rafmacalaba/datause-encoder-data huggingface dataset:
However, the picture differs with respect to innovative capacity, as the _Annual Report of Regional Innovation Capability of China_ (2001 and 2002) shows. Beijing lagged behind Shanghai in 2001, but generally surpassed Shanghai one year later (see Table 6). According to this report, Beijing has enhanced the innovation capability of enterprises as measured by the outputs of new products and technical innovations. This may be the result of success in attracting enterprises and research institutes of both domestic and multinational corporations to Beijing. Shanghai remained stable in most subentries.

**Table 6: Ranks of Beijing and Shanghai in** **_Annual Report of Regional Innovation Capability of China_**<sup>**_5_**</sup>

||Knowledge<br>creation|Knowledge<br>diffusion|Innovation<br>capability of<br>enterprises|Innovation<br>environment|Economic<br>performance<br>of<br>innovation|General|
|---|---|---|---|---|---|---|
||||2001||||
|Beijing|1|4|4|1|2|2|
|Shanghai|2|1|2|2|1|1|
||||2002||||
|Beijing|1|6|2|1|1|1|
|Shanghai|2|2|1|2|2|2|

_Source_ : based on Annual Report of Regional Innovation Capability of China 2001 and 2002.

**Table 7: Selected responses to on-line survey comparing investment environment in Beijing and Shanghai**<sup>**6**</sup>

|Which city is better? (% of responses)|Beijing|Shanghai|No<br>opinion|
|---|---|---|---|
|_General condition_||||
|Social economic performance|14|65|21|
|Openness and global linkages|6|80|14|
|_Business environment_||||
|Regional economy|7|88|5|
|Financial services|12|74|14|
|R&D human resources|51|23|25|
|Innovation capability of enterprises|16|63|21|
|Education|51|17|32|
|_Government services_||||
|Transparency|18|51|31|
|Government efficiency|10|66|24|
|Place promotion|11|64|25|
|_Living condition_||||
|Culture amenity|18|59|23|
|Foreigner’s living convenience|19|53|29|
|Environmentalquality|13|69|18|

> 5 Annual Report of Regional Innovation Capability of China is written by the academic research group on China’s science and technology development strategy. The members of the group are coming from National Science and Technology Bureau, Chinese Academy of Sciences, The State Counsel, Chinese Academy of Social Sciences, Tsinghua University, National Planning Commission, Beijing Institute of System Engineering, etc. This seriate report has been published since 2001.

6 This on-line survey was jointly conducted by China Economic Times, MNC Research Center of Nankai University, and China Foreign Investment Network in 2003, in which 36 questions covered four issues: regional economic environment, business environment at firm’s level, government services, and living condition. To the date of this report was compiled, there are 828 responses came from users of China Foreign Investment Network, more details about the survey can be obtained from: http://www.chinafiw.com/e_m/site/mysystem8936.asp 12
having a disability makes it hard to precisely estimate patterns in the data beyond simple correlations.

Despite these limitations, but keeping them in mind, the data are nevertheless revealing. Consistent with other similar surveys, the 11 surveys analyzed here identify on the order of 1 to 2 percent of the population as having a disability. Countries with two surveys and varying definitions suggest that the percentage is not always sensitive to the exact definition (e.g. different definitions can give similar prevalence rates, and _vice versa_ ). In addition, other aspects of the surveys, such as the training of enumerators or the use that interviewees expect the survey to be put, might affect the overall estimated rates.

Youth with disabilities sometimes live in poorer households—but the extent of this concentration is typically neither large nor statistically significant. On the other hand, youth with disabilities are almost always substantially less likely to participate in schooling—and significantly so. Children with disabilities are less likely to start school, and in some countries have lower transition rates resulting in lower schooling attainment. The order of magnitude of the school participation disability deficit is often larger than those associated with other characteristics such as gender, rural residence, or economic status differentials. The data do not suggest that there are typically interactive effects— although the small number of disabled youth in these surveys makes hard to identify those effects.

