rafmacalaba/datause-encoder-data huggingface dataset:
_Figure 4 Guinea-Bissau’s projected electricity demand (MW) and the optimal least-cost energy mix 2020-2035, Least Cost Generation Expansion Plan, 2020._ <!-- Start of picture text --> 100%<br>286 90%<br>273<br>260 80%<br>247<br>234 70%<br>221<br>208 60%<br>195<br>169 182 50%<br>157<br>6390 68103 74118 79130 85142 90 96 101 107 112 118 123 129 134 140 145 40%30%20%10%<br>0%<br>2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035<br>Scénario Bas Scénario Haut Solaire PV Thermique Fioul Hydro Imports ENS<br><!-- End of picture text --> _Source(s) Figure taken from Guinea-Bissau: Power Sector Policy Note 2020, World Bank. Note(s): Solaire PV = Solar PV, Thermique Fioul = heavy-fuel oil, ENS = suppressed demand._ The least-cost approach employed in that study calculates the lowest cost energy mix for a specified level of electricity demand, based on assumptions regarding future technology costs (LCOEs) and a set of constraints, such as decarbonization goals or storage options. However, as discussed above, such approaches overlook the path dependency of technology diffusion and the role that endogenous cost reduction may play.

# **5.3 FTT-FLEX baseline for Guinea-Bissau’s power sector**

Based on historical power sector share data in 2020 and short-term projections up to 2025 that reflect the latest power sector developments mentioned above, FTT-FLEX calculates the power generation mix from 2026 onward to meet the projected electricity demand (growing at around 5% pa) coming from the Guinea-Bissau MFMod macroeconomic model. Due to a lack of data for cost, cost distribution, lifetime, and lead time, the FTT-FLEX analysis reported below excludes electricity imports from neighboring countries as competing technologies. With more data, these could be included.

The projected power sector baseline assumes that the rest of the world's energy transition is limited, consistent with the IEA’s World Energy Outlook 2022 Stated Policies Scenario (STEPS). However, within the global power sector, solar is anticipated to expand even when there are limitations on energy transitions in other areas. In FTT-FLEX, this manifests as a significant reduction in solar costs over time due to exogenous global learning – with the result that the projected levelized cost of electricity (LCOE) for solar in Guinea-Bissau is estimated to decrease from $62 per MWh in 2030 to $37.5 per MWh in 2050 in the baseline without any additional policies (all prices presented in real 2020 price terms).

The resulting baseline projections using FTT-FLEX show a notable increase in the share of solar in Guinea-Bissau, rising from just over 30% in 2025 to 56% in 2050. While diminished, oil generation is expected to retain a significant share at 36% in 2050 in this baseline scenario.

13
with the product, whereby the information that the firm sells the same product in many other markets would not be relevant to an importer interested in an upgrade as a way of establishing trustworthiness.

**Table 5 – Competing risks model with upgrade defined by PRODY (1) and average price (2)**

||upgra<br>|(1)<br>de by PROD<br>|Y<br>|upgrad<br>|(2)<br>e by avg. pri<br>|ce<br>|
|---|---|---|---|---|---|---|
||death|upgrade|diff|death|upgrade|diff|
|Firm sold prod TH first|0.93|0.71***|-|0.918|0.79***|-|
|No prior importing (ref)|1|1||1|1||
|Prior imports other dest.|0.77***|0.90|***|0.86|0.83**|-|
|Prior imports current dest.|0.74***|1.03|***|0.70***|0.92|***|
|Prior experience w. prod|0.65***|0.49***|***|0.59***|0.53***|***|
|Prior experience w. dest.|0.56***|1.47***|***|0.41***|1.42***|***|
|Familiarity with prod|0.95***|0.92***|**|0.95***|0.93***|**|
|Familiarity with dest.|0.98***|1.07|**|0.85***|1.08|***|
|Peers|0.92***|0.87***|**|0.91***|0.88***|**|
|RCA|1.06|0.87|-|0.75**|1.25*|***|
|Log of pop. weighted dist.|1.22***|1.02|***|1.28***|1.01|***|
|Log of initial value|0.92***|0.93***|***|0.98|0.88***|-|
|Log of dest. GDP|0.99|0.88***|-|0.99|0.91***|-|
|Log of dest. GDP/capita|0.69***|0.75***|***|0.67***|0.80***|***|
|Destination GDPgrowth|0.90***|0.88***|***|0.89***|0.89***|***|
|N||11706|||11706||

