rafmacalaba/datause-encoder-data huggingface dataset:
- 2.3 -

a World Bank stLAdy t be undertaken in early 1988. Instead this study will consider a limited range of alternative sources of revenue which have been suggested, and which are of direct relevance to the cocoa Industry.

One suggested alternative Is to replace, elther fully or partially, the cocoa tax by extending the present Income tax to cocoa farmers. At present, in prlnciple, all noncocoa agricultural income Is liable for direct taxation, though Income from cocoa Is exempt. It Is therefore Interesting to examine the case for such a chtngo, to see whether It Is warranted on efficiency and equity grounds, and whether It would be administratively sensible. Taxes on reported agricultural Income are not only hard to monitor, hut also suffer from the usual disincentive effects, and varlous economists, together with the _NF,_ have therefare argued for a land tax, effectively a tax on presumptive or po' ntlai Income, rather than a tax on agricultural Income. How far does this argument apply In Ghana? Would a land tax, assuming It to be feasible, be preferable to the present cocoa tax? If so, would It be administratively feasible In Ghana, elther now or in the Rikely future?

> Both agricultural Income taxes and land taxes are natural alternatives to the current cocoa tax, though both present formidable administrative problems. If, as seems likely, these difficulties rule them out, then alternatNVe sources of revenue will be iequired. One obvious alternative Is a broad based Indirect tax, such as sales taxes and/or hmport tariffs. Another Is an excise tax on transport fuel and/or purchase taxes or Increased licence fees on transport vehicles. Both would affect cocoa producers, though they would also have wider inpacts. Indirect taxes would reduce the purchasing power of cocoa farmers, whiist transport taxes would raise the cost of transporting cocoa from the farmer to the point of export. Nevertheless, because both taxes fall on other sectors of the economy, the net burden of taxes on cocoa farmers would fall, and one would like to know by how much.
across countries are shown in Figure 10a in comparison to observed EV market shares in 2020. For the five countries that have above-average incentives, the simulated market share would be lower than the observed market shares. However, for the the rest of the countries, increasing incentives to the world average level would help them catch up in market shares with the leading countries.

The accessibility of charging infrastructure is also highly uneven across countries. We then examine how the charging infrastructure helps to explain cross-country differences in EV adoption. Figure 10b presents simulated EV market shares assuming charging infrastructure level measured by the number of charging ports per thousand new cars sold to be at the sample average. The simulation results show a much larger impact compared to the incentive case. Norway and Netherlands would see a decrease in EV market share, while EV penetration would deepen in all other countries. This highlights the importance of building up charging infrastructure in global EV diffusion.

In both cases, reducing the disparity incentive or charging infrastructure would lead to more uniformly distributed EV penetrations across countries. The dispersion of market shares is reduced by about 17% and 69% after removing the cross-country variation in incentive levels and charging infrastructure, respectively.

# **Cost-Effectiveness Comparison**

We calculate the average government spending per induced sale as the ratio between the aggregated government spending and the induced sales under each policy. For illustration purpose, we compare the induced cost and average government spending per induced sale under scenarios with no incentive and no charging station.<sup>10</sup> The aggregated government spending from financial incentives is calculated based on incentive levels and sales in the data. The aggregated government spending of installing charging ports is calculated based on the number fast and slow charging ports in the data and the construction cost for each type of charging port.<sup>11</sup> On average, to induce one unit of EV sale, it would cost $10,872 in terms of government

> vehicles and therefore our analysis cannot directly estimate the substitution.

> 10We simulate sales by reducing the number of charging ports to one instead of zero, since our model uses the logarithm scale of charging ports.

> 11China, US, and European countries have different slower charger construction and fast charger construction cost that we account for in the aggregate construction cost. China: $1,449 (slow AC) and $14,493 (fast DC); US ad Canada: $5,440 (slow AC) and $81,818 (fast DC); Europe and Japan: $7,273 (slow AC) and $173,580 (fast DC). Cost estimates are from Nicholas (2019) and Mathieu (September 2018).

13
<!-- Start of picture text --> 74<br>7.2<br>7.0<br>6.8<br>Average<br>SWB<br>(0-10) 66<br>6.4<br>6.2<br>6.0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text -->
# variations.<sup>18</sup> The ...ndings are largely similar.

# [Table 3 about here]

The vertical production linkage between foreign owned subsidiaries and parent ...rms also determines that the former should exhibit a greater advantage over local competitors when host countries experience a greater incidence of the crisis, in particular, a larger negative demand shock. MNC subsidiaries sharing an input-output relationship with their parent ...rms tend to engage in more intra-...rm trade, thereby less dependent on host-country domestic demand conditions. When the demand in host countries contracts, the intra-...rm demand will help absorb part of the shock, leaving the subsidiaries less a¤ected by the crisis. This advantage of vertical MNC subsidiaries is particularly important when the demand shocks in the host countries are relatively large.

