rafmacalaba/datause-encoder-data huggingface dataset:
The main principle is to separate the motivation for a subsidy (transport affordability) with the mechanism used to distribute the benefit. To fulfill the objective of a subsidy policy, the most effective and progressive instrument should be used, even if this operates outside of the transport sector.

- _5) More cooperation between transport specialist with social and welfare professionals is called for_

In order to improve transport affordability measures, more collaboration is needed between transport specialists and social and poverty specialists in each country. This will enable sectoral policy makers to evaluate the range of opportunities available to implement pro-poor affordability measures, avoid untargeted subsidies and integrate transport social concerns into wider poverty alleviation efforts.

It may well be that the urban/rural income divide is so wide in a country that any transport policy benefiting the urban poor will be regressive. In this case, the whole issue of public transport affordability may be called into question. The general message is that social issues in transport policy should not be analyzed in isolation.

- _6) Data-based policy decisions are required_

As an end note, the lack of quantitative data on the sources, distribution and impact of subsidies is alarming, in particular in developing countries. The only way to improve policy design is to use robust estimation techniques to simulate the impact of the available set of policy options. This requires a conscious effort to generate the necessary data (in particular, frequent travel surveys with socioeconomic variables) and a firm commitment to use the data when designing particular policies and openly debate the results of each of the policies being analyzed.

43
Figure 5: All-India Total Factor Productivity: Panel Decomposition with Baseline Exit Assumptions <!-- Start of picture text --> All−India Total Factor Productivity: Dynamic Decomposition<br>Randomly Selecting Exiters from Pool of Potential Exiters<br>Year<br>Aggregate Average Survivors<br>Reallocation Survivors Entrants<br>Exiters<br>19851986198719881989199019911992199319941995199619971998199920002001200220032004<br>.2<br>.15<br>.1<br>TFP<br>.05<br>0<br>−.05<br><!-- End of picture text --> Total factor productivity (TFP) decompositions for the panel of firms, conducted at the all-India level, using the MP method. “Aggregate” indicates market-share weighted mean productivity, “Average Survivors” and “Reallocation Survivors” indicate unweighted mean productivity and the covariance between market share and productivity for surviving firms, respectively, and “Entrants” and “Exiters” indicate the contributions of entering and exiting firms. Exit is assigned randomly among the pool of potential exiters.

40
11 metropolitan areas, namely Beijing, Chongqing, Dalian, Guangzhou, Harbin, Ningbo, Qingdao, Shanghai, Wuhan, Xiamen, and Xi’an. The price survey was implemented by China’s National Bureau of Statistics (NBS). Using the data from these 11 cities, the ICP has estimated national average prices and the PPP rate. To properly assess the implications of China’s new PPP we need to look carefully for possible sample bias associated with the fact that the ICP price survey only included these 11 cities.

# **Sources of bias in the 2005 PPP for China**

Our discussions with NBS staff responsible for implementing the ICP price survey revealed that the choice of these 11 cities was influenced by expectations about the likely availability of the types of goods referred to in the ICP survey, notably the more ‘international’ goods, not readily available throughout China. One would not expect to find that all the commodities identified in the ICP price survey schedule are readily available in most rural areas of China, or even in many urban areas.

Here we try to assess what bias this might entail in the PPP for China. The estimation of “national prices” from the data for 11 cities by the ICP did attempt to re-weight the data to make them nationally representative. Each of the 11 cities was assigned to one of four regional clusters (Capitals, Coastal, Northern, Inner China) and weights were then applied based on urban and rural expenditures shares across eight commodity groups, derived from NBS household surveys.<sup>7</sup> The issue at hand is whether there is sufficient common support between the data for 11 cities and the national distribution to believe that the bias could be eliminated by such a reweighting the data.<sup>8</sup> The data for the 11 cities included surrounding “rural” areas, but only about one fifth (22%) of the 1700 outlets from which prices were obtained were in these non-urban areas (World Bank, 2008). We discussed the survey design with the senior statistician of NBS managing the unit implementing the ICP for China and other staff of NBS in Beijing. We were assured that the “rural” coverage was little more than the suburban areas at the urban fringe, and could not be

> 7 The method is described in World Bank (2007, p.68) and in greater detail in Dikhanov (2005).

> 8 Re-weighting the data can only be used to correct for sampling bias as long as the sample spans the range of values found in the population; in other words, there must be adequate common support to believe that re-weighting is feasible.

