rafmacalaba/datause-encoder-data huggingface dataset:
|Economic Institutions CapacityBuildingProject<br>Cyrus P. Talati|
|---|
|Economic Institutions CapacityBuildingProject<br>Cyrus P. Talati|
|Economic Institutions CapacityBuildingProject<br>Cyrus P. Talati|
|Economic Institutions CapacityBuildingProject<br>Cyrus P. Talati|
|Renewable EnergyProject<br>Ahmet Gurhan Ozdora|
|Renewable EnergyProject<br>Ahmet Gurhan Ozdora|
|Renewable EnergyProject<br>Ahmet Gurhan Ozdora|
|Renewable EnergyProject<br>Ahmet Gurhan Ozdora|
|Renewable EnergyProject<br>Ahmet Gurhan Ozdora|
|Renewable EnergyProject<br>Ahmet Gurhan Ozdora|
|Renewable EnergyProject<br>Ahmet Gurhan Ozdora|
|Renewable EnergyProject<br>Ahmet Gurhan Ozdora|
|HIV/AIDS Control Project<br>Peter Okwero|
|HIV/AIDS Control Project<br>Peter Okwero|
|HIV/AIDS Control Project<br>Peter Okwero|
|HIV/AIDS Control Project<br>Peter Okwero|
|HIV/AIDS Control Project<br>Peter Okwero|
|HIV/AIDS Control Project<br>Peter Okwero|
|HIV/AIDS Control Project<br>Peter Okwero|
|HIV/AIDS Control Project<br>Peter Okwero|
|HIV/AIDS Control Project<br>Peter Okwero|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Cotton Sector Reform Project<br>Nicolas Ahouissoussi|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|Power and Communications Sectors Modernization and Rural ServicVictoria Susan Bogach|
|India: Kerala State Transport Project<br>Arnab Bandyopadhyay|
|India: Kerala State Transport Project<br>Arnab Bandyopadhyay|
|India: Kerala State Transport Project<br>Arnab Bandyopadhyay|
- 31 -

central govenment considerably detracts from local government fiscal flexibility and discretion. As we saw in box 3, there is wide agrmet on te principles that should determine the selection of local taxes. Most notably, it is recommended that local govemment should levy taxes on inmnobile bases and preferably bases that reflect benefits received from local govenmment services. Other characteristics of a good local tax are that it be visible, have a stable and predictable yield, be relatively easy to administer and not be 'exportable" 63. Property Taxes. Most of these criteria of a good local tax support the use of a ax on immobile property and there is also considerable intuitive appeal to the use of property taxes. Local property values are closely linked to the extent of the infrastructure services that support them, such as street construction, maintenance and lighting. Property taxes are therefore attractive on "benefit"

**-S.**

64. The use of property taxes is widespread. Out of 24 OECD coutries for which data are available, for instance, 20 levy some form of property taxation. Property taxation has been a mainstay of local governments in English-speaking countries, but is not restricted to them. The property tax is the main source of local govenment taxation in Holand, and bas also been introduced in recent years in Mediterranean countries such as Portugal, Spain and Italy. The case of Italy is perhaps most interesting, because it is Albania's neighbor and the principal national donor of aid to Albania. It has long been recognized in Italy that the country's local governments have been overly dependent on transfers and need to acquire their own sources of revenues to increase their autonomy. A national property tax has been introduced in taly and is to become a local property tax with locally established rates in 1994.
fractionalization in a country along ethnic, linguistic and religious lines. The source for these variables is Alesina et al. (2003).

Next, there is now substantial evidence showing that the legal tradition of a country is a strong proxy for various aspects of the institutional environment. For example, Djankov et al. (2002) show that entry barriers are much lower in common law relative to the French civil law countries. Similar findings are reported for shareholder rights (Djankov et al. 2008), contract enforcement (Djankov et al. 2003) and the flexibility of labor markets (Botero et al. 2004). We control for the legal tradition of a country using dummy variables. These traditions include French, German, Scandinavian, Socialist and the omitted category of English common law. The data source for the variable is Djankov et al. (2007).