_Exponentiated coefficients. * p<0.10;** p<0.05;*** p<0.01. Source: Own calculations based on Lao PDR customs data. The time dependence of the hazard is introduced as log(time) in all models_ One exception to the “trap” effect is the prior experience with destination: having already sold a product on a certain market in the past increases the odds of experiencing an upgrade by ~40%. Firms which know well a destination and whose reputation is established with a particular partner are more likely to be awarded an upgraded contract, as hypothesized (a "springboard" effect). The same effect is seen for RCA, but only in model (2) and with a lower level of significance.

Another such effect, although weaker, is the impact of familiarity with the destination in both models. In this case, selling another product on the same market not only increases the odds of survival (as was found in the previous section) but also slightly increases the odds of signing up to export a more sophisticated or a more expensive one (or at least it leaves the odds of an upgrade unchanged because the coefficient is not significant). This type of experience can be an accurate proxy for reliability from the point of view of the importer – if the two partners interact repeatedly in many goods they will be more likely to trust each other and extend this interaction to superior products.

26
- Monteiro, J. A. and Trachtman, J. P. (2020) Environmental laws, in _Handbook of Deep Trade Agreements_ (Eds.) A. Mattoo, N. Rocha and M. Ruta, World Bank, Washington, DC.

- Morgenstern, O., Braesicke, P., Hurwitz, M. M., O’Connor, F. M., Bushell, A. C., Johnson, C. E. and Pyle, J. A. (2008) The world avoided by the montreal protocol, _Geophysical Research Letters_ , **35** .

- Murdoch, J. C. and Sandler, T. (1997) The voluntary provision of a pure public good: The case of reduced cfc emissions and the montreal protocol, _Journal of Public Economics_ , **63** , 331–349.

- Rigby, M., Park, S., Saito, T., Western, L. M., Redington, A. L., Fang, X., Henne, S., Manning, A. J., Prinn, R. G., Dutton, G. S., Fraser, P. J., Ganesan, A. L., Hall, B. D., Harth, C. M., Kim,

- J., Kim, K. R., Krummel, P. B., Lee, T., Li, S., Liang, Q., Lunt, M. F., Montzka, S. A., Mu ̈hle, J., O’Doherty, S., Park, M. K., Reimann, S., Salameh, P. K., Simmonds, P., Tunnicliffe, R. L., Weiss, R. F., Yokouchi, Y. and Young, D. (2019) Increase in CFC-11 emissions from eastern China based on atmospheric observations, _Nature_ , **569** , 546–550.

- Sohngen, B., Mendelsohn, R. and Sedjo, R. (1999) Forest management, conservation, and global timber markets, _American Journal of Agricultural Economics_ , **81** , 1–13.

- Taylor, M. S. (2011) Buffalo hunt: International trade and the virtual extinction of the north american bison, _American Economic Review_ , **101** , 3162–95.

- Velders, G. J., Andersen, S. O., Daniel, J. S., Fahey, D. W. and McFarland, M. (2007) The importance of the montreal protocol in protecting climate, _Proceedings of the National Academy of Sciences_ , **104** , 4814–4819.

- Western, L. M., Redington, A. L., Manning, A. J., Trudinger, C. M., Hu, L., Henne, S., Fang, X., Kuijpers, L. J., Theodoridi, C., Godwin, D. S. _et al._ (2022) A renewed rise in global hcfc-141b emissions between 2017–2021, _Atmospheric Chemistry and Physics Discussions_ , pp. 1–25.

- World Meteorological Organization (2018) Executive summary: Scientific assessment of ozone depletion: 2018, World Meteorological Organization, Global Ozone Research and Monitoring Project, Report No. 58, Geneva, Switzerland.

