rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7825

# **Abstract**

The impact of the growth of the local supply of public schools in the post-Colonial period on intergenerational mobility in education is a first-order question in the Arab World. This question is examined in Jordan using a unique dataset that links individual data on own schooling and parents’ schooling for adults, from a household survey, with the supply of schools in the subdistrict of birth at the time the individual was of age to enroll, from a school census. The identification strategy exploits the variation in the supply of basic and secondary public schools across cohorts and subdistricts of birth in Jordan, controlling for year and subdistrict-of-birth fixed effects and interactions of governorate and year-of-birth fixed effects. The findings show that the local availability of basic public schools does, in fact, increase intergenerational mobility in education. For instance, a one standard deviation increase in the supply of basic public schools per 1,000 people reduces the fatherson and mother-son associations of schooling by 18–20 - percent and the father-daughter and mother-daughter asso ciations by 33–44 percent. However, an increase in the local supply of secondary public schools does not seem to have an effect on the intergenerational mobility in education.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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 assaad@umn.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
# **Figure 14 Non-residential capital-output ratios in the OECD area and China**

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1986 1991 1996 2001 2006 2011 2016<br>Average OECD country China<br>Source: Author estimates and OECD Economic Outlook Database<br><!-- End of picture text -->

# **_Infrastructure and government and residential sector capital-output ratios_**

Across countries, there is much less information available that would permit a comparison of the capital stock in the government and infrastructure sectors. Information is available for investment in the general government sector but not all infrastructure is undertaken by the general government sector. Moreover, at the industry level, few countries publish detailed capital stock data that would enable an exact comparison to be made with these estimates for China. Cross-country comparisons are complicated by the differences in the classification of infrastructure investment across countries. One country, the United States, does publish estimates for the non-residential capital stock in the private sector by industry and for the total government sector. However, some of the sectors shown as infrastructure in China are in the private sector in the United States. Moreover, even in the United States, no industrial breakdown of the private sector capital stock has been published for the years after 2001.

An approximate comparison between China and the United States of the combined capital stock in the government and infrastructure sectors can be made. It requires an assumption that private infrastructure has remained a constant share of private non-residential investment since 2001. On this basis, the capital-output ratio for the infrastructure and government sector in China had reached the same level as in the United States by the beginning of this decade (Figure 15, Panel A). Since then, it has moved steadily above the level in the United States.

# **Figure 15 Capital-output ratios in the United States and China**

A. Infrastructure and government B. Housing <!-- Start of picture text -->
1.2 1.4<br>1.0 1.2<br>1.0<br>0.8<br>0.8<br>0.6<br>0.6<br>0.4<br>0.4<br>0.2 0.2<br>0.0 0.0<br>United States China United Sates China<br><!-- End of picture text -->

_Source:_ BEA Fixed Asset Database and author estimates for China 22
Table B6: Urban and poverty status changes in 16 SSA countries

|**(A) Original defn**|**itions to DOU**|**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|---|---|---|---|---|---|---|---|---|---|
|**Poverty status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DOU:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**Ofcial urban def**|**nition:**|||||||||
|Non-poor|Urban|78.8|21.2|0.0|0.0|77.7|21.1|1.2|0.1|
||Rural|48.2|51.8|0.0|0.0|46.4|50.0|1.4|2.3|
|Poor|Urban|0.0|0.0|73.9|26.1|4.8|4.7|69.9|20.5|
||Rural|0.0|0.0|37.4|62.6|0.1|1.9|36.8|61.1|
|**(B) Original defn**|**itions to DB**|**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|**Poverty status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DOU:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**Ofcial urban def**|**nition:**|||||||||
|Non-poor|Urban|69.8|9.5|0.0|20.7|78.4|20.6|0.9|0.1|
||Rural|30.0|28.0|0.0|42.1|55.0|40.9|2.2|1.9|
|Poor|Urban|0.0|0.0|72.8|27.2|6.7|4.4|71.0|17.9|
||Rural|0.0|0.0|27.5|72.5|0.6|2.1|43.4|53.9|
|**(C) DOU to DB**||**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|**Poverty status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DB:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**DOU:**||||||||||
|Non-poor|Urban|73.2|22.3|0.0|4.4|94.7|4.7|0.6|0.0|
||Rural|4.3|17.6|0.0|78.1|21.1|77.4|1.1|0.5|
|Poor|Urban|0.0|0.0|81.6|18.4|3.3|0.4|85.4|11.0|
||Rural|0.0|0.0|5.2|94.8|0.3|2.0|19.3|78.4|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line. In Panel (A), welfare is deflated using official spatial deflators. In Panel (B) and (C), welfare is deflated using our updated spatial deflators.

