rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4. Changes in Prevalence of Depression and Anxiety Symptoms <!-- Start of picture text --> a. Prevalence<br>30<br>20<br>10<br>0<br>Baseline 5 weeks 3 months<br>Control Treatment<br><!-- End of picture text --> Source: Saraf, 2019; World Bank Group, 2018 Looking at the movement of individuals across the four categories of severity measured by the PHQ‐ADS (none, mild, moderate, and severe) provides a clearer picture of how these changes affect individual entrepreneurs at various points along the depression‐anxiety spectrum. Table 6 (Columns 1‐2) shows the changes across the severity categories between the treatment and the control group participants. This analysis suggests that the _treatment was most effective at improving outcomes for those at the margins or onset of depression and anxiety (that is, those in the “mild” category)_ , as well as those _experiencing moderate levels of depression and anxiety_ , the latter especially at the 5‐week mark. This becomes clearer when data is restricted to only those entrepreneurs who were interviewed at all 3 rounds (columns 3 and 4). The proportion of treatment group entrepreneurs in the mild and moderate categories go from 19.3% at baseline to 8.2% at the 3 months mark, which is a substantial reduction. While reduction is also observed in the control group (from 24.2% to 17.6%), the percentage reduction is much smaller.

Table 6. Changes in Depression and Anxiety Category across 3 Rounds

|||**A**|**ll**|**Restricted**|**sample***|
|---|---|---|---|---|---|
|**Time‐**<br>**points**|**Categories**|**_Treatment_**<br>**_Group_**|**_Control_**<br>**_Group_**|**_Treatment_**<br>**_Group_**|**_Control_**<br>**_Group_**|
|||**(1)**|**(2)**|**(3)**|**(4)**|
|Baseline||_N, f (%)_|_N, f (%)_|_N, f (%)_|_N, f (%)_|
||None (0‐9)|92(78.0)|82(70.1)|76(77.6)|66(72.5)|
||Mild (10‐19)|15(12.7)|24(20.5)|12(12.2)|15(16.5)|
||Moderate (20‐29)|8(6.6)|8(6.8)|7(7.1)|7(7.7)|
||Severe (30‐48)|3(2.5)|3(2.6)|3(3.1)|3(3.3)|
|5 weeks||||||
||None (0‐4)|90 (85.7)|80(77.7)|86(87.8)|72(79.1)|
||Mild (5‐9)|12(11.4)|16(15.5)|10(10.2)|12(13.2)|
||Moderate (10‐14)|1(1.0)|7(6.8)|0 (0.0)|7(7.7)|
||Severe (15‐19)|2(1.9)|0(0)|2(2.0)|0(0.0)|
|3 months||||||
||None (0‐4)|92(90.2)|81(81.0)|88(89.8)|73(80.2)|
||Mild (5‐9)|5(4.9)|13(13.0)|5(5.1)|12(13.2)|
||Moderate (10‐14)|3(2.9)|4(4.0)|3(3.1)|4(4.4)|
||Severe (15‐19)|2(2.0)|2(2.0)|2(2.0)|2(2.2)|

19
**Table 6 Ethnicity**

||Prod|uctivity|||Wages<br>||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|t|P>jtJ|Coef.|Std. Err.|t|P>ItI|
|europea|1.839|0.729|2.523|0.012|-0.116|0.355|-0.326|0.744|
|<br>empleuro|-0.253|0.137|-1.841|0.066|0.152|0.067|2.273|0.023|
|<br>textasia|<br>1.725|0.536|3.216|0.001|1.045|0.261|4.000|0.000|
|woodasia|0.812|0.665|1.222|0.222|0.753|0.324|2.327|0.020|
|emplasia|-0.179|0.080|-2.224|0.026|-0.059|0.039|-1.503|0.133|

The tests of the null hypotheses that workers of European or Asian ethnic origins are generally not paid more than African workers is not rejected. However, the findings from the production functions show that on average Europeans are more productive than Asians and Africans. Europeans employed in larger firms are paid (statistically significantly) more than Africans but are also less productive. The overall effect, however, is that Europeans are more productive than any other ethnic group.