23
(figure 4). The difference in means between formal and informal businesses is more pronounced for businesses located in rural towns _-_ in rural areas, productivity gains would be marginal if transitioning to formal. It therefore appears rational for many enterprises in rural areas to stay informal to avoid associated cost increases and benefit from their relatively low barriers to entry. In rural market towns, however, becoming formal appears an attractive option. Excepted in (gold) mining sector, productivity differences by sector are not pronounced (figure 5).

**Figure 4: Median Sales per Day of Labor by Location and Registration, 2005**

<!-- Start of picture text --> Rural Towns Rural Areas<br>Informal Informal<br>Formal Formal<br>0 1 2 3 0 1 2 3<br>US$/Day of Labor<br><!-- End of picture text -->

**Figure 5: Median Sales per Day of Labor by Sector and Registration, 2005**

<!-- Start of picture text --> Informal Formal<br>Trading Trading<br>Services Services<br>Manufacturing Manufacturing<br>Agroprocessing Agroprocessing<br>Mining & Quarrying Mining & Quarrying<br>Construction Construction<br>Two or More Sectors Two or More Sectors<br>0 2 4 6 8 0 2 4 6 8<br>US$/Day of Labor<br><!-- End of picture text --> Source: 2005 Tanzania RICS One-person enterprises are relatively more productive than their larger counterparts. On average, these enterprises generate about US$ 1.5 on sales revenue per working day.<sup>10</sup> Overall, rural labor productivity tends to decline with enterprise size. The exceptions are enterprises that employ more than four workers. Seasonality is a hallmark of the Tanzanian rural nonfarm sector, a variation largely due to labor supply, demand for rural products, and availability of raw materials. More than 75% of Tanzanian enterprises are heavily affected by seasonality. Sales in all sectors usually peak

> 10 This finding runs against mainstream evidence. A possible explanation is use of family labor, suggesting that larger rural firms use a higher amount of relatively less productive household family labor.

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<!-- Start of picture text --> ® Economic reasons<br>® Family reunification<br>Other reason a Study<br>® Crime insecurity<br>® Natural disaster<br>Critee = Other reason<br>Economic reasons Family reunification Study ee<br>Ty<br><!-- End of picture text --> <!-- Start of picture text --> a a<br><!-- End of picture text --> <!-- Start of picture text --> a a<br><!-- End of picture text -->
**Appendix A:** Pre-Analysis Plan for Round 4 Schooling, Income and Health Risk in Malawi (SIHR) Data **(** which can also be found at https://www.socialscienceregistry.org/trials/36)

# **<u>Principal Investigators:</u>**

Sarah Baird, University of Otago Ephraim Chirwa, Chancellor College Craig McIntosh, UCSD Berk Özler, World Bank and University of Otago

# **<u>Analysis plan:</u>**

The core analysis will compare the impact of the Conditional Cash Transfer (CCT) and Unconditional Cash Transfer (UCT) treatment to control EAs for the baseline schoolgirl stratum, and will compare the CCT treatment to the control for the baseline dropout stratum. Most of the analysis will consist of Round 4 cross-sectional regression (using OLS unless not appropriate), although where possible we will also pursue panel difference-in-differences analysis for variables that have been consistently collected in multiple rounds. For consistency, the analysis will include the full set of controls used in the paper by Baird, McIntosh and Özler (2011). These controls include baseline values of the following: a household asset index, highest grade attended, a dummy variable for having started sexual activity, dummy variables for age, and strata dummies. Standard errors will be clustered at the EA level, and results will be weighted to make them representative of the target population in the study EAs.

Only if a significant impact is found in the core analysis will further heterogeneity of impact be explored. Heterogeneity will be explored along both experimental dimensions including the amount of the transfer and the split between the parent and the girl, as well as based on the age at which you got the program and differences between rural and urban.

# **<u>Note on construction of indexes:</u>**

To construct indexes for classes of variables, we will adhere to the following rubric:

- a) For each sub-question in a family of variables, first align answers so that higher numbers always have a consistent meaning (good or bad).