The above mechanism conversely implies that the advantage of vertically linked MNC subsidiaries should be negatively associated with the incidence of the crisis in MNCs’home countries. A better demand condition at home will enhance the advantage of vertical subsidiaries abroad, while a worse incidence of the crisis in home markets adversely a¤ects those MNC subsidiaries’performance overseas.

We examine the above hypotheses in Table 4. The incidence of the crisis, in particular, the degree of the demand shock is proxied here by the average percentage decrease of sales in each host country and industry. The measure was constructed using either all establishments in the entire WorldBase dataset or the subset that excludes MNC subsidiaries and their local matches. The results were similar.<sup>19</sup> The estimates obtained with the latter measure are reported in Table 4.

# [Table 4 about here]

We ...nd that, ...rst, the e¤ect of vertical production linkages on MNC subsidiaries’ relative performance increases in the incidence of the crisis in host countries. Vertical MNC subsidiaries exhibited a greater advantage over local counterfactuals in countries with larger negative demand shocks. In contrast, the e¤ect of vertical production linkages is not signi...cantly associated with the incidence of the crisis in MNCs’home countries.

> 18 Recall that country-industry speci...c factors that uniformly a¤ect the performance of foreign owned and domestic establishments have been controlled for in the matching analysis by the pairing of foreign owned subsidiaries and local establishments from the same country and industry. Estimated parameters of country dummies and intercepts are suppressed in the table because of space consideration.

> 19 We also used countries’GDP growth rates and import growth rates as alternative proxies and found similar results.

17
Subte stations were closed: initially, only 39 of 106 stations were kept open, extended to 50 stations in April 2020 and remained at this number during the October 2020 analysis period. By October 2021, all the stations of the network were operating, while the available supply on the rail riles measured in train-kilometers was still 15 to 20 percent below October 2019 levels.

The pandemic led to an increased urgency to implement policies that benefit and protect active travelers (World Bank 2021). Also in the case of the Bogota and Buenos Aires metropolitan areas, albeit in the latter case concentrated in CABA, aggressive measures were taken to facilitate active mobility. Bogota implemented 84 km of temporary bike lanes. Buenos Aires built 17 km of new bicycle lanes on two of the city's main avenues, Córdoba and Corrientes.

# **5. The impacts of the pandemic on mobility in Buenos Aires and Bogota: Comparative results**

- _5.1. Initial changes (2020): Decline in public transport use, non-home based, and peak-hour travel_

The initial reduction in total trips was much more significant in AMBA (25 percent) than in Bogotá (15 percent) (Figure 3). The fall was stronger in the central cities of both metropolitan areas, at 18 percent in Bogota D.C. and 33 percent in CABA on weekdays. The decline in travel was somewhat lower on Saturdays compared to weekdays although the trends on Sundays differed between the two metropolitan areas. Total trips in Bogota fell by 7.4 percent on Saturdays and by 2.7 percent on Sundays, while the corresponding reductions in AMBA were 14.0 percent and 24.8 percent.

**_Figure 3:_** _Change in the number of daily trips on weekdays in October 2020 vs. 2019_

<!-- Start of picture text --> a. Bogota b. AMBA<br>45,000,000 45,000,000<br>40,000,000 40,000,000<br>35,000,000 35,000,000 -24.9%<br>30,000,000 30,000,000<br>25,000,000 -15.2% 25,000,000<br>20,000,000 20,000,000<br>15,000,000 15,000,000<br>10,000,000 10,000,000<br>5,000,000 5,000,000<br>0 0<br>2019 2020 2019 2020<br>Source: Authors based on CDR data<br><!-- End of picture text --> The overall fall in the number of trips was driven mostly by falling trip generation rates (TGRs) per person, although population out-movement also played a role. In October 2019, Bogota’s residents made on average 2.5 trips per day on weekdays and 2.2 trips and 1.8 trips on Saturdays and Sundays, respectively. In contrast, by October 2020, the TGRs had fallen by 15 percent on weekdays although only by 7 percent on Saturdays and 1-2 percent on Sundays. In AMBA, the TGRs on weekdays, Saturdays, and Sundays in 2019 were, respectively, 2.8, 2.46, and 2.21. By October 2020, they had declined by about 25 percent on weekdays and Sundays and by 14 percent on Saturdays.

