rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

The diffusion of knowledge is central to endogenous growth theories (Romer (1986, 1990); Grossman and Helpman (1991b,a); Rivera-Batiz and Romer (1991)). Due to its public good nature, new knowledge can be generated from all existing knowledge or R&D investments. Despite their appeal, endogenous growth theories rely on a broad set of assumptions that remain untested. For instance, there could be differential effects of knowledge spillovers, depending on the types of innovations and the distance (geographical or technological) between the innovator and the source of existing knowledge. Simply put, it is not obvious that a poor country can easily adopt knowledge from technologically or geographically faraway economies; it is not clear that low-productivity firms can easily learn from firms operating at the technological frontier.

This paper explores the impact of knowledge spillovers on the generation of new knowledge using two types of data: patents granted by the United States Patent and Trademark Office (USPTO) and firm data on product-quality upgrading and the introduction of new goods from the World Bank’s Enterprise Surveys. Although patents might be appropriate to study innovation patterns and externalities in developed countries, they might be ill suited for analyzing innovation patterns in developing countries. Hence both approaches might be more revealing than focusing only on one type of innovation.

In a first set of econometric estimations, following Rivera-Batiz and Romer (1991), we test whether the so-called “cumulative knowledge” or the “R&D investment” function explains patenting activity at the country level. If knowledge spillovers are international, then patenting by one country should be influenced not only by the stock of patents received by nationals, but also by the stock of patents worldwide. More specifically, we estimate specifications of patent functions with elements of the cumulative-knowledge and R&D-investments models, and allow for countries’ patenting activity to be influenced by the accumulated domestic and foreign knowledge. In a second set of estimations with firm data we explore the determinants of
to Syria’s national absolute poverty line, estimated at $2.2/day in 2003-04 (El-Laithy and AbuIsmail, 2005).

Given the objective of presenting cross-country comparisons, we fix the poverty line at $2 a day (in 2005 PPP$) for all the countries in our sample. This line is close to the national absolute poverty thresholds for Syria and Egypt, although it is above the national absolute poverty lines in Yemen and below the national poverty lines in Tunisia, Jordan, and the Palestine territories. Therefore, it overestimates poverty in Yemen but underestimates poverty in Tunisia, Jordan, and Palestine (Figure 1).

Table 3 indicates some progress with poverty reduction in the 2000s. Poverty rates declined in Tunisia, Syria, Jordan and the Palestine territories and increased in Egypt and Yemen. The subjective well-being data, however, reveals a different picture from the household survey data. It shows a uniformly bleak situation in the period before the Arab Spring (Figure 2, top panels), although we do not make strict comparisons of the quantitative changes observed in each country because of the differences in the survey years captured with the objective and subjective data.<sup>19</sup> In all developing MENA countries on which we have sufficient information, the percentage of dissatisfied (unhappy) people – those with life evaluation scores below the lower cutoff point of 4 or 5 as discussed in Table 1 – was high and increased between 2007 and 2010. The deterioration was particularly large in the Arab Spring countries, especially Egypt and Syria. By the end of the decade almost half of the population in Syria and Egypt were unhappy about their life (Figure 2, top panels). Importantly, unhappiness rates in Arab Spring countries were mostly higher than those in other countries in the region (Figure 2, top panels).

> 19 Data are not available for all countries in 2007. Thus we use data in 2006 for Lebanon, and 2008 for Algeria, Iraq, Syria, and
in 43 percent of high-income countries and 10 percent of upper-middle and lower-middle-income countries, while this system is not found in any low-income country.

It is worth noting that certain categories of government data may not be suitable for publication under open data policies—for instance, in the case of personal data or data entailing national security concerns. Thus, public entities should evaluate potential issues of sensitivity in light of a data classification policy. This requires an ex ante assessment of the risks associated with the publication and adoption of protective measures to ensure the safe publication of personal information on open data portals, while allowing accountability. Data should be categorized taking into account the different levels of sensitivity across the different stages of the data life cycle, enabling effective and safe data reuse (Digital Guardian 2018). A data classification policy has been adopted by more than half of the countries across income groups, ranging from 26 percent of countries in the low-income group to 78 percent of high-income countries. The use of such classification is mandatory for government data in 13 percent of low-income countries, 20 percent of lower-middle-income countries, 42 percent of upper-middle-income countries, and 57 percent of highincome countries.