This analysis suggests that, in developing countries, disability is associated with long-run poverty in the sense that children with disabilities are less likely to acquire the human capital that will allow them to earn higher incomes. However, the results should be treated as tentative at best. Establishing clear and consistent measures of disability for use in household surveys, and implementing these in the context of samples that are large enough to identify sufficient observations to allow detailed analysis (perhaps in the context of a census), will be a pre-requisite for further work on the relationship between disability and poverty. This should be a high priority for building empirically grounded policies to address the issue of disability, poverty and schooling.

# **References**

Afzal, Mohammad. 1992. “Disability Prevalence and Correlates in Pakistan: A Demographic Analysis.” _Pakistan Development Review_ 31(3):217-257.

Benitez-Silva, Hugo, Moshe Buchinsky, Hiu Man Chan, Sofia Cheidvasser, and John Rust. 2004. “How Large is the Bias in Self-Reported Disability?” _Journal of Applied Econometrics_ 19(6):649-670.

Elwan, Ann. 1999. “Poverty and Disability: A Survey of the Literature.” Social Protection Discussion Paper No. 9932. World Bank, Washington, D.C.

15
regions. We focus on three environmental problems that have been identified as critical for poor people in developing countries: health damage from outdoor particulate air pollution; health damage from waterbome pathogens; and vulnerability of rural populations on fragile lands.

We base our optimization exercise on a welfare function that makes three basic assumptions about the Bank's decision environment: the desirability of some representation for all partner countries; the importance of relative enviromnental damage across countries; and equal valuation of damage abatement across countries. In the first exercise (case 1), countries' optimal investment shares are simply their shares of total environmental damage. In case 2, we maintain equal valuation of abatement across countries but drop the assumption that all countries have equal likelihood of project success. We estimate country success probabilities from thousands of actual cases reviewed by OED. In case 2, each country's optimal share of total investment is determined by the product of its environmental damage and project success probability.

**Table 6.1: Optimal Investment Shares (%) by Sector (Case 2)**

|**Region**|**Total**|**Water**|**Air**|**Fragile**<br>**Lands**|
|---|---|---|---|---|
|EastAsia,Pacific|44|38|_50_|44|
|SouthAsia|21|15|24|24|
|Sub-SaharanAfrica|19|34|6|15|
|Latin America,Caribbean|6|7|6|_5_|
|East Europe,CentralAsia|_5_|3|9|4|
|North Africa,MiddleEast|_5_|3|5|7|

Our overall results are summarized by region in Table 6.1. We provide a detailed presentation of our country data and results in Appendix A. We find that the largest share of total optimal investment goes to East Asia (44%), followed by South Asia (21%) and Sub-Saharan Africa (19%). Other regions get significantly lower shares (respectively 13
_Sisben_ data‖) contains the universe of individuals above and below the threshold of eligibility (whether or not they actually participate in the program). This information is then used to construct indicators of program participation and length of exposure. In order to focus on a period of time that is comparable to the one examined in the matching analysis, the final subsample is restricted to treatment municipalities that were covered during the first phase of expansion between 2001 and 2004. This implies that in addition to the comparison group, the samples are comprised of beneficiaries that could have been covered for up to nine years until 2009.<sup>14</sup> The two resulting data sets (―PSM data‖ and ―SIFA + _Sisben_ data‖) are merged with the administrative records on registration and results for the _Icfes_ test.<sup>15</sup> This exam is a nationally recognized and standardized test that is administered prior to graduation from high school and mandatory for entrance to higher education. Over four million students registered and took the test between 2003 and 2009. This database identifies test takes by date of birth, full name and national identification number. This dataset is merged with the cohorts of children identified in the ―PSM data‖ assembled to perform the matching analysis, and with the ―SIFA+SISBEN data‖ to implement the RDD strategy. In order to avoid problems of nonrandom mismatch, strict procedures were followed to merge the datasets including matching based on full name, birth date, national identification number, and a minimization of the phonetic _Levenshtein_ distance.<sup>16</sup> (see Appendix A for more details about the data merging procedures). The final matching rates are around 18 percent for the matching analysis and 24 percent for the RDD approach.<sup>17</sup> The long-term impacts of the program on the human capital of children are estimated on two outcome variables. The first is an indicator of high school completion that is measured through a dummy variable that identifies whether a child registered or not for the _Icfes_ test during the period 2003-2009. Although the test is given to students just prior to graduation (grade 11), registration to the test is a good proxy for high school completion since over 90 percent of the test takers end up finishing grade 11 (World

> 14 The final sample also excludes internally displaced people who became eligible to the program much later.

