rafmacalaba/datause-encoder-data huggingface dataset:
ETB 3.1 billion. _Simulation 3_ considers a scenario where PSNP transfers that reach non-poor children (estimated as ETB 2.5 billion) are redirected and equally distributed to monetarily poor children based on their status at the final income. This policy – at zero fiscal cost – amounts to a reduction in monetary poverty by 6.9% and inequality by 2.5%, with almost no difference between boys and girls. The poverty reduction effect of redistribution transfers from non-poor to poor children is larger for urban than for rural children, though more equalizing for rural children. The effects of retargeting PSNP transfers are substantially larger than those that result from doubling them. The joint welfare effects of doubling and retargeting PSNP transfers ( _Simulation 4_ ) are sizeable, as monetary child poverty would fall by 8.8%. That translates to a slight reduction in the child poverty headcount rate from 25.8% to 23.5%. With this joint policy change, girls and children in urban areas would benefit slightly better than boys and those in rural areas. The policy change is also associated with the largest drops in inequality for all children (3.1 %) and other child groups, compared with other simulations.

Two assumptions are made a priori about the simulations. First, the extra transfers can induce a reduction in the labor supply by beneficiaries. Such changes in behavior are ruled out by our simulations. Second, the simulations do not take into account the additional administrative costs related to enrolling currently unenrolled students and increasing the amount and redistribution of PSNP transfers. However, these limitations are unlikely to change the main takeaways from the simulation exercises.

# **6. Conclusion**

The study investigates the fiscal space for children in Ethiopia using the Commitment to Equity for Children (CEQ4C) methodology. The analysis is based on 13,820 children (0-17 years old) from the 2018/19 Ethiopia Socioeconomic Survey. Individual and household level information collected from the survey is combined with budget figures and administrative data on programs and subsidies. The study then examines the burdens of taxation and the benefits from government transfers and spending in rural and urban settings, boys and girls, as well as poorer and richer children. It also analyzes the effect of these taxes and transfers on poverty and inequality.

The incidence analyses show that the fiscal system on average is progressive and mainly driven by direct taxes and indirect in-kind transfers. However, important differences in the distribution of some of the elements of taxes and transfers exist. For example, indirect taxes are regressive while public spending on primary education is by far the largest in-kind transfer and is generally progressive across levels of child deprivation. Secondary education spending is regressive, while public spending on health care is progressive across all children. However, in rural areas spending on primary education and health is neutral, in sharp contrast to strong progressivity in urban areas. Regarding impacts on poverty and inequality, the fiscal system reduces poverty by 21% from market 23
Policy Research Working Paper 5616

# **Abstract**

Progress in educational development in the world since 1900 has been slow and uneven between countries. Providing basic education for all children in developing countries has been and remains an unmet challenge of governments and international organizations alike. This is in sharp contrast to recent findings in the economics literature on the catalytic role of human capital for economic growth and social development in general. Using a newly constructed matched data set on education and national accounts in the 1950 to 2010 period, this paper estimates the loss of income and equity associated with not having a faster rate of human capital accumulation, using alternative methodologies and specific country examples. Such loss is projected backward (1900–1950) and forward (2010–2050) using plausible assumptions regarding what countries could have done in the past or may do in the future to accelerate human capital formation. The findings suggest that the welfare loss in terms of per capita income conservatively ranges from about 7 to 10 percent. Improved educational attainment is also shown to have an effect in reducing income inequality.

This paper is a product of the the Education Team, Human Development Network. 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 author may be contacted at hpatrinos@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
_log(Pt_<sup>_C_</sup> _) = μ + β1log(Pt_<sup>_AG_</sup> _) + β2log(MUVt) + β3t + εt._ _[1]_ _Pt_<sup>_C_</sup> and _Pt_<sup>_AG_</sup> denote the price of cotton and the agricultural commodity price index in year _t_ (expressed in nominal dollar terms), _MUVt_ denotes the deflator, _t_ is the time trend, and _εt_ denotes the error term; _μ_ , _β1_ , _β2_ , and _β3_ are parameters to be estimated. The first two rows of table 2 report results for the 1960-2009 and 196010 periods, respectively (the exclusion/inclusion of 2010 was motivated by the desire the capture the effect of remarkable recovery in cotton prices that took place in 2010). The estimate of _β1_ is 0.61 (without 2010) and 0.66 (with 2010) and highly significant in both cases with adjusted- _R_<sup>_2_</sup> s equal to 0.91 and ADF statistics of -6.03 and -6.21, implying a very strong co-movement between cotton prices and the agricultural commodity price index.