# _4.3.3 Enablers: private intent data_

The private intent data refers to data collected with the original intent of pursuing commercial purposes. Private data can generate productivity gains and growth in both developed and developing countries, supporting innovation, efficiency, and inclusion (World Bank 2016). Data on consumer behavior and preferences become the secret weapon to further propel business growth (Argenton and Prüfer 2012). MYbank, an online private commercial bank in China, uses big data to assess loan applications instead of hiring thousands of loan officers or lawyers for offline, face-to-face due diligence work. It provided loans

> 21 The text of the law can be accessed at:

<u>https://www.law.go.kr/%EB%B2%95%EB%A0%B9/%EA%B3%B5%EA
1-5. Panel D is constructed from multiple rounds of PODES and Susenas from 1990-2014, as described in Section 3. Panel D describes pooled observations of districts across PODES and Susenas years. Preschool counts are obtained from PODES and child age 3-6 population from Susenas; densities are defined as preschool count divided by 1,000 children in the district. Districts often split over time; by 2014, there were 511 districts. In our analyses, we maintain the 1993 district boundaries to allow comparisons over time.

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9|6.0|2.4|40.8|
|Access to improved latrines|% pop|9.9|40.8|47.6|1.4|
|Access to traditional latrines|% pop|50.1|30.8|28.6|30.4|
|Open defecation|% pop|40.3|22.2|21.3|14.3|
|Domestic water consumption|liter/capita/day|72.4|39|44|165.9|
|Revenue collection|% sales|92.7|96.0|96.0|100.0|
|Distribution losses|% production|34.3|37.0|25.5|26.8|
|Cost recovery|% total costs|56.0|—|70|80.6|
|Operating cost recovery|% operating costs|65|—|98|145|
|Labor costs|connections per<br>employee|158.6|170.4|210.8|368.7|
|Total hidden costs as % of revenue|%|162.7|184|124|140.4|
|||**Mali**|Countr|ies with scarce w|ater resources|
|Residential tariff (US cents per m<sup>3</sup>)||25.2||60.26||

Source: Demographic and Health Survey and AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. DHS figures are as of 2006. Utility numbers are as of 2001 and 2007. Note: Tariff data as of mid 2000s In less than two decades, Mali has managed to provide access to improved water and sanitation to more than half of its population, or in the worst case (figure 10). The share of
|
|LPI logistics|LPI|Competence and quality of logistics<br>services(1-5=high)|<br>connectivity|2007/10|Full except TWN & BRN|
|LPI customs|LPI|Efficiency of customs clearance<br>process(1-5=high)|connectivity|2007/10|Full except TWN & BRN|
|LPI overall|LPI|Overall (1-5=high)|connectivity|2007/2010|Full except TWN & BRN|
|Export time|WDI|Time to export (days)|connectivity|2005-2011|Full except TWN|
|Import time|WDI|Time to import (days)|connectivity|2005-2011|Full except TWN|
|Investment<br>freedom|Heritage<br>foundation|Restrictions imposed on investment<br>(100=best)|<br>investment &<br>tradepolicy|1995-2011|Full except BRN|
|STRI WB|Borchert, Gootiz<br>and Mattoo<br>(2012)|,<br>Services trade restrictiveness index,<br>simple mean|<br>investment &<br>trade policy|2008/09/10|Full except TWN, BRN, CHE, CYP, EST, HKG,<br>HRV, ISL, ISR, LUX, LVA, MLT, NOR, SVK,<br>SVN,SGP|
|FDI inflows|WDI|Foreign direct investment, net<br>inflows(% of GDP)|investment &<br>tradepolicy|1995-2011|Full except TWN|
|Services trade|WDI|Exports and imports of services (%<br>of GDP)|investment &<br>tradepolicy|2005-2011|Full except TWN|
|Openness|WDI|Exports and imports of goods and<br>services(% of GDP)|investment &<br>tradepolicy|1995-2011|Full except TWN|
|Political<br>stability|WGI|Perceptions on political stability<
������������������� where the dependent variable, yearly FDI inflows as a percentage of GDP (���),<sup>9</sup> is modeled as a function of a series of location-related, macroeconomic, institutional, and transition-related determinants (����, as discussed before. We include country fixed effects (��,) in all the specifications in order to control for unobserved country specificities. The error term is represented by ���.

