rafmacalaba/datause-encoder-data huggingface dataset:
7 ( _BHv_ 2) _xy ∈/ EM_ = 0 _._ 01 _∗_ ( _BHv_ 1) _xy ∈/ EM ⇒_ ( _BHv_ 2) _xy ∈/ EM >_ 100 _m_ (3) Next, for BH pixels showing a height between 50-100 m without an EM point in their vicinity (meaning here, EM is still supposed to be reasonably complete), we assume that BH is overestimated because otherwise EM should indicate the presence of a high-rise. In this case, the BHv1 value is reduced by 90% of the difference between the current BHv2 value and a fixed virtual height of 50 m (see eq. (4)). The 50 m height is used because it represents the empirically determined completeness threshold for tall buildings in EM (see Appx. Section B): ( _BHv_ 2) _xy ∈/ EM_ = ( _BHv_ 1) _xy ∈/ EM_ +0 _._ 01(50 _−BHv_ 1) _xy ∈/ EM ⇒_ 50 _m <_ ( _BHv_ 2) _xy ∈/ EM >_ 100 _m_ (4) In order to generate the new building volume product BVv2 (m<sup>3</sup> ), the BHv2 building heights are merged with the building areas given by BAv1. See eq. (5).

The improvements in BH achieved by integrating EM are documented by a comprehensive global validation campaign (see Appx. Section A for details). The WSF3Dv2 data can be accessed free and open at: https://geoservice.dlr.de/web/maps/eoc:wsf3d. Likewise, the replication files for the analysis and the country- and city-level data sets can be found here: https://doi.org/10.7910/DVN/TQ1AS5.

In Figure 1 below, this improvement can be observed in Manhattan by comparing the BH (building height in m) layer of the original WSF3Dv1 (top-right panel B) and the EM-improved WSF3Dv2 (bottom-left panel C). The top-left panel A shows the BA (building area in m<sup>2</sup> ) layer, whereas the bottom-right panel D shows the BV (building volume in m<sup>3</sup> ) layer. Redder pixels have more buildings (A), taller buildings (B-C), or more voluminous buildings (D).<sup>3</sup> Figure 2 illustrates the distribution of WSF3Dv2 heights for selected areas. Redder bars indicate a higher frequency of taller buildings. Asia has more tall buildings than Africa and Latin America, and Europe has more tall buildings than North America. China has taller buildings than the U.S. India has almost as many tall buildings as the United Kingdom. Seoul appears as tall as New York. Bogota, Cairo, Karachi, Paris, and Lagos are relatively flat.

3Appendix Figures C.1 and C.2 show the maps for some of the world’s largest cities and at global scale, respectively.
# **Table 2. EMDEs with statistically significantly above-average or below-average employment ratios (continued)**

||Significa|ntly below- <br>|average|Signific<br>|antly above<br>|-average|
|---|---|---|---|---|---|---|
|||Percent<br>||Number<br>|Percent<br>||
||Number of<br>countries|of<br>countries|Average<br>deviation|of<br>countries|of<br>countries|Average<br>deviation|
|**Aggregate EWAP:**|||||||
|East Asia and Pacific|0|0|...|4|40|22|
|Europe and Central Asia|9|47|-17|1|5|17|
|Latin America and the Caribbean|0|0|...|2|11|17|
|Middle East and North Africa|9|69|-21|3|23|28|
|South Asia|1|20|-15|0|0|...|
|Sub-Saharan Africa|9|24|-18|8|22|22|
|Commodity exporters|15|22|-18|15|22|22|
|Commodity importers|13|37|-19|3|9|22|
|**Non-agricultural EWAP:**|||||||
|East Asia and Pacific|1|10|-10|4|40|18|
|Europe and Central Asia|5|26|-17|6|32|22|
|Latin America and the Caribbean|1|5|-13|10|53|17|
|Middle East and North Africa|4|31|-15|4|31|43|
|South Asia|3|60|-14|0|0|...|
|Sub-Saharan Africa|22|59|-18|2|5|14|
|Commodity exporters|26|38|-18|15|22|25|
|Commodity importers|10|29|-16|11|31|25|
|**Agricultural EWAP:**|||||||
|East Asia and Pacific|1|10|-13|2|20|20|
|Europe and Central Asia|12|63|-16|1|5|10|
|Latin America and the Caribbean|11|58|-13|0|0|...|
|Middle East and North Africa|12|92|-15|0|0|...|
|South Asia|0|0|...|1|20|24|
|Sub-Saharan Africa|5|14|-10|16|43|30|
|Commodity exporters|21|31|-14|19|28|28|
|Commodity importers|20|57|-14|1|3|28|