As with total trips, TGRs per person fell disproportionately more in Bogota D.C. and CABA than in the overall metropolitan areas, from an initially higher level. The reduction in weekday TGRs was greater in Bogota’s northern localities – from an initially higher level – and smaller in the western ones. Trip generation on weekdays fell by more than 25 percent in Usaquén, Chapinero, and Teusaquillo, compared to just 2 percent in 9
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Voeten, E. & Merdzanovic, A. (2009), United Nations General Assembly Voting Data, hdl:1902.1/12379 unf:3:hpf6qokddzzvxf9m66yltg= =, Georgetown University.

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14
In the remainder of this section, we argue that Korea’s catch-up strategy involved a combination of interventions to promote export-led growth (even though import substitution efforts were initially pursued) and support for innovation-grade industries. In short, the anti-export bias created by importsubstitution was offset by pro-export measures and the government maintained close monitoring of performance in the international market in prioritizing areas for intervention.

_Export-led_ . Growth in select industries was powered by Korean exports. Between the early 1980s and 2004, the share of output exported increased from 38 to 64 percent in electrical machinery which was Korea’s leading industry, and from 5 to 33 percent in transport, its second largest industry (table 1, columns 8-9). The overall share of exports in GDP increased from 23 to 43 percent between the 1970s and 2006. Export-led catch up could not have been possible without the competitiveness of its leading industries; the trends in the RCA of these industries validate this description of Korea’s growth and catch up strategy (figure 6).

_Trade liberalization_ . After sharpening its technological capabilities, around 1984, Korea liberalized its trade to level the playing field. Its tariffs dropped from about 18-20 percent in 1988 to less than 10 percent in the 1990s and are presently around 3-5 percent (see figure 5). A good example is the electronics industry which was nurtured with the aid of import substitution and domestically financed innovation, but evolved into a world class industry through strong export orientation. By 1993, Korea had already become a global power in the international market for electronics.

<!-- Start of picture text --> Figure 5: Korea: Trade liberalization (reductions in weighted tariffs)<br>South Korea: Reduction in tariffs (weighted, %)<br>18<br>15<br>12<br>9<br>6<br>3<br>0<br>1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br>Textiles Wearing apparel<br>Iron and steel Non-ferrous metals<br>Fabricated metals Non-electrical machinery<br>Electrical machinery Transport equipment<br>Professional &Scientific equip<br>Source: TRAINS database<br>Weighted tariff rates (%)<br><!-- End of picture text --> _Industrial policy_ . The Korean state was committed to creating a strong economy based on domestic ownership, quite reminiscent of ‘big push’ type industrialization. Underpinning its innovation strategy were Korea’s national champions or chaebols. Selective industrial policies targeted high-tech industries centered on chaebols that were protected from global competition and given open access to subsidized finance. The two key factors in this stage were government commitment and state-owned banks that bore the risk of technological innovation (Amsden 1989).

13
|**ZAF**|South Africa|4,8%|0,3%|
|---|---|---|---|
|**ZMB**|Zambia|5,1%|0,0%|
|**XEF**|Rest of EFTA|15,5%|0,8%|
|**XWS**|Rest of Western Asia|4,6%|5,9%|
|**XNF**|Rest of North Africa|36,4%|20,7%|
|**XWF**|Rest of Western Africa|2,8%|0,4%|
|**XCF**|Central Africa|0,0%|0,1%|
|**XEC**|Rest of Eastern Africa|9,7%|8,8%|

# **Table B3: Applied AVEs for Agricultural Goods and Manufacturing Goods for the regions of our model**

||**Agricultural**<br>**goods**|**Manuf**<br>**goods**|**acturing**|
|---|---|---|---|
|**China**|6,1%||5,1%|
|**Kenya**|14,6%||0,3%|
|**Rwanda**|0,0%||4,8%|
|**Tanzania**|22,2%||47,4%|
|**Uganda**|3,9%||0,0%|
|**USA**|14,8%||3,3%|
|**SADC**|4,5%||0,4%|
|**COMESA**|27,5%||20,1%|
|**EU**|27,0%||2,3%|
|**ROW**|17,4%||5,0%|

# **References**

Anderson, James and J. Peter Neary (1996), "A new approach to evaluating trade policy," _Review of Economic Studies_ , 63 (1), 107-125.

