rafmacalaba/datause-encoder-data huggingface dataset:
objectives in this policy environment will demand analysis and evaluation of complex trade-offs.

For example, one of the most important issues facing Russia policy makers is whether to place priority on policies that foster the production of knowledge by Russian scientists or the consumption of knowledge by Russian enterprises. Put differently, at this stage in its development and competitive position in the global knowledge economy, should Russia emphasize its role as a producer of global knowledge or its need to consume knowledge produced outside Russia? And if Russia is going to be a producer of knowledge, should it focus on intemal or external sources of demand? On basic, fundamental research or on more applied, commercially-oriented research?

Russian policy makers have not yet addressed these issues in a satisfactory, comprehensive fashion. For example, most Russian policy makers would argue that at a time when technology is a leading factor of production and the primary source of national wealth and economic growth, Russia should use its vast S&T assets as a resource for domestic economic growth. But what does this mean in practice and how can this be accomplished in reality?

At first glance this objective would appear sensible, logical and self evident. Russia has an abundance (an over abundance according to the 1993 OECD analysis) of trained scientists. So it stands to reason that these human resources should be used to revive the domestic economy. But how will this be accomplished? Despite the economic upturn of the past few years, there is still no or very low internal demand for science. Many Russian enterprises still do not have the financial resources to finance new plant and equipment, not to mention large scale R&D programs. As a result, one could argue that much of the future demand for Russian science will most likely come from outside Russia. This, in turn, would suggest that if Russia hopes to remain a world class producer of knowledge, both its research and commercialization programs should be more deeply integrated into the global economy. Accomplishing this objective will require a greater emphasis on the search for strategic partners who can help to shepherd Russian R&D projects from the laboratory to the market, strategic partnerships with foreign firms who are active in complementary R&D fields, a search for high tech, sophisticated customers who would be likely to purchase the output of Russia's R&D establishment, and foreign direct investment within Russia by both traditional high tech firms (e.g., Intel, Microsoft) and such old economy firms as Boeing, Pratt and Whitney, and General Motors.

A complementary (or perhaps alternative) approach, which would appear to be favored by Russian policy makers, would be to increase the internal demand for Russian science. But this approach, however desirable in theory, is fraught with numerous problems and practical implementation challenges. For example, Government policy may need to need to place a greater relative emphasis on applied research, thereby reducing the emphasis on "prestige" basic research. To date, Russian policy makers have been reluctant to

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3.67 **In Pakistan** , the Improvement to Financial Reporting and Auditing (PIFRA) system has gaps because government agencies with departmentalized accounts at both the federal and provincial levels do not use the system for detailed ex ante transaction entry and control. This includes the self-accounting agencies with departmentalized accounts, such as the defense department, railways, the post office, and others at the federal level, and the public works department, forest, irrigation, and others at the provincial level. The system includes only periodic summaries of transactions and the corresponding accounts ex post in the Financial Accounting and Budgeting System databases. Because Pakistan records a large portion of budgetary transactions ex post, there was no way to ensure that all transactions are recorded and that the system’s financial reports are complete.

**_Finding 17: Benefits from an FMIS will accrue only if the system’s control protocols are diligently applied. The practice of incurring budgetary commitments outside the system is prone to the accumulation of arrears. Benefits from FMIS necessitate a diligent application of system protocols._**

3.68 **The FMIS needs an underlying commitment to manage public expenditures efficiently and with integrity, accountability, and adherence to the budget law to be effective.** This requires significant political capital to overcome political economy issues and resistance from those with potentially vested interests.<sup>53</sup> Automated financial management systems offer much stronger control over public finances, but benefits from an FMIS will accrue only by diligently applying the system’s control protocols (Diamond 2013 and Hashim 2014).

3.69 **A functioning FMIS in Malawi that did not deliver because of a lack of political commitment illustrates this point.**<sup>**54**</sup> The government successfully implemented an FMIS with a set of core modules, but expenditure management was still lacking because of weaknesses in the control environment. Security firewall protocols were dismantled without authorization and other control mechanisms were ignored because of a breakdown in the accountability chain. This led to a major corruption episode. Disregard for existing systems and processes caused this situation, not an FMIS technical failure.<sup>55</sup> Therefore, it is essential to communicate these aspects to counterparts in government and donor partners and obtain government commitment to address these as required before starting an FMIS project.

> 53 See Abdulbaqi and Ahmad (2012) for the relationship between FMIS and corruption.

> 54 See project documentation for FMIS operations in Malawi.

> 55 Like the FMIS, the scope of the human resource management information system must be comprehensive, and its controls should cover all civil service positions to avoid introduction of ghost workers. Similarly, the human resource management information system remains vulnerable to weaknesses in the control environment or to collusion by officials at the central level with those at the line ministry level, as was evidenced in Malawi.