23
restrictions as to where foreign insurance companies can do business. Foreign insurance companies are restricted to joint ventures with firms in nonlife insurance (51% foreign ownership) and life insurance (50% foreign ownership) in the PRC.<sup>8</sup> As a whole, the PRC’s openness to FDI and foreign participation in service sectors has been rated by McKinsey & Co. and the International Monetary Fund as stronger than that in Japan or the Republic of Korea.

# **IV.  Conclusions**

Over decades of rapid growth, the PRC has become a microcosm of East Asia. At one end of the spectrum are lagging interior provinces (e.g., Guizhou and Shanxi) resembling the low-income countries such as Cambodia and Lao People’s Democratic Republic; at the other end are leading coastal provinces (e.g., Guangdong and Shanghai) aspiring to be high-income countries such as Japan and the Republic of Korea.<sup>9</sup> From the angle of total GDP, the province of Yunnan is similar to Viet Nam, Sichuan to Malaysia, Guangdong to Indonesia, Shanghai to Finland, and Jiangsu to Switzerland (see Figure 5). What connect all these regions are increasingly efficient infrastructure and shared market institutions. Infrastructure can help reduce economic distances within the PRC, but it will never equalize transport costs between coastal cities and the world market, or between cities in the interior and the world market. It takes more than 5,000 yuan per kilometer to truck a 20foot container to the relevant seaport from Taiyuan; less than 400 yuan from the port of Shanghai. It takes more than 15,000 yuan per kilometer to ship the same container from Chengdu; while it can take more than 20,000 yuan from cities in the western regions (Gill and Kharas 2007, World Bank 2010). There is not much one can do to change this feature of economic geography.

Over time, the growing concentration of economic activities in the coastal and central areas would raise labor and land costs in coastal areas so much that it may be more profitable to relocate to the central and western regions. Some of this may already be happening, but in the meantime, there is a growing concentration of production in the coastal areas, which can be potentially beneficial for the PRC’s economic growth. One has to be patient about spreading out economic production. Economic growth is inherently unbalanced (spatially), and to try to spread out economic activity is to discourage growth. But development can still be inclusive, in that even people who start their lives far from economic opportunities can benefit from the growing concentration of wealth in a few

> 8The PRC has not opened its securities and fund management industries to foreign competition. For example, joint venture securities firms cannot trade in “A” shares on the PRC’s exchanges.

> 9The concluding section draws from the World Bank (2010, 146–49).

19
medium-term effect. Column 1 shows that despite the two treatments having significant impacts on the likelihood of self-employment in the short-term, the treatments have no significant effect on total earnings. In part this reflects the lower likelihood of wage employment. The point estimates are positive, but are equivalent to only about 7 percent of the control group’s income. In contrast, the impacts are larger in the medium-term, with increases in work income of 1494 Rs for the training only group (significant at the 10 percent level), and 697 Rs (not significant) for the training plus cash group. We also can not reject that the possibility that the two treatments have effects which are equal in magnitude. The medium-run point estimates reflect increases in income of 14 to 30 percent relative to the control group mean. The estimates suggest that the training-only treatment costs of approximately 18,600 Rs per person would be repaid after 12 months of the medium-term treatment effect, but the training plus cash cost would take 48 months of the medium-term treatment effect to be repaid.