To examine the divergence between agriculture and cotton prices, [1] was reformulated by introducing two dummy variables, _D1_ (equal to 1 during 19602001 and zero elsewhere) and _D2_ , applied to both _μ_ and _β1_ . Hence, [1] becomes: Results from [2] are reported in columns 3-6 of table 2. To examine the sensitivity of the model, columns 3 and 4 show results corresponding to _D1_ taking the value of one during 1960-2001. In view of the large cotton price increase in 2010, the model was run with and without the last observation, 2010. Columns 5 and 6 correspond to _D1_ taking the value of one during 1960-2002, again with and without 2010. The choice of 2001 or 2002 as the break year is expected to capture the introduction of biotech cotton in China and India.

Regardless of the specification considered, the econometric evidence overwhelmingly shows that while there was a strong relationship between the price of cotton and the other agricultural commodity prices up to 2002 that relationship weakened considerably during 2003-10. While it is unclear whether the gap will persist much longer, the fact that it has persisted for almost a decade implies considerable welfare losses for non-users of biotech cotton varieties. The next section discusses the reasons behind the divergence.

# **_b. Causes of the divergence_**

There are three reasons why cotton prices did not join the commodity price boom (they are discussed in order of importance.) The first, and perhaps most important, reason is the rapid and massive expansion of cotton production that took place in China and India, as a direct consequence of these countries’ adoption of biotech cotton. During the five-year period 2002-07, China increased its cotton output by 55 percent (from 5.2 to 8.1 million tons) while India increased its out9
Table A.5: Other studies considered

||Study|Intervention|Country|Reason for not inclusion|
|---|---|---|---|---|
||Adoho et al.(2014)|Job or business<br>training|Liberia|Positive effect of business training on self-employment but short-term hori-<br>zon (outcomes measured 7 months after intervention)|
||Attanasio et al.(2015)|Microcredit|Mongolia|Public use data does not include migration|
||Attanasio et al.(2017)|Vocational training|Colombia|10-years evaluation requires access to several administrative data sets with<br>focus on formal employment|
||Augsburg et al.(2012)|Microfnance|Bosnia and<br>Herzegovina|Positive impact on self-employment but short-term horizon (outcomes mea-<br>sured 14 months after intervention)|
||Bandiera et al.(2017)|Graduation program|Bangladesh|Public use data does not include migration|
||Bandiera et al.(2018)|Vocational training|Uganda|No attempt to track respondents who moved outside BRAC branch offce area|
|4|Banerjee et al.(2015);<br>Banerjee et al.(2016)|Graduation program|West Bengal|Public use data does not include migration|
|4|Berge et al.(2014)|Business training<br>and grant|Tanzania|No effect on business survival and short-term horizon (outcomes measured<br>4-7 months after intervention)|
||Blattman and Annan(2016)|Agricultural training +<br>in-kind grants of $125|Liberia|Short-term horizon (outcomes measured 14 months after intervention) and<br>no effect on self-employment|
||Blattman and Dercon(2018);<br>Blattman et al.(2019b)|Cash grants of $300<br>or job offer|Ethiopia|Respondents location was not accurately reported at baseline and follo-ups|
||Bruhn and Zia(2011)|Business education|Bosnia and<br>Herzegovina|Short-term horizon (outcomes measured 5-6 months after intervention) and<br>no effect on business survival|
||Calderon et al.(2013)|Business skills course|Mexico|No signifcant effect on frms survival|
||Card et al.(2011)|Job training<br>program|Dominican<br>Republic|Small impact on employment in the short run (10-14 months after training)<br>and randomized design potentially compromised|
||Cho et al.(2013)|Vocational training|Malawi|Follow-up after only 4 months and 46% attrition rate|
||Cr ́epon et al.(2011)|Expansion of MFI|Morocco|No effect on business start-up|
||Drexler et al.(2014)|Business training|Dominican<br>Republic|Does not report effects on frms survival or start-up|
# Discrimination as a Coordination Device

