rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> RCEP Output, billion $ Output, %<br>Wearing apparel and leather Mikes 7<br>Textiles [les C—O<br>Food, beverages, tobacco Nc ee<br>Other agriculture [Milles |<br>Machinery and equipment nec MIRIINIIIIII 7eee_.<br>Electronic equipment libs ———<br>Transport equipment Mil Cy<br>Metals SII ee<br>Natural resources / mining MENNININUU Ts EE_ eee<br>Chemical, rubber, plastic products NINN ES<br>Other manufacturing Milles lee<br>Social services ills ed<br>Communication and business services neC Milissssssss lee<br>Finance and other business services ERIM ccs: —O—————E<br>Trade and trarsport SIIIIIUUIUINIIIIIIIIIID :ttttt eee<br>Utilities Miles: eee<br>Construction EET..." 7<br>0 100 200 300 400 500 600 700 800 0 5 10 15 20<br>mStandard mi Productivity kick mStandard mTM Productivity kick<br><!-- End of picture text -->

<!-- Start of picture text --> RCEP Exports, billion $ Exports, %<br>Wearing apparel and leather [lees ;tese<br>Textiles a<br>Food, beverages, tobacco StTTT ss es<br>Other agriculture [iy Pl<br>Machinery and equipment nec St 0 Ee<br>Electronic equipment RRINIIIIIIIIIT sss Cs<br>Transport equipment SST Le]<br>Metals SSSI ee<br>Natural resources/ mining [SSS es<br>Chemical, rubber, plastic products [INSU 000 BN<br>Other manufacturing [ilies C—O<br>Social services "ss 2<br>Communication and business services NEC | Le<br>Finance and other business services 2<br>Trade and transport [es Ee<br>Utilities =,<br>Construction , —<br>20 0 20 40 60 80 100 120 140 160 180 6 0 5 10 15 20 25 30<br>Standard m Productivity kick mStandard TM Productivity kick<br><!-- End of picture text -->

<!-- Start of picture text --> RECP Imports, billion $ Imports, %<br>Wearing apparel and leather [ills ee<br>Textiles TL. es<br>Food, beverages, tobacco tttttt ttt tt ttt ttttt sss reereee<br>Other agriculture EEE.<br>Machinery and equipment nec RSS<br>Electronic equipment [UT pl<br>Transport equipment [Ts es<br>Metals EES cs NS<br>Natural resources / mining S ESSSSSUSSS T es<br>Chemical, rubber, plastic products SSNScesses es<br>Other manufacturing ML aes<br>Socialservices [iF ee<br>Communication and business services nec [ENN ees ee<br>Finance and other business services [ills Ly<br>Trade and transport [ETes EE<br>Utilities | Pl<br>Construction fh ee<br>te) 20 40 60 80 100 120 140 160 te) 5 10 15 20 25 30<br>m@Standard mTM Productivity kick Standard mTM Productivity kick<br><!-- End of picture text -->
<!-- Start of picture text --> Support Ratio Support Ratio<br>trough peak<br>\1<br>|\<br>I\<br>II<br>I 1<br>&a° sRarco ' AsaSo I<br>2 ar ' at 1<br>Ss ! !<br>= 1 1<br>Fy | ASR ; ask 4<br>a22 i11ar I ar<br>1\<br>1I<br>||<br>I1<br>1<br>Main driver:a8 1|! 1|!<br>Child DR ! Child DR | Aged oR<br>to T JU———__,—_____________Jty t Y t, time) (t)<br>SCHIIat OR ACHAEORar _ Chador OR <0<br>Aged OR SAged OF Aged OR 0<br>a >o ar >o ar *<br>PCHIEOR4 Child OR | , | O AgedAged OROF PEHIEORSChiId OR, | |,| SAgedAged OROF ial4 Chitd OR Sey Aged OR Ce<br><!-- End of picture text -->
unskilled production workers and administrative workers is the adoption of the complex software which can perform some high-skilled tasks. However, it is interesting to highlight that there is no statistically significant reduction in the demand for skilled production workers, indicating this employment category still has a role in the production process.