- b) Calculate the mean and SD of the responses to each sub-question in the sample _in the control group – separately for baseline schoolgirls and baseline dropouts_ .

- c) Create normalized variables that have the mean subtracted off and are divided by the SD. d) Calculate the raw mean of the normalized variables for all sub-questions within a family of variables.  This mean is the ‘index’ for those variables. _This summary index can further be normalized if desired._

For the core analysis we will not pursue the analysis of sub-variables within an index unless the index as a whole is significant.

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# <u>Tables</u>

## **<u>Table 1: Changes across survey rounds – Household Outcomes</u>**

||**2007**|**2009**|**Difference**|**Stdev**|**Pval**|
|---|---|---|---|---|---|
|**Respondent Information**||||||
|Correctly names Ward Member/President|61%|78%|17%|0.020|0.000|
|Heard of MNREGS|24%|61%|37%|0.017|0.000|
|Heard of Gram/Ward Sabha|20%|56%|36%|0.020|0.000|
|**Participation**||||||
|Attended Gram/Ward Sabha|11%|26%|16%|0.011|0.000|
|Attended any meeting in last year|5%|16%|11%|0.012|0.000|
|Feels President is responsive to needs|27%|21%|-6%|0.017|0.000|
|**Government Transfers**||||||
|House provided under government scheme|15%|25%|9%|0.007|0.000|
|Government transfer for housing|19%|26%|8%|0.008|0.000|
|Eligible and received BPL card|53%|85%|32%|0.019|0.000|
|**Contribution and Payments**||||||
|Paid tax last year|60%|76%|16%|0.023|0.000|
|Made contributions last year|70%|65%|-5%|0.039|0.165|
|Proportion of infrastructure contributed to|0.09|0.10|0.01|0.006|0.186|

***Note:** The standard errors for the difference in means are clustered at the level of randomization—the Gram Panchayat.

**–** **<u>Table 2: Changes across survey rounds Village Activity and Works</u>**

||**2007**|**2009**|**Difference**|**Stdev**|**Pval**|
|---|---|---|---|---|---|
|Activity in last 12 months – Roads|42.9%|63.5%|20.6%|0.052|0.000|
|Activity in last 12 months - Transportation|5.1%|9.0%|3.9%|0.027|0.154|
|Activity in last 12 months - Schools|6.1%|15.5%|9.4%|0.034|0.006|
|Activity in last 12 months – Health|7.1%|14.6%|7.5%|0.036|0.042|
|Activity in last 12 months – Drinking Water|44.9%|36.2%|-8.8%|0.052|0.092|
|Activity in last 12 months - Sanitation|21.2%|37.1%|15.8%|0.051|0.002|
|Activity in last 12 months - Electrcity|34.3%|35.0%|0.7%|0.053|0.902|
|Activity in last 12 months - Irrigation|5.6%|12.5%|6.9%|0.033|0.036|
|Agr. extention officers visit|54.5%|79.5%|25.0%|0.052|0.000|
|Taluk Engineer visits|33.8%|54.5%|20.7%|0.060|0.001|
|EO visit|29.3%|46.0%|16.7%|0.056|0.004|
|NGO active in village|9.6%|23.0%|13.4%|0.036|0.000|
|Traditional Panchayat active|60.6%|46.0%|-14.6%|0.053|0.006|
|Male agricultural wage|55|92|37|1.733|0.000|
|Female agricultural wage|31|50|18|1.312|0.000|
|Oligarchy|0.10|0.08|-0.02|0.010|0.140|

***Note:** The standard errors for the difference in means are clustered at the level of the Gram Panchayat.

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# **1. Introduction**

After a decade of unparalleled social gains, the years since 2011 have been marked by a protracted economic slowdown in Latin America and the Caribbean (LAC). For five consecutive years, growth trended downwards and even culminated in a contraction in regional economic activity in 2015 (de la Torre et al., 2015, 2016). In the decade or so after 2003 that coincided with the commodity boom time, the region had been characterized by sharp reductions in poverty and inequality and moved towards becoming a middle class region as increasingly large proportions of the population moved into the middle class. In the recent context, however, the question is how much of the achievements of the previous decade are being put at risk because of this protracted slowdown?