Markets and the Emergence of Identity<sup>∗</sup> Kaushik Basu The World Bank and Cornell University JEL Classification Numbers: D4, D82, J7 Keywords: Discrimination, Group Identity, Coordination, Focal Points, Market

> ∗I am grateful to Tito Cordella and Jose Luis Diaz Sanchez for discussion and comments. For editorial assistance I would like to thank Gabriela Calderon-Motta.
30

# **Box 8: Improved Environmental Performance in**<sup>**Mexico**</sup>

Altos Hornos de Mexico (EHMSA), an integrated steel<sup>mill,was sold toaconsortiumofintemationaland</sup> domestic investors in 1991. The Mexican govemment had expected a higher sale<sup>priceif environmental</sup> requirements were clearly expressed at the time of the sale.<sup>Therefore,anenvironmentalauditwascarriedout,</sup> and its results were used to agree on a three-year environmental<sup>complianceplan.Between1991and1995the</sup> company decreased its dust emissions by more than<sup>70percentandtotalwaterdischargesbymorethan60</sup> percent, reduced the amount of solid waste generated per<sup>unit of production,and,introduced programs toimprove</sup> the surrounding environment. The improvements came from process changes and<sup>upgrades,environmental</sup> investments, and changes in plant management.

Source: Lovei, 1999
|**Country**|**Intervention**|**Effect**<br>**Sizes**|**Country**<br>**GNI Per**<br>**Capita,**<br>**2015 PPP**|**Labor**<br>**Share of**<br>**Income,**<br>**latest**<br>**available**<br>**year**|**Average**<br>**income,**<br>**2015**<br>**PPP**|**Return to**<br>**literacy**|**Percentage**<br>**change in**<br>**earnings**|**NPV$**|
|---|---|---|---|---|---|---|---|---|
|Kenya|Health and Literacy<br>Intervention (HALI)|0.11|3,060|0.35|1,079|0.59|0.06|1,132|
|Kenya|Health and Literacy<br>Intervention (HALI):|0.13|3,060|0.35|1,079|0.59|0.08|1,338|
|India|The Shishuvachan<br>programme: reading classes<br>(teacher training) and a<br>library|0.13|6,020|0.29|1,769|0.51|0.07|1,792|
|Kenya|The Primary Math and<br>Reading (PRIMR) Rural<br>Expansion Programme|0.14|3,060|0.35|1,079|0.59|0.08|1,440|
|Chile|The Collaborative Language<br>and Literacy Instruction<br>Project (CLLIP)|0.14|21,740|0.38|8,235|0.72|0.10|13,138|
|Uganda|Northern Uganda Literacy<br>Project (NULP)|0.19|1,780|0.25|447|0.59|0.11|809|
|Brazil|Decentralised schooling<br>system|0.19|15,020|0.44|6,552|0.72|0.14|15,966|

45
sampling frame of an impact evaluation (IE) study that was conducted in 2013-14 to assess the effect of the 4P program on children’s health and education outcomes (Orbeta et al. 2014). This IE study also used the RDD method, and its data was collected between October-December 2013. To create the list of eligible households for our study, we identified households that had children in the age-group 0-12 years old in 2013, such that at the time of data collection for our IE study (i.e., 2019) – 6 years hence – these children would be in age group 6-18 years old and therefore, these households would still be eligible to get the 4P transfer. Therefore, the list of eligible households provided to the survey firm included a sub-set of households that were sampled in Orbeta et al. (2014), i.e., those that had children strictly below the age of 12 years (in 2013).

The firm randomly drew a required subsample of households and validated that the respondent households were indeed residents of the barangay via coordinating with barangay officials and local community leaders. If the household was not recognized by the local official, the household was replaced. The field team also validated the 4P status of the household by coordinating with barangay officials and local 4P leaders. They also checked whether the target female respondent/primary grantee were present in the barangay.