# **7. Robustness Checks and Heterogeneity of Impacts**

# **7.1 Robustness Checks**

We test the robustness of our main findings on firm-level occupation shares and task indexes to different concerns. First, we test the robustness of our findings to an alternative methodology to compute the firm-level task indexes, following Autor and Handel (2013).<sup>16</sup> We are interested in assessing whether changing the methodology used to define the task measures results in different task content of occupations and different impacts of the adoption of complex software. Second, we test the robustness of our findings to the use of an alternative measure of the regional adoption of new technologies in the first-stage of the instrumental variable methodology. Our objective is to test whether regional household computer use is capturing the regional adoption of new technologies or is capturing other regional trends. The alternative measure is the share of households in the region with at least one cell phone.<sup>17</sup> Third, we explore two alternative weighting schemes to the ELE survey’s cross-sectional sampling weights (exploited in our main results). In particular, we use either no sampling weights or a new set of adjusted weights accounting for the fact that only some firms in the ELE survey are part of the panel. To do that, we estimate a model for the probability of a firm belonging to the panel using firm characteristics in 2007 as control variables. Then, we adjust the sampling weights multiplying them by the inverse of the probability of being selected into the panel sub-sample. Fourth, we test the robustness of our results to the inclusion of the group of 140 firms exhibiting very large changes in employment composition between 2007 and 2013 (which are excluded from the estimating sample used in Section 6). Fifth, we explore whether our results are driven by the fact that only a small share of firms in our sample (25%) actually changes their status in the adoption of complex software over time. We re-estimate our main models ignoring the panel structure of the data thus including region and sector fixed

> 16 Panel B of Table B3 (Appendix B) reports the average of each task measure for each occupation using Autor and Handel’s (2013) methodology.

> 17 Again, this variable is interacted with the sector ICT intensity in 2003 obtained from the Chilean Input-Output matrix.

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All the surveyed participants agreed on the important role of political economy drivers in decision-making in different sectors and in shaping the way that policies are implemented, and ultimately in influencing the implementation of projects. It was acknowledged that a good understanding of the political context is likely to enhance the effectiveness of project implementation and the quality of policy dialogue. Overall, PE studies are seen as useful by providing insight into the country and sector context and facilitating a better and quicker understanding of political realities and thus possibly adapting policy dialogue to local conditions.

However, TTLs pointed out that the usefulness of political economy analysis also depends on what level they are carried out, whether it is at a country or sector level. The studies conducted at a country-level provide a broad assessment of the country’s environment while sector studies deal with the dynamics of a particular sector and provide deeper and broader understanding of specific issues. For team members working on projects in the field, sector-level analysis appears to be more useful. The usefulness depends on the knowledge that TTLs and teams already have about the sector in which they are operating and on their involvement in carrying out the analysis.

Sectoral PE studies may be valuable specifically in sectors where donors have long focused on supporting reforms and where the implementation of reforms has lagged behind or has never materialized. In Zambia, the electricity, telecommunications and land sectors are examples where historically there was lack of progress in implementing reforms. Therefore, it was felt that a good understanding of the political economy drivers in these sectors could enable staff to identify feasible reform options and better target interventions. The telecommunications and energy sectors are good examples of how the use of PE studies has changed the manner in which policy dialogue is conducted, contributing to progress in the sector.

The World Bank had been active in the telecommunications sector for several years, urging policy makers to improve the efficiency of ZAMTEL (until recently the government owned telecommunications company), address issues relating to its monopoly over the international gateway and proposing privatization as a solution. In particular, Zamtel’s monopoly over the international gateways had translated into high prices for international telephone calls and poor quality of service. After years of limited results, and using the results of the political economy diagnostic, the focus of policy dialogue was changed from improving efficiency to how high telecommunication costs were not only adversely impacting Zambia’s competitiveness by contributing to the high cost of doing business but were also contributing to high telecommunication costs for the individual citizens, many of whom now enjoyed access to mobile telephone services. The high cost of doing business was adversely 11
measures for victims. The law gave an additional impetus to the expansion of DEAMs, which are considered to be an important part of the infrastructure needed for implementation of the Maria da Penha law.

