rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 presents summary statistics on total patent flows and patent flows for each highIP cluster, using a concordance between the technology codes in the International Patent Classification (IPC) and sectors in the North American Industrial Classification System (NAICS) to map PATSTAT data to broader industry groupings.<sup>11</sup> The summary statistics are computed over both the full sample (including zero flows) and the sample with only positive flows. Note that because the patent-industry concordance consists of weights less than unity, some patent flows in the positive-only sample appear close to zero. This also explains patent flows that are less than one.

The figures in Table 2 show that the clusters with the smallest mean positive cross-border patent flows are biopharmaceuticals and medical devices. In contrast, the clusters with the largest average flows, as regards both zeros and non-zeros, are information and communication technology and “Other.” Despite that, the cluster with the smallest number of positive international applications is the ICT category.

Table 3 lists summary statistics for total patent flows among members and among countries who are not members of IPA agreements, again for all flows including zeros and for positive observations. These figures are organized with a different logic for comparison purposes. The “between members” data demonstrate flows pre- and post-IPA implementation among all country pairs in which both are in the agreement. The “non-members” data depict flows between all country pairs that never sign an IPA with each other, before and after implementation of an agreement between the destination country and any third party or parties. The rationale for this comparison is that it allows us to discern whether this change in the destination’s status correlates with changes in extra-agreement patent registrations. That is, does this evident change in destination _j’s_ IPRs regime influence applications from country _i_ within that pair? The data indicate that, following the establishment of IPAs containing three or more core BTRIPS provisions, the average volume of patent applications between countries increases for member-to-member pairs and non-member-to-member pairs. This pattern holds also for PTAs classified in the World Bank’s database as featuring any legally enforceable IPR provision. Notably, this relationship is reversed for IPAs in which the United States or EU/EFTA is a partner.

> 11 The concordance is in Lybbert and Zolas (2014).

12
Roodman, David and J. Morduch. 2011. The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence (Revised 2011), Working Paper 174, Center for Global Development.

van der Klaauw, Wilbert. 2008. “Regression–Discontinuity Analysis: A Survey of Recent Developments in Economics,” Labour 22 (2) 219–245.

20
regional legislatures. This altered the political landscape from top to bottom, shifting power away from Soeharto's Golkar party towards now-President Megawati Soekarnoputri's PDI-P and a host of newly established political groupings that were allowed to organize in rural areas. Second, the legislature passed a set of laws that initiated substantial decentralization of government services from the center to districts (mostly by-passing provinces)<sup>9</sup> . Third, as the center weakened there was an expansion in local activity that addressed past and present grievances through both violent (e.g. riots, land seizures, stoning local government offices (and officers)) and more "democratic" means (a free press).

# **_II)_** _Local Level Institutions Study Household Data_

> We are going to estimate the relationship between social activities and the perceptions of _desa_ government performance using multivariate regressions. To do that we need to specify the (a) the construction of each of the four social variables, (b) the empirical variables used to measure "governance," (c) the way we propose to distinguish between private and community impacts of social activities, (d) the non-social variables included in the regressions, and (e) the functional form.

The Local Level Institutions study (LLI) is a large, complex research endeavor carried out in 48 _desas_ in three provinces (six districts), first in 1996 (LLII) and again in 2000/2001 (LLI2). The study combined both qualitative and quantitative work on issues related to local governance, including documenting the array of social activities of development" to citizens as a means of sustaining legitimacy but less concerned with either local or national mechanisms of "voice"from citizens.

> 9 It should be noted that, as part of the decentralization effort, the 1979 law on village government has been revoked. Change has not been immediate, however, and most of the structures it created still persist throughout the research area (Wetterberg 2002).

9
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Bibliography and references

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project Web site: http://www.infrastructureafrica.org. For papers go to the document page (http://www.infrastructureafrica.org/aicd/documents), for databases to the data page (http://www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (http://www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (http://www.infrastructureafrica.org/aicd/tools/maps). The references for the papers that were used to compile this country report are provided in the table below.

