rafmacalaba/datause-encoder-data huggingface dataset:
Table 7: Estimates of Relative Differences in Achievement in Vocational and Other Tracks in <u>2000 and 2006, and for the 10th and 11th grade special sample</u>

||2000 9<sup>th</sup>grade|2006 10<sup>th</sup>grade|2006 11<sup>th</sup>grade|
|---|---|---|---|
|ISCED 3A + 3B|513.6|544.4|552.7|
|ISCED 3C|357.5|388.8|384.1|
|Difference|156.0|155.6|168.6|
|(standard error)|(7.5)|(10.2)|(10.3)|

# <u>Decomposition results</u>

We present the results of a simple decomposition in an attempt to explain one of the possible pathways by which the reform may have led to improved student achievement. Table 8a and 8b presents the results of production function estimates along with the decomposition results in reading. Overall, two-thirds of the observed test score differential between PISA 2000 and 2006 is explained by the variables in the model. Thus, only one-third is unexplained, or in this case, due to the returns to characteristics. In terms of unexplained, most of the difference is due to returns to student characteristics, and of that, all is due to age. That is, the returns to being older increased immensely over time. In terms of the unexplained, most is due to school characteristics. Moreover, of that, almost all is due to hours of instruction. That is, receiving more than four hours per week of reading classes is associated with a higher score. The returns to hours of reading class increased over time, but much more of an increase was seen in the proportion of students that received more than four hours of language class, from 1 percent in 2000 to 76 percent in 2006.

24
- 21 -

Table 9: Reduced model, Brazil secondary achievement, 1988

||Mathem|atics|Portu|guese|
|---|---|---|---|---|
|Independent<br>variable|Coeff.|t-stat|Coeff.|t-stat|
|Fixed|||||
|Female|-1.47***|<br>-7.04|0.52*|2.72|
|Age|-0.37***|<br>-5.87|-0.48***|<br>-8.26|
|Number of hours working|-O.03***|<br>-4.63|-0.02**|-3.61|
|Mean achievement|||||
|Intercept|7.18*|2.19|17.92***|<br>41.84|
|Average SES|.02***|<br>6.66|3.17***|6.74|
|Federal technical school<br>|7.31***|<br>4.09|2.68**|2.87|
|Class size|0.17**|3.71|-||
|Hours of mathematics|1.71*|2.77|-||
|Percent female|-3.39|-1.84-|||
|Average working hours|-0.09|-1.92|-0.08*|-3.42|
|Organizational Complexity|<br>-|-|0.40|1.55|
|SES Achievement Gap|||||
|Base|0.10|0.67|0.18|1.43|

*p <.05, **p <.10, ***p <.001

32. These reduced models explain as much of the variance in average achievement as was explained by the larger models (Table 10). At this point, we also tested, for possible inclusion, three other variables for their effect on average mathematics or Portuguese achievement (day school, high fee private school, and number of teachers currently attending university); none were significantly related to average achievement.
|OBC||30.8|24.3|
|---|---|---|---|
|FC||16.2|12.4|
|NS|28.1|22.6|18.0|
|Allgroups|31.7|25.8|20.9|
|||**Rural + Urban**||
|ST|63.5|62.4|45.6|
|SC|60.2|51.0|40.6|
|OBC||37.9|30.0|
|FC||23.0|17.6|
|NS|39.3|31.5|24.9|
|Allgroups|45.5|37.9|29.9|

Next, we report the national poverty rates by religious groups. In Table 4, we show the poverty rates at Lakdawala lines in rural and urban India and the country taken as a whole. Three observations follow. First, at the aggregate level (rural plus urban), poverty rates show a decline between every pair of successive surveys in the case of Hindus, Muslims, Christians, Jains and Sikhs. Poverty among the Buddhists also declines steadily with the exception of between 1983 and 1987-88. With one exception (Muslims in rural India between 1987-88 and 1993-94), the pattern of declining poverty rates between any two successive surveys also extends to the rural and urban poverty rates in the case of the two largest religious communities, Hindus and Muslims.