The wage gap between Asians and Africans increases with firm size and Asians are paid marginally -- although not significantly -- less than Africans. Moreover, the Asians are statistically significantly less productive in larger firms than in smaller compared to employees of other ethnic background.

In the textile and wood sectors Asians are paid higher wages, and, furthermore, they<sup>are</sup> significantly more productive in the former sector than in other sectors. However, the test for parameter equality indicates that wages and productivity are in line in textiles, the<sup>p-</sup> value is high, indicating that the coefficients are not different.

# **6.1.6 Gender differentials**

Are men and women equally productive and paid the same? The regression analyses reveal that female employees are paid statistically significantly less than their male<sup>co-</sup> workers, controlling for individual and other characteristics. It seems dubious whether 21
**TABLE 3: Real Wage growth and Sectoral Wage Differentials, Men**

|ARGENTINA||Avg. % gro|wth per yea|r||Wage|Differentia|ls|
|---|---|---|---|---|---|---|---|---|
|||199|7/1988||19|88|1|997|
|quantiles|All|Formal|Informal|self-e|IS/FS|SE/FS|IS/FS|SE/FS|
|5%|1.8|2.5|1.0|-0.4|0.552|0.740|0.492|0.586|
|10%|1.9|2.7|4.9|0.0|0.536|0.841|0.621|0.679|
|25%|2.6|2.6|3.9|0.4|0.643|1.007|0.700|0.844|
|50%|2.6|2.6|4.2|1.3|0.627|1.058|0.698|0.960|
|75%|_1.5_|1.2|3.8|1.4|0.584|1.091|0.707|1.105|
|90%|0.9|0.5|4.6|2.9|0.489|1.097|0.666|1.332|
|95%|1.2|1.0|3.0|2.7|0.447|1.102|0.521|1.261|
|Mean|1.6|_1.5_|3.9|1.9|0.556|1.055|0.662|1.086|
|BRAZIL|||||||||
|||199|5/1989||19|89|1|995|
|quantiles|All|Formal|Informal|self-e|IS/FS|SE/FS|IS/FS|SE/FS|
|_5%_|7.2|4.9|12.7|6.4|0.463|0.714|0.630|0.764|
|10%|6.0|7.9|11.9|5.9|0.544|0.884|0.634|0.811|
|25%|7.8|8.6|6.8|6.4|0.600|0.976|0.558|0.893|
|_50%_|6.3|6.0|9.6|3.9|0.461|1.032|0.533|0.933|
|75%|2.9|3.4|8.9|0.9|0.385|1.026|0.491|0.900|
|90%|2.1|2.6|6.8|0.5|0.367|1.000|0.446|0.893|
|95%|2.1|2.1|4.6|2.1|0.393|0.889|0.444|0.889|
|Mean|3.4|3.8|6.6|2.3|0.426|0.984|0.486|0.914|
|COSTA RI|CA||||||||
|||199|5/1989||19|89|1|995|
|quantiles|All|Formal|Informal|self-e|IS/FS|SE/NS|IS/FS|SE/FS|
|5|1.3|1.0|2.8|2.3|0.479|0.593|0.528|0.635|
|10|0.9|1.5|2.0|0.5|0.547|0.734|0.564|0.694|
|25|0.2|0.6|1.5|-0.5|0.591|0.820|0.623|0.768|
|50|0.8|0.7|1.4|0.1|0.603|0.901|0.630|0.874|
|75|0.7|1.1|0.8|1.9|0.552|0.896|0.540|0.935|
|90|2.1|2.0|2.2|2.8|0.493|0.976|0.501|1.019|
|95|2.7|2.0|4.3|3.5|0.463|0.964|0.520|1.039|
|Mean|1.1|1.2|1.8|1.1|0.550|0.916|0.569|0.911|

30
POLICY RESEARCH WORKING PAPER 2302

# **Summary** findings

Deininger and Olinto use a large panel data set from Zambia to examine factors that could explain the relatively lackluster performance of the country's agricultural sector after liberalization. Zambia's liberalization significantly opened the economy but failed to alter the structure of production or help realize efficiency gains. They reach two main conclusions.