4
# 3.1 The Basdine Scenario

The analysis presented by Cline (1992) is certainly the most thorough study of climate change and, therefore, it will be used as a reference for our baseline scenario in the absence of uncertainty. However, it is worth underscoring that two aspects from the original model have been modified: First, the expected increase in temperature from global warning will be explicitly linked to the CO2 emission rather than to be determined by a linear approximation between the long-term global warming and the current level of tempeature. Second, the ratio of unavoidable damages to total damages in the absence of intervention will not be fixed during the entire period, but it will vary over time in response to the variations in the stock of CO 2 .

## Expected Costs

In the recent literature, the cost of abaten"'nt polices are generally detennined by: (i) afforestation or diminishing deforestation; (ii) energy substitution -non-fossil fuel for fossil fuel energies; (ffl) non-energy inputs substitution --capital and labor for energy; (iv) change in product mix; (v) adaptive measures such as population migrations; (vi) 'climatic engineering' such as ocean fertilizaon.

Accordingly, Cline considers that the costs of abatement policy basically arise from the reduction in fossil fuel emissions due to output reduction (Q), the need tO set aside land for affoation (FA), and the need to curtail frontier agricultural land use and thereby carbon release from deforestation (FD). These costs are expanded by the proportion (w) to take into account likely costs associated with curtailing all greenhouses gases in a way commensurate with reducing carbon dioxide emissions. This cost is further increased by considering the portion of the cost that would have 8
# **Table 10. Effects of Standards on Firm Export Exit: Role of Firm Size and Network Effects**

|||Dependen|t Variable:||
|---|---|---|---|---|
||Export Exit<br>Dummyc,i,k,d,t<br>(1)|<br>Export Exit<br>Dummyc,i,k,d,t<br>(2)|Export Exit<br>Dummyc,i,k,d,t<br>(3)|<br>Export Exit<br>Dummyc,i,k,d,t<br>(4)|
|Rel_restrictivenessc,k,d,t|0.413<br>[0.128]***|0.450<br>[0.131]***|0.470<br>[0.137]***|0.505<br>[0.140]***|
|Log Agro_exportsc,i,t‐1|‐0.093<br>[0.003]***|‐0.095<br>[0.003]***|‐0.095<br>[0.003]***|‐0.096<br>[0.003]***|
|Rel_restrictivenessc,k,d,tX Log Agro_exportsc,i,t‐1|‐0.033<br>[0.009]***|‐0.033<br>[0.009]***|‐0.036<br>[0.009]***|‐0.036<br>[0.009]***|
|Product_sharec,i,k,t‐1||‐0.084<br>[0.037]**||‐0.076<br>[0.037]**|
|Rel_restrictivenessc,k,d,tX Product_sharec,i,k,t‐1||‐0.070<br>[0.046]||‐0.069<br>[0.046]|
|Destination_sharec,i,d,t‐1|||‐0.084<br>[0.017]***|‐0.076<br>[0.017]***|
|Rel_restrictivenessc,k,d,tX Destination_sharec,i,d,t‐1|||‐0.032|‐0.031|
||||[0.046]|[0.046]|
|Log 1+Tariffc,k,d,t|0.008|0.008|0.007|0.007|
||[0.010]|[0.010]|[0.010]|[0.010]|
|Log GDP per capitad,t|‐0.603|‐0.600|‐0.593|‐0.592|
||[0.112]***|[0.114]***|[0.112]***|[0.113]***|
|Log Total Exportsc,k,t|‐0.073<br>[0.006]***|‐0.072<br>[0.006]***|‐0.073<br>[0.006]***|‐0.073<br>[0.006]***|
|Exporting Firm‐Importing Country‐Product Fixed<br>Effects|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes<br>Linear|Yes<br>Linear|Yes<br>Linear|Yes<br>Linear|
|Estimation method|Probability<br>Model|Probability<br>Model|Probability<br>Model|Probability<br>Model|
|Observations|240,074|240,074|240,074|240,074|
|R‐squared|0.709|0.709|0.709|0.709|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. The dependent variable is defined as follows: exit equals one if firm _i_ does not export product _k_ to destination _d_ in year _t_ but did so in year _t-1_ and zero if the firm exported product _k_ to destination _d_ in year _t-1_ and continues to do so in year _t_ .