After the validation with barangay officials, the field team visited households with the objective of confirming that the female respondent: (i) was currently living in the barangay; (ii) has a child aged 0-18 years old, and (iii) was under the age limit of 50 years old.<sup>9</sup> If the household could not confirm that the target respondent’s eligibility criteria as described above, the field team would replace this household.

Based on our power calculations, our target was to interview 1,124 individuals: 562 women and 562 men (partners of these women). Early stages of fieldwork proved that we had over-estimated the co-habitation rate in the Philippines (80% estimated, 55% actual). Therefore, while we met our target of interviewing female respondents (564 actual, 562 target), we were only able to reach 85% of our target of interviewing male respondents (478 actual, 562 target).

_Table 1: Sample Summary_

|**Respondents**|**Treatment**|**Control**|**Total**|
|---|---|---|---|
|**Male**|237|241|478|
|**Female**|284|280|564|
|**Total**|521|521|1,042|

We collected the data between November 7, 2019, and February 2, 2020, from 1,042 individuals, spread across 149 barangays. The survey data was collected using two types of questionnaire: the household questionnaire, which was administered to the most knowledgeable member of the household, and the individual questionnaire (administered to the target women and her partner). Each survey interview took

> 9 The motivation for the third criterion is that most GBV studies focus on women in reproductive age, i.e., 15-50 years old.

8
- Gerardi, Kristofer, Harvey Rosen and Paul Willen, 2009, “Do Households Benefit from Financial Deregulation and Innovation? The Case of the Mortgage Market.” _Review of Financial Studies_ , forthcoming.

- Gyntelberg, Jacob, Martin Johansson, Mattias Persson, 2007, “Using Housing Finance Micro Data To Assess Financial Stability Risks,” _Housing Finance International_ , September: 3-8.

- Holló, Dániel, 2007, “Household indebtedness and financial stability: Reasons to be afraid?” _MNB Bulletin_ , November (Budapest: National Bank of Hungary).

- International Monetary Fund (IMF), 2006, “Czech Republic, Republic of Estonia, Hungary, Republic of Latvia, Republic of Lithuania, Republic of Poland, Slovak Republic, and Republic of Slovenia—Export Structure and Credit Growth,” IMF Country Report No. 06/414 (Washington: International Monetary Fund).

- Jappelli, Tullio, Marco Pagano and Marco di Maggio, 2008, “Households’ Indebtedness and Financial Fragility,” Working Paper No. 208 (Centre for Studies in Economics and Finance).

- Johansson, Martin W. and Mattias Persson, 2006, “Swedish households’ indebtedness and ability to pay: a household level study,” _Sveriges Riksbank Economic Review_ , No. 3: pp. 24-40.

- Kang, S.J. and Y. Sawada, 2008, “Credit Crunch and Household Welfare: The Korean Financial Crisis,” _Japanese Economic Review_ , Vol. 59, No. 4: pp. 438-458.

- La Cava, Gianni and John Simon, 2005, “Household Debt and Financial Constraints in Australia,” _The Australian Economic Review_ , Vol. 38, No. 1: pp. 40–60.

- Mian, Atif and Amir Sufi. 2009. “The Consequences of Mortgage Credit Expansion: Evidence from the 2007 Default Crisis.” Quarterly Journal of Economics, forthcoming.

- National Bank of Slovakia (NBS), 2006, “Report on the Results of the Slovak Financial Sector Analysis,” (Bratislava: National Bank of Slovakia).

- Organisation for Economic Co-operation and Development (OECD), 2006, “Has the rise in debt made household more vulnerable,” _Economic Outlook No. 80_ (Paris: Organisation for Economic Cooperation and Development).

- Roy, Friedeman, 2008, “Mortgage Markets in Central and Eastern Europe - A Review of Past Experiences and Future Perspectives,” _European Journal of Housing Policy_ , Volume 8, Issue 2: 133-160.