Second, going by the poverty rates in 2009-10 in rural and urban areas combined, Jains have the lowest poverty rates followed by Sikhs, Christians, Hindus, Muslims and Buddhists in that order. Prosperity among Jains and Sikhs is well known but the lower level of poverty among Christians relative to Hindus is less well known. Also interesting is the relatively small gap of 5.8 percentage points between poverty rates among Hindus and Muslims.

Finally, the impact of accelerated growth on poverty between 2004-05 and 200910 that we observed across social groups can also be seen across religious groups. Once again, we see a sharper decline in the poverty rate for the largest minority, Muslims, relative to Hindus who form the majority of the population.

This broad pattern holds when we consider poverty rates by religious groups at the Tendulkar line, as seen in Table 5. Jains have the lowest poverty rates followed by Sikhs, Christians, Hindus, Muslims and Buddhists in that order. With one exception (Sikhs in rural India between 1993-94 and 2004-05), poverty declines between every pair of successive surveys for every religious group in rural as well as urban India. The only difference is that the decline in poverty among Muslims in rural and urban areas combined between 2004-05 and 2009-10 is not as sharp as at the Lakdawala lines<sup>14</sup> . As a result, we do not see a narrowing of the difference in poverty between Hindus and Muslims when taking rural and urban regions together. We do see a narrowing of the difference in urban poverty but this gain is neutralized by the opposite movement in the

> 14 Panagariya and More (2013) find that the poverty rates for all religious groups, with the exception of Jains, in rural as well as urban areas declines steadily between 2009-10 and 2011-12.

14
|Table 7. Effecti|ve nonresi<br>Level o|dential tari<br>Comm<br>f consumpt|ffs bylevel<br>ercial<br>ion(kWh)/|of consu<br>month|mption(cent<br>Indus<br>Level of|s)<br>trial<br>demand|
|---|---|---|---|---|---|---|
|||||||**100**|
||**450**|**900**|**2,500**|**5,000**|**10 kVA **|**kVA **|
|Benin|15.1|15.1|15.1|15.1|10.7|10.7|
|Burkina Faso|27.0|26.7|26.5|26.5|14.1|15.0|
|Cameroon|11.7|11.4|11.3|11.2|8.8|9.2|
|Cape Verde|21.8|21.8|21.8|21.8|17.7|17.7|
|Chad<br>|43.7<br>|44.7<br>|45.3<br>|45.5<br>|45.6<br>|38.8<br>|
|Congo, Dem.<br>Rep. of|11.0|11.0|10.8|10.8|14.6|14.6|
|Côte d’Ivoire|17.8|16.9|16.3|16.1|10.7|10.7|
|Ethiopia|9.8|8.3|7.3|7.0|4.8|4.7|
|<br>Ghana|12.6|13.9|15.2|15.6|5.7|6.4|
|Kenya|22.1|21.7|21.5|21.4|16.6|15.1|
|Lesotho|9.3|9.3|9.3|9.3|1.5|3.3|
|Madagascar|30.9|25.3|21.7|20.7|11.1|10.5|
|<br>Malawi|8.1|6.9|6.1|5.8|2.6|3.1|
|Mozambique|9.0|8.0|7.5|7.3|4.7|5.1|
|Namibia|15.2|14.0|13.2|13.0|12.7|13.6|
|Niger|13.4|13.2|13.0|12.9|9.0|9.3|
|Nigeria|5.1|5.0|5.0|5.5|5.0|5.1|
|<br>Rwanda|17.2|17.2|17.2|17.2|17.2|17.2|
|Senegal|23.8|22.8|26.2|26.0|15.8|15.8|
|<br>South Africa|11.4|7.7|5.3|4.7|2.7|2.7|
|Tanzania|8.6|8.0|7.6|7.5|5.0|5.4|
|Uganda|22.0|21.9|21.8|21.8|16.8|17.0|
|Zambia|5.1|4.4|3.9|3.8|2.3|2.5|
|Congo, Rep.|11.7|10.7|10.1|9.9|11.2|11.2|
|<br>Mali|23.2|23.2|23.2|23.2|||
|Botswana|7.7|7.2|6.9|6.8|3.3|4.0|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Notes:_ Average commercial consumption level is highlighted in bold — Not available.