**48**
effect of the intervention. As mentioned, the intervention consisted of two components; one component was applied to the general population, and the second component was an intensified<sup>6</sup> campaign based on the program’s target population (the treatment group). The intervention was the hand washing promotion via multiple communication channels. These included a local campaign carried out by television, radio broadcast, and billboards combined with the promotion of handwashing in a direct approach with consumer and interpersonal communication at the household level. However, due to substantial information exchange between households in the control and treatment clusters, the recall of exposure to basic hygiene elements was similar in treatment and control clusters. Thus the random assignment to treatment suffered from contamination between comparison groups which ultimately bias the identification of impacts. Still, given the exogenous variation between asset tenure and program outcomes it is possible to explore how the combination between exposure to the program (different intensity) and communication assets can lead to shifts in child development outcomes<sup>7</sup> . A main caveat of exploiting the data under this approach is that the marginal impacts of the two different hygiene information campaigns cannot be identified. Under this approach, the main assumption is that the presence of communication assets in the households ultimately facilitates the delivery of hygiene and sanitation messages and information contained in the hygiene campaigns.

> 6 Intensified campaign based on direct consumer contact events such as road shows, dramas, games, and street parades directed to engage and interact with caregivers and children to stimulate handwashing behavior; and interpersonal communication activities conducted one‐on‐one at the household level to convince caregivers to set up designated areas for handwashing with soap and water and influence other handwashing determinants such as beliefs or skills.

> 7 In the Impact Evaluation surveys, there are no significant differences shown between comparison groups for most health outcomes. This may be due to recall bias. Recall bias represents a major threat to the internal validity of studies using self‐reported data. It arises with the tendency of individuals to report past health events in a manner that is different between the two comparison groups. This pattern of recall errors can lead to differential misclassification of the related variable among individuals with a subsequent distortion of outcome measures. Conversely, socio‐emotional tests collected are less ambiguous and show reduced recall bias because tests are applied directly to children and caregivers, the former being in direct contact with tested children.

8
29

# **Table 4.19: Incidence of Poverty by Educational Level of Head of Household, 1985-93 (Poverty Line=N395.41)**

||||**_RURAL_**||||
|---|---|---|---|---|---|---|
|||**1985/86**|||**1992/93**||
|**Education**<br>**Level**|**P0**|**P,**|P2|**P0**|**P1**|**P2**|
|No Education|0.519|0.187|0.095|0.495|0.219|0.129|
|Primary Educ.|0.475|0.142|0.069|0.289|0.118|0.064|
|Second. Educ.|0.288|0.075|0.032|0.257|0.096|0.058|
|Post Secondary|0.162|0.000|0.000|0.215|0.112|0.069|
|_All Rural_|_0.495_|_0.169_|_0.085_|_0.364_|_0.167_|_0.098_|
||||_URBAN_||||
|||**1985/86**|||**1992/93**||
|**Education Level**|**P0**|**P1**|**P2**|**Po**|**P,**|**P2**|
|No Education|0.378|0.148|0.065|0.428|0.162|0.077|
|Primary Educ.|0.286|0.102|0.038|0.282|0.113|0.065|
|Second. Educ.|0.211|0.053|0.018|0.214|0.087|0.046|
|Post Secondary|0.238|0.000|0.000|0.235|0.084|0.044|
|_All Urban_|_0.317_|_0.124_|_0.051_|_0.304_|_0.140_|_0.076_|

Source: NCS, 1985/86 and 1992/93.

**Table 4.20 Incidence of Extreme Poverty by Educational Level of Head of Household (Poverty Line=N197.71)**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**Education**<br>**Level**|**P0**|**P,**|**P2**|**P0**|**P,**|**P2**|
|No Education|0.148|0.049|0.018|0.162|0.061|0.032|
|Primary Educ.|0.065|0.023|0.007|0.109|0.038|0.020|
|Secondary Educ.|0.089|0.004|0.004|0.078|0.031|0.018|
|Post Secondary|0.000|0.000|0.000|0.093|0.030|0.017|
|_All Nigeria_|_0.120_|_0.042_|_0.016_|_0.136_|_0.085_|_0.034_|
|**Contr**|**ibution of**|**Extreme Pove**<br>**1985/86**|**rty Incidence**|**by Educatio**|**n of Head**<br>**1992/93**||
|**Education**<br>**Level**|**P0**|**PI**|**P2**|**P0**|**PI**|**P2**|
|No Education|80.0|89.2|90.6|71.5|81.18|80.7|
|Primary Educ.|18.5|10.7|9.3|18.2|12.9|13.1|
|Secondary Educ.|_1.5_|0.1|0.1|7.0|4.0|4.3|
|Post Secondary|0.0|0.0|0.0|3.3|1.8|1.9|
|_All Nigeria_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|

Source: NCS, 1985/86 and 1992/93.