These efforts appear to be paying off at least in some areas. For instance, over time more women are denouncing the perpetrators, as the culture gradually acknowledges that domestic violence is not to be tolerated. From 2007 to 2009 calls to the women’s help hotline Ligue 180 doubled (Comunicacao Social, Secretaria de Politicas para as Mulheres 2013). Other indicators suggest a less optimistic picture: in spite of a dip in 2007 after the passing of the Maria da Penha law, the female homicide rate continues rising as it has over the last decade (Figure 1). This seeming incongruity with the increase in anti-violence efforts by both women denouncing and policy makers providing more resources underscores the importance of a careful analysis of the effectiveness of interventions.

<!-- Start of picture text --> Homicide Rate per 100,000 women<br>Ages 15-49<br>2004 2005 2006 2007 2008 2009<br>year<br>Source: Sistema de Informacao sobre Mortalidade. Only municipalities with high quality data.<br>8<br>Female<br>6<br><!-- End of picture text -->

**Figure 1**

Thus, this paper attempts to make a twofold contribution: first, it adds to the literature on domestic violence by providing quantitative evidence on the effects of women’s police stations, an intervention that has been gaining popularity in the array of tools to address domestic violence.

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# **Table 6. Nontariff Measure Coverage Ratios for OECD Imports from Developed, Developing and Sub-Saharan African Countries**

||1992 Im|port ($million)||NTB|Coverage Rat|ios|
|---|---|---|---|---|---|---|
|Product Group (SITC)|Developed<br>Countries|Developing<br>Countries|Sub-<br>Saharan<br>Africa|Developed<br>Countries|Developing<br>Countries|Sub-<br> <br>Saharan<br>Africa|
|ALL NON-FUEL ITEMS (0 TO 9-3)|1,900,481|540,783|25,137|9.7|16.6|10.8|
|All Foods (0+1+22+4)|190,602|79,053|8,022|24.6|17.1|23.4|
|Food and Live Animals (0)|152,772|69,241|7,044|28.1|18.2|24.5|
|Oil Seeds and Nuts (22)|5,849|2,509|72|1.3|3.6|6.3|
|Animal & Vegetable Oils (4)|5,046|2,841|171|5.7|5.7|0.1|
|Agricultural Materials (2-22-27-28)|53,386|20,303|2,719|1.3|1.3|0.3|
|Ores and Metals (27+28+67+68)|116,438|42,227|7,521|13.6|10.1|5.7|
|Ferrous Metals (67)|55,326|11,294|1,097|38.2|35.9|38.6|
|Non-Ferrous Metals (68)|37,753|15,192|3,677|0.0|0.0|0.0|
|Mineral Fuels (3)|86,298|164,851|19,654|21.5|16.4|17.4|
|All Manufactures (5 to 8 - 68)|1,499.800|383,871|6,524|8.5|18.8|5.6|
|Chemicals (5)|216,755|22,039|769|6.0|3.9|0.2|
|Other Manufactures (6 to 8-67-68)|1,283,045|361,832|5,755|8.8|19.9|6.4|
|Leather (61)|5,004|3,749|237|3.0|1.2|0.0|
|Textile Yarn & Fabric (65)|**49,545**|19,485|275|4.4|52.5|18.7|
|Clothing (84)|43,250|79,659|1,019|3.4|62.5|44.8|
|Footwear (85)|12,142|15,864|17|12.2|32.0|1.3|
|ALL ITEMS (0 to 9)|1,986,779|705,634|44,791|10.2|16.6|13.1|

Note: The following measures were included in the computation of the nontariff barrier coverage ratio: tariff quotas; increased duties, safeguard duties, retaliatory duties and customs surcharges; variable levies and flexible import fees; non-automatic licensing and discretionary licensing; quotas and prohibitions; voluntary export restraints, MFA quotas and other restraints including textile restraint agreements, orderly marketing arrangements; other quantitative restrictions; other restrictions imposed under the Multifiber Arrangement; minimum, reference or other import price controls; voluntary export price restraints; state monopoly of imports; and local content regulations.