Last, starting with the seminal work of Max Weber, a number of studies have highlighted the importance of religion in shaping the quality of institutions. For example, Stulz and Williamson (2003) show that the low level of creditors’ protection present in Catholic countries is due to the anti-usury culture pervasive in the Catholic tradition. Following this literature, we use dummy indicators identifying the main religious group in the country as either Muslim, or Catholic, Protestant and the residual category of all other religions. The source for these indicators is La Porta et al. (1999).

In addition to these variables, we briefly discuss our results with a number of other controls. Examples include the level of regulation across countries at the beginning of the sample period, the degree of openness of the economy, foreign aid, the level of education, and mortality rate for early European settlers in the previously colonized countries.

10
**Figure 4.2b: Regional disparity trends** in **unitary countries**

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Its weighted CV has been around three times that of the US. The decline may be because Chile's development stage is on the right side of the "inverted U". Morevover market liberalization in Chile may be causing convergence. Economic activity is heavily concentrated in the central region. However, centralizing trends appear to have stopped, as a result of the mining boom in the north and the economic dynamism achieved in the extreme south by salmon breeding, tourism and large-scale methanol production. Tourism and export agriculture are strong engines of growth in the centre-north, while forestry, tourism, fruit production and traditional agriculture are important to the centre-south regions. Further, the traditionally strong centralist bias is being reversed through a gradual devolution of power to the municipalities, regional offices of ministries and public agencies, and other regional organizations (Source: EIU Country Profile).

24
supervisors that directly monitor, discipline, and influence banks, are more likely to report major obstacles due to corruption in lending. On the other hand, a supervisory strategy that focuses on empowering private monitoring of banks by forcing banks to disclose accurate information to the private sector tends to lower the degree to which corruption of bank officials is an obstacle to firms raising external finance. Table 6 reports the results of a regression of Corruption in Lending on firm characteristics and other country characteristics. We also control for general financing obstacle to control for the fact that firms that report higher overall financing obstacles might put part of the blame on corruption. Supervisory power, which is a principal component indicator of 14 dummy variables proxying for specific power of bank supervisors to intervene in banks, enters positively and significantly. Private Monitoring, which is the principal component indicators summarizing the extent to which banks are forced to publish information and the absence of explicit or implicit deposit insurance, enters negatively and significantly.

While Bangladesh is not part of the Beck et al. (2006a) sample due to some missing firmlevel variables, we can use the regression results to gauge the effect of powerful bank supervision in the case of Bangladesh. Bangladesh takes a value of 0.44 for Supervisory Power – Bangladesh Bank had 11 out of 14 powers summarized by this indicator in 1997 – while Private Monitoring takes a value of -0.80.<sup>24</sup> Bank supervisors are thus much more powerful in Bangladesh than in the average country, but supervision relies much less on monitoring and disciplining by market participants. 19% of all firms in Bangladesh reported corruption in lending as major obstacle to operation and growth, compared to 8% across the 37 countries in the Beck et al. sample. Were Supervisory Power and Private Monitoring at the sample means of

> 24 Unfortunately, there are no data available in the more recent version of the Bank regulation database for Bangladesh.

26
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<!-- Start of picture text --> 500<br>=<br>=<br>oO las 1! . W\on<br>c '<br>ie 450 1<br>wwS’“<br>_oO ' t a<br>c 1 "<br>(<b) 1 . iv<br>or) 1Na WtyiM /Vi| AT<br>7g «400 v A wae ane<br>ne) ra Aa i “ a 4 4<br>Tif rn Wy evn rN S i‘<br>Ko) ‘ ss re on y x ‘ , :<br>Ee we ‘\ aan ky if ‘nl<br>350<br>O1jan2020 Olapr2020 01jul2020 Otloct2020 O1jan2021 Otlapr2021 O1jul2021 01oct2021<br>date<br>—— Sweden predicted using own stringency index<br>---- Sweden predicted using France's stringency index<br><!-- End of picture text -->
- 27 -