63
# **6.4 Threats to Validity**

## **6.4.1 Contemporaneous Investment in Lagging Areas**

The post-reform magnitudes from equation (1) are inversely related to pre-reform levels of schooling. If the government matched the FPE program with increased levels of investment in lagging regions of the country, these investments would be correlated with post-reform levels of the intensity measure. Examining the correlation between pre-reform education levels and the change in regional spending on education would provide insight into how funding was allocated following the implementation of the reform; finding a strong negative correlation would suggest a disproportionate increase in funding to areas with lower pre-reform levels of schooling. Levels of regional per-student spending in 1993, the first year for which data are available, exhibit a strong positive correlation with pre-reform education levels, as would be expected. Then comparing pre-reform education levels with the growth in spending through 1996, as the reforms are implemented, and through 2001, well after the implementation, yields correlations of 0.01 and 0.17, respectively (World Bank, 2005). This indicates that there is very little relationship between pre-reform education levels and the post-reform investment decisions of the regional governments.

Furthermore, the inclusion of the MTI reform in the analysis does not change the article’s main results. A beneficial characteristic of including the MTI reform is that the identification strategy of the combination of the FPE and MTI reforms does not simply exploit a change in policy at a single point in time; the variation exploited by the joint reforms is introduced at four points in time, in different parts of the country. The initial returns to MTI in Tigray were found to be positive; this was followed by the introduction of MTI with script change in three additional regions, which initially put downward pressure on schooling prior to the removal of school fees (Chicoine, 2019). The remaining seven regions of the country were then positively affected by the removal of school fees in 1995. The pattern of results for schooling, fertility, and the mechanisms linking the two are largely consistent; therefore, any alternative explanation would have to follow this pattern, significantly reducing the possibility that the intensity measure is capturing spurious correlations that could be assigned to a competing policy. The pattern of implementation is likely unique to the combination of the FPE and MTI reforms.

## **6.4.2 Quality**

One concern is that increases in class size that occurred after the implementation of the reform could lead to a reduction of quality of education following the reform. However, this reduction in education quality would not directly impact the first stage, which measures years of schooling, not learning. Any reductions in quality of education correlated with larger increases in enrollment would simply make it less likely that 16
- 83 -

# VI. Lessons for Future Investment

6.01 The principal lesson to be drawn from the analysis is that economic circumstances have great impact on the success of VET investments. It follows that the investment designs and institutional models employed in lending should vary with economic circumstances. In middle income countries, continuing support to enhance the quality and efficiency of the national training systems established over the past d cade will be required. In the small, poor countries of Sub-Saharan Africa simpler models, based on nonformal delivery modes, should have prio'rity. In tlis latter circumstance, sustained investment over time in institutional capacity, perhaps supported by sub-regional professional institutions, will be required. The record suggests that the Bank has an opportunity to improve the quality of lending for VET significantly, if sufficient policy and operational support can be provided.

# INTRODUCTION

6.02 Although based on a large and representative sample of VET components, the foregoing analysis has a number of limitations. The analysis of broad lending trends is comprehensive, and its conclusions regarding shifts in emphasis within VET towards nonformal training modes and training for industry, and away from secondary vocational schools and training for agriculture, can be taken as conclusive.

6.03 The analyses based on the sample of components can be accepted as valid for the general patterns identified. However, some detail is inevitably lost in sampling. The components not chosen may contain significant innovations, or b_ otherwise of interest.