In Nigeria, as elsewhere, education can play a key role in reducing poverty over the long term, both indirectly through improving the productivity and efficiency of the labor force,
Policy Research Working Paper 10482

# **Abstract**

Quality assurance systems have been implemented or are under development in a number of low- and middle-income countries in an effort to observe the quality of education and deploy targeted measures to improve quality. This paper shares lessons learned on the potential ability of quality assurance systems to observe quality and inform action, drawing on data from a pre-primary quality assurance system in the Arab Republic of Egypt. A nationally representative study of kindergarten classrooms was conducted, using a detailed diagnostic research tool administered by independent enumerators from a data collection firm. A subsample of these kindergarten classrooms was randomly assigned to also be observed through a short quality assurance system tool, half of them by independent enumerators, and the other half by the existing cadre of government kindergarten supervisors. The quality assurance system tool was developed for scale and financial sustainability; thus, it could be administered in roughly one-third of the time of the diagnostic tool, at one-third of the cost. Overall, the results illustrate that at the national level, the quality assurance system tool can identify important areas for improvement, and thus inform broad policy actions. Further, the results are consistent whether an independent data collection firm or a government kindergarten supervisor acted as enumerator, suggesting that quality assurance system data collection efforts can be embedded within ministries of education and implemented in a regular and sustainable manner. At the school and teacher level, however, there were several areas where the quality assurance system data were inconsistent with the diagnostic data. This underscores how quality assurance systems are best used as a formative system, a starting point for quality enhancement, and not as a summative system that directly targets, punishes, or rewards specific schools.

This paper is a product of the Education Global Practice. 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 cgkrafft@stkate.edu.

_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
# **Table 3: Effect of transportation cost and conflict on welfare indicators in Democratic Republic of Congo**

||||Multi-dim<br>|ensional<br>|
|---|---|---|---|---|
||Wealth|Index|poverty|indicator|
||(1)|(2)|(3)|(4)|
|VARIABLES|OLS|IV|OLS|IV|
|Log of transport cost|-0.0903***|-0.170***|0.0222***|0.0235**|
||(-9.679)|(-10.04)|(4.094)|(2.398)|
|Log of no. of fatality near markets in the past<br>5 yrs (50 km Kernel)|0.0125|0.0203|0.00348|0.0613***|
||(0.826)|(0.482)|(0.376)|(2.646)|
|Log of no. of fatality near households in the<br>past 5 yrs (50 km Kernel)|0.0532***|-0.359***|0.00785|0.0230|
||(2.998)|(-5.163)|(0.935)|(0.693)|
|Agri. potential|0.00435|-0.00634|-0.00201|-0.00175|
||(0.395)|(-0.535)|(-0.346)|(-0.301)|
|Dummy: HH agriculturally involved|-0.384***|-0.437***|0.0898***|0.0980***|
||(-16.69)|(-15.91)|(6.810)|(7.209)|
|Log of age of HH head|-0.0253|-0.0268|-0.0515***|-0.0508***|
||(-0.859)|(-0.791)|(-2.870)|(-2.803)|
|Dummy: Female HH head|-0.139***|-0.147***|0.0531***|0.0523***|
||(-5.880)|(-5.813)|(3.761)|(3.675)|
|log of no. of hh members|0.103***|0.0806***|0.0603***|0.0645***|
||(3.763)|(2.674)|(3.491)|(3.716)|
|log of no. of female HH members aged 15-49|||||
|<br>yrs|0.188***|0.207***|-0.0793***|-0.0825***|
||(5.807)|(5.804)|(-4.136)|(-4.283)|
|log of no. of male HH members aged 15-59<br>|***|***|**|**|
|yrs|0.130|0.141|0.0308|0.0313|
||(6.288)|(6.336)|(2.292)|(2.322)|
|log of no. of children in HH aged 0-5 yrs|-0.109***|-0.105***|0.0312**|0.0290**|
||(-5.240)|(-4.683)|(2.546)|(2.358)|
|Dummy: Rural area|-0.539***|-0.531***|0.0982***|0.110***|
||(-19.65)|(-16.25)|(6.246)|(6.123)|
|Control for region dummies|Yes|Yes|Yes|Yes|
|Constant|12.55***|11.36***|0.571***|0.785***|
||(107.7)|(48.09)|(8.586)|(8.960)|
|Observations|6,550|6,550|6,550|6,550|
|R-squared|0.654|0.599|0.251|0.243|
|Robust t-statistics in parentheses|||||
|***p<0.01,**p<0.05,*p<0.1|||||

33
systematic evidence on these issues.