The remainder of panel A looks at the impact of the treatments on business profits, sales, capital stock, and business practices. These results are all conditional on running a business. We asked the potential business owners about business practices only in rounds 4 and 5. We can never reject equality of the two treatment effects, but the point estimates on profits and sales are higher for the training only treatments, the impact on business practices is similar in magnitude for the two treatments, and the point estimate of the effect on capital stock is larger for the cash plus training treatment. The results for the training only intervention show significant impacts on profits and sales in the rounds 4 and 5 surveys, with the magnitudes equivalent to a 43 percent increase in profits and a 40 percent increase in sales, both relative to the control mean.

There is no difference in the rates of business ownership by rounds 4 and 5 of the survey, but our analysis has shown that both treatments led to women with lower Raven scores starting businesses and the cash plus training treatment led to women with lower wealth starting businesses. We would expect this selection to bias the OLS estimates downwards, since lower ability and poorer individuals might be expected to earn lower profits. In this sense, the estimates in panel A might be considered lower bounds for the treatment effect.

How sensitive our results are to this selection? To provide some indication, we use the sample of business owners to estimate a multinomial logit for the probability of being in each of our three treatment groups. We use the set of variables that were used in Table 2 to predict training take-up, including wealth, ability, risk attitudes, interest in attending training, interest in 23
barley and sorghum. Lime is promoted only in regions where soils suffer from acidity, which applies to Amhara and Oromia, but not to Tigray. The project encourages the joint application of these individual components as a key feature of ISFM.

# Treatment description

The core elements of the extension intervention are the following: In each treatment mws, three so-called ‘farmer research and extension groups’ (FREG) were formed, each consisting of 16 or 17 members, leading to a total of around 50 FREG members per mws. FREG farmers were selected in a non-random manner by extension agents and village heads, based on farmers’ interest and social involvement. The FREGs conduct regular meetings, typically once or twice per month, to discuss agricultural topics. Each group is led by three of its members, called ‘model farmers’, whose central activity is the establishment and maintenance of demonstration plots. For this purpose, model farmers receive trainings on ISFM from public extension agents and are provided with all necessary inputs. Demonstration plots are on-farm trials on which the package of ISFM practices is applied, next to plots that are managed according to traditional farming practices. Hence, the benefits of ISFM in comparison to traditional practices, such as yield improvements, become clearly visible to farmers (cp. Supplementary Online Appendix S1, available with this article at _The World Bank Economic Review_ website). In each mws, ‘farmer field days’ are conducted twice per harvest cycle; at critical stages around mid-season and just before harvest. During these field days, model farmers share and discuss their experience with FREG members; extension agents are present to complement information. Field day activities are mainly targeted at FREG members, although in some communities, other farmers do also participate. Overall, the extension treatment aims at creating awareness and know-how about ISFM through a knowledge sharing process from extension agents to model farmers, and from model farmers to other FREG members. FREG members are then encouraged to share the information with the broader population of farmers in the communities. Hence, this model 6
|US States|48|41|30|3|yes|
|---|---|---|---|---|---|
|Canada -<br>Provinces|8|3|2|4|yes|
|Germany|Yes|||||
|New Zealand|Yes|||||
|Sweden|||yes|||
|Switzerland|yes|yes|yes|yes||
|Brazil, 2000-|Yes|Yes|Yes||Yes<br>including<br>prison terms|
|Argentina, 2004-|Yes|Yes|yes|||
|Argentina -<br>provinces|17|17|17|||
|India, 2003-|yes|Yes||||
|India-States|yes|yes||||
|Sources: Various i|ncluding Fi|nance Canada|(2005).|||

Mature federations vary a great deal in terms of fiscal policy coordinating mechanisms. In the USA, there is no overall federal-state coordination of fiscal policy and there are no constitutional restraints on state borrowing but states’ own constitutional provisions prohibit operating deficits. Intergovernmental coordination often comes through establishment of fiscal rules established through acts of Congress such as the Gramm-Rudman Act. Fiscal discipline primarily arises from three distinct incentives offered by the political and market cultures. First, the electorates are conservative and elect candidates with a commitment to keep public spending in check. Second, pursuit of fiscal policies that are perceived as imprudent lower property values thereby lowering public revenues. Third, capital markets discipline governments that live beyond their means (see Inman and Rubinfeld, 1992).

