rafmacalaba/datause-encoder-data huggingface dataset:
43 ‘journals’ (Regs 4.3 and 4a.3) and ‘royalties per capita’ (Regs 4.4 and 4a.4) exhibit positive and statistically significant coefficients.

One reason for the sharp reduction in statistical significance of the estimated coefficients of the knowledge indicators on economic growth on the inclusion of the openness and institution variables could be because of substantial multicollinearity between the openness and institution variables and the other knowledge variables. Table 4b shows the correlation matrix between all of the knowledge variables used in the regressions in Tables 4 and 4a. We see that institutional quality is indeed highly correlated with many of the other knowledge variables. More specifically, note that the correlation between institutions and the human capital stock, patent and the ICT variables are greater than 0.8. On referring back to Tables 4 and 4a, we see that these are precisely the knowledge variables whose independent effects on economic growth were ‘eliminated’. On the other hand, we see that the correlation between institutions and the ‘journals’ and ‘royalties per capita’ are relatively lower, and these are precisely the innovation variables that managed to maintain their statistically significant positive effects on economic growth. Thus, the correlation table suggests that the reason why quite a few of the estimated coefficients of our knowledge indicators ‘lost’ their statistical significance is because they are highly correlated with institutional quality.<sup>49</sup>

# **8. Summary and Conclusion**

Economic theory postulates that knowledge leads to increases in total factor productivity and hence economic growth. However, unlike traditional factors of production, knowledge is not subject to diminishing returns. If true, the increased creation, use, adoption and flow of knowledge in various forms will significantly improve the prospects of sustainable long-term economic development for many countries. In this light, this paper focuses on empirically assessing the effects of knowledge on economic growth.

We argue that knowledge can become the main engine of growth if the economy satisfies certain preconditions. These preconditions include a sufficiently high level of quality human capital stock, a high intensity of domestic innovation and technological adoption, the information and communications infrastructure is well established, and the overall economic and institutional regime is conducive for knowledge to propagate and

> 49 One possible reason for this high correlation between institutional quality and the other knowledge variables could be because a good institutional quality environment is a pre-requisite for investment in education or human capital stock accumulation and ICT infrastructure to take place.  We will defer to analysis the determinants of investment in education and ICT infrastructure to a future study.
<!-- Start of picture text --> —chemicals (Ihs) —#— inotor (Ihs) —— machother motor (lhs)<br>ironsteel (Ihs) textiles (rhs) leather (rhs)<br>rubber (rhs) —— cookwood (rhs) paper (rhs)<br>4500000_ esa 450000_<br>3500000 — ess —_TM-_ 350000<br>~~ ~~ e— ~~ _”<br>1500000 ma_ - rt 150000<br>500000 50000<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->
The follow up or endline survey was conducted during November – December 2012, after two years of implementation of the program. The baseline survey consists of 3,195 households, of which 1,594 participated in the EGPP (or beneficiaries) and the rest belong to a comparison group who did not participate but nevertheless were eligible based on the two targeting criteria (or eligible non-beneficiaries). The survey follows a multistage clustered sampling design. Initially 5 _upazilas_ from each of the seven divisions in the country were selected depending on the proportion of people living in poverty in each _upazila_ . Then in the next stage two unions from each _upazila_ were selected with probability proportional to the population. Finally, from these 70 selected unions, two villages/ _mauzas_ were selected which constitute the primary sampling units (PSU). The villages/ _mauzas_ were also selected with probability proportional to the population of the village _/mauza_ . In order to identify the EGPP participants and the corresponding comparison group a census of all households in the selected 140 PSUs was conducted. A total of 34,369 non-participant households and 2,540 participant households living 140 clusters were listed in the census (Mitra and Associates, 2010). Then for each PSU, 12 non–beneficiary households and 12 beneficiary households were randomly selected from the set of eligible non-participant and participant households. In areas where there were less than 12 participating households all were selected for the baseline survey. The household level eligibility criteria, i.e. household land ownership and the occupation of the household head as a casual laborer, were used to identify similar non-beneficiary households who would otherwise be both willing and eligible to participation in EGPP.<sup>15</sup> When conducting the endline survey, 207 households or about 6.5 percent of the baseline respondents were not found, and 127 new “replacement households”<sup>16</sup> were added in the end-line survey. The number of the original EGPP participants in the survey sample declined from baseline to end-line - only a third of the baseline beneficiary households continued to receive benefits from the program during the endline survey (see Table 1). This reduction in the number of observations of “baseline” EGPP

> 15 90% of the participant/beneficiary households have < 0.4 acres of land while all the non-participant/non-beneficiary households have <0.32 acres of land. 95% of the household heads of the beneficiary households were casual laborers whereas the corresponding figure for the non-beneficiary households is 92%.