# Table 7: Net Liability of Major<sup>Borrowers to</sup> The Banking Sector (including NIB)

||Private<br>Public<br>Business<br>Enterprises|Central<br>Government and<br>Public Authorities|
|---|---|---|
|Net Liabilitv OutstandinQ|(In billion Egyptian LE)<br>||
|1. Banking Sector|7.0<br>2.8<br> <br>|37.6<br>|
|1 a. National Currency<br>1 b. Foreign Currency|7.4<br>3.9<br>-.4<br>-1.1|37.9<br>-.3|
|2. National Investment Bank|0.0<br>26.8|0.0|
||(As percent of GDP)||
|Net Liability Outstanding<br>1. Banking Sector<br>|7.8<br>3.1<br> <br>|41.2<br>|
|Ia. National Currency<br>1b. Foreign Currency|8.2<br>4.3<br>--<br>-1.3|41.6<br>--|
|2. National<br>Investment<br>Bank|<br>--<br>29.4|--|

Source: Authors' estimates based<sup>on data from The Central Bank of Egypt.</sup> With a net liability of nearly 38 billion<sup>Egyptian pounds (41.2 percent of</sup> GDP) outstanding as of December 1989, the<sup>government sector is the major debtor</sup> to deposit banking institutions. By contrast,<sup>public enterprises draw mostly on</sup> the resources of NIB to meet their financing<sup>requirements, even though over the</sup> last few years, more and more public enterprises' investments have been financed through overdrafts on deposit banks.<sup>Public enterprises also depend on deposit</sup> banking institutions to raise funds<sup>for their working capital requirements, and</sup> they hold, as a group, a large sum of<sup>deposits with these institutions. Their</sup> net liability outstanding with deposit banks<sup>on the whole is about 4.3 percent</sup> of GDP, as of December 1989. To the extent<sup>that the adjustment program will</sup> succeed in getting public enterprises on the<sup>same footing as private enterprises</sup> in the financing of their investment<sup>program, the yields on the alternative</sup> financing sources will be equalized.<sup>This would mean, for instance, that for</sup> every ten percentage point increase in<sup>interest rates, and based on thecurrent</sup>
**Revisiting the National Innovation System in Developing Countries**<sup>**1**</sup>

William. F. Maloney World Bank

**JEL:** 032, 038, B52, L2, O47

**Keywords:** National Innovation Systems, Technological Diffusion, Firm Capabilities, Innovation Benchmarking.

> 1 My thanks to Jose Miguel Benevente, Laura Chioda, Xavier Cirera, Charles Edquist, Keun Lee, Bengt Ǻke Lundvall, and Juan Rogers for very thoughtful comments. This work partially financed by the Knowledge for Change Trust Fund.
# Improving Enrollment and Learning through Videos and Mobiles: Experimental Evidence from Northern Nigeria<sup>*</sup>

Victor Orozco-Olvera, _DIME, The World Bank_ Ericka Rasc ́on-Ram ́ırez, _Middlesex University_

**Keywords:** school enrollment, aspirations, learning, disruptive technologies, self-efficacy, early marriage, early parenthood, gender norms. **JEL Classification:** I21, J12, J13, J16, L82, O15, Z13.

> *This research is part of the entertainment-education program of the World Bank’s Development Impact Evaluation Department (DIME). Computational replicability was verified by DIME Analytics. We thank the Nigeria Federal and the State ministries of education of Kano and Jigawa, in particular Dr. Folake Olatunji-David, for supporting the testing of innovations and for facilitating access to local communities. We thank Impact(Ed) International, Curious Learning, Global Digital Library, Breaking Barriers in Development, and MTN for producing the interventions’ materials or supporting their delivery. We are immensely grateful to our field coordinator Edwin Ikhuoria and Hanovia Ltd, the survey firm that conducted the data collection. Laura Costica and Tomas Koutecky provided excellent research assistance. We thank seminar participants at the Royal Economic Society, Centro de Investigaci ́on y Docencia Econ ́omicas (CIDE), Universidad de Granada, Universidad de Sevilla, University of Maryland, and World Bank DIME. This project was funded by the World Bank Nigeria Partnership for Education Project, the World Bank Research and Support Budget, and the Norwegian Agency for Development Cooperation. This study received ethical clearance from Solutions IRB (IORG0007116) and was preregistered at the AEA RCT Registry `https://www.socialscienceregistry.org/trials/3619` prior to baseline data collection. The views expressed in this article are those of the authors and do not necessarily reflect the views of their institutions. All errors are the authors’ sole responsibility.
those that are not reached will continue to be listed in the bank of possible phone numbers to be tried each month, and the same household is potentially contacted for a number of survey rounds.<sup>10</sup> Regardless of the exact numbers of households in the database of phone numbers, in each survey 20 percent of responses are from new households and the remaining responses are from households surveyed at least once before.

