rafmacalaba/datause-encoder-data huggingface dataset:
the sub-region and the capital of the partner country.<sup>10</sup> Second, we exploit financial information disaggregated at the sub-regional and provincial levels from the Bank Association of Turkey.<sup>11</sup> The data contains information on the number of branches of local and foreign banks and the number of loans in each Turkish sub-region. The definition of the level of financial development in each sub-region ( _Financial developmentit_ ) is the ratio of credit to gross value added computed at the sub-regional level. The measure is popular in the financial development literature. As King and Levine (1993) initially pointed out, the measure can be used as proxy because lending to the private sector rather than only to government and state-owned enterprises is more likely to stimulate growth in the sector by promoting corporate control capacities and risk evaluations. While we are aware of the limitations of this variable, it is the best indicators available given the data and the subnational nature of the analysis.<sup>12</sup>

# **3 Results**

We first examine the effects of foreign banks on exports, which are summarized in Table 2. The intensive margin and the extensive margin of the information channel and the level of financial development positively affect the exports of Turkish sub-regions. We compare the effects of each variable on exports in separate regressions in columns (2) to (4), by examining the standardized coefficients. A one standard deviation increase in financial development leads to a 0.03 standard deviation increase in _Log(Exportsijt)_ . Similarly a one standard deviation increase in the presence of foreign banks will increase the log of exports by 0.02 standard deviations. When the share of bank branches increases by one standard deviation, the log of exports increase by 0.08 standard deviations. These initial regressions suggest that while the level of financial development and the extensive margin of the information channel are equally important for exports, the intensive margin of the information channel is most important for exports.

The intensive and extensive margins of the information channel remain important after accounting for the sub-regional level of financial development in the regressions in the last two columns of Table 2. The level of financial development is important, and as important as the extensive margin of the information channel, but the intensive margin of the information channel is more important for the sub-regions exports. All coefficients for financial development and the information channels are statistically significant. The signs on the

> 10See Celbis, Nijkamp, and Poot (2014) for the different sources of information to construct the variable. 11https://www.tbb.org.tr

> 12Refer to Levine (2005) for a discussion on the measurement of financial development.

7
their quality, their cost and their institutional nature should complete the picture. This last aspect is especially relevant for water, which is often provided by local operators and regulated at the local rather than the national level.

# **V. Conclusion**

The review of the infrastructure and development literature performed in this paper sustains a number of conclusions in terms of potential research areas and associated data development need, which can be organized in three related parts, relating to macroeconomic, microeconomic and economic geography aspects. In terms of data development, the main effort should be concentrated in the microeconomic part of the picture, through a strategy to gather data from both household and firm-level (ICAs) survey on aspects including access, quality and costs of services. Indicators, aggregated at different levels, could then be used both in macro-level and economic geography types of estimations.

At the macro-level, the relevant challenges imply to move away from a long string of contributions that have tried to estimate the link between output or growth and aggregate indicators of infrastructure (public capital or physical indicators) and rather concentrate on how aspects linked to the political, institutional and regulatory environment have affected the delivery and efficiency of services in the different sectors. This also implies continuing with ongoing efforts to develop large databases on these features of the environment, and stepping up theoretical effort to understand the underlying mechanisms at work.

The economic geography approach appears very promising, but infrastructure related work, both at the theoretical and at the empirical level, is still in its infancy. Significant efforts are necessary to develop the relevant theories, include additional aspects of infrastructure beyond transportation, and develop suitable empirical strategies to test the models. The main related recommendations are summarized in Table 5.