First, not owning productive assets (in Zambia, draft animals and implements) limits improvements in agricultural productivity and household welfare. Owning oxen increases income directly, allows farmers to till their fields efficiently when rain is delayed, increases the area cultivated, and improves access to credit and fertilizer markets.

Second, the authors reject the hypothesis that the application of fertilizer is unprofitable because of high input prices. Rather, fertilizer use appears to have declined because of constraints on supplies, which government intervention exacerbated instead of alleviating. (Extending the use of fertilizer to the many producers not currently using it would be profitable, but increasing the amount applied by the few producers who now have access to it would not be.) Policies to foster accumulation of the assets needed for agricultural production (including draft animals and implements) and to provide complementary public goods (education, credit, and good agricultural extension services) could greatly help reduce poverty and improve productivity.

- - This paper a product of Rural Development, Development Research Group is part of a larger effort in the group to analyze determinants of rural growth and market participation. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Fernandez, room MC3-508, telephone 202-473-3766, fax 202-522-1151, email address mfernandez2@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The authors may be contacted at kdeininger@worldbank.org or polinto@Hworldbank.org. March 2000. (22 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas abont development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the autho,s and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. 7hey do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._

## Produced by the Policy Research Dissemination Center
<u>Panel 2</u>

|||G<br>|eorgia<br>|K<br>|enya<br>|
|---|---|---|---|---|---|
|||Men|Women|Men|Women|
|Openness|Mean|2.92|2.98|3.02|2.93|
||SD|0.57|0.50|0.58|0.56|
|Conscientiousness|Mean|3.09|3.19|3.26|3.21|
||SD|0.57|0.51|0.50|0.50|
|Extraversion|Mean|2.47|2.54|2.85|2.83|
||SD|0.51|0.52|0.61|0.58|
|Agreeableness|Mean|3.13|3.18|2.88|2.87|
||SD|0.55|0.52|0.57|0.54|
|Emotional|Mean|2.63|2.51|2.71|2.68|
|stability|SD|0.65|0.70|0.49|0.47|
|Grit|Mean|2.78|2.79|2.74|2.67|
||SD|0.62|0.59|0.61|0.58|
|Decision-making|Mean|3.27|3.39|3.16|3.14|
||SD|0.50|0.45|0.52|0.51|

<u>Panel 3</u>

|||Uk<br>|raine<br>|Vie<br>|tnam<br>|
|---|---|---|---|---|---|
|||Men|Women|Men|Women|
|Openness|Mean|3.02|3.10|2.90|2.70|
||SD|0.50|0.59|0.60|0.62|
|Conscientiousness|Mean|2.93|3.09|2.86|2.79|
||SD|0.45|0.54|0.50|0.49|
|Extraversion|Mean|2.49|2.76|2.69|2.74|
||SD|0.53|0.64|0.48|0.53|
|Agreeableness|Mean|2.81|2.98|3.02|3.01|
||SD|0.54|0.64|0.52|0.52|
|Emotional|Mean|2.77|2.40|3.07|2.74|
|stability|SD|0.53|0.71|0.52|0.54|
|Grit|Mean|2.79|2.78|2.80|2.75|
||SD|0.56|0.67|0.54|0.49|
|Decision-making|Mean|3.02|3.20|2.91|2.87|
||SD|0.48|0.61|0.61|0.60|

_Note_ . Noncognitive skills are self-reported. Each scale consists of 3 to 5 items scored on a 4-point scale from “Almost never” to “Almost always.” Highlighted cells indicate statistically significant differences between scores for men and women. The sample includes 25- to 64-year-olds in self-employment or wage work.

