rafmacalaba/datause-encoder-data huggingface dataset:
30 As summarized in Table 6, RZ find that the coefficient estimate for the interaction between external dependence and total capitalization measure, External(k)*Total Capitalization(i), is positive and statistically significant at the one-percent level. This implies that an increase in financial development disproportionately boosts the growth of industries that are naturally heavy users of external finance.

RZ note that the economic magnitude is large. Compare Machinery, which is an industry at the 75<sup>th</sup> percentile of dependence (0.45), with Beverages, which has low dependence (0.08) and is at the 25<sup>th</sup> percentile of dependence. Now, consider Italy, which has high total capitalization (0.98) at the 75<sup>th</sup> percentile of the sample, and the Philippines, which is at the 25<sup>th</sup> percentile of total capitalization with a value of 0.46. Due to differences in financial development, the coefficient estimates predict that Machinery should grow 1.3 percent faster than Beverages in Italy in comparison to the Philippines. The actual difference is 3.4, so the estimated value of 1.3 is substantial. Thus, financial development has a big impact on industrial growth by facilitating external finance.

# **_2.5.2. Firm level studies_**

Demirguc-Kunt and Maksimovic (henceforth DM, 1998) use firm-level data and test whether financial development influences the degree to which firms are constrained from investing in profitable growth opportunities. They focus on the use of long-term debt and external equity in funding firm growth. As in RZ, DM focuses on a particular mechanism through which finance influences growth: does greater financial development remove impediments to firm growth. In contrast to RZ, DM estimate the external financing needs of each individual firm in the sample.
26 pocket payments than immiserized families. Nonetheless, as a count-based measure it is not bad, capturing as it does the idea that from a poverty-reduction perspective impoverishing and immiserizing payments are worse than catastrophic payments (at least if defined in terms of whether they leave a family close to the poverty line), which are in turn worse than non-catastrophic payments, which are in turn worse than no payments (especially if this group is restricted to users). Moreover, this index is easy to explain to a non-technical audience.

We have illustrated our ideas using data from 51 countries for which out-ofpocket spending data are available in the World Health Survey. We use $1.25-aday, $2-a-day and $5-a-day international poverty lines, and a catastrophic payment threshold of 40 percent. While intended to be only illustrative, the results are not without interest. For the 51 countries as a whole, we find very small shares of households falling into the impoverishment and catastrophic spending categories, and a much larger fraction falling into the immiserizing category. We also find a larger fraction of households in the catastrophic payment category when catastrophic payments are defined as payments that leave a family’s nonmedical consumption below _Z_ times the poverty line. Unsurprisingly, as we raise the poverty line, the share of households falling into the immiserizing spending category increase, and the shares falling into the impoverishing and catastrophic spending categories decrease. In our more detailed analysis of the BRICS countries (Brazil, China, India, Russia and South Africa), we see appreciable fractions of the population in some countries falling into the impoverishing and catastrophic
27 implications. Higher level services such as hospitals and universities necessitate more costly capital inputs and are likely to be used relatively more by the wealthier groups. Thus, it can be expected that ignoring capital will tend to result in an underestimation of the inequality in the distribution of public expenditures. Second, from a policy point of view when we are interested in allocating expenditures between sectors in the most cost effective way, total public costs will sometimes be more relevant than recurrent expenditures on their own. It will be important to keep these points in mind when drawing conclusions from the incidence estimates.

The analysis of health and education costs and budgets in Indonesia is not straightforward for several reasons. There are numerous budgetary sources for the two sectors, including at the central, province, and district government levels. In addition, there are numerous ministries besides the major ones (the Ministry of Health and the Ministry of Education and Culture) as well as foreign funds, contributing to overall expenditure levels. No central accounting system exists to keep track of the total amounts being spent and the composition of expenditures is not clear from outlay accounting classifications. Calculating total recurrent spending, let alone the per visit subsidies, is therefore a complex task.1<sup>6</sup> With these difficulties in mind, the paper tries to fo-llow the methodology detailed in Meesook's study for Indonesia in the late 1970s as closely as the available data permit.