38
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33
<!-- Start of picture text --> Prob. of being inactive<br>rural bows tural girls<br>=|-I<br>00 .<br>oo” . oo” “7 -<br>Bt Bt SS<br>be) ca =e wT.<br>a ee a<br>bi] log per capita6 expenditure7 dasa walue] bi] log per capita6 expenditure7 asiva value Ht]<br>bandwidth= .3 bandwidth= .3<br>urban boys urban girls<br>=|-!<br>00<br>ae ay 2<br>gtd et ms<br>m4 m<br>bi log per6 capita expenditure7 8 agg value 10 bi log per6 capita 7expenditure8 aga value 10<br>bandwidh= & bandwidh= &<br><!-- End of picture text -->
<!-- Start of picture text --> 1 2<br>( ) ( ) (3)<br>trade trade trade<br>RTA 0.02 ∗∗∗ 0. 0.10 ∗∗∗<br>27 ∗∗∗<br>(4.78) (32.39) (12.70)<br>Domestic trade NO YES YES<br>Border controls NO NO YES<br>N 28 6 660 660<br>9 74 933 933<br><!-- End of picture text -->

**Note:** the variable RTA measures the world average effect of trade agreements. _t_ statistics in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

Table 1: Comparison to the literature in South Asia in 23% = exp(0.10 + 011) _−_ 1, a 12% more than the world average effect of RTAs. This additional effect includes both intra-regional RTAs and RTAs with the rest of the world. For this reason, column 3 adds a specific term for intra-regional RTAs. It shows an additional effect of of RTA within the region is 22% = exp(0.20) _−_ 1 for a total effect of 49% = exp(0.10 + 0.10 + 0.20) _−_ 1, while RTAs of South Asia countries with the rest of the world see a total effect of 22% = exp(0.10 + 0.10) _−_ 1.

This is a very encouraging result for South Asia, showing strong trade creation effects for trade agreements in the region, with a stronger effect within the region itself. An interesting step would be to see how these results change if we control for large trade agreements like the European Union (EU) common market and RTAs signed between the EU and other countries. The reason for this control is that the EU is well-known for putting in place a deep trade agreement among its many members and this could affect the world average effect of RTAs and provide more insights. Table (3) introduces these controls. As expected, the world average effect is reduced to 8%, with the EU common market having an additional effect of 10%. The results for South Asia do not change much (the RTA with the rest of the world strengthens slightly with the reduction in the world average), but these results show that RTAs, both within the region and with the rest of the world, have had a stronger effect than the average of the EU trade agreements.<sup>3</sup>

> 3While this is a positive result for South Asia, it is later discussed in the conclusions section and put into the context of a lower starting level of trade flows.

8
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19
The trade intensity indices of NAFTA<sup>and the EU moved in the opposite</sup> direction. That of NAFTA rose between<sup>1980 and1996 but droppedoff by1998and,</sup> thereafter, remained virtually unchanged.<sup>The EU's indexfluctuated a bit but generally</sup> rose steadily between 1980 and 2000.<sup>5</sup> There are few clear regional trends<sup>that emergein the foregoingdiscussion</sup> although it does reinforce the argument<sup>thatEast Asia'stradecontinues to be openand</sup> global but with a strong intraregional<sup>component. While the resultsbased on two</sup> different measures - trade share and<sup>trade intensity-are not always consistent,some</sup> general observations can be made. Despite<sup>some decline, the U.S. remains a major</sup> trading partner of most East Asian countries,<sup>including Japan.There isstill a significant</sup> share (although declining) of East Asia's<sup>tradethat isconducted with the EU,although</sup> the trade intensity index reveals no<sup>bias.36It does not appear from the analysisthat East</sup> Asia's trade is necessarily becoming<sup>moreintraregional,infact, thereissome indication</sup> of the opposite based on trade intensity.<sup>However,within the region itself there are</sup> shifts; for example, ASEAN's trade<sup>with Japanis decliningwhilethat with Chinaand</sup> South Korea is rising. Japan, on the<sup>other hand, is tradingslightly less with extraregional</sup> partners and more intraregionally. China<sup>is exporting much moreto the U.S. and slightly</sup> more to the EU but importing more<sup>from within the region.</sup> Trade within the region will almost<sup>assuredly continue to change significantly</sup> over the next decade and developing<sup>trendswill beaffectedin no small way by further</sup> progress in China's reforms, by developments<sup>in Japan'seconomy,as well as in the</sup> global economy, and by the direction<sup>andextent of regionalintegrationefforts within</sup> East Asia. The region has the power<sup>tosteer these changesin a direction that will be</sup> advantageous to its growth and development<sup>but this will take someconcerted effort and</sup> a well-developed cooperative strategy.<sup>Somerelevantsuggestionsin this area will be</sup> made later in this paper.

