rafmacalaba/datause-encoder-data huggingface dataset:
Murphy et al. (1991) in using engineering density derived from Maloney & Valencia (2022) and census data. Figure 4 shows the evolution of the logarithm of copper revenue by Chilean nationals, and US nationals in the US, and Figure 5 shows the evolution of the number of domestic engineers for both countries. Note Chile has a higher initial revenues, but is overtaken by the US within the first decades. On the other hand, US has a higher initial entrepreneurial capital and keeps investing in it at a higher pace than Chile.

To replicate the observed data with our model we start, for each country, with an initial _τ_ 0 equal to the inverse of the number of engineers in the population in 1850, and an initial _μs_ equal to the logarithm of copper revenue in US in 1850. The initial _μr_ is also set equal to the initial _μs_ , the second _μr_ is set equal to the initial logarithm of copper revenue for each country, and _t_ is set to infinity, that is the signal reflects the true expected return of the risky project. The depreciation parameter _δ_ is set to 8 and the cost of investing in entrepreneurial capital _c_ is set to 0 _._ 5 for both countries. The process for the risky projects arising each period follows a normal distribution with mean equal to _μs_ and variance equal to the inverse of _τ_ given the perfect informativeness of the signals.

We use the same seed for the realized returns of the risky project for both countries. Figure 6 shows the generated evolution of copper revenue, while Figure 7 shows the implied evolution of entrepreneurial capital. Note the numerical exercise replicates the observed paths in Figures 4 and 5. Despite Chile having a higher initial return, US quickly surpasses it because of a higher initial entrepreneurial capital and a lower initial return.

To gain further clarity, we decompose the effect of each by assigning the U.S. initial value to each as a base and then varying them individually. Figures 8 and 9 show the evolution of the returns and the entrepreneurial capital, respectively, for the case where both countries have the same initial entrepreneurial capital, but a higher initial return for Chile reduces the incentives to invest further. Even when returns moderate, the resulting lag in entrepreneurial capital implies that it is no longer profitable to invest further and frontier adjusted stock continues to depreciate. The model implies a similar path as the previous figures, except that the US overtakes Chile later given the latter’s higher initial stock of entrepreneurial 27
Figure D.4. Raw Data on Empathy and Perspective Taking <!-- Start of picture text --> 24.28<br>23.68 23.62<br>20.07<br>19.68<br>19.14<br>Empathy Scale Perspective-taking Scale<br>Control Game Video<br>26.00<br>24.00<br>22.00<br>20.00<br>18.00<br><!-- End of picture text --> _Notes:_ This figure illustrates the raw means of the empathy and perspective taking scale variables and their respective confidence interval. Dependent variables are constructed using the scales of the Interpersonal Reactivity Index (IRI) originally formulated by Davis (1980, 1983). (i) The Empathic scale consists of eight statements assessing the tendency to feel sympathy and compassion for others in distress. (ii) The Perspective-taking scale comprises seven statements measuring the inclination to adopt others’ psychological viewpoints in daily life. Respondents rate each of the 15 statements on a scale from zero to four, where zero indicates “does not describe me well” and four indicates “describes me very well.” Scores range from zero to four for each statement, with the total score being the sum of partial scores.

50
is ambiguous. On the one hand, they receive too much factor given their extremely low productivity (or, perhaps more accurately, too many female workers may be stuck working for female-owned establishments).<sup>5</sup> This contributes to more misallocation. On the other hand, misallocation is less among female-owned establishments, which contributes to less misallocation in the unorganized sector.

Two further results must bear notice in table 1. First, the lower level of output misallocation for female-owned establishments is the outcome of a lower level of factor misallocation. While for output, misallocation was worse across groups than within groups, the opposite is true for factors. For factors, female-owned establishments unambiguously contribute to less misallocation because (i) between-group misallocation is lower than within-group misallocation and (ii) there is less misallocation among female-owned establishments.

The reason why within-group misallocation is less severe for output is that even if establishments receive factors independently of their productivity, the most productive establishments will produce much more output than the less productive establishments. Here, despite extremely large productivity differences between female- and male-owned establishments, within-group heterogeneity is even greater.

