentum trading. But these bivariate relations provide no means of testing joint significance. Is lag-one momentum trading still significant after controlling for lag-zero momentum trading (i.e., after controlling for contemporaneous price effects)? Is cross-country contagion trading still significant after controlling for own-price effects via lag-zero and lag-one momentum trading? Are these relations robust to including local-market index returns and the **US-** market index return?

**A** regression-based approach provides a natural framework for addressing these questions. At the whole-fund level, the questions of the previous paragraph can be addressed **by** estimating:

Here, %t and 4,t-1 are own-stock returns, as before. These variables capture lag-zero and lag-one momentum trading, respectively. The variable **16,,** is the contemporaneous return on a Latin American equity index.18 This variable captures cross-country contagion trading. The fourth variable, Ru,t, is the local-market index return. This variable does not enter the analysis introduced in the previous sections, and is intended here as a control for countrylevel systematic factors. The last variable, Rus,t, is the US-market index return. This variable also does not enter in the previous sections, and is intended here as a control for systematic **U.S.** factors, which have well established effects on emerging equity markets.

At the nianager-only and investor-only levels, the dependent variable in equation **(Al)** is replaced with:

> **18** We do not attempt to remove the own-country portion of the broader Latin American index. Note, thought, that the own-country index is also in the regression, and our results are able to distinguish quite sharply between them. In fact, the own-country index is never significant, so it is highly unlikely the effects are confounded.

22
continued working afterwards. Then the analysis focuses on labor market entries and job transitions.

# **6.1 Income Reductions**

Our interest now is to assess whether for workers who continued to work during the pandemic, income reductions were associated to the possibility of working from home and whether there were asymmetries by gender.<sup>20</sup> Unfortunately, we only have a discrete measure of individual income going up, down or staying the same, and we cannot construct a household income measure to assess the relative income falls across genders. However, knowing what happened to women’s earnings from work is still useful to assess possible changes in their economic autonomy as a result of the pandemic.

We estimate a variant of model 2 where the dependent variable is an indicator that takes the value 1 if individual _i_ from country _c_ suffered a fall in her income in wave _w_ relative to the pre-pandemic period. We estimate this model for the sample of individuals who were working both before and during the pandemic. Table 9 reports the results.<sup>21</sup> Once again, the estimated coefficient for the WFH variable is negative and significant, indicating that the probability of suffering an income fall was lower among workers whose pre-pandemic jobs were more compatible with working from home. As expected, the possibility of working from home seems to have played a much more important role in avoiding job losses than income reductions for those who did not lose their jobs —notice that the coefficient of the variable WFH in the first column of Table 4 is more than 50 times larger than the corresponding one in Table 9.

Regarding gender differences, the coefficient of the variable _Women_ is positive and significant, meaning that the probability of suffering income reductions for workers in jobs incompatible with remote work was higher for women than for men. On the other hand, the estimated coefficient of the interaction between gender and WFH is negative but very small and not statistically significant, implying that the possibility of working from home did not have a differential effect on incomes of men and women who continued to work during the pandemic. Additionally, the results in columns 2, 3 and 4 suggest that income reductions are not associated with the presence of children at home for these workers.

Another interesting result that emerges from Table 9 is that the estimated coefficient of the _Employee_ dummy is negative, large in absolute value, and highly significant in all the specifications, indicating that salaried workers were less exposed to income reductions than self-employed workers.

> 20We would also like to assess the effects on hours worked but unfortunately this information is not available from the HFPS.

> 21These results should be taken with caution because the dependent variable _Income fall_ presents a lot of missing data, especially for the second wave.

15
2004). However, data constraints generally limit these studies to comparisons of environmental ratings before and after program implementation, or comparisons of compliance status between rated and unrated firms. Unfortunately, intertemporal rating comparisons are subject to confounding effects from time-varying factors such as technology change, while cross-sectional comparisons can be subject to significant selection bias.

# **3.  China’s Green Watch Program**

Despite long-standing efforts to control pollution with traditional regulatory instruments, China continues to have severe pollution problems. This has led China’s State Environmental Protection Administration (SEPA) to test the effectiveness of environmental performance rating and disclosure in a program supported by the World Bank. In 1999, SEPA launched its Green Watch program in Zhenjiang City, Jiangsu Province and Hohhot City, Inner Mongolia Autonomous District. Zhenjiang implemented a relatively complex rating system, as shown in Figure 1, while Hohhot used a simpler rating system that was suited to its lower level of economic and institutional development (Wang et al., 2004). As shown in Figure 1, Green Watch rates firms’ environmental performance from best to worst in five colors – green for superior performance, blue for full compliance, yellow for meeting major compliance standards but violating some minor requirements, red for violating important standards, and black for more extreme non-compliance.

The first Green Watch ratings were disclosed through the media in 1999. The program was extended from Zhenjiang to the entire Jiangsu Province in 2001, and to eight other provinces during 2003-2005. Nationwide implementation of Green Watch has been promoted since 2005. Overall, the available evidence suggests a positive impact for the program. Table 1 shows that in Zhenjiang, the percentage of firms with positive ratings (green, blue and yellow) increased from 6
Duflo, E., Dupas, P. & Kremer, M. 2015. "School governance, teacher incentives, and pupil–teacher ratios: Experimental evidence from Kenyan primary schools," _Journal of Public Economics_ , Vol. 123(C), 92‐110.

The Economist. 2014. “China’s Economy: Poverty Elucidation Day.” Oct. 20, 2014.