# _Goveri ent Subsidies for Education_

In the education sector, subsidy levels differ at each schooling tier. At the primary level, where no school fees are charged, recurrent expenditures divided by the total number of public students in grades one to

**I'** The complexities involved in calculating budgets in Indonesia are further discussed in World Bank 1991a.
18 _period_ that would make agents indifferent between experiencing and not experiencing the shock. If _c_<sup>_∗_</sup> is steady state consumption, _ct_ is steady state consumption with the shock and _r_ is the discount rate, then the welfare measure is the value of ∆ such that We assume a discount rate of 4 percent (Web Appx. Section E discusses why). In the most severe scenario, welfare decreases in the LICs by 1 _._ 9 percent in perpetuity, 3 _._ 0 percent in the MIC and 4 _._ 3 percent in the HICs (see Figure 4(d)). Given the discount rate, this is equivalent to a one-time loss of 74 percent, 89 percent and 111 percent of GDP respectively.<sup>36</sup> This adds up to 102 _._ 4 percent of World GDP in 2019 ($86 _._ 7 trillion). The cost of human capital decumulation is high also. In year 10, once the shock is over, ∆= 2 _._ 0 percent in the LIC, 3 _._ 0 percent in the MIC and 3 _._ 4 percent in the HICs.<sup>37</sup> If the shock is to schooling only, then welfare decreases by 1 _._ 3 percent in the LIC, 1 _._ 9 percent in the MICs and 1 _._ 8 percent in the HICs (Web Appx. Fig. A.5). The experience shock reduces welfare by 1 _._ 6 percent in the LICs, 1 _._ 6 percent in the MICs, and 2 _._ 6 percent in the HICs (Ibid.). Fifty-six percent of the impact on the LICs is from the shock to employment/experience, whereas it is 45 and 57 in the MICs and HICs respectively.

That said, much of this has to do with the short run impact of the shock, whereas after 10 years (once the shock is over) it is the disruption to schooling that has the largest impact. At year 10, the total impact of schooling accounts for 86, 91 and 77 percent of the continuing welfare cost from the pandemic in the LIC, MIC and HIC respectively.

Finally, it is worth pointing out that the (conservative) assumption that college education is not disrupted implies that one effect of experience – its interaction with college schooling – is absent. Were it present, it would increase the impact of experience disruptions to some extent, primarily in the HIC where college education is common.

# **5. Welfare Impact for Different Scenarios**

So far we examined the welfare impact of the extreme pandemic shock scenario, to get a sense of how different factors would impact economies at different income levels. In this section, a range of feasible scenarios are considered.

> 36The losses equal $73 in PPP terms per year in the LIC, $423 per year in the MICs and $2 _,_ 342 per year in the HICs. Alternatively, this is equivalent to a one-off hit of $1 _,_ 812, $10 _,_ 585 or $58 _,_ 549 respectively. 37 This is equivalent to a one-time hit in year 10 of $214, $8 _,_ 831 or $44 _,_ 339 respectively.
available data for any country, and for any number of climate models. No matter how many models are included, the method will summarize information for each grid point in the nine-cell matrix portrayed in our Ethiopian illustrations. The information is computed for each square in a fine-gridded map (372 squares for Ethiopia alone). It includes the relative likelihood of future climate regimes, along with associated changes in average temperature and rainfall, and the seasonal distributions of those variables. We believe that this spatial information is sufficiently disaggregated to be useful for local planners and policymakers. At the same time, our method is invariant to scale. After aggregation of grid cells, it can be applied to national subregions, nations, or entire regions. We therefore believe that it can provide a generally-useful tool for policymakers and planners who need to understand the basic implications of the myriad climate projections that are now available.