In Canada, there are elaborate mechanisms for federal-provincial fiscal coordination. These take the form of intergovernmental conferences (periodic first ministers’ and finance ministers/treasurers’ conferences) and the Council of the Federation (an interprovincial consultative body). The majority of direct program expenditures in Canada are at the sub-national level but Ottawa (i.e. the Canadian federal government) retains flexibility and achieves fiscal harmonization through conditional transfers and tax collection agreements. In addition, Ottawa has established a well- knit system of institutional arrangements for intergovernmental consultation and coordination. But much of the discipline on public sector borrowing comes from the private banking sector monitoring deficits and debt at all levels of government. Overall financial markets and electorates impose a strong fiscal discipline at the sub-national level.

In Switzerland, societal conservatism, fiscal rules and intergovernmental relations play an important part in fiscal coordination. Borrowing by cantons and communes is restricted to capital 16
**Table 1.  Descriptive Statistics**

||**201**<br>**_N = 1_**|**3**<br>**_907_**|**201**<br>**_N =_**|**5**<br>**_299_**|**201**<br>**_N = 2_**|**6**<br>**_507_**|
|---|---|---|---|---|---|---|
||_Mean_|_SE_|_Mean_|_SE_|_Mean_|_SE_|
|**Consumption Variables**|||||||
|Non-Food Expenditure|49820|1272|46238|1762|80297|1561|
|Value of Food Consumed|81538|1320|85993|3163|116858|1641|
|Food Purchases|60291|1635|61539|2851|100956|2026|
|Calorie Consumption|17959|196|16963|558|15677|149|
|Food Consumption Score|58.652|0.471|44.331|1.093|52.172|0.448|
|School Participation Rate|0.799|0.009|0.864|0.018|0.790|0.008|
|**Weather Shocks†**|||||||
|Weather Shock|0|0|0.288|0.026|0.400|0.010|
|Current Shock|0|0|0.288|0.026|0.310|0.009|
|Sequential Shock|0|0|0|0|0.090|0.006|
|**WFP Distribution**|||||||
|WFP HH Distribution Rate|0.394|0.017|1.028|0.050|1.651|0.046|
|WFP * Weather Shock|0|0|0.344|0.042|0.755|0.032|
|WFP * Current Shock|0|0|0.344|0.042|0.501|0.026|
|WFP * Sequential Shock|0|0|0|0|0.253|0.021|
|**HH Receipt of Aid**|||||||
|Aid|0.126|0.008|0.207|0.023|0.156|0.007|
|Aid * Weather Shock|0|0|0.077|0.015|0.066|0.005|
|Aid * Current Shock|0|0|0.077|0.015|0.039|0.004|
|Aid * Sequential Shock|0|0|0|0|0.027|0.003|
|**Explicit HH Targeting Characteristics**|||||||
|Dummy, Female Head|0.232|0.010|0.318|0.027|0.251|0.009|
|Dummy, Children<3|0.424|0.011|0.368|0.028|0.352|0.010|
|Dummy, All Members>59|0.016|0.003|0.037|0.011|0.022|0.003|
|Dummy, HH Member Disabled|0.261|0.010|0.211|0.024|0.257|0.009|
|**Wealth**|||||||
|Wealth Index|0.254|0.005|0.212|0.009|0.276|0.004|
|# Mobile Phones<br>**Notes:**The table is constructed across all h|0.902<br>ousehold observat|0.027<br>ions used in|0.746<br>the analysis.|0.050<br>†**Weather**|1.081<br>**Shock**is a di|0.025<br>chotomous|

**Notes:** The table is constructed across all household observations used in the analysis. † **Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