## General

AICD (Africa Infrastructure Country Diagnostic). 2010a. ―Africa’s Infrastructure: A Time for

- Transformation.‖ Foster, Vivien, and Cecilia Briceño-Garmendia, eds. Paris and Washington,

- DC: Agence Française de Développement and World Bank. http://www.infrastructureafrica.org.

- ———. 2010b. ―ECOWAS’s Infrastructure: A Regional Perspective.‖ Africa Region, World Bank, Washington, DC.

- World Bank. 2009a. Country Assistance Strategy for Burkina Faso FY10-12. Washington, DC: World Bank.

- ———. 2011. Doing Business 2011: Making a Difference for Entrepreneurs. Washington, DC.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2009. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure

- Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

46
|Percent of faculty that are women|-0.000868<br>(0.000789)|-0.0655<br>(0.0965)|-0.00500<br>(0.00517)|
|---|---|---|---|
|_Other practices_||||
|Percent of governing body that belongs to current students||0.0112<br>(0.210)||
|Min. scores in HS GPA or national entry test is an admission<br>requirement|0.0902**<br>(0.0449)||0.761***<br>(0.288)|
|Student body, program, and HEI characteristics (indexes)|✓|✓|✓|
|Noise controls, and field-fixed effects|✓|✓|✓|
|Observations|1,214|1,201|1,206|
|R-squared|0.230|0.325|0.247|
|Mean of dependent variable|0.348|23.81|2.294|
|Average wage (USD 2019 PPP)|||300.6|
|AdjRsquared|0.214|0.303|0.232|

_Source_ : Own estimations using individual-level data from administrative sources and program-level data from the WBSCPS for Ecuador. _Notes_ : This table shows coefficients from the (second stage) OLS regressions of student outcomes on the quality determinants selected by LASSO in the first stage. The unit of observation is an individual. Outcomes (dependent variables) are the following labor market outcomes pertaining to the 12-month period following graduation: whether the student is formally employed at least one month (col. 1); percent of months she is formally employed (col. 2); and average monthly wage (col. 3). Average monthly wage is computed over the months worked; it is zero if the individual does not work formally at all. Regressions are weighted using WBSCPS sampling weights. All specifications include noise controls as well as fixed effects for graduation year, state, and field. They also control for PCA scores for the following: student body characteristics (based on survey data), student characteristics (based on individual-level data, including proxies for previous labor market experience), program characteristics (based on survey data), HEI characteristics (based on survey data), and peer characteristics (based on individual-level data, including proxies of peers’ previous labor market experience). See Appendix 1 for the list of the variables included in each index. Standard errors clustered at program level are in parenthesis. Significance levels: * p<0.1, ** p< 0.05, *** p<0.0 91
Paper Series 5508, The World Bank.

Foster, A. and M. Rosenzweig, 2004, “Agricultural development, industrialization and rural inequality, _mimeographed_ , Harvard University.

Haggblade, S., P. Hazell and T. Reardon, 2006, _Transforming the Rural Nonfarm Economy_ , Johns Hopkins Press, Baltimore.

Harris, John and Michael Todaro (1970). Migration, Unemployment, and Development: A Two Sector Analysis, American Economic Review, 40: 126-142.

Hossain, M. and A. Bayes, 2009, _Rural Economy and Livelihoods: Insights from Bangladesh_ , AH Development Publishing House.

Hossain, Mohammad Ismail, and Wim Verbeke. 2010. “Evaluation of Rice Markets Integration in Bangladesh.” Lahore Journal of Economics 15 (2): 77–96.

Hymer, Stephen and Stephen Resnick (1969), “A Model of an Agrarian Economy with Nonagricultural Activities”, American Economic Review, Vol. 59, No. 4, Part 1 (Sep., 1969), pp. 493-506 Kuznets, S. (1973) ‘Modern economic growth: findings and reflections’ (Nobel Lecture), American Economic Review 63, 247–58.

Lanjouw, Peter and Rinku Murgai, 2009.Poverty decline, agricultural wages, and nonfarm employment in rural India: 1983-2004, _Agricultural Economics,_ vol. 40(2), pages 243-263, 03.