<!-- Start of picture text --> Figure 8. Increasing, decreasing, and neutral tariff structures<br>20<br>15<br>10<br>5<br>0<br>domestic commercial industrial<br>type of effective tariff structure<br>increasing neutral decreasing<br>number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database 19
Second, selectivity bias also leads to understatement of the differences in the costs of bringing equity to market in the two counltries. Firms in the United States were much less likely to issue common stock because most found the cost issuance prohibitive. Thus only well-seasonied firms (those with relatively low information and transaction costs) issued stock. From 1900 to 1913 the volume of net bond issues (net of retirements) in the United States was roughly the same as stock issues. During the same period in Germany gross bond issues (which are greater than net bond issues) were only half the volume of equity issues. Moreover, to the extent that equity was issued in the United States during this period, it was typically associated with corporate reorganization, rather than the financing of new capital investmeint Looking at ba:ance sheets of nonfinancial corporations in the two countries in 1912, bonds and notes accounted for more than half of the book value of corporate equity in the United States, but only iO percent in Germany (Calomiris i995, table 5).

The data on commissions for common stock issues earned by German banks from 1893 to 1913 include all firms in the electrical industry (including manufacturers of electrical equipment and operating power W!ants) and firms in2 he metal manufacturing industry whose names begin withi the letters A thirough K (table 7.3). Both of these industries are important producers of new products, and both are central to the second industrial revolution.

The metal manufacturing industry includes many small firms, while the electrical industry is dominated by large firms. Togethier these two industries can provide some evidence **oni** the role of **firm** size and issue size in determining bankers' commissions. For both industries I divide the sample into small and large issues (less than or greater than one million marks, which equals $220,000 in 1913 dollars). For metals I also report data for firms with small total capital in 1913 (less than 2 million marks). Bankers' commissioris averaged 3.67 percent for the electrical industry and 3.90 percent for metal manufacturing. Comniissions oni small and large issues are essentially the same: although small 4
# **7.0 Bibliography**

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Atlas Narodov Mira. Glavnoe Upravlenie Geodezii Kartografii. _<u>ATLAS MIRA [WORLD ATLAS].</u>_ Moscow, 1964 Benson, Michelle and Jacek Kugler, 1998. “Power Parity, Democracy, and the Severity of Internal Violence _.” Journal of Conflict Resolution_ . 42(2): 196-209.

Boardman, Jason, 2003. _Multilevel Modelling Handout_ . Boulder, Colorado, Department of Sociology, University of Colorado.

" " Boardman, Jason D., and Jarron Saint Onge, 2005. <u>Neighborhoods and Adolescent Development.</u> _Children, Youth, and Environments_ 15(1):138-164.

Cederman, Lars-Erik & Kristian Skrede Gleditsch, 2004. “Conquest and Regime Change: An <u>Evolutionary Model of the Spread of Democracy and Peace.”</u> _International Studies Quarterly_ 48(3): 603–629.

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Collier, Paul and Anke Hoeffler, 2004. _“The Challenge of Reducing the Global Incidence of Civil War.” Copenhagen Consensus_ , Copenhagen.

Collier, Paul, L. Elliot, et al., 2003. _Breaking the Conflict Trap_ . World Bank and Oxford University Press.

Doyle, Michael and Nicholas Sambanis, 2000. “International Peacebuilding: a Theoretical and Quantitative Analysis.” _American Political Science Review_ , 94(4): 779-802.

Elbadawi, Ibrahim A., 2001. “Civil Wars and Poverty: The Role of External Interventions, Political Rights and Economic Growth.” Unpublished paper, _World Bank_ from <u>http://econ.worldbank.org/programs/conflict/library/doc?id = 13208</u> Fearon, James and David Laitin, 2003. _“_ Ethnicity, Insurgency, and Civil War _.” American Political Science Review_ 97(1): 75-90.

Fearon, James and David Laitin, 2003. “Additional Tables for Ethnicity, Insurgency, and Civil War.” Available from http://www.stanford.edu/group/ethnic/ 29
other words, absenteeism should be negatively correlated with access. Figure 3 plots for each state the percentage of citizens who had access to goods (government run school and health care) on the vertical axis, and absenteeism on the horizontal axis. As expected, the two measures are negatively correlated.