20
warming. Knowing what causes climate change, and what does not, is the most powerful predictor of both stated intentions to take voluntary actions and to vote on hypothetical referenda to enact new government policies to reduce greenhouse gas emissions. Identifying bogus causes (e.g., insecticides) correlates with the belief that the globe will warm, but is only weakly related to voluntary actions and not at all related to support for government policies. General proenvironmental beliefs and perceptions that global warming poses serious threats to society also help to explain behavioral intentions. The explanatory power of an air pollution framework is substantial in bivariate analyses, but has little explanatory power in multivariate analyses that include knowledge, risk perceptions, and general environmental beliefs. Translating public concern for global warming into effective action requires real knowledge. General environmental concern or concern for the negative effects of air pollution appear not to motivate people to support programs designed to control global warming.

Brechin, Steven 2003. “Comparative Public Opinion and Knowledge on Global Climatic Change and the Kyoto Protocol: The U.S. versus the World?’ International Journal of Sociology <u>and Social Policy</u> 23(10): 106-134.

Brechin compiles public opinion surveys on public understanding of climate change from 15 nations between 1991 and 2001. He finds that even as scientific consensus on climate science increases, knowledge regarding causes of climate change by the public is minimal. There is some evidence for increased public understanding regarding the causes of climate change over the decade from 1991 to 2001, however citizens in all nations studied remain largely uniformed. In the 2001 survey, citizens of Mexico knew the most about the causes of climate change, but even here only one quarter of respondents correctly identifying burning fossil fuels as the primary cause of global warming. Americans, who were tied with the citizens of Brazil at 15%, were in the middle of the pack. The article includes survey data collected shortly after President Bush's withdrawal from the Kyoto Protocol in 1991 on attitudes regarding this event. The U.S. public is much more supportive of Bush’s withdrawl than were the citizens of European nations (where there was considerable outrage expressed).

Brechin, Steven R 2008. Ostriches and Change: A Response to 'Global Warming and Sociology' <u>Current Sociology</u> 56(3): 467-474.

The threat of climate change should be more than sufficient to mobilize the world to respond meaningfully and quickly. However, Brechin considers reasons why this has not been the case, including the fact that response must be made to conditions not yet fully realized. Brechin claims that most people in the rich and powerful Northern countries do not really feel like they are living on the edge. Environmental concerns such as climate change are seen as “no more than background noise.” 49
Shoes. Table 2 also shows the average distance branch plants locate from headquarters (conditional on having a headquarters). These numbers are very small, providing further evidence that the pull of the parent is significant.

In addition to the role of firm family structure, we also explore the role played by a variety of other urban agglomeration attributes (e.g., indicators of economic development, infrastructure, education, and inter/intra-industry spillovers).<sup>4</sup> Table 3 summarizes these attributes by region, and Table 4 demonstrates that many of these variables are highly correlated with one another. This will make it difficult to infer their separate causal roles.

# **4. An Estimation Strategy to Examine the Impact of Regional Subsidies**

Our estimation strategy is designed to answer the following questions: (i) were the CF’s effective in inducing firms to locate new establishments in Brazil’s lagging regions, (ii) what role does the spatial distribution of a multi-unit firm play in the location decision, and (iii) what local attributes are important factors in firms’ location decisions. In answering these questions, we face a number of challenges. First among these is separating the role of idiosyncratic firm characteristics from those of local attributes and CF allocations. In particular, in deciding where to place a new branch plant, a multi-unit firm may take into account proximity to the firm’s headquarters. The location of each firm’s headquarters will be unique, and it is unclear _a priori_ in what direction ignoring this factor would bias our conclusions.<sup>5</sup>

> 4 For further description of the variables used, refer to Da Mata, Deichmann, Henderson, Lall and Wang (2005a and 2005b).

> 5 Given that most firms’ headquarters were located in the Southeast, our expectation is that failing to account for the pull of parent firms would lead us to overstate the quality of the Southeast region for entry by branch plants.  Because this region did not receive any CF support, this would likely have the effect of biasing downward the estimated impact of the CF’s.

12