6.04 Finally, the large sample compensates to some extent for uneven treatment of investments in Staff Appraisal and Project Completion Reports. Nevertheless, the data are limited in many respects, notably in regard to the educational and economic outcomes of training.

6.05 Despite these caveats, lessons can be drawn that suggest directions for future investment in vocational education and training. Clearly, these cannot be taken as rigid prescriptions for investment design. Adjustments, sometimes significant ones, will inevitably be required in individual country circumstances.

6.06 The lessons, moreover, relate primarily to the design of VET systems and investments; the scope ar_ data of the study permit only the most rudimentary conclusions regarding the econov'z justification
Figure 23: Probability of accessing public support by country type (severity of initial lockdowns) <!-- Start of picture text --> 60<br>49<br>44<br>40 37<br>34<br>20<br>0<br>Below median Above median<br>First round Latest round<br>P-value of test that exporters in above median countries are different than exporters in below median:<br>First round: 0.071; Latest round: 0.005<br>Probability of accessing support<br><!-- End of picture text --> _Notes:_ The sample includes 38,908 firms in 42 countries.

# **6. Conclusions and Policy Recommendations**

This paper provides novel evidence on the impact of the COVID-19 pandemic and the subsequent recovery for globally engaged firms, as well as on their adjustment mechanisms. This evidence has been grouped into a set of six stylized facts on trade outcomes; changes in sales and financial fragility for these firms; their labor market adjustments; the heterogeneity across attributes pertaining to firm, sector, and country; their response in terms of digitalization, product innovation, and changes in trading partners; and finally their access to public support. In all cases these patterns are compared not only to non-engaged firms but also across different types of global engagement using a rich survey spanning three waves of data collection covering nearly 65,000 firm-year observations across 45 countries. Our analysis from the BPS data shows an immediate negative impact of the COVID-19 pandemic on trade outcomes at the firm-level, which is also confirmed in the customs data. The adverse impact is more pronounced for GVC firms, that is, those that participate in both exports and imports perhaps because shocks in the multiple nodes of global production networks magnify for these firms. The adverse impact of the pandemic on total sales (and other outcomes such as financial fragility) of firms are recovering faster for globally engaged firms, possibly due to their stronger pre-pandemic productivity, management practices and digital readiness relative to non-engaged firms.

While there is wide geographic, and weaker sectoral, heterogeneity in the impact of the pandemic, globally engaged firms were affected similarly across size, source and destination of exports. On the surface it may appear surprising that the immediate impact of the crisis was sharper for bettermanaged exporters, however, the noticeable recovery for these firms suggests that the very practices such as lean manufacturing and just-in-time production which make them more productive in normal times can create potential vulnerabilities in times of global shocks. Nevertheless, these are 35
# **8. References**

Axinn, W.G., and J.S. Barber. 2001. “Mass Education and Fertility Transition.” _American Sociological Review_ 66(4):481–505. Available at: http://www.dspace.cam.ac.uk/handle/1810/1153.

Azevedo, J.P., G. Inchauste, S. Olivieri, J. Saavedra, and H. Winkler. 2013. “Is Labor Income Responsible for Poverty Reduction? A Decomposition Approach.” _World Bank Policy Research Working Paper_ 6414(April).

Azevedo, J.P.W. De, C.M. Nguyen, and V. Sanfelice. 2012. “ADECOMP: Stata module to estimate Shapley Decomposition by Components of a Welfare Measure.” Available at: https://ideas.repec.org/c/boc/bocode/s457562.html.

Beegle, K., J. De Weerdt, J. Friedman, and J. Gibson. 2012. “Methods of household consumption measurement through surveys: Experimental results from Tanzania.” _Journal of Development Economics_ 98(1):3–18. Available at:

- http://linkinghub.elsevier.com/retrieve/pii/S0304387811001039 [Accessed February 16, 2015].

- Bhandari, P., and D. Ghimire. 2013. “Rural agricultural change and fertility transition in Nepal.” _Rural Sociology_ 78(2):229–252.

- Bista, B.G. 2003. “Population Policy and Reproductive Health.” In _Population Monograph of Nepal 2003_ . Kathmandu, Nepal, pp. 263–275. Available at: http://cbs.gov.np/sectoral_statistics/population/population_monograph_nepal_2003_all.