We first uncovered new stylized facts on the emergence and dissemination of green technologies over the last two decades using textual analysis of patents. We identified a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact to climate change mentioned in WIPO green patents data set over the period 2000-2021. The textual analysis of these data suggests that green innovation was primarily driven by new technologies from the construction, energy and transportation sectors, including technologies such as biofuels, geothermal energy, LED technology, drones and hybrid/electric vehicles.

We have then provided evidence on patterns of adoption and diffusion of these green technologies across firms and countries drawing on the textual analysis of ECTs. We followed a growing literature seeking to infer firm-level technology adoption using general-text classification methods for identifying business-relevant keywords associated with specific technologies; and by counting the number of times these words are mentioned in conference calls that public listed firms host with financial analysts. Using unique firm identifiers, we were able to merge these firm-level textual based indicators with information on inter-firm relationships and firm-level information for the period 2012-2021. These multi-country firm-level data cover buyer-supplier relationships, thereby allowing us to inspect for evidence of dissemination of green technologies across global value chains. They also contain information on innovation partnerships between firms.

The textual analysis of earnings conference calls transcripts reveals a growing importance of green technologies since 2019, as revealed by a rapid increase in the share of ECTs mentioning keywords associated with specific green technologies, as well as in the set of firms that have ECTs mentioning these green technologies. This increase is primarily driven by a few green technologies, including construction material, carbon capture technology, hybrid/electric vehicles, and wind turbines, which tend to be mentioned together in the same ECT. It is concentrated in firms operating in a few industries— especially manufacturing and utilities and among larger firms within industries. We also find evidence of an increase in the spatial spread of these green firms, with a growing presence in large emerging countries in Asia, Latin America and the Middle East and North Africa, in addition to North America and Europe. Importantly, we find that firms that initially had a high degree of emissions intensity that mention green technologies in their ECTs are significantly more likely to observe a subsequent reduction in carbon emissions. This evidence supports the hypothesis that ECTs capture firm investments in green technologies among these firms.

13
**Figure 1: Definition of income concepts in Chile’s incidence analysis**

<!-- Start of picture text --> Market income<br>- Income from work (monetary and in-kind)<br>- Income from capital<br>- Self-provision of goods<br>- Private transfers<br>- Private pensions<br>- Remittances and alimony<br>- Imputed rent<br><!-- End of picture text --> <!-- Start of picture text --> +<br>Contributory pensions<br>Market income plus pensions<br>Direct taxes and contributions<br>- Personal income tax -<br>- Contributions to health<br>- Contributions to unemployment insurance<br>Net market income<br>Direct transfers<br>+<br>- Cash transfers<br>- Near-cash transfers<br>Disposable income<br>Indirect subsidies<br>+ - Public transportation<br>Indirect taxes<br>- Water<br>- Value added tax -<br>- Gas in the Magallanes region<br>- Sales tax: alcoholic and non-alcoholic beverages<br>- Excise taxes: tobacco and fuel<br>Consumable income<br>In-kind transfers<br>+ Monetized value of public<br>Public education and health - and subsidized education, and<br>public health<br>copayment and fees<br>Final income<br><!-- End of picture text --> _Source_ : Author’s elaboration based on Higgins and Lustig (2017).

8
# **4.4 Robustness**

## **Using different instruments**

The core results use the industry-size rank in the 2001 US Census. This section shows that results are robust to the choice of the exogenous size-distribution used as an instrument. Table A1 presents the first stage and IV coefficients on tax inspection, for six different definitions of the instrumental variable. Columns 1-3 report results using each of the last three waves of the US firm Census (1991, 2001, 2015). We obtain the same ranking of WBES size and similar IV coefficients.<sup>26</sup> Since the validity of the instrument rests on the assumption that the firm-size distribution in the US is undistorted by tax enforcement, using size rankings from other high fiscal capacity OECD countries should yield similar results. We test this by constructing the IV with British and German firms in the Amadeus database. In Great Britain the data are derived from audited annual reports presented to shareholders. In Germany, the data come from annual financial statements filed with the business registry. While these firms represent a selected sample compared to the US Census, they are subject to stringent reporting requirements and hence it is less likely that size proxies are used by tax authorities. Column 4 in Table A1 shows that the Amadeus size-ranking predicts a similar first-stage, and a large and significant IV coefficient.

## **Restricting the sample to manufacturing**

To exclude the possibility that results are driven by a small set of peculiar ISIC3 industries, we limit the sample to manufacturing, which is over-sampled in the WBES (60% of WBES firms) and where most industries are well represented across countries. Table A2 shows the reduced form and IV results on the intensive and extensive margins of tax inspection. The 1st stage coefficient is the same as the coefficient in the full sample. The reduced form and IV coefficients are larger than in the full sample.

> 26The first stage is constant across development-levels for each census wave (Figures A1 & A2).

20