> 15 More recently called _Saber 11_ .

> 16 The _Levenshtein_ distance measures the difference between two strings, in terms of edits you have to do to convert one string into the other.

> 17 We considered 10 points above and below the threshold to check the matching for the RDD sample.

13
|P144497|2015|2020|12/30/2019|9 Active|Tanzania|
|---|---|---|---|---|---|
|P144497|2015|2020|5/19/2020|10 Active|Tanzania|
|P144500|2013|2014|11/29/2013|1 Active|Morocco|
|P144500|2013|2015|7/6/2014|2 Active|Morocco|
|P144500|2013|2015|1/7/2015|3 Active|Morocco|
|P144500|2013|2016|8/16/2015|4 Active|Morocco|
|P144500|2013|2016|4/10/2016|5 Active|Morocco|
|P144500|2013|2017|8/4/2016|6 Active|Morocco|
|P144500|2013|2017|3/7/2017|7 Active|Morocco|
|P144500|2013|2018|9/6/2017|8 Active|Morocco|
|P144500|2013|2018|6/3/2018|9 Active|Morocco|
|P144500|2013|2019|6/14/2019|10 Active|Morocco|
|P144500|2013|2021|7/23/2020|11 Active|Morocco|
|P144520|2013|2013|5/27/2013|9 Closed|Timor-Leste|
|P144520|2013|2014|8/11/2013|10 Closed|Timor-Leste|
|P144520|2013|2014|3/14/2014|11 Closed|Timor-Leste|
|P144520|2013|2015|8/31/2014|12 Closed|Timor-Leste|
|P144520|2013|2015|3/17/2015|13 Closed|Timor-Leste|
|P144520|2013|2015|6/7/2015|14 Closed|Timor-Leste|
|P144520|2013|2016|6/26/2016|15 Closed|Timor-Leste|
|P144522|2014|2014|6/12/2014|1 Closed|Yemen,Republic of|
|P144522|2014|2015|12/12/2014<br>|2 Closed|Yemen,Republic of|
|P144522|2014|2015|6/16/2015<br>|3 Closed|Yemen,Republic of<br>|
|P144522|2014|2016|12/11/2015|4 Closed|Yemen,Republic of|
|P144522|2014|2016|6/18/2016|5 Closed|Yemen,Republic of|
|P144522|2014|2016|6/23/2016|6 Closed|Yemen,Republic of|
|P144531|2015|2015|11/25/2014|1 Active|China|
|P144531|2015|2015|5/12/2015|2 Active|China|
|P144531|2015|2016|11/2/2015|3 Active|China|
|P144531|2015|2016|5/8/2016|4 Active|China|
|P144531|2015|2017|11/21/2016|5 Active|China|
|P144531|2015|2017|6/9/2017|6 Active|China|
|P144531|2015|2018|11/22/2017|7 Active|China|
|P144531|2015|2018|6/28/2018|8 Active|China|
|P144531|2015|2019|1/8/2019|9 Active|China|
|P144531|2015|2019|6/26/2019|10 Active|China|
|P144531|2015|2020|12/26/2019|11 Active|China|
|P144531|2015|2020|6/30/2020|12 Active|China|
|P144534|2014|2015|12/31/2014|1 Active|Turkey|
|P144534|2014|2015|6/30/2015|2 Active|Turkey|
|P144534|2014|2016|1/6/2016|3 Active|Turkey|
|P144534|2014|2017|9/30/2016|4 Active|Turkey|
|P144534|2014|2017|5/12/2017|5 Active|Turkey|
|P144534|2014|2018|12/18/2017|6 Active|Turkey|
|P144534|2014|2018|6/30/2018|7 Active|Turkey|
|P144534|2014|2019|2/12/2019|8 Active|Turkey|
|P144534|2014|2020|8/12/2019|9 Active|Turkey|
**Table T3.IR:  Father's Education and Educational Expenditure on Children**