92
# **Tables**

Table 1: Definition of Conflict Variables

|Variable|Definition|
|---|---|
|_cohortt_|Dummy equal to one when the individual belongs to the exposed cohort<br>(definition depends on the variable of interest), and zero otherwise. Appears<br>interacted with one of the five variables below:|
|_*casualtiesj_|the total number of conflict-related casualties in district_j_during 1996-2006, per<br>1000 inhabitants (as of the 1991 Census of Nepal, omitted hereafter)|
|_*schooldestrj_|the total number of schools destroyed in district_j_during 1996-2006, per 1000<br>inhabitants|
|_*abductionsj_|the total number of abductions by Maoists in district_j_during 1996-2006, per 1000<br>inhabitants|
|_*control1j_|Dummy equal to one when the individual is in a district controlled by Maoists in<br>2003 according to both the Maoists’ own classification and that used by the<br>government, and zero otherwise. (Definition 1)|
|_*control2j_|Dummy equal to one when the individual is in a district controlled by Maoists in<br>2003 according to the government’s, and zero otherwise. (Definition 2)|
|_casualties_by_surveyjt_|Total number of conflict-related deaths at the district level, per 1000 inhabitants,<br>from the (Nepali) year of birth to the year of the survey in which most data were<br>collected (2001 DHS: 2057; 2006 DHS: 2062; NLSS: 2060)|
|_casualties_age1age2jt_|Number of conflict-related deaths at the district level, per 1000 inhabitants, during<br>the period when the individual was aged between age1 and age2 (in turn, between<br>6 and 10 and between 11 and 14).|

Table 2: Definition of Outcome Variables

|Variable|Definition|
|---|---|
|Primary|Dummy equal to one if the individual has completed primary education and zero<br>otherwise.|
|Years of education|Number of years of education completed by the individual at the time of interview.|
|Enrolled|Dummy equal to one if the individual was enrolled in education at the time of the<br>survey, and zero otherwise.|
|Marriedby“age0”|Dummy equal to one if the individual was married by age0. Age0 varies according to<br>the age of the youngest observation included in the sample (e.g., where the youngest<br>person in the cohorts included in the regression is interviewed at 21 years old, the left-<br>hand-side variable is “marriedby21” to account for the truncated nature of the marriage<br>information).|

37
_HOw_ **_Fuc Mu CAba. 1Ay Oeu_** _t_ **_is_** TABLE 10: aoIO DATA FOR 15 BLgCNz (100 MWoN YUAN, 1930 PRC=) (On*imAsoa)

|branch<br>C. GVIO<br>|1980<br>discrepancy: d<br>|1981<br>erived- publishd<br>|1982<br>1983<br>u X of derived figurn<br> <br>|1984<br>|1935|
|---|---|---|---|---|---|
|1|0.56|0.96|0.84<br>0.86|0.20|1.00|
|2|1.48|2.33|2.30<br>4.04|4.62|1.64|
|3|2.69|3.32|2.92<br>2.99|3.03|1.79|
|4|1.67|1.23|1.23<br>1.53|-0.07|10.70|
|_5_|1.72|1.72|1.81<br>2.70|2.74|2.89|
|6|3.56|3.77|3.84<br>4.06|4.93|5.40|
|7|7.55|7.65|7.70<br>7.45|7.30|8.11|
|8|11.16|9.63|10.40<br>10.74|10.86|11.07|
|9|-370.84|-393.14|-409.15<br>-41S.11|-398.31|-300.67|
|10|-IS.SS|-19.86|-9.93<br>-16.11|-20.49|-27.53|
|11|83.33|83.76|84.60<br>84.67|84.41|78.11|
|12|61.03|61.64|62.09<br>64.59|66.81|63.80|
|13|-232.80|-193.49|_-116.6S_<br>-120.31|-43.85|-38.20|
|14|62.34|84.01|0.42<br>2.66|-52.30|-80.64|
|_1S_|8.62|10.08|9.60<br>11.66|15.76|19.94|
|tota|_-0.15_|0.34|0.53<br>0.87|1.00|1.64|
|A7. Revi|sed calcula*io:|os sad. coal per|Yi yua CIVIO,P80|||
|1|_16.55_|15.81|_1S.17_<br>14.84|14.29|13.40|
|2|10.06|10.19|9.84<br>9.79|9.85|9.36|
|3|16.70|16.30|16.01<br>15.73|1S.58|_15.95_|
|4|8.48|7.70|7.44<br>7.06|6.59|6.72|
|5|15.12|13.89|12.94<br>12.17|11.38|10.11|
|6|3.37|3.24|3.04<br>2.69|2.41|2.00|
|7|23.56|2..4|23.32<br>22.65|20.85|19.62|
|8|4.16|4.13|3.76<br>4.26|3.76|3.85|
|9|2.79|2.59|2.52<br>2.62|2.57|2.51|
|10|2.82|2.61|2.71<br>2.S9|2.38|2.21|
|11|0.42|0.41|0.46<br>0.43|0.41|0.33|
|12|1.75|1.66|1.77<br>1.64|1.52|1.44|
|13|S.20|4.92|7.44<br>7.65|11.50|11.17|
|14|3.05|7.63|1.22<br>1.27|0.76|0.64|
|_15_|8.48|8.16|7.82<br>7.53|7.29|7.32|
|Toal|7.83|7,26|7.07<br>6.76|6.32|5.78|
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