_Source_ : STEP Surveys (2014).

Table A1 in the appendix presents descriptive statistics for the control variables used in the analysis. The men and women in our sample average around 40 years of age—the women 19
insufficient to meet existing needs. For example, preschool starts relatively late, typically around age 6, only lasts for one year, and covers less than half of the population. In addition, the supply of secondary education is also insufficient and skewed towards urban and more prosperous areas of the country. In the departments with the highest poverty rates, there is only one secondary school for every 400-500 secondary-school age youth, while in lower poverty departments, this ratio is closer to one school for every 100 youth.

Yet there is a pressing need for targeted and scalable action in the short term among the large pool of youth already at high risk of dropping out. Evidence from across Latin America shows that greater shares of youth who are out of school and out of work contribute to a range of negative social and economic outcomes, including higher rates of crime, long-term reductions in labor productivity and growth, and increases in inequality (De Hoyos, Rogers, and Székely 2016).

# 3. Intervention and study design

The ENTRE program In 2017, motivated by the challenges and constraints described above, the Government of Guatemala, with support from the World Bank, designed the ENTRE program as a scalable and low-cost early warning system. The program is aimed at reducing dropout in the transition from primary to lower secondary school, where almost a third of students stop their formal education.

Like most early warning systems, the key goals of ENTRE are to provide targeted support to students at-risk and to take timely, preventive actions. At a minimum, this requires that school principals and teachers have a basic knowledge of effective dropout prevention strategies, are able to identify students at risk, and implement these strategies in a timely manner. However, quantitative and qualitative data—including focus groups with teachers and school principals, as well as interviews with public officials conducted to inform the design of ENTRE — showed important gaps in all these dimensions.

First, school actors have limited knowledge of, or support on, dropout prevention strategies, and school principals—who play a leading role in these initiatives— are usually regular teachers 7
Streck and Lin 2008: Streck, Charlotte / Lin, Jolene, Making Markets Work: A Review of CDM Performance and the Need for Reform, 19 European Journal of International Law 409 (2008). Streck and Chagas 2008: Streck, Charlotte / Chagas, Thiago, The Future of the CDM in a Post-Kyoto World, Carbon & Climate Law Review, Vol. 1, No 1, CCLR 1/2007, p.53 (2007). Streck and Scholz 2006: Streck, Charlotte / Scholz, Sebastian. The role of forests in global climate change: whence we come and where we go, International Affairs 82 (5), p. 861-879 (2006). Sutter and Parreño 2007: Sutter, Christoph / Parreño, Juan Carlos. Does the Current Clean Development Mechanism (CDM) Deliver its Sustainable Development Claim? An Analysis of Officially Registered CDM Projects, 84 Climate Change 75 (2007). UNEP Risoe 2008: UNEP Risoe CDM/JI Pipeline Analysis and Database, June 2009. UNFCCC 2007: Investment and Financial Flows Relevant to the Development of an Effective and Appropriate International Response to Climate Change, 2007. UNFCCC 2007a: Call for Input on Non-Binding Best-Practice Examples on the Demonstration of Additionality to Assist the Development of PDDs, Particularly for SSC Project Activities, 2007. UNFCCC 2007b: Bali Action Plan Ad Hoc Working Group on Long-term Cooperative Action under the Convention Document FCCC/CP/2007/L.7/Rev.1 Accessed November 28th, (2008), Accessed June 22, 2009 at: www.unfccc.int/files/meetings/cop_13/application/pdf/cp_bali_act_p.pdf, UNFCCC 2008a: Update on Investment and Financial Flows, December 2008. UNFCCC 2008b: EB 36 Agenda Item 5. Regional distribution of project activities. Accessed June 22, 2009, at: http://cdm.unfccc.int/EB/036/eb36rep.pdf Wara 2008: Wara, Michael. Measuring the Clean Development Mechanism’s Performance and Potential, 55 UCLA Law Review 1759 (2008). Wara 2007: Wara, Michael. Is the Global Carbon Market Working? 445 Nature 595 (2007). Ward et al 2008: Ward, Murray / Streck, C. / Winkler, H. / Jung, M. / Hagemann, M. / Höhne, N. / O’Sullivan, R.. The Role of Sector No-Lose Targets in Scaling up Finance for Climate Change Mitigation Activities in Developing Countries, DEFRA (2008). Wara and Victor 2008: Wara, Michael / Victor, David. A Realistic Policy on International Carbon Offsets, PESD Working Paper, Stanford University, April (2008). World Bank 2007: State and Trends of the Carbon Market 2007. Accessed June 22, 2009 at: http://carbonfinance.org/docs/Carbon_Trends_2007-_FINAL_-_May_2.pdf. World Bank 2008: State and Trends of the Carbon Market 2008. Accessed June 22 ,2009 at: <u>http://carbonfinance.org/docs/State___Trends--formatted_06_May_10pm.pdf.</u> 41
# **3.1 Growth Consistent with Reasonable Time Paths of Growth Drivers**