Source: World Bank-UNCTAD SMART Data Base. The statistics in this and the tables that follow reflect nontariff barriers which are applied in all OECD markets with the exception of Iceland and Turkey. Developed countries are defuned as all OECD members less Turkey while developing countries are all countries less the OECD plus Turkey. The countries included in the sub-Saharan group are listed in Table 1.
# **Figure 3** . **South Asia: retail vs. optimal unit prices by economy and fuel, 2020**

<!-- Start of picture text --> 3b. Coal<br>3a. Natural gas<br>US$/kilojoule US$/kilojoule<br>0 5 10 15 0 5 10 15 20 25<br>Bangladesh Bangladesh<br>Bhutan Bhutan<br>India India<br>Maldives Maldives<br>Nepal<br>Nepal<br>Pakistan<br>Pakistan<br>Sri Lanka<br>Sri Lanka<br>3c. Gasoline 3d. Diesel<br>US$/liter US$/liter<br>0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5<br>Bangladesh Bangladesh<br>Bhutan Bhutan<br>India India<br>Maldives Maldives<br>Nepal Nepal<br>Pakistan Pakistan<br>Sri Lanka Sri Lanka<br>3e. LPG 3f. Kerosene<br>US$/liter 0.0 0.2 0.4 0.6 0.8 1.0 1.2 US$/liter 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4<br>Bangladesh<br>Bangladesh<br>Bhutan<br>Bhutan<br>India India<br>Maldives Maldives<br>Nepal Nepal<br>Pakistan Pakistan<br>Sri Lanka Sri Lanka<br>Supply Cost Local Congestion Accidents Road VAT Global Retail price<br>pollution damage warming<br><!-- End of picture text --> Source: Authors’ estimates based on data and methods in Parry, Black and Vernon (2021). Afghanistan is not shown, due to lack of available data.

Currently, retail prices in most South Asian economies are far below these optimal prices, in many cases not even covering supply costs because of direct subsidies. In other words, governments are setting domestic fossil fuel prices below international reference prices. Especially in India, Bangladesh, and Sri Lanka, natural gas and coal prices are considerably below their optimal levels, while Nepal’s prices (especially LPG and kerosene) are generally closer to these levels (World Bank, 2022a). As a result, raising retail prices to achieve this efficient price will require both reducing the explicit subsidy 6
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declined by about 5 to 6 1/2 per cent over this period, depending on the base period used, relative to prices of exports to all destinations, or an average of between .13 and .18 per cent per year. If the average decline in prices for exports to developing countries relative to exports to the world is calculated from fitted equations in logs, the rate of decline is somewhat larger -- between .16 and .21 per cent per year, with the later weights indicating larger declines in the relative prices of exports to developing countries. Thus, an estimate of the terms of trade of developing countries based on data for developed-country exports to all countries would be biased downward slightly on this account.

The Effect of Changing Base Years Price indexes have been calculated using several different base years, 1963, 1975, and 1986. The composition of trade in manufactures has, of course, changed substantially over this period. One way it has changed is that world trade has moved steadily toward more high-tech products, in the sense of products of industries in which R & D expenditures are important relative to value added or sales (Table 2).

Table 2 Distribution of Market Economy Exports by Technology Class

||Low|Medium|High||
|---|---|---|---|---|
||Technology|Technology|Technologv|Total|
|1966|48.4|37.2|14.4|100.0|
|1977|43.2|39.0|17.8|100.0|
|1982|41.2|39.4|19.4|100.0|
|1986|34.3|41.2|24.5|100.0|
|1990|34.2|39.8|26.1|100.0|

Source: Lipsey and Kravis (1992) and later UN Trade Tapes.

Whether because of the sh5 ft over time to high-tech products or the more
Figure 2: CFC Event Study Around Signature of RTA <!-- Start of picture text --> −3 −2 −1 0 1 2 3<br>Event Time (RTA Signature)<br>1.0<br>0.5<br>0.0<br>% Deviation from MP<br>−0.5<br>−1.0<br><!-- End of picture text -->

**Notes:** This figure presents event study coefficients for CFC emissions (represented as percent deviations from target). Black lines correspond to coefficient estimates and 95% confidence intervals for RTAs without ODS provisions, red lines correspond to coefficient estimates and 95% confidence intervals for RTAs with ODS provisions.