Lanjouw J. and P. Lanjouw, 2001, “Rural Non-farm Employment: Issues and Evidence from Developing Countries,” _Agricultural Economics_ , vol. 26, no. 1, pp.1 –24.

Lewis, A. (1954) ‘Economic development with unlimited supplies of labor,’ Manchester School 22, 139– 91 Mellor, J, 1976, _The New Economics of Growth: A Strategy for India and the Developing World_ , Cornell University press, Ithaca.

Ranis, G. and F. Stewart, 1973. “Rural Non-Agricultural Activities in Development: Theory and Application.” _Journal of Development Economics_ , vol. 33, o.7 pp. 1045-1060.

31
Figure 4: Poverty rates across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Poverty rate (%)<br>Poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the distributions of poverty rates over different geographic areas in 16 SSA countries. WorldPop 250m is used for the DOU and DB methods. The dashed lines represent the average national poverty rate in the sample.

Figure 5: Comparison of urban poverty rates between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>60 60 MWI 60 MWI<br>MWI<br>50 50 50<br>NER NER<br>40 40 40<br>UGA UGA UGA<br>TZA TZA TZA<br>30 NER 30 30<br>TCD TCD<br>ETH ETH<br>20 ETH 20 LSO 20 LSO<br>BFATCD LSOAGO BFA AGO BFAAGO<br>10 GABGINSENMRTCIVGNBGHA 10 GABGINSENMCIVRTGNBGHA 10 GABMRTGINSENGHACIVGNB<br>10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60<br>Urban poverty rate (%) Urban poverty rate (%) Urban poverty rate (%)<br>Official definition Official definition DOU<br>DOU DB DB<br>Urban poverty rate (%) Urban poverty rate (%) Urban poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

16
# **Hukou and Highways**

**The Impact of China’s Spatial Development Policies on Urbanization and Regional Inequality**

Maarten Bosker<sup>1</sup> , Uwe Deichmann<sup>2</sup> and Mark Roberts<sup>3</sup> JEL Codes: R11, R23, R42 Key words: China, transport investments, highways, migration, new economic geography Sectors: Transport, Urban Development 1 Department of Economics, Erasmus University Rotterdam, The Netherlands and CEPR <bosker@ese.eur.nl> 2 Development Research Group, World Bank, Washington, D.C., USA <udeichmann@worldbank.org>; 3 Social, Urban, Rural and Resilience Global Practice, World Bank, Washington, D.C., USA <mroberts1@worldbank.org>.

The authors thank Harry Garretsen, Vernon Henderson, Bert Hofman, Laura Hering, Diego Puga, Karlis Smits, Maisy Wong, and participants at the 2<sup>nd</sup> Urbanization and Poverty Reduction Research Conference and the 2014 North American Regional Science Conference for helpful comments and suggestions. Financial support from the World Bank’s Knowledge for Change Program is gratefully acknowledged.
equals 27.4. Across countries, the variable ranges between 2.9 percent (Yemen) and 53.7 percent (Senegal).

In separate regressions, we also use the percentage of women among production workers ( _Women production workers_ ) and among non-production workers ( _Women non-production workers_ ) as dependent variables. Under the assumption that non-production jobs carry higher wages than the production jobs, we check if non-discrimination boosts women’s employment in higher paying jobs or not.

Since our dependent variable is women’s employment _relative_ to men’s employment, factors such as overall economic development, job availability and labor market conditions that may affect the employment of men and women equally are excluded from spuriously affecting our results. For later reference we define _Absolute women employment_ as log of (1 plus) total number of permanent full-time workers at the firm that are women. _Absolute men employment_ is analogously defined.<sup>4</sup>

# _Main explanatory variable_

Our main explanatory variable, _Non-discrimination_ , equals 1 if the country has laws or clause mandating non-discrimination in hiring practices on the basis of gender and 0 otherwise. Data source for the variable is Women, Business and the Law database, World Bank (2011). Two conditions have to be met for a country to have the non-discrimination clause included in the data from the Women, Business and the Law database: (a) the clause has to explicitly mention the word sex or gender; (b) the clause has to explicitly refer to the hiring process and not just

> 4 We use log values here to curb any unduly large effect of some firms in the sample that have very high employment levels of men and women workers. Our results are qualitatively unaffected if we use actual level instead of log values provided that extreme values of women and men workers are excluded from the sample. These extreme values were identified using Stata’s _rreg_ command.