# **_3.4 Within and between misallocation: Organized and unorganized sector_**

We repeat the same decomposition exercise but perform it on the organized and unorganized sectors instead of male- and female-owned establishments. The results are reported in table 2. Again, we focus our attention on 2010. The results for the other years of data exhibit similar patterns. Starting with the misallocation of output, we can note that, although the between-group component _M∆_ is indicative of more misallocation than the within group components, _M_<sup> ̃</sup> , they are both of roughly similar magnitude. This is in sharp contrast with the previous decomposition.

5This last point is certainly consistent with female-owned establishments being more labor intensive and having access to less in terms of land and buildings.

13
change, which is conducted for Ghana as a whole and also for each locality, respectively. Section V uses, again, entropy class measures and regression analysis to determine household characteristics that affect inequality and expenditure. Finally, Section VI provides main findings and policy recommendations. Technical details are presented in Appendix A and B.

# **_II._** _Data_

The data used in this study are Ghana Living Standards Surveys (GLSS) conducted in 1987/88 (GLSS1), 1988/1989(GLSS2) and 1991/92 (GLSS3), respectively. The GLSS is a nationwide household survey undertaken by the Ghana Statistical Service. The GLSS provides data on various aspects of demographic characteristics, and economic and social activities at both household and individual levels. Since the survey questionnaire of GLSS3 was changed from that of GLSS 1 in several significant ways, the comparability of the two data sets have been questioned by several studies (Coulombe and McKay, 1995, Jones and Ye, 1995, Demery and Mehra, 1997). In Demery and Mehra's study, it is demonstrated that GLSS2 and GLSS3 are more comparable than GLSS1 and GLSS3 for expenditure based analysis. The corrections on expenditure levels by Demery and Mehra, however, do not affect distribution in any significant way. The sensitivity analysis shows that the comparison between GLSS1 and GLSS3 and GLSS2 and GLSS3 yield consistent results. We present all results from the comparisons of GLSS1 and GLSS3 and the main results from GLSS2 and GLSS3, with a focus on the comparison of GLSS 1 and GLSS3.

# **_III._** _The changes in inequality and social welfare_

Using Gini as an index, income distribution has improved slightly between 1988 and 1992 in Ghana, by 4 percent. This section uses entropy class measures to extend the analysis that Gini has provided. The advantage of entropy class of measures to Gini are two folds. First, they can be decomposed into within- and between-group inequality. The within-group index can explain how inequality has changed within each locality while Gini coefficient can not be decomposed into sub-groups. The between-group inequality index can examine how household characteristics such as occupation and education level of household head might explain inequality.

Second, different entropy class of measures are sensitive to different parts of the distribution while Gini coefficient is mainly sensitive to the changes in the middle part of a distribution. This study uses three entropy class of measures: E(O), the mean log deviation, sensitive to the changes at the lower end of a distribution; E(1), the Theil index, equally sensitive to changes across a distribution; and E(2), sensitive to the changes at the higher end of a distribution.' In this section, stochastic dominance For a detailed explanation and the formulas of these indices, see Ahuja et al. (1997) appendix A, which is 2
Inequality and Economic Growth: The Role of Initial Income by Markus Brueckner and Daniel Lederman* _Key words:_ Income Inequality, Economic Growth _JEL codes:_ O1

> * Australian National University (Brueckner) and World Bank (Lederman). We are grateful to three anonymous referees and the associate editor for thoughtful comments that significantly improved the paper. The findings, interpretations, and conclusions of this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent.  Corresponding author's email: <u>markus.brueckner@anu.edu.au. Address: Australian National University, LF Crisp</u> Building, 0200 Acton, ACT, Australia.
Table 1b: Summary statistics on business environment indicators