40
a relatively more signi fi cant e ff ect on spatial patterns of specialization. This will be particularly true in less developed countries where poor infrastructure endowment has led to a concentration of economic and political functions in one or very few large urban centers (e.g., Henderson, 2002). We control for two types of accessibility in our regressions: access to nearest town or urban municipality with a population of at least 5000 inhabitants,<sup>13</sup> and access to the two major growth poles in the country. In Bangladesh, the capital city Dhaka and the port city of Chittagong in the Southeast are the primary urban centers, accounting for a large share of urban and overall population<sup>14</sup> . They act as the main domestic and international trading hubs and are the dominant seats of major administrative and economic functions. The travel time to urban centers is often taken as the indicator of accessibility in the existing literature (see for example, Fafchamps and Shilpi, 2003; Jacoby, 2000). The use of travel time in de fi ning access to urban centers raises concerns about the possible endogeneity in the placement of road infrastructure (van de Walle, 2002).<sup>15</sup> In order to avoid the potential endogeneity problem of travel time, we use minimum arc distance from surveyed primary sampling units to either of the two metropolitan centers (Dhaka or Chittagong) as an indicator of accessibility. These distances are estimated using the Haversine formula. These distances, popularly known as the crow fl y distances, do not depend on the actual placement of road and other transport infrastructure. Yet, they provide good measures of the geographical dispersion of the surveyed locations with respect to Dhaka and Chittagong.

In order to complete the econometric speci fi cation, next we de fi ne our dependent variable. There is considerable evidence on the heterogeneity of non-farm activities consisting of high and

> 13 This measure of access to towns is used in the regression mainly to conform with existing literature. It should also be noted that using access to other towns of larger sizes (e.g. 10,000 inhabitants) do not change the results signi fi cantly.

> 14 Population in these cities accounts for 87 percent of total population in metropolitan cities, and 48 percent of total urban population in the country.

> 15 Roads are often built in areas with greater agricultural and perhaps also nonfarm potential.

10
Rao, N., et al. (2015). Early childhood development and cognitive development in developing countries: a rigorous literature review. _Department for International Development, University of Hong Kong. Available at:_ _<u>http://eppi.ioe.ac.uk/cms/LinkClick.aspx?fileticket=9reL_ORWZmI%3d&tabid=3465.</u>_ Rodrigues, C. G., Pinto, C. X. C., & Santos, D. D. (2010). The impact of daycare attendance on math test scores for a cohort of 4th graders in Brazil. Report to the Inter-American Development Bank.

Sabol, T. J., & Pianta, R. C. (2012). Patterns of school readiness forecast achievement and socioemotional development at the end of elementary school. _Child Development,_ 83(1), 282-299.

Samii, C. (2016). Inverse covariance weighting versus factor analysis, <u>http://cyrussamii.com/?p=2177.</u> Accessed on August 23, 2016.

Segal, L., Opie, R., & Dalziel, K. (2012). Theory! The Missing Link in Understanding the Performance of Neonate/Infant Home-Visiting Programs to Prevent Child Maltreatment: A Systematic Review. _Milbank Quarterly,_ 90(1), 47-106.

Sweet, M. A., & Appelbaum, M. I. (2004). Is Home Visiting an Effective Strategy? A Meta-Analytic Review of Home Visiting Programs for Families With Young Children. _Child Development,_ 75(5), 1435-1456.

UNESCO. (2011). International Standard Classification of Education. Available at <u>http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf. .</u> UNESCO. (2015). EFA Global Monitoring Report 2015.

UNICEF (2015). Transforming our world: the 2030 Agenda for Sustainable Development. Available at: <u>https://sustainabledevelopment.un.org/post2015/transformingourworld.</u> World Health Organization Multicentre Growth Reference Study Group (2006). WHO Child Growth Standards based on length/height, weight and age. _Acta Paediatr Suppl,_ 450, 76-85.

Yoshikawa, H., et al. (2015). Experimental impacts of a teacher professional development program in Chile on preschool classroom quality and child outcomes. _Dev Psychol,_ 51(3), 309-322.