13
Table A.3: Summary Statistics for firms in IPC fields

||**Firms **|**in the Sa**<br>**IPC feld**|**nctioned**<br>**s**|**F**|**irms in o**<br>**IPC feld**|**ther**<br>**s**|
|---|---|---|---|---|---|---|
||Mean|Medium|Std. Dev.|Mean|Medium|Std. Dev.|
|Total Patents|25.34|3|181.99|10.96|2|78.50|
|Domestic Patents|24.97|2|179.62|10.81|2|78.32|
|EPO/USPTO/PCT Patents|3.69|0|60.48|1.17|0|7.26|
|High-tech Patents|10.30|1|81.98|2.11|0|17.17|
|Triadic Patents|0.33|0|6.27|0.13|0|0.93|
|Patent Stock|46.59|5.30|397.23|21.26|5.13|133.96|
|Patent Quality|1.57|0.92|3.83|1.74|1.00|3.35|
|Priori Average Patent App.|0.20|0.00|1.45|0.31|0.00|5.02|
|Patenting Age|8.87|7|7.51|9.23|8|7.74|
|No. of Observations|104,813|||244,846|||

Table A.4: Summary Statistics after matching and balancing

||**Firms i**<br>**IP**|**n Sanctioned**<br>**C felds**|**Firms **<br>**IPC**|**in other**<br> **felds**|**Difer**|**ence**|
|---|---|---|---|---|---|---|
||Mean|Std. Dev|Mean|Std. Dev|Dif|Std|
|Total Patents|5.41|8.35|5.30|8.62|0.11|(0.06)|
|High-Tech Patents|2.24|4.15|1.11|2.64|1.13***|(0.03)|
|Domestic Patents|5.23|8.27|5.17|8.58|0.06|(0.06)|
|EPO/USPTO/PCT Patents|1.19|2.97|0.90|2.48|0.29***|(0.02)|
|Triadic Patents|0.17|0.85|0.12|0.61|0.05***|(0.00)|
|Controls|||||||
|Patent Age|8.14|6.44|8.14|6.00|0.00|(0.04)|
|Patent Stock (log)|1.77|1.02|1.77|1.04|0.00|(0.01)|
|Prior Average Paent App.|0.12|0.27|0.12|0.27|0.00|(0.00)|
|Dummy Patent|0.74|0.44|0.74|0.44|0.00|(0.00)|
|Lagged Total Patent App (log)|1.20|0.97|1.20|0.99|0.00|(0.01)|

Note: This Table compares the mean and standard deviation of firms in sanctioned and unsanctioned sectors before the sector bing sanctioned. Patent is a dummy variable that equals 1 if a firm at year _t_ has also patented at _t −_ 1. Patent _×_ Total Patent is the number of patents firm at year _t_ patented at _t −_ 1. Last column is the standard error of t-test. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

47
Policy Research Working Paper 10908

# **Abstract**

This study expands the inventory of green job titles by incorporating a global perspective and using contemporary sources. It leverages natural language processing, specifically a retrieval-augmented generation model, to identify green job titles. The process began with a search of academic literature published after 2008 using the official APIs of Scopus and Web of Science. The search yielded 1,067 articles, from which 695 unique potential green job titles were identified. The retrieval-augmented generation model used the advanced text analysis capabilities of Generative Pre-trained Transformer 4, providing a reproducible method to categorize jobs within various green economy sectors. The research clustered these job titles into 25 distinct sectors. This categorization aligns closely with established frameworks, such as the U.S. Department of Labor’s Occupational Information Network, and suggests potential new categories like green human resources. The findings demonstrate the efficacy of advanced natural language processing models in identifying emerging green job roles, contributing significantly to the ongoing discourse on the green economy transition.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at snasozen@worldbank.org and draju2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about 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 authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table D.6: Effects of combining various measures of self control

|||||I|TT regres|sion (N=94|7)||
|---|---|---|---|---|---|---|---|---|
||||Ther|apy only|Cas|h only||Both|
|Outcome|Round|Control|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
|||mean|||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Self control, z-score|2–5w|-0.037|0.085|[.098]|-0.147|[.104]|0.037|[.096]|
||12–13m|-0.070|0.159|[.090]*|-0.025|[.095]|0.244|[.095]**|
|Time preferences, z-score|2–5w|-0.202|0.179|[.098]*|0.071|[.099]|0.318|[.099]***|
||12–13m|-0.149|0.149|[.102]|0.105|[.102]|0.209|[.105]**|
|Executive function, z-score|2–5w|-0.103|0.076|[.075]|0.059|[.077]|0.024|[.085]|
||12–13m|0.110|-0.094|[.077]|-0.078|[.076]|-0.109|[.078]|
||2–5w|-0.066|0.166|[.072]**|0.016|[.072]|0.221|[.073]***|
|Self control + time preferences|12–13m|-0.212|0.183|[.088]**|0.060|[.088]|0.255|[.091]***|
|Self control + executive|2–5w|-0.095|0.100|[.070]|-0.006|[.072]|0.037|[.077]|
|function|12–13m|0.060|-0.012|[.073]|-0.072|[.074]|0.009|[.075]|
|Self control + time preferences|2–5w|-0.038|0.097|[.042]**|0.009|[.042]|0.129|[.043]***|
|+ executive function|12–13m|-0.124|0.106|[.051]**|0.035|[.051]|0.149|[.053]***|