The aim of this paper is to describe through a set of stylized facts how the slowdown is affecting households in the region. The paper is organized around two central questions: what is happening with poverty and inequality and what is behind these trends? The first section documents how the slowdown is manifesting in key welfare indicators and presents new emerging trends in the region. The second section further describes what is behind recent trends by looking at the main channels through which the impact of the slowdown is manifesting. Lastly, the paper concludes with a discussion of some of the main implications and challenges that the new scenario poses to policy makers in the region. It does so from the perspective that the lower economic growth in the region is likely the reflection of a “new normal,” driven in turn by the bursting of the commodity price boom and lower growth in China. If that is the case, the future pace of social gains in the region is also likely to be affected and this is likely to pose challenges for policy makers to protect the social gains achieved. In addition, to the extent that the rapid pace of social gains seen in previous years may have been internalized by the population at large and created expectations for future gains, the new normal may result in societies facing a mismatch between expectations and actual social gains and mobility.

# **2. How is the slowdown affecting poverty and inequality?**

As economic growth slows in the region, a particular concern is that it may translate into a slowdown—or even a reversal—of the social gains achieved during the recent period of sustained high growth. Indeed, we find evidence that the slowdown has already translated into a slower pace of poverty reduction and a stagnation of the growth of the middle class (Figure 1).<sup>7</sup>

> 7 The numbers presented in this brief are based on comparable household and labor force surveys in the SEDLAC and LABLAC databases. See Annex 1 for more details about the data and the definitions of the different economic groups. All poverty rates are calculated based on international poverty lines, in particular, $4 per day adjusted for 2005 purchasing power parity (PPP).

2
# **<u>Figures</u>**

**Figure 1: Export Growth and Deviations from Radial Growth Rates**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>-20% -15% -10% -5% 0% 5% 10% 15% 20%<br>Basket Growth Rate<br>Deviation From Radial Growth<br><!-- End of picture text --> Source: Authors’ calculations based on COMTRADE

**Figure 2: Patenting Concentration and Performance**

<!-- Start of picture text --> JPN<br>DEU<br>FRAGBR<br>CAN<br>ITA CHE<br>SWENLD KOR<br>BEL<br>AUS<br>AUTISRFIN DNK<br>ESPRUSNOR HUN HKG<br>IRL CHN MEXBRANZL CZE SGP<br>SVK<br>ARG VEN IND<br>GRCPOLUKRSVNMYSPRTBGRCOLPHLTHACHL<br>BLRTURROMEGY HRV PERIDN CRI<br>IRN URYTTOECUGTM LKA CYP SYRLVA KAZ<br>ARM ESTSLV JAM PAN<br>UGA AZE BOLPAKNGAJOR LTUNIC<br>KGZMUS<br>SEN GIN<br>.2 .4 .6 .8 1<br>concentration<br>15<br>10<br>performance<br>5<br>0<br><!-- End of picture text --> Source: Authors’ calculations. Performance is log of average counts across observations (given the implicit log-linear specification of the negative binomial regressions presented below), concentration is the average Gini across observations, only observations included in estimation are shown.

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Figure 19 <!-- Start of picture text --> INDICATORS OF NOMINAL & REAL WAGE<br>rAGTS AND ACTUMl. O 5 10921 J4<br>a<br>7<br>4<br>3<br>-1<br>1992 1993 1994<br>=TARET R N - TARGET 1f CPI - TAR CPI AR<br><!-- End of picture text --> 28
**Table 6. Occupations (ISCO08 two-digit) by gender preferences**