8
- <mark>Dang, H. A., & Do, M. N. (2023). COVID-19 Pandemic and the Health and Well-being of Vulnerable People in Vietnam. In</mark> _<mark>Handbook of Social Sciences and Global Public Health</mark>_ <mark>. Cham: Springer International Publishing.</mark>

- <mark>Dang, H. A., Nguyen, C., & Carletto, C. (2023). Did a Successful Fight against COVID-19 Come at a Cost? Impacts of the pandemic on employment outcomes in Vietnam.</mark> _<mark>World Development</mark>_ <mark>,</mark> 161, 106129.

- <mark>Dang, H. A., Dhongde, S., Do, M., Nguyen, C. V., & Pimhidzai, O. (2023).</mark> _<mark>Rapid Economic Growth but Rising Poverty Segregation: Will Vietnam Meet the SDGs for Equitable Development</mark>_ <mark>? IZA Discussion paper no. 15196.</mark>

- Deaton, A., & Kozel, V. (2005). Data and dogma: the great Indian poverty debate. _World Bank Research Observer_ , 20(2), 177-199.

- Dhongde, S. (2017). Measuring Segregation of the Poor: Evidence from India. _World Development_ , 89, 111-123.

- Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-level estimation of poverty and inequality. _Econometrica_ , 71(1), 355-364.

Ferreira, F.H. (2010). “Distributions in Motion”. In _Oxford Handbook of the Economics of Poverty_ .

- <mark>Ferreira, I. A., Salvucci, V., & Tarp, F. (2022).</mark> _<mark>Poverty, inequality, and growth: trends, policies, and controversies</mark>_ <mark>(No. wp-2022-43). World Institute for Development Economic Research (UNU-WIDER).</mark>

- <mark>Forbes, K. J. (2000). A reassessment of the relationship between inequality and growth.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>90</mark>_ <mark>(4), 869-887.</mark>

- Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. _Econometrica_ , 52(3): 761-766.

- Fujii, T. (2018). Has the development gap between the ethnic minority and majority groups narrowed in Vietnam?: Evidence from household surveys. _World Economy_ , 41(8), 2067-2101.

- Glewwe, P., & Dang, H. A. H. (2011). Was Vietnam’s economic growth in the 1990s pro-poor? An analysis of panel data from Vietnam. _Economic Development and Cultural Change_ , 59(3), 583-608.

- <mark>Gradín, C. (2021).</mark> _<mark>Trends in global inequality using a new integrated dataset</mark>_ <mark>(No. 2021/61). WIDER Working Paper.</mark>

- Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. _Econometrica_ , 1371-1395.

23
**DRAFT – Not for citation**

Whalley, J., & Zhang, S. (2007). A numerical simulation analysis of (Hukou) labour mobility restrictions in China. Journal of Development Economics, 83(2), 392-410.

- World Bank (2007), Domestic trade impacts of the expansion of the national expressway network in China. EASTR Working Paper No. 8, Transport Sector Unit, Infrastructure Development East Asia and Pacific Region, The World Bank, Washington, DC.

- World Bank (2009), _World Development Report 2009: Reshaping Economic Geography_ , Washington, D.C.

- World Bank and DRC (2014), _Urban China – Toward efficient, inclusive, and sustainable urbanization_ , World Bank, Washington, D.C., and Development Research Center of the State Council, Beijing.

- Zhang, J., and Zhao, Z. (2013). Measuring the income-distance trade-off for rural-urban migrants in China, IZA discussion paper, no.7160.

- Zhao, Y. (1999a), Labor migration and earnings differences: the case of rural China. _Economic Development and Cultural Change_ , Vol.47(4), 767-782.

- Zhao, Y. (1999b), Leaving the countryside: rural-urban migration decisions in China. _American Economic Review,_ 89(2): 281-286.