Having calibrated the model, I now move to analyze the long‐term growth scenarios for the country. The first approach consists of choosing reasonable paths of drivers of growth and uses the model to solve for the resulting growth paths. Specifically, I assume the following time paths:

- **Growth rate of human capital per worker:** �� ��. �% **.** The historical trend in human capital index from the PWT 8.1 provides guidance regarding the reasonable path of human capital per worker going forward. The PWT 8.1 reports the index based on the Barro‐Lee method that takes into account both the years of schooling as well as returns on education. The PWT 8.1 data is available for the period 1990 – 2010. After reaching a peak during the second half of 1990s, the growth of human capital index has been on a downward trend. The average growth rate stood just below 1.4 percent during the period 2006 – 2010. It seems likely that the growth rate of human capital will continue its gradual decline with contribution from gains in primary education diminishing as it reaches absolute levels. As such, the human capital per worker growth rate is assumed to be 1.3 percent per annum going into the future.

<!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1991 ‐ 1995 1996 ‐ 2000 2001 ‐ 2005 2006 ‐ 2010<br>Source: PWT 8.1<br>Growth Rate of HC Index (%)<br><!-- End of picture text -->

- **Growth rate of TFP:** �� ��%. A TFP index for the country is calculated using the data and methodology listed in PWT 8.1 and using the labor share of 0.51 obtained from the GTAP database. The growth rate of TFP is generally volatile and it is also true for the case of Bangladesh as seen in the figure below. The TFP was on an upward trend from the 1980s till it suddenly dropped in 2001. After remaining in the negative territory for another two years, the TFP growth started trending upwards again. The average growth rate of TFP during 1991 – 2011 has barely managed to remain in the positive

9
|Going to _t_ +1|Reinsurers<br>Capital at _t_|
|---|---|
||Reinsurers<br>|
|Expected|MarketEquilibrium<br>choose|
|Losses|<br>_Or_, _Er_<br>_Market Price_<br>_π_<sup>_d_</sup><br>_r_<br>Realized Loss<br>Final wealth|
|Figur<br>Table <br>|Possible bankruptcy<br>e 2: Model timing schematics<br> 1: Variables and Parameters|
|Market variables||
| ̃_L_<br>_pa_<br>_p_<sup>_t_</sup>|Distribution of market loss to be reinsured<br>Actuarial price of losses<br>Market price of reinsurance at time _t_|
|Market parameters||
|_d_0<br>_δ_<br>_α_<br>_πlim_<br>_κ_<br>_cr_<br>_Sr_|Demand parameter<br>Price demand elasticity<br>Rate of return of risk-free investments<br>Limit probability of default<br>Limit percentage of capital that reinsurers can buy back<br>Cost of external capital<br>Shareholder cost|
|Reinsurers' specic variables||
|_r_<br>_NR_<br>_W_ <sup>0</sup><br>_r_<br>_W _<sup>_t_</sup><br>_r_<br>_E_<sup>_t_</sup><br>_r_|Reinsurer index<br>Number of reinsurance rms<br>Initial wealth of the rm<br>Wealth of the rm at the beginning of time _t_<br>External capital acquired by the rm at time _t_|
|_O_<sup>_t_</sup><br>_r_|Share of the market risk covered by reinsurer _r_ at time _t_|
|_π_<sup>_d,t_</sup><br>_r_|Probability of default of the rm at time _t_|