|ISCO|Occupation Titles|Nopreference|Male|Female|
|---|---|---|---|---|
|11|Chief executives, senior officials and legislators|0.006|0.007|0.007|
|12|Administrative and commercial managers|0.196|0.239|0.190|
|13|Production and specialised services managers|0.025|0.027|0.022|
|14|Hospitality,retail and other services managers|0.007|0.010|0.007|
|21|Science and engineering professionals|0.062|0.067|0.049|
|22|Health professionals|0.008|0.007|0.011|
|23|Teaching professionals|0.024|0.015|0.066|
|24|Business and administration professionals|0.053|0.045|0.056|
|25|Information and communications technology professionals|0.163|0.146|0.092|
|26|Legal,social and culturalprofessionals|0.036|0.022|0.035|
|31|Science and engineering associate professionals|0.010|0.017|0.007|
|32|Health associate professionals|0.031|0.029|0.044|
|33|Business and administration associate professionals|0.190|0.153|0.209|
|34|Legal, social, cultural and related associate professionals|0.012|0.010|0.012|
|35|Information and communications technicians|0.009|0.010|0.010|
|41|General and keyboard clerks|0.022|0.022|0.030|
|42|Customer services clerks|0.008|0.009|0.019|
|43|Numerical and material recording clerks|0.002|0.003|0.001|
|44|Other clerical support workers|0.001|0.001|0.001|
|51|Personal service workers|0.008|0.021|0.010|
|52|Sales workers|0.069|0.067|0.067|
|53|Personal care workers|0.012|0.008|0.020|
|61|Market-oriented skilled agricultural workers|0.001|0.001|0.001|
|62|Market-oriented skilled forestry, fishery and hunting workers|0.000|0.000|0.000|
|63|Subsistence farmers,fishers,hunters andgatherers|0.000|0.000|0.000|
|71|Building and related trades workers, excluding electricians|0.004|0.005|0.004|
|72|Metal, machinery and related trades workers|0.002|0.004|0.001|
|73|Handicraft and printing workers|0.001|0.001|0.001|
|74|Electrical and electronic trades workers|0.003|0.006|0.003|
|75|Foodprocessing,wood working, garment and other craft and related trades workers|0.005|0.006|0.007|
|81|Stationary plant and machine operators|0.004|0.009|0.004|
|82|Assemblers|0.000|0.001|0.000|
|83|Drivers and mobileplant operators|0.006|0.016|0.005|
|91|Cleaners and helpers|0.001|0.001|0.001|
|92|Agricultural, forestry and fishery labourers|0.000|0.000|0.000|
|93|Labourers in mining, construction, manufacturing and transport|0.001|0.001|0.001|
|94|Food preparation assistants|0.002|0.004|0.002|
|95|Street and related sales and service workers|0.000|0.000|0.000|
|96|Refuse workers and other elementaryworkers|0.013|0.007|0.007|

_Source_ : Authors.

_Note_ : The proportions of occupations in each gender preference are presented.

# 4. Job application and selection

## 4.1 Applications

This section analyzes job market transactions, namely application and selection. One of the unique data features of online job portals is transaction data, which cannot be obtained from labor force surveys. Over the period from 2012, the number of applications per job posting is 25
_Figure 17: In Croatia and in Slavonia, firms’ management score falls with age_ <!-- Start of picture text --> Median Management Scores (by Age Categories)<br>Croatian Firms (Manufacturing and Services), 2017<br>0-10 11-19 >= 20<br>All Regions, Except Slavonia Slavonia<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text -->

# _f. Size_

In the United States, Bloom et al. (2014) show that better managed firms are significantly larger than poorly managed firms: In the United States, their work shows that a one standard deviation of management is associated with a 40 log point increase in employment size. We find similar results in Croatia and in Slavonia, where firm size is significant and positively correlated with management score.

_Figure 18: In Croatia and in Slavonia, firms’ management score rises with size_ <!-- Start of picture text --> Median Management Scores (by Size Categories)<br>Croatian Firms (Manufacturing and Services), 2017<br>06-10 11-20 21-34 >= 35<br>All Regions, Except Slavonia Slavonia<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text -->

# _g. Exporters_

In manufacturing, the exporters have adopted significantly higher management practices, especially in Slavonia. On average, an exporter in Slavonia has adopted 60% of the management practices, whereas a non-exporter has only 51%. However, in the case of services, the difference is not very clear whether exporters are better managed, especially in Slavonia (Figure 19).<sup>22</sup>

> 22 See Annex Table A.4 for detailed results.

21