39
Policy Research Working Paper 9532

# **Abstract**

Low levels of investment into modern technologies, and limited use of measures that have low monetary cost but the potential for high yields, are often regarded as obstacles to further agricultural development. This paper investigates farmers’ demand for one such measure, namely agricultural advisory services. These have modest (most frequently zero) monetary user cost but, according to some recent research, have the potential to result in large increases of yields. Yet, demand for these extension services is often low. This study proposes that costly attention may be part of the explanation for this. In the model, advisory services are available free of charge, but positive effects on production are only realized if farmers devote attention to listening to and implementing the provided advice. Modeling farmers as rational decision makers facing scarce attention, the study identifies the circumstances under which farmers may optimally abstain from demanding advisory services. The model complements the insights of other theories commonly used to explain suboptimal farm decisions and outcomes, and generates testable predictions, which are consistent with empirical evidence based on a large farm-level panel dataset from Sub-Saharan Africa.

This paper is a product of the Development Policy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dominik.naeher@ucd.ie.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
23 exclusion restriction for the distance-weighted instrument is not satisfied as openness of neighbours’ economies increases discoveries there and in the home country as the neighbours’ discoveries provide additional geological information about the home country. By controlling directly for neighbours’ distance-weighted discoveries, this concern is addressed (see Table S1). We find that the point estimate on market orientation is virtually unchanged relative to the baseline regression, but the estimate for the impact of neighbours’ discoveries is always positive albeit not always significant.

The significance of our core results in Table 4 is unaffected if standard errors are clustered at the country-resource level instead of at the year-resource level (see Table S2). If the human capital index and GDP from the Penn World Tables as are added as controls in our 3-way panel, the results are broadly the same even though the point estimates of the effect of openness on discoveries are somewhat smaller (see Table S3).

To allow for a more even comparison between countries (even though the fixed effects included in all regressions already address issues such as different sizes of countries), we also estimated the regressions with discoveries _per capita_ as the dependent variable (see Table S4). The point estimate on openness remains positive and highly significant. With discoveries per capita as the dependent variable the estimates are in fact particularly robust and are both qualitatively and quantitatively unchanged when additional control variables (such as GDP per capita) are added (Table S5). Instead of using a count variable (the number of discoveries), we have also used a simple dummy variable as the dependent variable, again confirming the significance of market orientation for natural resource discoveries (see Table S6).

Our core empirical results also hold when excluding any of the individual country groups (see Table S7) or excluding any particular decade (see Table S8). Across all regressions the coefficient on market orientation remains positive and significant. This is important since it suggests that our results are not driven by one region or one specific time period only.

Given our rich data set for discoveries, we can estimate equation (I) individually for different natural resources �∈�����, ���, ���, ������, ������, �������, ����, �������. This list covers over 90% of all discoveries for our period of analysis, 1950-2004, corresponding to 2171 out of a total of 2392 discoveries. The estimates for the impact of openness on discoveries
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# **Table 2: The Relationship between Outcome of Public Spending and**

# **Accountability**

|Dependentvariable:Gross secondary enr|ollmentratio<br>||||
|---|---|---|---|---|
||(1)||(2)||
|Urban population|0.25|**|0.25|**|
||_0.09_||_0.09_||
|GDP per capita|14.59|**|14.58|**|
||_2.02_||_2.04_||
|Pupil/teacher ratio, secondary|-0.44|**|-0.43|**|
||_0.20_||_0.20_||
|Public secondary education spending|0.39|**|0.37|**|
||_0.14_||_0.14_||
|Voice and accountability|**3.87**|******|2.73|*|
||_1.26_||_1.52_||
|Control of corruption|||1.61||
||||_1.79_||
|Sub-Saharan Africa|-25.62|**|-23.99|**|
||_7.08_||_7.43_||
|South Asia|-32.04|**|-31.42|**|
||_10.43_||_10.12_||
|Middle East and North Africa|-27.03|**|-26.77|**|
||_7.15_||_7.12_||
|East Asia and Pacific|-21.88|**|-20.39|**|
||_6.53_||_6.85_||
|Europe and Central Asia|-29.80|**|-28.83|**|
||_6.90_||_7.13_||
|Latin America and the Caribbean|-33.49|**|-31.84|**|
||_6.92_||_7.25_||
|Constant|-45.13||-46.11|**|
||_17.71_||_17.88_||
|# of obs.|185||185||
|R^2|0.77||0.77||

(standard deviation shown in italic) The second step consists of examining if taxation can improve the accountability of government when oil-dependency lowers it.