iteration involves the approximate solution of a large linear system using the method of Preconditioned Conjugate Gradients (PCG).

# 4.2 Scenario

## Reinsurance loss coverage

To build the insurance market demand for reinsurance, we proceed in the following manner. We use model-based aggregate insured loss data from Risk Management Solutions Inc. for the four main reinsurance disaster markets: Japan Earthquake, European Windstorm, US Hurricane and US Earthquake, including four types of risk covered (agricultural, commercial, residential and 10
motor vehicles exports were increasing by 17 percent in the reshoring by leading economies are now contracting by 18 percent. In Europe and Central Asia where computer and electronics were expanding 45 percent before, they are now decreasing by 1 percent.

Table 2. Sectors with top three increases in exports for the “Reshoring leading economies” scenario. Exports compared with COVID L-shape scenario

|Country/region|Sector|Reshoring<br>leading<br>economies|Reshoring<br>all|GVC friendly<br>liberalization<br>+ TF imports|
|---|---|---|---|---|
||Livestock|51|101|51|
|China|Accommodation, food and service activities|2|4|-1|
||Meat products (inc. fisheries) and other food|2|20|-4|
|Rest of East Asia &|Motor vehicles, parts and transport equipment|17|-18|57|
|<br>Pifi|Refined oil|16|-19|36|
|acc|Computer, electronic and optical products|7|-27|41|
||Electricity|-3|-5|-6|
|Western Europe|Communications|-4|-6|-5|
||Natural resource products|-4|-2|-6|
|E & Ctl|Computer, electronic and optical products|45|-1|97|
|urope enra<br>Ai|Motor vehicles, parts and transport equipment|15|-14|35|
|sa|Other manufacturing|15|-14|39|
|Latin America and|Computer, electronic and optical products|21|-12|41|
|<br>Cibb|Air transport|18|3|22|
|arean|Wood and paper products|13|-12|36|
|Middl E & Nh|Computer, electronic and optical products|43|-7|123|
|e ast ort<br>|Metals|11|-13|17|
|Africa|Motor vehicles, parts and transport equipment|9|-17|29|
|Rest of Hih-Income|Natural resource products|7|5|5|
|g<br>Ci|Air transport|0|3|-4|
|ountres|Electricity|0|-3|-3|
||Livestock|103|129|103|
|South Asia|Motor vehicles, parts and transport equipment|26|-26|71|
||Computer, electronic and optical products|26|-31|124|
||Computer, electronic and optical products|51|5|115|
|Sub-Saharan Africa|Motor vehicles, parts and transport equipment|18|-12|70|
||Refined oil|14|-20|25|
||Natural resource products|3|2|0|
|United States|Electricity|-1|-4|-5|
||Non-metallic minerals|-4|-9|-5|
||Natural resource products|-2|-5|-4|
|World|Electricity|-3|-7|-6|
||Metals|-3|-9|-6|

Source: ENVISAGE simulations.

Eliminating intermediate input tariffs and implementation of trade facilitation measures under _GVCfriendly_ scenario leads to faster integration of developing countries into regional and global value chains. Exports of several sectors deeply integrated into GVCs expand such as motor vehicles and transport equipment in Sub-Saharan Africa, or computer, electronic, and optical products in South Asia. Table 3 displays percentage increases as compared to the COVID L-shape recovery scenario. Often these impressive export growth rates apply to a very small base. In value terms, the fastest increase of exports 15