The Economist. 2014. “Poverty in Indonesia: Muted Music.” May 3, 2014.

Edward, P. and Sumner, A. 2013. “The Future of Global Poverty in a Multi‐Speed World: New Estimates of Scale, Location, and Cost.” _King’s International Development Institute, King’s College London._ Ferreira et al. 2015. “A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results.” _World Bank,_ Policy Research Working Paper 7432.

Fiszbein, A. et al. 2009. _Conditional Cash Transfers: Reducing Present and Future Poverty._ Washington, DC: World Bank.

Gertler, P. et al. 2012. “Investing Cash Transfers to Raise Long‐Term Living Standards.” _American Economic Journal: Applied Economics,_ Vol. 4, No. 1, 164‐192.

Gill, I. and Kharas, H. 2007. _An East Asian Renaissance: Ideas for Economic Growth._ Washington, DC: World Bank.

Gilligan et al. 2009. “An Impact Evaluation of Ethiopia’s Productive Safety Net Program.” _Mimeo, International Food Policy Research Institute_ . Washington, DC.

Gockowski, J. 2012. “Policy‐led Intensification and Returns to Input Use among Ghanaian Cocoa Farmers.” _Sustainable Tree Crop Program of the International Institute of Tropical Agriculture._ Government of Ghana. 2013. “The Livelihood Empowerment Against Poverty (LEAP) Programme.” _Ministry of Gender, Children and Social Protection,_ Briefing Paper.

Greatrex H. et al. 2015. “Scaling Up Index Insurance for Smallholder Farmers: Recent Evidence and Insights.” _CGIAR Research Program on Climate Change, Agriculture and Food Security,_ CCAFS Report No. 14.

Handa, S., M.J. Park, R.O. Darko, I. Osei‐Akoto, B. Davis, S. Diadone. 2013. _“_ Livelihood Empowerment against Poverty Impact Evaluation,” _Carolina Population Center, University of North Carolina._ Hanson, G. 2012. “Understanding Mexico’s Economic Underperformance.” _Migration Policy Institute._ Heckman, J. 2012. “The Case for Investing in Disadvantaged Young Children”. _HeckmanEquation.Org_ .

Inchauste, G. and Saavedra‐Chanduvi, J. 2014. “Opportunity Knocks: Deeping Our Understanding of Poverty Reduction.” In _Understanding Changes in Poverty,_ 1‐13. Washington, DC: World Bank.

Inoue, K. et al. 2015. _Out‐of‐School Youth in Sub‐Saharan Africa: A Policy Perspective._ Directions in Development. Washington, DC: World Bank Islam, R. 1985. _Strategies for Alleviating Poverty in Rural Asia._ Geneva: International Labour Organisation.

Jonas, O. 2013. “Pandemic Risk.” Background paper for the _World Development Report 2014_ .

Jonas. O. 2014. “Pandemic Risk”. _Finance and Development,_ December 2014.

Kim, J. 2014. Speech by World Bank Group President Jim Yong Kim: “The Importance Of Safety Nets: Mexico’s Leadership.” 49
— in Figure S4 is that ultra-poor individuals use their phones very differently than nonultra-poor individuals, and machine learning algorithms can use those differences to predict ultra-poor status.

Our main analysis uses a gradient boosting model, which generally out-performs several other common machine learning algorithms for this task (see Table S3). The feature importances for the trained model are shown in Table S2. To limit the potential for overfitting, probabilistic predictions are generated via 10-fold cross-validation, with folds stratified to preserve class balance.<sup>4</sup> Additional details on the machine learning methods are provided in Appendix A.

**Combined Methods** We also evaluate several approaches that use data from multiple sources to predict ultra-poor status. Our main _combined method_ trains a logistic regression to classify the ultra-poor and non-ultra-poor households using the predicted ultra-poor probability from the CDR-based method (i.e., the output of the gradient boosting algorithm described above), as well as asset and consumption data collected in the TUP survey. For comparison, we similarly evaluate the performance of methods that combine only two of the available data sources (i.e., assets plus consumption, assets plus CDR, and consumption plus CDR). Predictions for each of the combined methods are pooled over 10-fold cross-validation.

# **2.5 Targeting Accuracy Evaluation**

**Evaluation on Matched Subsample** Our main analysis focuses on the 535 households for which we observe both CDR and survey data, and evaluates whether machine learning methods leveraging CDR data can accurately identify households designated as ultra-poor by the TUP program (using the two-step hybrid approach described in Section 2.1). We compare the performance of the CDR-based method to the performance of methods based on the wealth index, consumption data, and combinations of these data sources.<sup>5</sup> Each targeting method is evaluated based on classification accuracy, errors of exclusion (ultra-poor households misclassified as non-ultra-poor) and errors of inclusion (non-ultra-poor house-

> 4While cross validation is a standard evaluation strategy in the machine learning literature, for robustness we present results using a basic single train-test split in Table S6.

> 5The CDR-based method uses supervised learning to model the ultra-poverty outcome, whereas the assetand consumption-based approaches do not. To assess the importance of this difference, we experiment with applying machine learning methods to the asset and consumption data to model the ultra-poverty outcome. In results shown in Table S4, we find that a machine-learned asset predictor provides slight improvements on the standard asset-based wealth index and consumption measures. We continue to use the standard asset and consumption measures as benchmarks in the remainder of the paper, however, as they are the targeting methods most frequently used in practice.

9