> 16 Replacement households were selected from the original sample list prepared during the baseline survey.

14
Note: The sample includes households from Indonesia, Mongolia, Myanmar, Philippines, and PNG that are not missing coping and income data. The reference period for food insecurity is the past 30 days for all countries except Indonesia, for which it is the past 7 days.

The final dimension that we analyze that may have long term implications relates to children’s access to education. School closures may have a real and detrimental effect on learning outcomes for children, particularly if prolonged over an extended period of time. In Indonesia, an estimated 62.5 million students from pre-primary to higher education have been affected by school closures that started in March 2020 and continued late into the year.<sup>28</sup> In the absence of online learning platforms or other tools that enable effective home learning, school closures have the potential to make a lasting impact on human capital accumulation for school-age children. Estimates suggest that students in the EAP stand to lose an average of about two-thirds of a year of learning-adjusted years of schooling, with loses twice as high in countries such as Cambodia, Lao PDR and Myanmar (World Bank 2021c). These losses may have the longer-term effect of negatively impacting the future earning potential of individuals, reducing educational and income mobility (Narayan et al. 2018, World Bank 2018).

Across most countries analyzed, children in poorer households were at greater risk of being left behind: Among households with children attending school pre-pandemic, those in the bottom quintile were on average 16 percentage points less likely than those in the top quintile to engage in face-to-face learning or utilize online, mobile, or video platforms that allow more interactive learning (Figure 11). In the Philippines, for instance, students from the richest quintiles were twice as likely to engage in these more interactive educational activities than those at in the bottom quintile (38 vis-à-vis 16 percent, respectively). Most of these mediums require access to technology, particularly the internet or a computer or mobile phone, presenting a greater challenge for children at the bottom of the welfare distribution to engage in more interactive methods of learning at home.

> 28 https://blogs.worldbank.org/eastasiapacific/covid-19-and-learning-inequities-indonesia-four-ways-bridge-gap

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# **c. Data**

We use annual data from the IMF GFS from 1970-2012. As mentioned previously, we use the expenditureGDP ratios to identify when expenditure episodes occur. For force we use GFS expense data across the main 2-digit economic classification, namely compensation of employees; consumption of fixed capital; grants; subsidies; use of goods and services; social benefits; and other expenses.

Given our definition of extreme fiscal episodes, there were 254 episodes; 153 for emerging and developing and 101 for advanced economies.<sup>12</sup> The exact number of episodes we consider for the noise-signal ratio varies depending on the specification we use for force. We have data available on expenditure episodes and force for 114 countries.

The distribution of expenditure force is shown in Figure 3 for advanced economies and in Figure 4 for emerging economies. The figures plot the frequency of different force magnitudes in the sample. The force numbers presented is the change in the 1-year momentum for total expenses. Due to the short time period, expenditure force can vary widely from year-to-year. For example, in the year 2000 we had, on the one hand, Thailand with a negative force of around 46 percentage points, while on the other hand Moldova with a positive force of around 31 percentage points.