|||**RURAL**<br>**IHDS**<br>|**INDIA**<br>**2005**<br>||
|---|---|---|---|---|
||**(6-22) Year**<br>|**s Children**<br>|**(8-23) Year**<br>|**s Children**<br>|
|Father's Sch.|153.9***|145.4***|169.2***|158.7***|
||(9.08)|(8.77)|(11.0)|(10.6)|
|**Father's Sch. * Daughter**|**-22.8****|**-21.9***|**-24.0***|**-22.4**|
|**Dummy**|**(11.4)**|**(11.3)**|**(13.9)**|**(13.7)**|
|**Daughter Dummy**|**-182.5*****|**-137.1*****|**-211.0*****|**-134.0****|
||(43.5)|(43.7)|(52.3)|(52.7)|
|Intercept|772.3***|1585.4***|877.6***|1949.3***|
||(33.3)|(54.7)|(39.8)|(67.9)|
|No. of Children||-274.4***||-361.9***|
|||(17.2)||(21.8)|
|No. Observations|23058|23058|19002|19002|
|||**NSS**<br>|**1995**<br>||
||**(6-18) Year**|**s Children**|**(8-18) Year**|**s Children**|
|Father's Sch.|35.2***|35.2***|36.5***|37.5***|
||(1.425)|(1.425)|(1.535)|(1.567)|
|**Father's Sch. * Daughter**|**-3.7***|**-3.7***|**-3.4**|**-3.5**|
|**Dummy**|(2.083)|(2.083)|(2.285)|(2.281)|
|**Daughter Dummy**|**-74.1*****|**-74.1*****|**-65.2*****|**-58.5*****|
||(8.694)|(8.694)|(9.700)|(9.729)|
|Intercept|426.9***|426.9***|461.0***|507.7***|
||(5.420)|(5.420)|(5.882)|(9.728)|
|No. of Children||||-21.6***<br>(3.749)|
|No. Observations|37144|37144|32499|32499|

Notes: (1) The data used are IHDS 2005 and NSS 1995 respectively. (2) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
the top 10 trading partners; it increases the likelihood of an antidumping tariff by 33%.

The final specification of Table 2 examines the standard errors of our estimates by implementing the variance estimator of Cameron, Gelbach and Miller (CGM) (2011) in a logit model.<sup>17</sup> The CGM procedure constructs a variance estimator that allows two-way nonnested clustering. In our application, one might be concerned that errors are correlated with industry groups, _k_ , and within country groups, _i_ . The marginal effects reported in column (5) from the logit model are similar to the marginal effects from the probit model reported in column (4) and have no discernable quantitatively different effect on the predicted probabilities reported in the bottom panel of Table 2. However, the CGM variance estimator yields standard errors that are larger than the Huber-White robust standard errors reported for the probit specifications in columns (1) - (4). In terms of hypothesis testing, using the CGM standard errors, the marginal effect of the growth of imports is statistically significant at the 1% level, but the statistical significance of estimates on the natural log of the inverse sum of the export supply and import demand elasticities and of the bilateral real exchange rate declines to the 10% level. Using the CGM procedure, the estimate of the marginal effect of the standard deviation of import growth is no longer statistically from zero.

# **3.2 Robustness Checks: Market Share, Safeguards, and China**

Table 3 introduces a new explanatory variable to proxy for the unexpected import surge in the Bagwell and Staiger model. Some of the papers in the literature on the terms-of-trade theory of trade agreements (Bagwell and Staiger, 1999; Ossa, 2011) emphasize the importance of the market access implied by a negotiated tariff rate over tariff rates and import volumes. In mapping the repeated static environment of Bagwell and Staiger (1990) to an empirical environment characterized by domestic economic and trade growth, in Table 3 we use country _i_ ’s _share_ of the importing country’s market as our measure of expected import volume. From this, we define an import surge at _t −_ 1 as an increase in country _i_ ’s share of the US’s market for _k_ between _t −_ 2 and _t −_ 1.

The first column of Table 3 reports our basic specification using the market share variable in lieu of the import growth measure. The results are consistent with those of the baseline specification (1) of Table 2. A one

> 17Judson Caskey provided the STATA code for the CGM variance estimator in a logit model.