In addition, the equation laid out above was estimated using an alternative method of random effects. Although the results are remarkably similar across both specification, the Hausman test confirms in most cases that estimation under fixed effects is preferred to random effects (as the latter estimator turns out to be inconsistent).

**Table 7. Im-Pesaran-Shin unit-root test results**

|Variable|P value|
|---|---|
|FDI net inflow|0.0000|
|Inflation|0.0000|
|GDP growth|0.0074|
|Openness|0.0000|
|Law and Order|0.0000|
|Bureaucracy|0.0000|
|Government stability|0.0000|
|Corruption Control|0.0005|
|Investment Profile|0.0207|
|Exchange rate risk|0.0000|
|FDI Stock|0.6377|
|Tertiary Educ enrolment|0.9501|
|Tel and mobile subscription|1.0000|
|Δ Tertiary Educ enrolment|0.0004|
|Δ Tel and mobile subscription|0.0000|

Source: authors’ calculations.

> 9 FDI inflows  (% GDP) come from the UNCTAD database, and are defined as follows: “For associates and subsidiaries, FDI flows consist of the net sales of shares and loans (including non-cash acquisitions made against equipment, manufacturing rights, etc.) to the parent company plus the parent firm ́
mental provisions (although this increase is not statistically significant). Forest-related production and exports exhibit qualitatively similar patterns–increased output and trade with RTA entry into force that is offset by inclusion of environmental provisions—but none of our forest-related estimates are statistically significant. These findings corroborate the view that environmental provisions in RTAs affect deforestation primarily through their impact on agricultural trade and production.

# **4.2 High-Risk Areas and Ecosystems**

Because our identification strategy above measures net outcome variables at the RTA level, it can obscure the areas and ecosystems that are driving our results. To explore this idea further, we reconstruct our panel dataset of agreement-level deforestation using only a subset of country-level forest loss across agreement signatories. We consider tropical and nontropical forest loss, forest loss in developed and developing countries, and forest loss in countries within the upper quartile of global biodiversity and the countries within the 75th percentile of global biodiversity. Along each of these dimensions, we only aggregate forest loss from RTA signatories that belong to the subgroup under consideration. All other model specifications follow our main approach. We present this subsample analysis in Table 3. Panel A presents subsets we consider “high-risk”—tropical, developing, and biodiversity-rich countries. Panel B presents lower risk subsets—the complements of the subsets in Panel A. Our main qualitative findings are consistent across all subsets: forest loss increases following entry into force of RTAs without environmental provisions and provision inclusion largely offsets these increases. However, the difference in the magnitudes of these effects is quite dramatic, with our findings driven by tropical deforestation rather than non-tropical forest loss. Likewise, our results are driven by forest loss in developing countries rather than developed economies. The only dimension along which our estimated magnitudes are similar is biodiversity, however the statistical significance is concentrated in high-biodiversity countries.

Taken together, our results suggest that forest loss arising from trade liberalization, as well as mitigating effects from inclusion of environmental provisions, is concentrated in relatively higherrisk countries.

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**Table 1.3. Shorrocks Mobility Index and Short-Term and Long-Term Inequality (balanced sample for the whole period 1994-** **<u>2015)</u>**

|||**Gini Ind**<br>|**ex**<br>||**Variance of Lo**<br>|**g Income**<br>|**No. of**|**No. of**|
|---|---|---|---|---|---|---|---|---|
|**Period**|_M_<sup>_s_</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|_M_<sup>_s_</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|<br>**observations**|<br>**individuals**|
|1994-1998|0.21|0.42|0.33|0.42|0.72|0.41|3 336|834|
|1998-2004|0.21|0.38|0.30|0.45|0.55|0.30|5 004|834|
|2004-2009|0.16|0.31|0.26|0.36|0.35|0.22|5 004|834|
|2009-2015|0.16|0.27|0.22|0.31|0.24|0.17|5 838|834|
|1994-2004|0.27|0.39|0.28|0.52|
_βt_ thus denotes the dynamic treatment effect of firms exposed to certain NTMs compared to firms that are not exposed on the outcome _Yit,pc_ in year-month _t_ compared to the baseline month March 2020.