||OE|CD|EE+ De|veloping|
|---|---|---|---|---|
|**Variable**|**Mean**|**SD**|**Mean**|**SD**|
|_Growth & Innovation_|||||
|Annual compound employment growth rate|5.66|21.54|6.00|23.48|
|ln(Total Factor Productivity)|3.28|2.49|3.19|2.40|
|Whether Firm Introduced a New Process (0/1)|29.6%|45.6%|36.7%|48.2%|
|Whether Firm Introduced a New Product (0/1)|34.7%|47.6%|43.1%|49.5%|
|Whether the firm does R&D (0/1)|18.9%|39.2%|23.0%|42.1%|
|R&D Spending/ Total Sales|0.4%|2.8%|0.7%|4.1%|
|_Establishment-level Business Environment_|||||
|Whether the firm exports (0/1)|28.1%|44.9%|28.0%|44.9%|
|Whether the firm uses internet (0/1)|77.8%|41.6%|59.8%|49.0%|
|Whether the firm is part of a business association (0/1)|59.9%|49.0%|59.2%|49.2%|
|Whether the firm has ISO certification (0/1)|26.1%|43.9%|21.1%|40.8%|
|Whether the firm offers formal training programs (0/1)|47.2%|49.9%|47.3%|49.9%|
|Fraction of borrowing in foreign currency|23.0%|33.5%|11.9%|29.7%|
|Fraction of investment capital from local banks|13.9%|25.4%|20.1%|30.9%|
|ln(avg annual wage)|9.54|0.89|8.08|1.79|
|Whether the firm established a new foreign joint venture (0/1)|6.6%|24.8%|6.4%|24.5%|
|Whether the firm established a new licensing agreement (0/1)|8.4%|27.8%|9.0%|28.7%|
|_Enterprise Characteristics_|||||
|Whether the firm is government owned (0/1)|1.6%|12.6%|6.8%|25.2%|
|Whether the firm is foreign owned (0/1)|9.5%|29.4%|12.4%|32.9%|
|Whether the firm is incorporated (0/1)|57.8%|49.4%|52.6%|49.9%|
|Fraction of workforce comprised of management employees|5.4%|12.0%|9.9%|14.7%|
|Fraction of workforce comprised of skilled production employees|51.5%|30.3%|38.9%|30.1%|
|Fraction of workforce comprised of unskilled production employees|23.0%|28.9%|34.3%|31.6%|
|Fraction of workforce comprised of female employees|17.4%|23.9%|28.2%|30.1%|
|_Sectoral & Country Business Environment_|||||
|US 4 digit ISIC sector average R&D intensity|1.7%|1.8%|1.6%|1.4%|

_Source: World Bank Enterprise Surveys and Sharma et al. (2010)._ 28
data are derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).

# 4. Empirical Results

FAO has identified 16 Agro-Ecological Zones in Africa. Table 1 shows the classification of AEZs and several descriptive statistics by AEZs. The AEZs are classified into dry savannah, humid forest, moist savannah, semi-arid, and sub-humid by the length of the growing season. Within each AEZ, they are further broken down by elevation into high, mid, and low elevation. The other remaining zone is desert. Table 1 also shows the average profit per hectare of land in USD for each AEZ in the survey period. Farmers earn higher profits in high elevation moist savannah and sub humid zones and mid elevation dry savannah and sub humid zones. Farmers earn lower profits in high elevation dry savannah, humid forest, and semi arid zones, the lowland semi-arid zone, and in the desert zone.

Figure 1 shows the distribution of the 16 agro-ecological zones across the continent. The Sahara desert occupies a vast land area in the north. There are also desert zones in the eastern and southern edge of the continent. Just beneath the Sahara in West Africa is a lowland semi-arid zone, followed by lowland dry savannah, lowland moist savannah, and lowland sub-humid zone. The lowland humid forest then stretches from Cameroon across Central Africa. Eastern Africa is composed of some desert, lowland dry savannah, and some high elevation humid forest and high elevation dry savannah which are located around Mount Kilimanjaro and part of Kenya. Southern Africa consists of lowland or mid elevation moist savannah, and lowland or mid elevation dry savannah.

Farms in different agro-ecological zones clearly face different conditions for farming. Hence, we expect that farms in favorable ecological zones for agriculture earn higher profits while farms in unfavorable zones earn much less per hectare. In order to examine the climate sensitivity of farms in each AEZ, we examine the variation of farm profits across different climate zones.

In Table 2, we show four different specifications of the Ricardian model of net revenue per hectare of land. For all the regressions, the dependent variable is net revenue from both crops and 7
that VR training can help welding students to be more efficient using materials, such as plates and electrodes (Stone et al. 2011) and can expedite the time students take to perform surgical procedures such as laparoscopy surgery (Larsen et al. 2009), robotic heart surgery (Valdis et al., 2015) and hip arthroplasty (Logishetty, Rudran, & Cobb, 2018). Results from Logishetty, Rudran, & Cobb (2018) also indicate that students who are exposed to VR training are less likely to make mistakes when performing real surgery procedures (see Table A12 in the annex). Of course, due to the limited number of experiments used to draw these conclusions, these results need to be interpreted with care and should not be generalized. Nonetheless, these findings are indicative of the potential of VR simulators to be not only an effective, but also an efficient learning mechanism, especially in the fields of health and safety and technical education.