_Notes:_ The table reports intent to treat estimates of the effect of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. Heterosketastic robust standard errors are reported in brackets. Because there are two endline surveys per round per individual, each surveyed individual enters twice into the regressions, and standard errors are clustered by individual. *** p<0.01, ** p<0.05, * p<0.1 xxxvii
additional 15-20% market share in each of these services. Public banks collect a stable 11% share in savings accounts and a declining 15% share of deposits in current accounts. They provide a significantly lower share of term deposits. The significant market shares of financial cooperatives in saving term deposits (21%) and other credit institutions (such as commercial finance companies and leasing companies) in term deposits (23%) is the result of their regulatory restrictions on the type of deposits they can accept.

19. **Private domestic banks cater more towards small savers compared to public banks.** Private domestic banks in Colombia collect almost 90% of savings deposits below COP 5 million and have the smallest estimated average savings amount (see Figure 1.7), as they hold by far the largest number of savings accounts in this category. Private domestic banks therefore appear to cater more towards small savers than public banks, which can likely be attributed to the specific business focus of some banks (such as _Banco Agrario, Caja Social,_ Colmena or Megabanco) on low income clients.

20. **Checking accounts by public banks may be more accessible for lower income clients.** Public banks show a significant market share in current accounts with a deposit size below COP 5 million, with 27% (see Figure 1.7). Public banks also have by far the largest number of accounts in the small deposit segment below COP 5 million (see Table 1.1). This may indicate that the checking account products of public banks serve smaller savers and lower income clients better than those of private banks.

**Figure 1. 7  Average amounts of current and savings accounts, per type of institution**

<!-- Start of picture text --> Market share of current accounts by deposit level Market share of savings accounts by deposit level<br>100 27 15 16 17 18 20 100 113 93 105 107 13 18<br>80 80 10<br>21 20 18 17 16 12<br>60 20 60<br>40 40 86 88 85 ` 82 77<br>64 64 65 65 65 69<br>53<br>20 20<br>0 0<br><5 >5 & <10 >10 & <20 >20 & <50 >50 & < >100 <5 >5 & <10 >10 & <20 >20 & <50 >50 & < >100<br>100 100<br>Million COP Million COP<br>Private Domestic Banks Private Foreign Banks Public Banks Private Domestic Banks Private Foreign Banks Public Banks<br>Source: Own calculations based on Superintendencia Bancaria data<br>% Market share of current accounts % Market share of savings accounts<br><!-- End of picture text --> 24
# Corporate Responses to Size-Based Tax Rates in Lithuania<sup>∗</sup>

Thiago Scot<sup>†</sup> Pablo Garriga<sup>‡</sup> Keywords: Corporate Taxation, Tax compliance, Firms & Productivity, Lithuania JEL Codes: H25, H32

> ∗The results in this paper were produced in the context of a World Bank project with funding by the European Union in cooperation with the European Commission’s DG REFORM. The views expressed herein can in no way be taken to reflect the official opinion of the European Union. The findings, interpretation and conclusions expressed in this paper do not necessarily reflect the views of the Executive Directors of the World Bank, the European Commission or the Government of Lithuania. We thank Statistics Lithuania, in particular Edita Baltuse and Aleksandra Golubovic, for the collaboration to produce the data used in this study. We thank Alberto Leyton, Cristina Savescu, Munawer Kwahja, Dzelila Kramer, Louise Jensen, Vytautas Adomatis and Vaidas Navickas for continued feedback and guidance during the project. We also thank Pierre Bachas and Camille Semelet for their fundamental role in the initial stages of this project. Javier Feinmann provided excellent research assistance.