The survey first collects information regarding a household’s location, and whether the household has been displaced. If the household is currently displaced, the survey inquires about the month and year of displacement and the origin governorate.<sup>11</sup> Given that households are followed over time, there are households that are displaced more than once, and also households that return to their homes following one or more initial displacements. We then merge the time and location of households with conflict data from the Uppsala Data Program (prior to 2016) and from the Armed Conflict and Event Data (ACLED) (2016 and later).<sup>12</sup> We match the violence data before, during, and after the time of displacement in the origin district.

The survey further collects a full food security module, including the number of times households in the week before the survey have consumed from important food groups, the number of times households have resorted to five common food coping strategies, and whether the household received food assistance in the past 30 days. From these data, we calculate the household Food Consumption Score (FCS) and the reduced Food Coping Strategies Index (rCSI). A higher FCS and a higher number of days consuming particular food items are associated with better food access; and a lower rCSI and a lower number of days relying on negative food coping strategies are associated with better food access.

The entire survey between August 2015 and December 2018 includes 18,078 separate households.<sup>13</sup> This analysis focuses on the households that became displaced after their first interview, which allows us to analyze how indicators of well-being were changing

> 10The WFP is unable to observe whether a phone number exists in the random digit dialing procedure, and thus we are unable to identify the share of non-responses related to active phone numbers.

> 11See Appendix 2 for a summary of the variables reported in each survey.

> 12 We use the number of fatalities caused by violent incidents and the number of violent incidents as our main indicators of violence.

> 13Although the mVAM continues through the time this paper was written, the displacement module was removed from the questionnaire.

10
Table 2: Marginal Effects of School Type Determinants: selected variables, full sample

||||**Men**|||**W**|**omen**||
|---|---|---|---|---|---|---|---|---|
||Public<br>general|Public<br>vocational|<br>Private<br>general|Private<br>vocational|Public<br>general|Public<br>vocational|<br>Private<br>general|Private<br>vocational|
|Personal characteristics|||||||||
|Middle Cohort|4.2|-13.4***|8.8***|0.3|-1.6|-15.6***|10.0***|7.1**|
||(2.6)|(1.8)|(2.5)|(2.0)|(2.9)|(2.3)|(3.2)|(3.0)|
|Recent cohort|-2.5|-13.4***|3.0|12.9***|1.3|-18.4***|7.7**|9.4***|
||(2.5)|(1.9)|(2.5)|(2.8)|(2.8)|(1.9)|(3.1)|(3.1)|
|Repeated grade in junior<br>secondary|-4.3|-3.8|5.5|2.6|3.6|-11.1|1.8|5.8|
||(6.2)|(5.1)|(5.3)|(4.7)|(9.1)|(10.1)|(10.7)|(6.1)|
|Lived in small town at age 12|2.2|-0.9|-2.7|1.3|4.2|-2.7|-1.7|0.2|
||(2.4)|(2.1)|(2.1)|(2.3)|(2.7)|(2.2)|(2.4)|(2.2)|
|Lived in big city at age 12|7.4**|-2.0|-6.5***|1.1|6.4*|-0.1|3.3|-9.7***|
||(2.9)|(2.5)|(2.4)|(2.7)|(3.3)|(2.8)|(3.0)|(2.2)|
|Height|0.0|-0.1|0.2*|-0.2**|0.0|-0.0|0.1|-0.1|
||(0.1)|(0.1)|(0.1)|(0.1)|(0.2)|(0.2)|(0.2)|(0.2)|
|Parental education|||||||||
|Father graduated elementary|4.2|-1.3|4.1|-7.0*|-6.5|-4.6|5.8|5.3|
||(4.5)|(3.9)|(4.2)|(4.2)|(6.2)|(5.3)|(6.2)|(6.5)|
|Father graduated junior secondary|6.2|-3.9|7.4|-9.8**|-5.6|-8.8*|8.0|6.4|
||(5.4)|(4.1)|(5.2)|(4.5)|(6.6)|(5.3)|(6.9)|(7.1)|
|Father graduated senior secondary|6.0|-6.8|13.0**|-12.2**|-1.5|-8.5|11.0|-1.0|
||(5.9)|(4.5)|(6.1)|(4.8)|(7.8)|(5.5)|(7.6)|(6.1)|
|Father graduated university|18.7**|-12.2***|12.4*|-18.9***|6.1|-11.3*|7.0|-1.8|
||(7.7)|(4.4)|(7.3)|(4.5)|(9.0)|(5.9)|(7.4)|(7.0)|
|Father attended vocational school|3.5|7.0|-5.2|-5.3|1.5|0.2|-8.9**|7.2|
||(5.0)|(5.5)|(4.2)|(4.4)|(5.4)|(4.6)|(4.2)|(5.3)|
|Mother graduated elementary|0.1|-4.6*|5.8*|-1.3|6.7|6.6*|-4.6|-8.7***|
||(3.2)|(2.5)|(3.2)|(3.1)|(4.5)|(3.5)|(3.6)|(3.2)|
|Mother graduated junior<br>secondary|3.0|-4.5|7.1|-5.6|13.2**|-2.0|1.9|-13.1***|
||(4.4)|(3.7)|(4.7)|(3.8)|(5.6)|(3.4)|(4.9)|(3.4)|
|Mother graduated senior<br>secondary|5.2|-8.8*|2.0|1.5|10.0|4.7|-2.2|-12.5**|
||(6.9)|(5.0)|(6.0)|(6.9)|(6.7)|(5.9)|(6.6)|(5.4)|
|Mother graduated university|18.6*|-5.3|-4.7|-8.7|17.2*|7.3|-11.4|-13.1**|
||(11.3)|(10.6)|(5.7)|(7.8)|(10.1)|(10.1)|(7.2)|(6.7)|
|Mother attended vocational<br>school|-0.9|7.1|-3.5|-2.7|3.9|-2.6|-5.1|3.9|
||(6.5)|(7.9)|(5.5)|(5.7)|(6.1)|(5.3)|(6.0)|(7.4)|
|Base case probability|12.8|30.8|18.4|38.0|50.6|19.8|17.7|11.9|
|Observations||2|,675|||2|,260||
|R-Squared||0|.099|||0|.116||