**Figure 12:  % decrease in inputs / performance errors per 1 hour of VR training compared to traditional training [Learning Efficiency]**

<!-- Start of picture text --> Lampi (2013) Configuration time - min<br>Lampi (2013) Troubleshooting time - min<br>Stone et al. (2011) Number of groove plates used<br>Finkelstein et al. (2005) Time on task - min<br>Stone et al. (2011) Number of plates used<br>Stone et al. (2011) Pounds of electrodes used<br>Valdis et al. (2015) Time on task - min<br>Logishetty (2018) Operation time - min<br>Larsen et al. (2009) Time on task - min<br>Valdis et al. (2015) Time on task - min<br>Logishetty (2018) Anteversion error - degrees<br>Logishetty (2018) Inclination error - degrees<br>-120.0 -100.0 -80.0 -60.0 -40.0 -20.0 0.0 20.0 40.0 60.0<br><!-- End of picture text --> _Note_ : studies in bold are in the field of engineering, science, and technical education. Underlined studies are in the field of health and safely. Other studies are in the field of general education. The bold vertical dotted line represents the average observed effects. Gray vertical dotted lines represent the standard deviation of the observed effects.

# **V. Conclusions**

The development of students’ skills remains a challenge for education systems worldwide. To address this challenge, educators are beginning to explore the possibility of using information technology to create learning experiences that would otherwise not be accessible to students. Simulations that rely on VR technology can provide students access to learning environments that 27
TABLE 111.5. MAKING COMPATIBLE THE NET FEDERAL DEBT STATISTICS AND THE NOMINAL DEBT STATISTICS IN R$: 1995-1998

|In R$ (millions)|Dec/94|Dec/98|Variation|Percentage<br>Variation|
|---|---|---|---|---|
|Net Federal Debt (+ Central Bank)|65,836|231,258|165,422|251.3%|
|+State's and Municipalities' Net Debt|51,091|130,905|79,814|156.2%|
|+ State Owned Enterprises' Net Debt|36,236|26,504|-9,732|-26.9%|
|=Total Net Debt|153,163|388,667|235,504|153.8%|
|-Balance Sheet Adjustment|0|39,516|39,516||
|+ Privatization Adjustment|0|30,656|30,656||
|=Net<br>Fiscal Debt without<br>Privatization|153,163|379,808|226,645|148.0%|

TABLE I11.6. MAKING COMPATIBLE THE NET FEDERAL DEBT STATISTICS AND THE NOMINAL DEBT STATISTICS IN percent OF GDP: 1995-1998

|In percent of GDP|Dec/94|Dec/98|Variation|
|---|---|---|---|
|Net Federal Debt (+ Central Bank)|12.25%|25.34%|13.10%|
|+State's and Municipalities' Net Debt|9.50%|14.35%|4.84%|
|+ State Owned Enterprises' Net Debt|6.74%|2.90%|-3.84%|
|=Total Net Debt|28.49%|42.60%|14.10%|
|- Balance Sheet Adjustment|0.C0%|4.33%|4.33%|
|+ Privatization Adjustment|0.00%|3.36%|3.36%|
|=Net<br>Fiscal Debt without<br>Privatization|28.49%|41.62%|13.13%|

Table III.7 shows the evolution and the composition of the public deficit during 1995-8.