15
**TABLE 23: ROAD FREIGHT CHARGES ON MAIN OVERLAND LEG AS A PERCENT OF FREIGHT VALUES**

||Trading partner|Percent of Imports value|Percent of Export value|
|---|---|---|---|
|Origin/Destination Country||||
|**Costa Rica**|Central America|5.9%|4.6%|
||Rest of World|5.2%|2.9%|
|**El Salvador**|Central America|4.6%|6.8%|
||Rest of World|3.4%|7.0%|
|**Guatemala**|Central America|4.8%|2.1%|
||Rest of World|2.9%|2.5%|
|**Honduras**|Central America|7.3%|5.5%|
||Rest of World|3.5%|3.7%|
|**Nicaragua**|Central America|4.8%|3.9%|
||Rest of World|3.7%|6.9%|
|**Panama**|Central America|1.3%|5.4%|
||Rest of World|0.2%|0.4%|

Author’s calculations using trucking survey and WITS database.

# CONCLUSIONS

Using detailed data on firm costs and characteristics and route-firm-specific data on prices and frictions along main land corridors for international trade in Central America, we estimate firm-level short run cost and price functions in the presence of constraints that trucking firms face. In doing so, we estimate the quantitative impact on prices of constraining factors such as traffic congestion (average speeds), road quality, wait times, empty backhaul, and informal payments. The results show that traffic congestion, high wait times, and informal payments are significant contributors to high costs in the region.

We find surprisingly little evidence of effects of road quality on the costs of service on main trading corridors. Poor road conditions or inadequate road linkages may cause important economic losses in transporting goods from producers – especially agricultural producers – to market, or consumption goods to smaller towns and villages. However, we are able to find no evidence that on the whole improving roads serving main trade corridors would have a sizeable direct effect on overland freight prices in Central America. There is one exception. Although lower average travel speeds do not affect average firm costs, routes with slower average travel speeds are priced higher, probably due to congestion which increases labor and fuel costs. Speeds are in turn affected by road quality as measured in the survey – in particular width and surface conditions – as would be expected. Thus although wholesale investment in improving the roads forming main trading routes would not result in appreciably lower prices, well targeted investments in road works may be worthwhile as part of a package of solutions to reduce traffic congestion where major bottlenecks are found.

In addition to route-level frictions, trucking firms are currently unable to reap economies of scale. Fleet sizes are smaller than optimal for minimizing costs, yet at the same time fleet utilization is low. Whereas high wait times, congestion, and other constraints contribute to low fleet utilization, they do not fully explain it. Market structure 34
# Information Deprivation

National level information deprivation has been drastically decreasing over time, in the urban and the rural areas alike (see Figure 36). In 2010, 33 percent of rural children of school going age were, according to the GHS data, deprived of information, such as phone, TV and radio, while this share had dropped to 9 percent over the subsequent 6 years period. This large reduction in severe information deprivation of children, holds, as Figures 37 a and 37 b show, across all geo-spatial zones. While the estimates from the MICS 2016/2017 survey, do not continue the trend of the third wave of the GHS survey these are very much in line with the GHS wave 2 estimates. One explanation for this discrepancy may be large heterogeneity at the state level, that the GHS, only representative at the regional level, cannot capture.

Unlike other deprivations, there is not such a clear north-south divide in terms of information deprivation. While North Eastern and North Western rural zones have the largest shares of information deprived children, the share of information deprived children in the North Central urban and rural areas is according to both surveys in the last wave comparable to the South Eastern and South Western zones. Figures 38 and 39 rather show that particular states stand out. Ebonyi stands out with about 15 percentage points more children being severely information deprived compared to neighboring states in the South East. Children living in the at the time of the survey accessible areas of Borno on the other hand are one of the least information deprived, even compared to those in the least deprived southern states and the FCT. Considering that most accessible enumeration areas of Borno were in and around it’s state capital city Maiduguri and exclude IDP camps, results hence suggest that children living in these areas are despite the conflict in the state still doing quite well in terms of household access to information. The surge in information deprivation in the urban North East from wave 3 to wave 4 may however be linked to the rising level of conflict and potentially a need to sell assets to meet consumption needs even in the accessible areas that will only in later rounds show as surge in monetary poverty in this region. This finding also gives reason to expect even larger within state heterogeneity in the non-accessible conflict affected areas, considering that above 20 percent up to a quarter of the children in other northern states with remote rural areas, such as Gombe, Taraba, Jigawa and Kebbi, live in household without access to any means to information.

47