We control for firm, HS-8 product, year-month, and partner country fixed effects. Standard errors are clustered at the 8-digit product level. The period of our analysis is February 2019-June 2021 and the frequency is monthly. The time period in our analysis is limited by the availability of high-frequency firm-level data from Panjiva. The omitted month is March 2020, when the WHO characterized Covid-19 as a pandemic. We plot the _βt_ coefficients associated with the outcome variables for each month.

# **Fact 5: Firms subject to Port of entry restrictions (C3) saw a large fall in their export quantities and transactions following Covid-19 compared to those that did not face any restrictions (Figures 27a and b).**

This is consistent with the evidence of major port congestion during Covid-19, as the pandemic led to disruptions in maritime trade due to port terminal closures and transportation delays (UNCTAD, 2022).

We show that the results hold even if we change the definition of treated firms and define the treated firms as firms that face above the median level of port of entry restrictions (Figures A25 and A26 in appendix A2).

We do not find any systematic evidence that other NTMs negatively affected firms’ ability to export during the pandemic. The disproportionately large effects of the port of entry restrictions NTM is therefore likely related to port congestion. These results are consistent with those of Majune and Montfaucon (2023), who find that imports subject to port of entry restrictions and preshipment inspections were more negatively affected by Covid-19-related lockdown measures by trading partners.

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**Figure 2.6: Despite diversification, exports concentrate in low-complexity products.** Cambodia’s Exports in Value (% of total) and export value, 2000 and 2020

<!-- Start of picture text --> 70 12,000<br> 60<br> 10,000<br> 50 Export Share<br> 8,000<br>Export value<br> 40<br> 6,000<br> 30<br> 4,000<br> 20<br> 10 2,000<br> - -<br>Apparel, knit Precious metals Footwear Articles of Travel and<br>,and not knit and stones leather tourism<br>% on total exports Export value in nominal USD<br>2000 2008 2018 2019 2020 2000 2008 2018 2019 2020 2000 2008 2018 2019 2020 2000 2008 2018 2019 2020 2000 2008 2018 2019 2020<br><!-- End of picture text -->

**Source:** Author’s elaboration based on data from the Atlas of economic complexity.

Cambodia primarily imports intermediate goods used as inputs for domestic production. In 2020, intermediate goods accounted for 48.6 percent of Cambodia's total imports while consumer goods accounted for 30.8 percent (Figure 2.7). Top products imported include inputs for the garment industry (8.1%), petroleum oils (6.5%), industrial machinery (6.22%), electrical equipment (5.9%), and vehicles (5.6%). These imports support a range of domestic production activities, including garment manufacturing, construction, and transportation. Overall, Cambodia's reliance on intermediate goods imports suggests a lack of domestic capacity, resulting in a comparative advantage to produce certain goods and importing intermediate inputs rather than producing them domestically.

5 | P a g e
significantly lower for social minorities and education by itself is not sufficient to dismantle the structural barriers faced by members of these communities to avail of remunerative employment opportunities.

# **4 Policy Recommendations**

On the basis of the empirical findings, we offer the following three major policy recommendations concerning education, financial infrastructure and structural barriers for minorities in society.

## **4.1 Education**

The empirical analysis showed that the completion of secondary education was a robust consistent predictor of non-farm employment for rural workers. Importantly, secondary education was positively correlated with employment in rural salaried work, and in service occupations. Salaried work, particularly in service occupations, are typically white collar positions which offer significantly higher returns to labor. This is confirmed in the analysis using the Consumer Pyramids database where we see that secondary education has a strong impact on the likelihood of rural workers being engaged in white-collar activities. Additionally, we see that households with a secondary educated worker have 14 percent higher monthly per capita consumption. If secondary education is reflective of skill endowments and serve as a necessary pre-condition for employment in non-farm salaried occupations offering higher wages, a plausible public intervention would be to invest in secondary and tertiary education of rural workers. This attains particular relevance when considering that in 2015, only a third of rural adults aged between 25 and 65 had completed secondary education.