- Central Bureau of Statistics. 1996. “Nepal Living Standards Survey Report 1996 Volue I: Main Findings.” Available at: http://cbs.gov.np/nada/index.php/catalog/7.

- Datt, G., and M. Ravallion. 1992. “Growth and redistribution components of changes in poverty measures: A decomposition with applications to Brazil and India in the 1980s.” _Journal of Development Economics_ 38(2):275–295. Available at: http://linkinghub.elsevier.com/retrieve/pii/030438789290001P.

- Inchauste, G., and S. Olivieri. 2014. “Understanding Poverty Reduction in Bangladesh: A MicroDecomposition Approach.” _Bangladesh Development Studies_ XXXVII(June):21–56.

- Mitra, S. 2014. “Synergies Among Monetary, Multidimensional and Subjective Poverty: Evidence from Nepal.” _Social Indicators Research_ . Available at: http://link.springer.com/10.1007/s11205-0140831-3.

- Muzzini, E., and G. Aparicio. 2013. _Urban Growth and Spatial Transition in Nepal: An Initial Assessment_ . Available at: http://elibrary.worldbank.org/content/book/9780821396599.

- Oxford Poverty and Human Development Initiative (OPHI). 2013. “Country Briefing : Nepal Multidimensional Poverty Index (MPI) At a Glance.” Available at: http://www.ophi.org.uk/wpcontent/uploads/Nepal-2013.pdf.

34
**<u>Table 6: Probability of levirate remarriage for remarried widows</u>**

||All areas|Urban areas|Rural areas|
|---|---|---|---|
|_Ref: dissolution after age 40. _<br>||||
|Dissolution before age 25|-0.098|-0.117|-0.107|
||(0.105)|(0.148)|(0.135)|
|Dissolution between ages 25-39|-0.113|-0.149|-0.043|
||(0.087)<br>|(0.126)|(0.113)|
|Last dissolution; rural resident|0.233<sup>***</sup><br>(0.075)|||
|Had a son at widowhood|0.249<sup>***</sup>|0.276<sup>**</sup>|0.246<sup>**</sup>|
||(0.080)|(0.131)|(0.107)|
|Number of marital dissolutions|-0.128<sup>*</sup>|-0.270<sup>*</sup>|-0.112|
||(0.072)|(0.158)|(0.091)|
|Polygamous last marriage|-0.115|0.094|-0.207<sup>**</sup>|
||(0.077)|(0.152)|(0.097)|
|Ever been to a French school|-0.274<sup>**</sup>|-0.216<sup>*</sup>|-0.316<sup>*</sup>|
||(0.124)|(0.128)|(0.186)|
|Fostered before age 15|0.054|0.017|0.023|
||(0.094)|(0.137)|(0.134)|
|_Ref: Wolof/Lebou_||||
|Serere|0.059|-0.025|0.112|
||(0.107)|(0.126)|(0.175)|
|Poular|-0.009|-0.122|0.001|
||(0.102)|(0.172)|(0.131)|
|Other ethnicity|-0.192<sup>**</sup>|-0.223|-0.168|
||(0.092)|(0.206)|(0.120)|
|Mean of dep. Var|0.49|0.31|0.60|
|N|140|52|88|
|Pseudo R2|0.189|0.275|0.140|

Note: Logit model, marginal effects shown. Sample of remarried widows<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12.

Source: Authors’ estimations using PSF1.

37
# ACKNOWLDGEMENTS

A number of persons provided us with important information for this work or assisted us in preparing it In other ways. The Estimates and Projections Section of the U.N. Populadon Division has been continually supportive in providing us with data, even before their publication as U.N. assessments. Partly as a result, nonessential differences between these projections and the biennial U.N. assessments have been minimized. We are grateful to the section chief, Larry Helichman, and to his continually accommodating staff, as well as to the head of the division, Shunichi Inoue, and hope that the collaboration will continue.

'orld Bank country economists, population specialists, and the few demographers in the operating divisions have also provided us with recent population estimates, bri-aging to our attention government reports we might not otherwise see.