- Zheng, S., & Kahn, M. E. (2013). China’s bullet trains facilitate market integration and mitigate the cost of megacity growth. _Proceedings of the National Academy of Sciences_ , 110(14), E1248-E1253.

35
Table S.28: Lee (2009)’s 95% confidence intervals of the treatment effects

||Confden|ce interval|No. of obs.<br>|
|---|---|---|---|
||Lower|Upper|having|
||bound|bound|non-missing|
||||values<br>at follow-up|
|Examination results (2013)||||
|Progression<br>|-0.031|0.099|850|
|Bengali|-0.094|0.220|850|
|English|-0.206|0.101|850|
|Math|-0.425|0.099|850|
|General science|-0.239|0.195|850|
|Islamic studies|-0.042|0.265|850|
|Bangladesh general studies|0.005|0.311|850|
|Examination results (2014)||||
|Progression|-0.104|0.035|845|
|Bengali|-0.210|0.484|846|
|English|-0.214|0.099|846|
|Math|-0.203|0.079|846|
|General science|-0.420|0.421|846|
|Islamic studies|-0.160|0.473|846|
|Bangladesh general studies|-0.268|0.023|846|
|Per-day minutes (average from Se|p 2013 to|Apr 2014)|spent on:|
|Study at school|-8.079|9.242|620|
|Study at home (total)|-1.265|25.375|620|
|Study at home (day)|-16.008|-2.208|620|
|Study at home (night)|11.719|29.442|620|
|Other activities|-38.530|29.535|620|
|Per-day hours using light sources||||
|Flashlight (rainy)|-0.319|-0.054|847|
|Flashlight (of-rainy)|-0.442|-0.061|847|
|Kerosene lanterns (rainy)|-0.850|-0.503|847|
|Kerosene lanterns (of-rainy)|-1.070|-0.657|847|
|Kerosene lamps (rainy)|-3.072|-2.450|847|
|Kerosene lamps (of-rainy)|-3.645|-2.845|847|
|Log of expenditures (BDT)||||
|Education|-0.088|0.119|842|
|Kerosene|-1.207|-0.626|631|
|Total|-0.092|0.006|847|

73
## **Median operating expenses**

<!-- Start of picture text --> 2007 0.12<br>Benchmark Europe 2011 0.09<br>2015 1.23<br>2007 2.00<br>WEST ECA 2011 1.63<br>2015 1.85<br>2007 4.80<br>EAST ECA 2011 3.92<br>2015 2.80<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br><!-- End of picture text --> Interest income is lower for all SOFIs in 2015 compared to 2007, except for SOFIs in East ECA (Figure 12c). Non-interest income is lower for all SOFIs in 2015 compared to 2007, except for SOFIs in Benchmark Europe. The SOFIs in Benchmark Europe appear minimally affected over the period under observation, while income appears to be dropping fast for West ECA during the reported period. At the same time, while operating expenses have been dropping among SOFIs in East ECA, there was only a relatively muted response in West ECA.

The overall banking sector’s profitability, as measured by the median ROE, also show a decrease over time. Commercial SOFIs outperformed the overall banking sector in 2011, but not in 2007 and 2015. Development SOFIs median ROE, although always positive, has always been lower than median profitability of the banking sector.

# 5.4.3 _Loan quality_

Median NPL rates<sup>15</sup> increased significantly both for Commercial and Development SOFIs, driven primarily by the direct lending operations (with a late pronounced hike in East ECA from 2011 to 2015, possibly related also to the 2014 oil price decline and sanctions on the Russian Federation). Median NPLs on direct lending do not seem consistently higher for Development rather than Commercial SOFIs, suggesting similar risk-taking behavior. Commercial SOFIs experienced a spike in median NPL rates earlier than the Development SOFIs (Figure 13a), possibly as a result of the on-lending/ direct lending instrument mix (with the latter allowing a more immediate reaction to the GFC), an issue of recognition, or reflecting their more immediate

> 15 The comparison of NPL rates needs to be approached with caution, as the NPL definition can vary significantly from country to country, in addition to within countries when some SOFIs are not subject to the same regulatory standards as the rest of the banking sector.

31