## **4.2 Financial Infrastructure**

The empirical findings find a limited role of financial infrastructure in affecting rural nonfarm employment. This is despite the fact that public sector banks continue to account for two-thirds of formal credit disbursement in the Indian economy and banks are obligated to channel 40 percent of their aggregate loan portfolios towards “priority sectors”, which include credit extensions to agriculture, small and micro-enterprises, and marginalized populations. Despite this, we see limited predictive power of either bank branch density, or per capita bank credit for rural non-farm employment. While there is a positive correlation of per capita bank credit (although not branch density) with
# **Measuring Financial Inclusion:**

## **The Global Findex Database**

Asli Demirguc-Kunt and Leora Klapper<sup>*</sup>

**This Version: April, 2012**

**Abstract:** This paper provides the first analysis of the Global Financial Inclusion (Global Findex) Database, a new set of indicators that measure how adults in 148 economies save, borrow, make payments, and manage risk. The data show that 50 percent of adults worldwide have an account at a formal financial institution, though account penetration varies widely across regions, income groups and individual characteristics. In addition, 22 percent of adults report having saved at a formal financial institution in the past 12 months, and 9 percent report having taken out a new loan from a bank, credit union or microfinance institution in the past year. Although half of adults around the world remain unbanked, at least 35 percent of them report barriers to account use that might be addressed by public policy. Among the most commonly reported barriers are high cost, physical distance, and lack of proper documentation, though there are significant differences across regions and individual characteristics.

_Keywords_ : Financial Inclusion; Financial Institutions; Emerging Markets _JEL Codes_ : G2, G21, O16

* Demirgüç-Kunt: World Bank, ademirguckunt@worldbank.org; Klapper: World Bank, lklapper@worldbank.org. We thank Franklin Allen, Oya Pinar Ardic Alper, Thorsten Beck, Massimo Cirasino, Robert Cull, Maya Eden, Asli T. Egrican, Tilman Ehrbeck, Michael Fuchs, Xavi Gine, Markus Goldstein, Ruth Goodwin-Groen, Raul Hernandez-Coss, Richard Hinz, Jake Kendall, Aart Kraay, Alexia Latortue, Sole Martinez Peria, Ignacio Mas-Ribo, Jonathan Morduch, Nataliya Mylenko, Mark Napier, Douglas Pearce, Bikki Randhawa, Richard Rosenberg, Armida San Jose, Kinnon M. Scott, Peer Stein, Gaiv Tata, Jeanette Thomas, Klaus Tilmes, Augusto de la Torre, Rodger Voorhies, and Alan Winters for their valuable and substantive
B. On the compatibility between the law data and the trade data and the interpretation of the empirical estimates Data on the value of bilateral trade by industry are sourced from the ITPD-E database. It collates only official and administrative data, and includes information on domestic trade. We consider annual data between 2000 and 2019, when trade in services is also measured. Considering this type of trade data that do not distinguish between different types of buyer (government vs others) has three main advantages over alternative approaches based on inter-country input-output (ICIO) tables that permit to split cross-border procurement from other trade flows. First, using aggregate trade data allows us to incorporate in our estimates the effect that protectionist laws in government procurement can have on the sourcing strategies of domestic firms. This is important because while governments might not vary much in their propensity to award contracts to domestic (rather than foreign) firms, they can impose different restrictions to the input sourcing of the awardees, for instance through “made in the country” provisions. Second, bilateral imports are computed from administrative data, while the trade data from ICIO tables are estimated based on different proportionality assumptions (Antràs and Chor 2022; Johnson 2018). Finally, data from ITPD-E database cover all countries in the world. We can thus include in the analysis all the countries for which we have coded information on procurement law (around 140 countries), whereas the ICIO data are generally available for a subset of countries. For these reasons, we carry out our baseline empirical analysis with the aggregate trade data, and discuss the results using estimated data for cross-border procurement flows from ICIO tables in the Appendix.

Before moving on to the econometric analysis linking the variable on protectionist rules in government procurement and the variable on cross-border trade flows, it is important to address some caveats that could limit the significance of a relationship between the two variables. First, it should be noted that the data resulting from the analysis of the text of national laws only captures ‘de jure’ protectionism