> †The World Bank, tscot@worldbank.org

> ‡The World Bank, pgarriga@worldbank.org
Overall, results indicate that there is no significant difference in efficiency between public firms and private firms operating under a RoR regime. Firms operating under a PC regime are the most efficient, whereas those operating under HR are in between .

# **The impact of the reforms on electricity prices**

Table 7 lists average household and industrial KWh electricity prices by country, both with taxes and without taxes, along with the share of private sector participation in the distribution activity and the type of regulatory regime under which firms’ operate.<sup>12</sup>

**Table 7: Average electricity** **<u>prices by country</u>**

||Private sector<br>participation<br>in distribution|Regulatory<br>regime|Pr<br>With|ices correspondi<br>taxes|ng to the year 20<br>Withou|01<br>t taxes|
|---|---|---|---|---|---|---|
|Country|(in %)||Household|Industrial|Household|Industrial|
|Argentina|70|Price cap|13.50|10.94|10.79|8.07|
|Bolivia|90|Hybrid|14.90|14.02|10.02|9.67|
|Brazil|60|Price cap|21.47|9.84|17.64|8.07|
|Chile|90|Hybrid|16.07|10.30|13.64|8.73|
|Colombia|50|Price cap|23.10|15.07|6.79|12.33|
|Costa Rica|10|Rate of return|15.04|17.75|||
|Ecuador|20|Hybrid|16.28|18.94|||
|El Salvador|40|Hybrid|33.84|38.75|||
|Mexico|10|Rate of return|11.12|7.71|||
|Panama|100|Price cap|20.16|16.52|20.16|16.52|
|Paraguay|0|Rate of return|24.13|13.80|22.02|12.62|
|Peru|60|Hybrid|22.58|12.87|22.56|12.93|
|Uruguay|0|Rate of return|18.81|9.58|15.32|7.52|
|Venezuela|20|Rate of return|6.46|3.29||2.06|

Notes: All prices are at the country level, and correspond to the year 2001, except for prices without taxes in Colombia, which correspond to the year 2000 and were converted into 2001 price levels by using the US Consumer Price Index.

Regulatory regimes and the share of private sector participation in the distribution activity for all countries but Panama, were obtained from Espinasa (2001). The share of private sector participation in the distribution activity for Panama was obtained from the regulator of the public services in that country. In order to harmonize electricity prices across countries, all prices are in 2001 US dollars per KWh of electricity and are expressed in terms of purchasing power parity.

The simple correlations between the share of private sector participation in the distribution activity and household prices with taxes, industrial prices with taxes, household prices without taxes, and industrial prices without taxes (all corresponding to

> 12 The data is from the OLADE data base which only reflects taxes as reported by governments and usually tend to ignore subnational taxes which in some countries may bias the analysis.

20
Let _y_ = 1 represent a household’s use of environmentally destructive fishing gear and _y_ = 0 the use of non-destructive fishing gear. We can estimate the probability a household chooses to use environmentally destructive fishing gear, which is given by: Where Φ represents the cumulative normal standard distribution and<sup>_x_</sup> _i_<sup>the vector of</sup> independent variables, fishing related factors, household’s livelihood strategies and endowments, institutional factors, village factors, discussed earlier.

However, we should consider the possibility that the household’s choice of fishing gear and type of fish to catch may be simultaneously determined. Results of the factor analysis of fishing technology and target species for households involved in fishing shows that a high proportion of the variation in fishing characteristics falls into logical groupings, in terms of reliance on near shore resources and use of specific fishing technology. This suggests decisions concerning where to fish, what type of species to target, and gear used are closely linked. In this case, we would then have to specify the following model: Where<sup>_y_</sup> 1<sup>denotes the choice of fish to target (pelagic or reef) and</sup> _y_ 2 denotes the choice of fishing gear. We estimate this structural model as a bivariate probit model and test the hypothesis that the choice of fishing gear and target fish species are simultaneously made by households. Assuming that _u1_ and _u2_ are jointly normally distributed with mean zero, the covariance matrix is given by: 23