This exercise was carried out using a personal computer program, ProjPC-II, written by Kenneth Hill of Johns Hopkins University.

This work for this paper was started as part of the Population, Health, and Nutrition Division's work program. The paper was completed as a product of the Papulation Policy and Advisory Service of the Population and Human Resources Department. We are grateful to the division chief, Dr. Anthony R. Measham, and the department director, Ann 0. Hamilton, for their support of this work We also wish to acknowledge the interest of the Bank's International Economics Socioeconomic Data Division (IECSE), which not only utilizes our figures-for such important Bank purposes as estimating per capita GNP--and incorporates them into the databases it maintains and publications It distributes but also helps coordinate communications with country eonomists. Sulekha Patel and Taranjit Kaur of that division have been particularly helpful in the past year.

v
Table B.6: **Test of Truncation Bias: Evidence from IFLS and Census 2000**

|||**Linear C**|**EF**||**Quadratic**|**CEF**|
|---|---|---|---|---|---|---|
||**IFLS**|**Census**|**IPW Corrected**|**IFLS**|**Census**|**IPW Corrected**|
||(1)|(2)|(3)|(4)|(5)|(6)|
||||**Daughters-Ye**|**ars of School**|**ing**||
|Father’s Edu|0.522***<br>(0.021)|0.489***<br>(0.007)|0.511***<br>(0.007)|0.501***<br>(0.053)|0.480***<br>(0.013)|0.516***<br>(0.014)|
|Father’s Edu Sq.||||0.002<br>(0.004)|0.001<br>(0.001)|-0.000<br>(0.001)|
|Observations|2,362|848,350|848,350|2,362|848,350|848,350|
|R-squared|0.484|0.408|0.428|0.484|0.408|0.428|
||||**Daughters-Pri**|**mary Comple**|**tion**||
|Father’s Edu<br>Father’s Edu Sq.|0.023***<br>(0.002)|0.019***<br>(0.001)|0.025***<br>(0.001)|0.056***<br>(0.005)<br>-0.003***<br>(0.000)|0.043***<br>(0.002)<br>-0.002***<br>(0.000)|0.055***<br>(0.002)<br>-0.003***<br>(0.000)|
|Observations|2,362|848,350|848,350|2,362|848,350|848,350|
|R-squared|0309|0160|0204|0325|0177|0225|
||.|.|.<br>**Sons-Year**|.<br>**s of Schooling**|.<br>|.|
|Father’s Edu|0.455***<br>(0.024)|0.433***<br>(0.006)|0.452***<br>(0.006)|0.571***<br>(0.068)|0.373***<br>(0.011)|0.408***<br>(0.012)|
|Father’s Edu Sq.||||-0.008**<br>(0.004)|0.005***<br>(0.001)|0.004***<br>(0.001)|
|Observations|2,361|1,199,814|1,199,814|2,361|1,199,814|1,199,814|
|R-squared|0.389|0.321|0.330|0.390|0.321|0.330|
||||**Sons-Prima**|**ry Completio**|**n**||
|Father’s Edu|0.013***|0.013***|0.017***|0.035***|0.029***|0.037***|
||(0.002)|(0.001)|(0.001)|(0.006)|(0.001)|(0.001)|
|Father’s Edu Sq.||||-0.002***<br>(0.000)|-0.001***<br>(0.000)|-0.002***<br>(0.000)|
|Observations|2,361|1,199,814|1,199,814|2,361|1,199,814|1,199,814|
|R-squared|0.263|0.101|0.129|0.272|0.111|0.142|

Notes: Robust standard errors in parentheses clustered at the district of birth. (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Years of schooling (Edu) in Census 2000 was calculated based on the education level completed. Years of schooling (Edu) in the IFLS was calculated based on highest grade completed in an education level. Primary completion takes the value of 1 if a child has completed 6 or more years of schooling and 0 otherwise. Family background is measured by father’s years of schooling. The Inverse Probability Weighting (IPW) estimates are calculated using 1975 district level density interacted with time trend. Data sources: Indonesia’s full count census 2000, IFLS and IFLS-East. 69