<!-- Start of picture text --> Figure 3: Advanced Economies - Distribution of Expenditure Figure 4: Emerging Economies - Distribution of Expenditure<br>Force – The change in the 1-year momentum for total Force – The change in the 1-year momentum for total<br>expenses expenses<br>(percent) (percent)<br>25 10<br>20 8<br>15 6<br>10 4<br>5 2<br>0 0<br>-30 -25 -19 -13 -8 -2 4 10 15 21 27 -50 -40 -30 -20 -10 0 10 20 30 40 50<br><!-- End of picture text --> Note: For presentational purposes, observations 2 standard deviations above or below the average are excluded from the calculation. For emerging economies, the histogram was truncated at -50/+50. Source: IMF GFS and authors’ calculations

> 12 County groupings from the April 2014 WEO are used. For some countries, their classification as advanced or emerging may have changed over the sample period.
# **Philippines**

Table 5a: Returns to levels of education - Employed for wages

||1988|2006|Change (%)<br>2006-1988|
|---|---|---|---|
|_Basic controls only_||||
|- Secondary/primary|0.415|0.492|_18.5_|
|- Tertiary/primary|1.075|1.184|_10.1_|
|_Basic controls + Industry dummies_||||
|- Secondary/primary|0.254|0.201|_-20.9_|
|- Tertiary/primary|0.691|0.593|_-14.1_|

# Table 5b: Returns to skill over time – Employed for wages

||1988|2006|Change (%)<br>2006-1988|
|---|---|---|---|
|_Basic controls only_||||
|- Some Sec. and above/ Primary. and below|0.415|0.492|_18.5_|
|- At least some tertiary/Secondary and below|0.660|0.693|_5.0_|
|_Basic controls + Industry dummies_||||
|- Some Sec. and above/ Primary and below|0.254|0.201|_-20.9_|
|- At least some tertiary/Secondary and below|0.437|0.392|_-10.3_|

14
-29-

# 11. POWER PROJECT AND SECTOR PERFORMANCE

# 2.1 Introduction

A retrospective study of the power sector has been carried out covering projects financed by the World Bank and IDA between 1965 and 1983. Altogether, the study covered about 300 projects, of which about 85% were completed by the end of 1986. While a few aspects of power project performance have been reviewed from time to time, no comprehensive study of Bank-financed power projects covering the last 20 years had previously been undertaken. However, a large sample of projects Implemented between 195i and 1968 was Included In a review of the power sector by OED In 1972. Therefore, a limited review of project and sector performance over the past 30 years has been made possible with the additional data collected for this retrospective study. The source of the data collected has been mostly limited to appraisal and audit reports and exlsting studies carried out by EGY over the past few years. Details of the source of data and how It was analyzed are Included In Annex 1.

Throughout the 1950s and 1960s, power sector lending was primarily confined to countries In Latin America, Asia and European countries, plus a few major hydroelectric projects In smaller countries. Several of these countries have since graduated from Bank lending (for example, Ireland and Slngapore), while others have continued to borrow funds for the power sector from the Bank (or IDA) almost continuously over the past thirty years (for example, Brazil, Colombia, India, Paklstan and Turkey). During the 19708 and 1980s, the number of countries borrowing to finance power projects Increased significantly and there still continues to be new lower-Income countries borrowing from the Bank or IDA for the first time for the power sector (Burma and Burundi). Over the time perlod covered In this retrospective study, 1965-1983, about 33% of projects approved were for Latin America, 20% for the EMENA regions, 17% for **East** 1/ "Power Sector Review", Operatlons Evaluation Department (OED), 1972
bilaterally— involving engagements among multiple organized, armed factions, occasionally leading to collateral civilian harm— or unilaterally, wherein a group targets civilians deliberately.” Furthermore, for the most precise depiction of areas severely affected by conflict, fatalities stemming from protests, riots, and strategic development (as per ACLED data) have been excluded, maintaining consistency with the WBG Classification of Fragility and Conflict Situation’s (FCS) objectives and the scope of this study. Our analysis focuses on conflict records categorized as ‘Battles’, ‘Explosions/Remote violence’, and ‘Violence against civilians’. These types of conflicts are selected due to their violent nature.