9
# **1. Introduction**

Early life investments can be beneficial for children both for short- and longer-term outcomes, such as better labor market returns (e.g. (Gertler et al., 2014), and programs targeting disadvantaged children can be socially efficient (Elango et al., 2015). Returns to investments in early childhood are higher than investments made later in life because beneficiaries have a longer time to reap the rewards, and because early childhood is a sensitive period during which adverse exposures as well as positive interventions can have the greatest effects on an individual’s developmental trajectory (Heckman & Mosso, 2014). Furthermore, early investments in human capital have dynamic complementarities, such that “learning begets learning” (Carneiro & Heckman, 2003).<sup>1</sup> Lack of adequate preparation for primary school through pre-primary education is one of the key risk factors for poor performance in primary school (Behrman et al., 2006).<sup>2</sup> Thus, a popular approach to trying to improve outcomes in children has to do with increasing enrollment in preschool programs, and/or trying to improve the quality of existing programs.<sup>3</sup> Children in low-resource settings are less likely to attend school, and they are not likely to learn when they are in the school setting – partly because they are unprepared for school when they get there.

1 Early child development (ECD) investments can take many forms, including promotion of good health and nutrition, support for safe and stimulating environments, protection from risks such as violence or abandonment, parenting support and early learning experiences, media, preschools, and community groups. Systematic reviews of ECD interventions in LMICs have shown success, particularly when interventions are high quality, targeted to the most vulnerable children and integrated with other services (Engle et al., 2011).

2 Pre-primary (or preschool) programs generally refer to an organized learning group that meets at least two hours per week and can be categorized as formal (institutionalized, intentional and planned through public organizations) or informal (not institutionalized, less structured, less organized) (UNESCO, 2011).

3 Enrollment in preschool programs has increased substantially in LMICs over the past several decades (Behrman et al., 2013), but is still far from optimal. UNESCO’s Global Monitoring Report of pre-primary enrollment rates in 2012 showed coverage ranging from 19% (86%) for low-income (high-income) countries (UNESCO, 2015).

2
|EmergencyFarm Reconstruction Project|Frauke Jungbluth|
|---|---|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Jordan Higher Education Development|Adriana Jaramillo|
|Improved Management and Transparencyof MiningSector|Boubacar Bocoum|
|Improved Management and Transparencyof MiningSector|Boubacar Bocoum|
|Employment Promotion Project(LIL)|Arvo Kuddo|
|Employment Promotion Project(LIL)|Arvo Kuddo|
|Employment Promotion Project(LIL)|Arvo Kuddo|
|Employment Promotion Project(LIL)|Arvo Kuddo|
|Employment Promotion Project(LIL)|Arvo Kuddo|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|INDIA - CFC PRODUCTION SECTOR CLOSURE PROJECT(ODS III)|Laurent Granier|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
|Gulf of Gabes Marine and Coastal Resources Protection Project|TaoufiqBennouna|
<!-- Start of picture text --> THATLAND (20043<br>|<br>ee<br>Voice and Accountability _ << [7 SO<br>___fT$<—<br>Political Stability Ssee<br>—<<<br>EEa L—————<br>Governnent Effectiveness aaaaOO[7esaJ———<br>S53<br>—a<br>Regulatory Quality ee———————<br>eeafT oy<br>|<br>a<br>Rule of Law [i<br><< a<br>Sa=!<br>Control of Corruption a$<el——<br>a 25 a8 fa 18086<br>Conparison between 260604, 26002, 2000, 1996, 1996 ttop—botton order?<br>Country”s Percentile Rank ¢6—-168>)<br>Source: D. Kaufmann: Ae Kraqus and He Hastrussd 2005<br><!-- End of picture text -->