# **Patterns of FDI Flows**

Although there is some variability country<sup>to country,overall,FDI plays a critical</sup> role in economic expansion for East<sup>Asian economies,as evidenced by the FDI shareof</sup> gross fixed capital formation shown<sup>in Table 4.7.</sup> 17
|General Charateristics|LACFirms<br>(%p|SSAFirms (%|
|---|---|---|
|Size -Micro (0-5employees)|48|Bl|
|Age- I0-veers arless|||
|Level ofeducation oflargestowner -No£ducetion|5|8|
|Largest ownerhas ajob inaformal business<br>|4<br>|12<br>|
|UseofFinance|r|r|
|<br>Use of accounts|b|30|
|Use ofloans|14|3|
|Working capitalfinance - Internal funds, femilyand moneylenders|af|75|
|Investmentfinance - /nternalfunds, familyand money lenders<br>|AB<br>|Da<br>|
|BusinessRegistration|||
|Realstered at startup|4|3|
|Like to register||59|
|Mainreasonfornotregistering - Taxes||26|
|Mostiraportantbenefitfromregistering-Accesstafinance||52|
resulting growth in agricultural productivity have increased demand for services, machinery repairs, small tractors and agricultural pumps in rural areas and brought about growth in non-farm employment opportunities (Winters 2002, World Bank 2004). The evidence presented here appears to corroborate this story in Bangladesh.

**<u>Table 6. Decomposition of growth in total expenditures, 1995-96 to 2000.</u>**

||Agricultural<br>wages|Non-<br>agricultural<br>wages|Remittances|Other<br>sources|Agricultural<br>production<br>(gross)|_Of_<br>_which_<br>_Rice_|Total<br>growth,<br>percent|
|---|---|---|---|---|---|---|---|
|**Urban**||||||||
|Poorest 30%|0.5|3.2|0.1|2.2|0.2|_0.1_|6.3|
|Middle 40%|0.0|0.7|0.1|0.5|0.1|_0.0_|1.3|
|Richest 30%|0.0|-0.9|-0.1|-1.1|-0.1|_0.0_|-2.2|
|Total|-0.1|-2.6|-0.3|-2.5|-0.2|_-0.1_|-5.7|
|**Rural**||||||||
|Poorest 30%|2.6|1.9|0.3|2.7|1.7|_1.0_|9.1|
|Middle 40%|0.1|0.2|0.0|0.3|0.2|_0.1_|0.8|
|Richest 30%|0.1|0.7|0.6|1.5|0.9|_0.6_|3.8|
|Total|0.7|0.8|0.3|1.3|0.9|_0.5_|4.0|
|**Rural net bu**|**yers**|||||||
|Poorest 30%|3.3|2.6|0.3|3.1|0.7|_0.3_|10.1|
|Middle 40%|0.3|0.4|0.1|0.5|0.1|_0.0_|1.4|
|Richest 30%|0.6|2.7|2.0|4.6|0.8|_0.3_|10.6|
|Total|1.8|2.3|0.7|3.1|0.7|_0.3_|8.7|
|**Rural net sel**|**lers**|||||||
|Poorest 30%|0.5|0.2|0.1|0.7|1.6|_1.1_|3.2|
|Middle 40%|-0.4|-0.5|-0.2|-1.3|-2.7|_-1.8_|-5.1|
|Richest 30%|-0.1|-0.6|-0.5|-1.5|-2.5|_-1.7_|-5.2|
|Total|-0.6|-0.7|-0.4|-2.0|-3.9|_-2.7_|-7.7|
|**Non-farmers**|**(rural)**|||||||
|Poorest 30%|4.8|3.6|0.5|4.4|1.0|_0.5_|14.3|
|Middle 40%|0.7|1.0|0.2|1.3|0.3|_0.1_|3.6|
|Richest 30%|-0.2|-0.8|-0.6|-1.4|-0.1|_-0.1_|-3.2|
|Total|0.5|0.6|0.2|0.8|0.2|_0.1_|2.2|
|**All farmers (**|**rural)**|||||||
|Poorest 30%|1.4|0.9|0.3|2.0|3.2|_2.0_|7.8|
|Middle 40%|0.3|0.5|0.2|1.1|1.6|_1.0_|3.7|
|Richest 30%|0.2|1.2|1.0|2.8|3.2|_2.0_|8.4|
|Total|0.9|1.3|0.8|3.2|4.3|_2.7_|10.5|

Note: the change in total expenditures in the two surveys was decomposed into growth by source of income, using a change in total expenditures and applying the income structure in 2000 to this change in expenditures. Source: Calculated from 1995-96 and 2000 HIES.

20