# _Settlement data_

To determine the urbanization level, we use the Global Human Settlement Layer (GHSL) which combines gridded population data estimated by CIESIN GPW v4.11 GHS-POP R2023 and built-up surface information from Landsat and Sentinel-2 data GHS-BUILT-S R2023 (Schiavina et al., 2023).<sup>5</sup> The settlement data are available at the 1km resolution. We consider the data for the year 2020, which is the closest available to the time period of interest for both countries. In case of Nigeria, we defined ‘urban’ areas as cells defined as high-density cluster,<sup>6</sup> ‘suburban’ as moderate-density cluster,<sup>7</sup> ‘rural’ as rural and low-density clusters<sup>8</sup> and ‘Uninhabited’ as very low density rural and water covered areas (Figure 1).<sup>9</sup>

> 5 - In Google Earth Engine, this Image collection is accessible through <u>https://developers.google.com/earth engine/datasets/catalog/JRC_GHSL_P2023A_GHS_SMOD.</u>

> 6 The ‘urban’ category includes the classes 30: “Urban Centre grid cell”, 23: “Dense Urban Cluster grid cell”.

> 7 The ‘suburban’ category includes the classes 22: “Semi-dense Urban Cluster grid cell” and 21: “Suburban or peri-urban grid cell”.

> 8 The ‘rural’ category includes the classes 13: “Rural cluster grid cell” and 12: “Low Density Rural grid cell”.

> 9 The ‘uninhabited’ category includes the classes 11: “Very low density rural grid cell” and 10: “Water grid cell”.

5
contrast to coffee, most of the causes of the crisis related to cocoa are more temporary in nature. The international cocoa situation is characterized by high prices, with high uncertainty about the future. International cocoa prices have a long history of wide year-to-year fluctuations.

When observing the current price spikes, it is important to recall that cocoa prices had been in a long-term downward trend in real terms since the last price peak in the mid-1970s. Nominal cocoa prices have returned to their highest level since the late 1980s.

In 2002 the International Cocoa Organization’s (ICCO) daily price averaged about $US 1800 per MT, almost 63% higher than in 2001 and double the average price in 2000 (which, at that time, had reached a 30-yeard low). Preliminary forecasts for 2002/3 predict a third consecutive year of a global supply shortfall, even though higher production is expected from some countries in Africa.

The decrease in global production and resulting undersupply has led to reduction of stocks. In 2001/2, stocks of cocoa beans declined by 5%. The world stocks-to-grindings (i.e., processing) ratio, a proxy for global cocoa availability, was about 38%, the same level as it was in 2000/1, reflecting a second year of supply shortfall. Stock levels of intermediate cocoa products, such as cocoa butter, cake and powder also declined.

**Figure II.2:  Cocoa Prices adjusted for Manufacturing Unit Values 1970-2002**

<!-- Start of picture text --> 7100<br>6100<br>5100<br>4100<br>3100<br>2100<br>1100<br>100<br>197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002<br><!-- End of picture text -->

**Sources: Cocoa Prices (ICCO) Manufacturing Unit Values (World Bank)**

A recent development that could have major repercussions for international markets is the liberalization of the cocoa sector in Cote d’Ivoire. The regulatory framework of the cocoa sector in Cote d’Ivoire was liberalized in August 1999. Deregulation of the cocoa sector coincided with the sharp decline in cocoa prices during the 1990s that bottomed out in 2000. Many changes occurred in Ivorian markets and, as a result, Cote d’Ivoire’s cocoa production in 1999/2000 reached a record high just prior to the beginning of civil unrest.

Global demand for cocoa can be approximated by FAO data for “grindings” (processed cocoa beans). The average annual growth rate for cocoa grindings were steady at about 2% for the 1027
<!-- Start of picture text --> 40%<br>m99%<br>m95%<br>35% mooy<br>m75%<br>650%<br>30% 625%<br>0 10%<br>m5%<br>1%<br>25%<br>2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> 0%<br>B%<br>m99%<br>7%<br>m95%<br>28 590%<br>5% m75%<br>4% 650%<br>396 625%<br>2% 0 10%<br>m5%<br>1%<br>1%<br>0%<br>2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->
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are not engaged in farm labor or wage employment are not classified as being economically active. Futhermore, inclusion in the economically active group, especially for women, varies across countries. In the _ILO Estimates and Projections 1950-2025,_ a working definition of the economically active is used which consists of the following:

[...] including among the economically active population all persons who contribute to the supply of labour for the production of goods and services, i.e. all persons in employment (employers, own-account workers, salaried employees, wage earners, unpaid family workers, members of producer co-operatives and members of the armed forces), as well as all the unemployed (both those woth previous job experience and those seeking work for the forst time). It should be noted that this definition of the economically active population is the one adopted in 1954 by the Eighth International Conference of Labour Statisticians (OCLS). A new definition of the economically active population and its componants (employment and unemployment) was adopted by the 13th ICLS in 1982. It was not possible to take account of the new definition in the present edition, data in accordance with the new definition not yet being available.[ILO 1986] In estimating the _World at Work,_ group specific estimates of the working age population are calculated as the sum over countries in each income group or region, using the groups defined in _1995 World Development Indicators_ [World Bank 19951. Group specific activity rates are calculated as the weighted mean of country specific participation rates. The weights used are the size of the working age population in 1995.

# 2.2. Unemployment

Cross-country comparability of unemployment statistics is not guaranteed (even within the OECD [OECD 1994 p. 6]). However, for present purposes, they are treated as equivalently defined shares of the labor force that is unemployed. There are several main sources for the data used here. First. a labor statistics database under construction [Topel, Levy and 3
**Table 7. Effect of Objective Financial Access Variables on Employment Growth**

||||De|pendent var|iable: empl|oyment gro|wth|||
|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|**Variable**|**World**|**World**|**World**|**World**|**World**|**AFR**|**EAP**|**ECA**|**LAC**|
|Laborsize|-0.024***|-0.023***|-0.022***|-0.024***|-0.026***|-0.029***|-0.032***|-0.022***|-0.023***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Age|-0.024***|-0.023***|-0.023***|-0.022***|-0.023***|-0.020***|-0.018***|-0.030***|-0.023***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.003)|(0.002)|(0.002)|
|Multi|0.019***|0.018***|0.019***|0.019***|0.019***|0.013***|0.017***|0.020***|0.022***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.006)|(0.005)|(0.004)|
|Manuf|0.0003|0.001|-0.000|-0.001|-0.000|0.010***|0.010**|-0.010***|-0.002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.005)|(0.003)|(0.003)|
|Exporter|0.022***|0.024***|0.024***|0.023***|0.021***|0.022***|0.023***|0.015***|0.028***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.004)|(0.007)|(0.004)|(0.004)|
|Foreign|0.013***|0.012***|0.013***|0.014***|0.014***|0.008**|0.019***|0.020***|0.016***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.006)|(0.005)|(0.005)|
|Govt|-0.009**|-0.011**|-0.011**|-0.010**|-0.008*|-0.008|0.024**|-0.015**|-0.016|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.010)|(0.011)|(0.007)|(0.020)|
|Loan|0.031***||||0.020***|0.014***|0.018***|0.023***|0.026***|
||(0.002)||||(0.002)|(0.003)|(0.004)|(0.003)|(0.003)|
|Credit constraint||-0.019***|||-0.010***|-0.004*|0.001|-0.024***|-0.012***|
|||(0.002)|||(0.002)|(0.002)|(0.004)|(0.003)|(0.003)|
|Sales credit|||0.026***||0.009***|0.010***|0.006|0.012***|0.005|
||||(0.003)||(0.002)|(0.002)|(0.005)|(0.003)|(0.003)|
|External finance||||0.042***|0.036***|0.021***|0.041***|0.036***|0.041***|
|||||(0.002)|(0.002)|(0.003)|(0.005)|(0.003)|(0.003)|
|Constant|0.163***|0.182***||0.166***|0.162***|0.174***|0.144***|0.165***|0.153***|
||(0.003)|(0.003)||(0.003)|(0.003)|(0.005)|(0.009)|(0.006)|(0.006)|
|Number of observations|34,894|35,641|36,722|36,722|34,524|10,270|3,971|9,423|9,911|
|Adjusted_R_-squared|0.131|0.125|0.123|0.138|0.146|0.144|0.155|0.154|0.128|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Regressions for the Middle East and North Africa and South Asia are excluded because of insufficient data.

Model: _EG_ = _b_ 0 + _b_ 1Laborsize + _b_ 2Age + _b3_ Multi + _b_ 4Manuf + _b_ 5Exporter + _b_ 6Foreign + _b_ 7Govt + _b_ 8 _FC(s)_ + Country Fixed Effects + _e. ; * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent Source:_ Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).

27