- Sawada, Y., K. Nawata, M. Ii, and J. Lee, 2007, “Did the Credit Crunch in Japan Affect Household Welfare? An Augmented Euler Equation Approach Using Type 5 Tobit Model,” CIRJE Discussion Papers F-Series No. 498 (Tokyo: University of Tokyo).

35
**<u>Appendix A: Review of Evidence on Main Policy Interventions</u>**

# **Table 1: Wage subsidies/Payroll tax subsidies**

|**Crisis and Context**|**Authors**|**Methodology**|**Main Findings**|**Comments**|
|---|---|---|---|---|
|**Korea (1997-2001)**<br>Employment stabilization<br>program.|Phang et al.<br>(2001).|Annual_Economically Active Population Survey,_<br>(1995-2000) from the National Statistical<br>Office are used in combination with statistics<br>provided by the Ministry of Labor and Korea<br>Labor Institute.<br>Net employment effect calculated as:<br>net employment effect = gross effect –<br>deadweight loss – substitution effect –<br>displacement effect.|Net employment effect of the employment<br>stabilization program<sup>1</sup>was 22.5% on<br>average, implying a high deadweight loss.<br>The effectiveness of wage subsides varied<br>with firm-size as well as sector. More<br>capital intensive sectors benefitted less<br>while small firms benefitted more.||
|**Czech Republic and**<br>**Hungary (1990-2005)**<br>Wage subsidies to help the<br>long-term unemployed|Fretwell, and<br>others (1999)|Matching in combination with regression<br>methods.|Employment impact was an increase of 10<br>pp in Czech Republic*** and 12 pp in<br>Hungary**.||
|**OECD and Post-Soviet**<br>**transition countries (1982-**<br>**1999)**<br>Mix of wage subsidies studied.|Dar and<br>others (1999)|Literature review: An analysis of 18<br>interventions in developed and developing<br>countries: 1 experimental, 11 quasi-<br>experimental, and 6 non-scientific.|<br>Additionality is typically in the order<br>of magnitude of 20-35%<br><br>Deadweight effects range between 20-<br>70%<br><br>Substitution effects range between 10-<br>80%.||

> Note: ** Impact statistically significant at the 95 percent level of confidence

> *** Impact statistically significant at the 99 percent level of confidence

20
Figure 1.12 Real Wage and employment growth in private and state enterprises in machine building sector (20062010) <!-- Start of picture text --> Private enterprises State and mixed ownership enterprises<br>25.0%<br>25%<br>20.0%<br>20%<br>15.0% 15%<br>10.0% 10%<br>5.0% 5%<br>0.0% 0%<br>-5.0% 2006 2007 2008 2009 2010 -5% 2006 2007 2008 2009 2010<br>-10.0% -10%<br>-15.0% -15%<br>-20.0% -20%<br>Real Wage growth Employment Growth<br>Real Wage growth Employment Growth<br><!-- End of picture text --> Source: Authors estimates based on the NBRB dataset.

**The second fact is the slow technological upgrade of production plants and adoption of organizational practices similar to those introduced in the transport equipment sector in other countries during the past decade.** Slow change has harmed capacity to meet increasing competition from China in traditional product lines and made impossible to move forward and compete in the higher quality market segment.

**Slow technological change is, at least in part, the result of an incentive system that does not foster innovation** . First, SOEs cannot appropriate the results from an innovation increasing their net revenue; their managers may benefit in part from successful innovations but bear the cost of unsuccessful ones. Second, and most relevant for the transport equipment sector, FDI inflows remain low. FDI could enhance growth and competitiveness of the host economy not only through the adoption of new technologies in the production process, but also through knowledge transfers, both in terms of labor training and skill acquisition and by introducing alternative management practices and better organizational arrangements. This reality contrasts with the experience of other Eastern and Central European countries which were able to upgrade their industrial enterprises through privatization and attracting foreign direct investment. For instance, in Czech Republic, a sale of Skoda to VW group gave access to modern technologies and know-how. Against this background, the likelihood of the Innovation fund (Box 2) to be the source of financing of cost reducing innovations is very low: as structured, it operates more as a tax on firms than as a source of financing of new technological and organizational innovations.

16