**Figure 3: Correlation Between Access to Public Schools, Healthcare and Absenteeism**

<!-- Start of picture text --> 3A: Access vs Teacher Absence 3B: Access vs Doctor Absence<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>10 20 30 40 15 25 35 45 55<br>Percentage of Teachers Absent in Public Schools Percentage of Doctors Absent in Public Hospitals<br>Percentage of Respondents with Access to "Goods" Percentage of Respondents with Access to "Goods"<br><!-- End of picture text --> _Source:_ Absenteeism data for teachers is from Kremer et al. (2005, p.660) and for doctors is from Muralidharan et al. (2011, Table 2).

Table 2 converts the absolute measure of access (from Table 1) to a relative ranking. This conversion is done to enable the combination and comparison of measures of access with measures of inequality in access (From Section 4.3). To develop the relative score, a value of zero is assigned to the state with the lowest access and a value of 1 is assigned to the state with the highest access. For example, from Table 1 Column 4, a score of 1 was assigned to Andhra Pradesh for goods (with 62 percent access) and a score of 0 was assigned to Bihar (with 22 percent access). All other states are assigned a value between 0 and 1 using the following formula: 15
10 effects of adjustment policies in seven countries.'<sup>3</sup> The methodology is to first identify key socioeconomic characteristics of poverty groups that are sensitive to adjustment policies. These characteristics include their production activities, consumption patterns, holdings of land and other assets, accessioility to government services and to formal markets, and other relevant information. Second, the causes of macroeconomic imbalances are analyzed, and third, all policy measures taken during the course of the program are examined. Fourth, the short-run impacts of particular adjustment measures are evaluated by analyzing the economic consequences for the poor in terms of the sources and uses of their income. Fifth, the medium- to longer-term impacts of structural changes on the earning capacity of the poor are assessed. The authors state that the effect on the uses side will be primarily short run, whereas the sources side will have to be analysed for the longer run since structural adjustment will have long-term effects on incomes. This framework is used in a very general qualitative analysis of seven countries.

A more detailed analysis is by Glewwe and de Tray (1988, 1989) who use Living Standard Measurement Surveys (LSMS) to identify key socioeconomic groups and their production and consumption patterns for Cote d'Ivoire and Peru. They create a detailed quantitative poverty profile and then do a qualitative analysis of the first round, direct effects of a change in policies (the short-term transitional costs of adjustment). This is done by identifying how the poor would be affected by policies through changes in incomes from rural and urban employment, changes in market prices of consumption goods, and changes in the level or quality of government services.

'<sup>3</sup> The seven countries are Chile, Dominican Republic, Ghana, Kenya, Philippines, Sri Lanka and Thailand.
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Glewwe, P. W., Hanushek, E. A., Humpage, S. D., & Ravina, R. (2014). “School resources and educational outcomes in developing countries: a review of the literature from 1990 to 2010.” in _Education Policy in Developing Countrie_ s, ed. Glewwe, P. University of Chicago Press: Chicago and London.

Glewwe, P., Kremer, M., and Moulin, S. (2009). “Many Children Left Behind? Textbooks and Test Scores in Kenya.” _American Economic Journal: Applied Economics_ , 1(1), 112–135.

Glewwe, P., Kremer, M., and Moulin, S., & Zitzewitz, E. (2004). “Retrospective vs. prospective analyses of school inputs: the case of flip charts in Kenya.” _Journal of Development Economics_ , 74, 251– 268.

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Jensen, R. T. (2010a). “Economic Opportunities and Gender Differences in Human Capital: Experimental Evidence for India.” NBER Working Paper No. 16021. National Bureau of Economic Research.