The net federal debt variation is slightly higher than the nominal deficits accumulated in the same period (226,645 - 219,999 = R$6,646). This difference occurred during 1995, when the Balance Sheet adjustments' methodology was not yet implemented. Therefore, an extra 20
<mark>experience with prior phone surveys in Nigeria, a response rate of 60% was assumed, implying that the required number of households to contact in order to reach the interview target was 3,000. These households were selected from the frame of 4,934 GHS-Panel households with at least one phone number for a household member or a reference individual. Given the large amount of auxiliary information available in the GHS-Panel for these households, a balanced sampling approach (using the cube method) was adopted</mark><sup>_35_</sup> <mark>. This balanced sampling approach enables selection of a random sample that still retains the properties of the pre-COVID sampling frame across selected covariates. Balancing on these variables results in a reduction of the variance of the resulting estimates, assuming that the chosen covariates are correlated with the target variable. Calibration to the balancing variables after the data collection further reduces this variance</mark><sup>_9_</sup> <mark>. The sample was balanced across several important dimensions: state, sector (urban/rural), household size, per capita consumption expenditure, household head sex and education, and household ownership of a mobile phone.</mark>

**Reduced form estimation:** The reduced form econometric methods applied here describe behavior of the various outcomes of interest in the data (e.g. food security, education, income loss). This setup allows us to make inference and conduct more complex statistical tests with multiple controls for differences across populations than simple univariate tests for differences in means<sup>_10_</sup> . For a given outcome, y, define the conditional expectation function (CEF)<sup>_36_</sup> as:

Here the CEF is the expectation, or sample mean, of individual outcomes yi, given a k× 1 vector of covariates Xi, which is held fixed. The conditional density function of yi is fy(t|Xi = x). In our case, the expectation is the mean in the population-weighted sample surveyed by the <mark>highfrequency phone surveys on COVID-19</mark> . Given that the calculated weights correct for selection bias and non-response bias, the CEF allows us to make unbiased inference about the impact of a specific xk (a single covariate element in X) on the outcome for the national population. Stated another way, we can use the weighted sample CEF to learn about the population CEF. Using the law of iterated expectations, we can rewrite the CEF as:

where εi is mean independent of Xi. For an arbitrary function β(Xi), the CEF solves: which is simply the minimum mean squared error prediction problem. Using the first order condition, the solution is β= E[XiXi′]−1E[Xiyi], which is the well-known least squares 20
<!-- Start of picture text --> Metering rate<br>90<br>80<br>70<br>w 60<br>oD<br>£50c<br>& 40<br>ov<br>= 30<br>20<br>10<br>0 os ems as me<br>2011 2012 2013 2014 2015 2016<br>= + Bottom 80% Top 20%<br><!-- End of picture text -->
# **I. Introduction**

There is growing recognition that access to good jobs is an important driver of social cohesion. The World Bank’s 2013 World Development Report on Jobs discusses the link between jobs and social cohesion as one of the three central pillars of its multidimensional framework, in addition to living standards and productivity (World Bank 2013, henceforth WDR). In a similar vein, the OECD, in its recent flagship report on social cohesion, argues that labor market outcomes are critical determinants of social stability, both because they influence the level and distribution of labor earnings and because jobs are critical loci of social interaction (OECD 2011: 154).

While economic dimensions of labor market outcomes are relatively well documented, evidence on the connection between jobs and social cohesion is still surprisingly scarce. Sociologists have traditionally discussed social cohesion as a wider concept, related to, but in no way limited to class and status divides grounded in occupational categories (Norton and de Haan 2012). Recent research on social cohesion by economists has also focused on wider societal outcomes – like levels of trust, civic attitudes or the quality of political institutions (see Knack and Keefer 1997, 1995, Easterly et al. 2006, Easterly and Levine 1997, Rodrik 1999). However, few of these studies have analyzed specifically how behaviors and interactions that relate to social cohesion vary along with people’s employment situation.

This paper, based on an earlier background report for the WDR 2013 on Jobs (Wietzke and McLeod 2012), seeks to fill the gap by discussing possible pathways between jobs and social cohesion. Following the WDR and the OECD recent report on the subject I distinguish between a narrower and a broader definition of social cohesion. The narrower definition focuses on the capacity of societies and groups to peacefully manage possible collective action problems (WDR 127ff, Woolcock 2011). This involves a concern with subjective outcomes like trust and wellbeing, as well as with ‘bridging’ professional relations that enable individuals and groups to communicate and collaborate across potentially divisive social and ethnic lines. The wider definition includes notions of social inclusion and mobility (OECD 2011, Club de Madrid 2009). This addresses concerns about social and political institutions that limit equal participation in the labor market (Silver 1994, Hills et al. 2007). The two definitions are linked because a fair distribution of social and economic opportunities is often identified as a primary source of peaceful relations between relevant