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Figure 2: Explaining variation in the change in sales among firms.

<!-- Start of picture text --> Calculated Shapley value to assess the relative power of<br>Average change in sales<br>size, sector, exporter status, and shocks suffered<br>.03<br>80<br>.02 62.4<br>60<br>.01<br>40<br>0<br>20<br>-100 -50 0 50 100 150<br>Percentage change in sales relative to same period of 2019 9.6 10.4<br>Variation between Variation within 4.2<br>country-size-subsector country-size-subsector<br>averages - No SAR groups - No SAR 0<br>Variation between Variation within<br>country-size-subsector country-size-subsector<br>averages - SAR groups - SAR<br>Size Sector Exporter status Shock suffered<br>Density<br>R2 explained by group<br>Relative Shapley Value (%)<br><!-- End of picture text --> _Note:_ Change in sales is measured by the difference between the current sales and that of the same period in 2019.

We will revisit this finding in Section 4 of the paper.

# **2.2 Financial fragility**

With COVID-19, liquidity pressures can escalate and may lead to solvency issues. This can be alarming as financial markets in South Asia are seen as ill-prepared to deal with the repercussions of the pandemic, with many banks having high shares of non-performing loans and low capital buffers (World Bank, 2020b). BPS results show that nearly two-thirds of the South Asian firms are likely to fall into arrears (Figure 3, left panel a), which is significantly higher than that in SSA (52%), and EAP (46%).

There is significant heterogeneity by firm characteristics. Within the firm-size groups, micro and small firms in SAR tend to face more liquidity challenges. The average probability of falling into arrears for micro and small firms in South Asia is close to 70%, which is significantly higher than that for large firms (52%). Compared to the rest of the world, South Asian firms are more financially fragile across all size groups (Figure 3, right panel a). We extend this analysis to other firm attributes in Appendix Figure A.4. Within sector groups, accommodation services are found to be most fragile, especially in South Asia. This relatively high level of financial fragility among smaller firms and those in vulnerable sectors can partly explain why they are less likely to continue their operation during the COVID-19 outbreak. South Asian exporters are slightly less financially fragile when compared with non-exporters. The probability of falling in arrears being 59% for exporters and 67% for non-exporters. Such difference by exporter status is not observed in the rest of the world. Consistent with global average elsewhere, we do not find significant difference in financial fragility between female- and male-led firms in South Asia.

While size and sector composition explain the cross-country differences in financial fragility, what explains the within-country differences? Using the Shapley decomposition of the probability of falling into arrears within countries, we observe that a significantly large proportion of the within7
Table D.21. Effects of Guerrilla Control on Migration Outcomes for the Highly Educated Population

||||Intern|ational Migrants||Always Lived in|Same Location|People who Arrived|Years since|
|---|---|---|---|---|---|---|---|---|---|
||During Control|At any time|Years since|Households who Received|Received Remittance from|same Location|as the Mother|During Control|Arrival|
||(Share)|(Share)|departure|Remittances (Share)|War Migrant (Share)|(Share)|(Share)|(Share)||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Guerrilla control|0.00151|0.00343|0.226|-0.00573|-0.00112|-0.00376|-0.00713|-0.00491|-0.469|
||(0.00452)|(0.00927)|(0.540)|(0.00463)|(0.00416)|(0.0127)|(0.0132)|(0.00535)|(0.531)|
|Observations|3,325|3,325|1,907|3,636|3,325|3,602|3,602|3,602|3,441|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|0.0200|0.100|6.220|0.110|0.0100|0.730|0.700|0.0800|17.68|

_Note:_ The table presents the results of equation 1 for our outcomes related to migration. Columns 1 to 5 focus on outcomes for international migrants. All information was obtained from the Population Census of 2007. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.22. Share of Individuals who Work in the Same Place as their Residence

||Work in the Same Place as Residence<br>(Share)|
|---|---|
||(1)|
|Guerrilla control|0.00333|
||(0.00320)|
|Observations|3,647|
|Bandwidth (Km)|2.271|
|Dependent mean|0.987|

_Note:_ The table presents the results of equation 1 for individuals who work in the same place as their residence. All information was obtained from the Population Census of 2007. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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