20
Policy Research Working Paper 8361

# **Abstract**

This study carries out a thorough investigation of the potential sources of mismatch in poverty and inequality levels and trends between the Tanzania National Panel Survey and Household Budget Survey. The main findings of the study include the following. First, the difference in poverty levels between the Household Budget Survey and the National Panel Survey is essentially explained by the differences in the methods of estimating the poverty line. Second, the discrepancy in poverty trends can be mainly attributed to the difference in inter-year temporal price deflators, and, to a lesser extent, spatial price deflators. The use of the consumer price index for adjusting consumption variation across years would show a decline in poverty during the past five years for the Household Budget Survey and the National Panel Survey. Third, despite noticeable differences in the methods of household consumption data collection, the Household Budget Survey and National Panel Survey show close mean household consumption levels in the last rounds, when using the consumer price index to adjust for inter-year price variations. Mean household consumption levels in the Household Budget Survey 2011/12 and National Panel Survey 2010/11 are comparable, and the mean consumption level in the National Panel Survey 2012/13 is around 10 percent higher. The difference is driven by higher levels of aggregate and food consumption by the better-off groups in the National Panel Survey. Fourth, the mismatch in inequality trends and pro-poor growth patterns between the two surveys could not be resolved and is a subject for further analysis.

This paper is a product of the Poverty and Equity 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://econ.worldbank.org. The authors may be contacted at nbelghith@ worldbank.org.

_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
One possibility is that these firms are not very productive, making it unprofitable for them to expand. To explore this possibility, we take a closer look at the performance of the formal manufacturing sector following a two-pronged strategy. We begin by summarizing the recent evidence for Ethiopia and Tanzania documented in Diao et al. (2021). We then turn to UN Industrial Development Organization data to gauge whether the results for Ethiopia and Tanzania are indicative of what is happening in the other African countries for which we have data.

Diao et al. (2021) provide an in-depth analysis of the formal manufacturing sectors in Ethiopia and Tanzania. The core of their analysis rests on two newly created panels of manufacturing firms using formal firm census data, one for Tanzania covering 2008-2016 and one for Ethiopia covering 1996-2017. In both cases, the panel covers firms with 10 employees or more. In both Tanzania and Ethiopia, large firms, exporters and foreign firms all have significantly higher levels of labor productivity. These results are consistent with a very large theoretical and empirical literature on manufacturing firm performance in Africa and elsewhere. For example, see Bigsten and Soderbom (2006) for a summary of early research on the manufacturing sector in Africa based on the Regional Program for Enterprise Development and Harrison and Rodriguez-Clare (2010) for a summary of the literature on the impact of trade and foreign direct investment on firm performance.

However, the firm and sector-level results for Tanzania and Ethiopia suggest that the bestperforming firms are not absorbing employment. In Tanzania, labor productivity growth in large firms is on the order of 8 percent per year, and 13 percent per year for large exporters. By contrast, labor productivity growth in firms that start with fewer than 50 employees ranges between negative 3 percent and zero. By contrast, employment growth in these small firms is as high as 13 percent while employment growth averages zero percent in firms with 50 or more employees. In Ethiopia, the picture is somewhat better although labor productivity growth is not as high as it is in Tanzania. Average sectoral employment growth in Ethiopia’s large firms is around 6.5 percent per year while it is around 7.5 percent per year for firms with between 10 and 49 employees. (Although the employment growth rates may seem high in Ethiopia, it is important to remember that the country is starting from an extremely low base.) Labor productivity growth is around 5 percent per year for both small and large firms. To complete the analysis of Ethiopia, the authors examine a group of mechanized firms with fewer than 10 employees using the Small-Scale Industries survey. Average annual employment growth among these firms is around 17 percent, while labor productivity growth is an average of zero, with considerable heterogeneity. The combined evidence for Ethiopia and Tanzania suggests that the 9
_Brazilian spatial labor dynamics Page 28_

# Labor Market Dynamics

<!-- Start of picture text --> E+, W+<br>E-, W+<br>E-, W-<br>E+, W-<br>N<br>W E<br>S<br>900 0 900 1800 Miles<br><!-- End of picture text -->

**Figure 1  Labor dynamics, Brazil**

## **Categorization of MCA by its labor dynamics**

<!-- Start of picture text --> Real wage change<br>E-W+ E+W+<br>Employment change relative<br>to population growth<br>E-W- E+W-<br><!-- End of picture text -->