Notes: *** 1% significance, ** 5% significance, * 10% significance; figures are marginal effects in percentage points; estimation includes province of junior secondary graduation fixed effects and all variables listed in Appendix 1; standard errors in <u>parentheses, they are robust to heteroskedasticity and clustered at subdistrict level.</u> 11
However, if stratification is purely exogenous, such that it does not take into consideration the dependent variable to stratify the sample, then the estimated parameters are consistent, regardless of differences between the estimation sample and the true underlying population. By contrast, under pure endogenous sampling, the marginal distribution of the dependent variable in the sample differs from that in the population, and as a result, the estimated coefficients are inconsistent. Since firms’ sales have not been used to stratify the WBES, we do not use country-specific weights for the estimation of the coefficients of the PF. Last, following the literature on PF estimation using the CFA, we bootstrapped the standard errors using 100 replications and country and year to construct the strata.

After estimating the PF elasticities, we use equation 4.1 to estimate TFPR. Then, with unbiased estimates of TFPR at the firm-level in hand, we pool all the observations and run an OLS regression of unbiased-TFPR on digital business solutions (e.g., email and website) to estimate the marginal effects of digitization on TFPR. The OLS coefficients are mathematically equivalent to the weighted average of the estimated coefficients obtained from the PF estimation, where the Markov coefficients vary at the sector-level (see Appendix B for the proof).

We assume homogeneous effects of digital-technology adoption on TFPR (instead of sector effects) for two reasons. First, the type of digitization we are interested in falls under the category of general-purpose technologies (instead of sector-specific technologies). The second reason is to gain efficiency and increase the degrees of freedom in the estimation, as several sectors have few observations once we lagged the explanatory variables used to endogenize TFPR to control for endogeneity. Provided we focus the interpretation of the results (inference) on the entire sample, our approach eliminates imprecisions coming from making estimations with small sub-samples.

Our empirical strategy has three stages. Stages 1 and 2 are the standard Control Function Approach stages, with the difference that we extend Ackerberg, Caves, and Frazer (2015) and De Loecker (2013) and endogenize TFPR as a function of four firm-choice variables, 14