rafmacalaba/datause-encoder-data huggingface dataset:
11 of inorganic fertilizers and seeds are derived for each household from the empirical CobbDouglas production functions estimated with household fixed effects using the 2003 and 2004 surveys (see Table A2-1 in Appendix A2 for details).<sup>15</sup> The marginal productivities for each input are calculated both for total crop production and maize and rice production only.

Contrary to the optimal targeting rule for increasing aggregate production, vouchers are going disproportionately to households with a lower marginal productivity for modern input use. The difference is substantial, with the marginal productivity among households that receive vouchers 50 to 320 percent lower than among households that are not eligible for vouchers.<sup>16</sup> Voucher recipients are also more likely to have used inputs before, i.e. vouchers appear not aimed at pulling in non-users, and they also tend to be less poor, with more land. In other words, the decentralized targeting does not appear to perform well on either criterion for optimal targeting.

A review of the social factors suggests that male headed households tend to have a slight advantage (5 percentage points more likely to receive vouchers). Similarly, Chagga households (who are more prevalent in the richer northern districts) are more likely to receive vouchers than Pare households (who are more prevalent in the poorer southern district), though there is no discernable difference based on religion. More trusting individuals on the other hand, see themselves left with less vouchers (7 percentage points). Slightly fewer vouchers might have been distributed in more remote villages and villages where extension agents were present, though none of the differences is statistically significant.

Finally, consistent with the guidelines, voucher recipients were more educated and less likely to be illiterate. They were also more likely to have planted maize/rice and had more land allocated maize/rice cultivation in the past. As they derived similar shares of

> 15 The marginal productivities are equal to the exponent of the fitted value in the production function multiplied by the coefficient of log of the variable in the household fixed effeccts estimation and divided by the value of the variable.

> 16 Only when comparing the marginal productivity of improved seeds for maize/rice cultivation is the difference not statistically significant.
following decomposition (under normality): 2 Since _cov_ � _βf Sf_<sup>_p_</sup> � _≥_ 0 , assuming independence in equation (10) will in general underestimate the role of the intergenerational transmission of schooling. The evidence on intergenerational income mobility in Denmark reported by Bingley and Cappellari (2019) suggests that the relaxation of the independence assumption is especially important; the estimated share of the intergenerational component (father's income) increases substantially as a result.

The decomposition in equation (11) relaxes two important restrictive assumptions in the standard specication (5): homogeneity in _βf_ and independence between _βf_ and _Sf_<sup>_p_,but</sup> it relies on the normality assumption which is rejected by the data in most of the cases. Bingley and Cappellari (2019) nd that imposing normality tends to underestimate the share of intergenerational inuences in sibling correlation. They relax the normality assumption by using an unrestricted form of the intergenerational correlation between the children and parents. In our empirical analysis, we will report estimates from both the classic methods (Bjorklund et al. (2010), Mazumder (2008), Solon (1999)), and the method due to Bingley and Cappellari (2019).

# (4) Data and Estimation Methods

A major hurdle for credible cross-country ranking of inequality of opportunity and intergenerational mobility is that data from dierent surveys may not be comparable. As noted earlier, the survey instruments used for education information by DHS are standardized across countries which makes the data much more comparable.<sup>27</sup> Even when trying to elicit the same information (say education of parents and children), dierent household surveys may contain dierent kinds of data. In the context of intergenerational educational mobility, there are two issues relevant here. First, whether data on educational attainment refer to years of schooling or education categories (primary, secondary etc.). The DHS data we use have information on years of schooling for both the parents (father) and children. Second, in many household surveys used for intergenerational mobility analysis in developing countries, data on parental

> 27We take the information from the household roster which is the same in all DHS surveys.

15
Kennedy, S. 2005. “Wind Power Planning: Assessing Long-Term Costs and Benefits.” _Energy Policy_ 33(13): 1661-1575.

LAZARD. 2008. “Levelized Cost of Energy Analysis – Version 2.0.” Retrieved from <u>http://www.narucmeetings.org/Presentations/2008%20EMP%20Levelized%20Cost%2</u> - <u>0of%20Energy%20 %20Master%20June%202008%20(2).pdf</u> on November 3, 2008.

- Liik, O., R. Oidram, and M. Keel. 2003. “Estimation of Real Emissions Reduction Caused by Wind Generators.” Paper presented at the International Energy Workshop. 24-26 June 2003, Laxenburg, Austria. Tallinn Technical University, International Institute for Applied Systems Analysis, Tallinn, Estonia.

Lund, H. 2005. “Large-Scale Integration of Wind Power into Different Energy Systems.” _Energy_ 30(13): 2402-2412.

- Maddaloni, J.D., A.M. Rowe, and G.C. van Kooten. 2008a. “Network Constrained Wind Integration on Vancouver Island,” _Energy Policy_ 36(2): 591-602.

- Maddaloni, J.D., A.M. Rowe, and G.C. van Kooten. 2008b. “Wind Integration into various Generation Mixtures,” _Renewable Energy_ In press, doi:10.1016/j.renene.2008.04.019.

- Martinot, E. 2002. “Grid-Based Renewable Energy in Developing Countries: Policies,

- Strategies, and Lessons from the GEF.” Presented at World Renewable Energy Policy and Strategy Forum. 13-15 June 2002, Berlin. Global Environmental Facility, Washington, DC. Retrieved from

<u>http://www.gefweb.org/Martinot_WCRE_June2002.pdf, June 13, 2008.</u>

- _____. 2005. “Renewables 2005: Global Status Report.” Prepared for Renewable Energy Policy Network for the 21st Century, Paris and Worldwatch Institute, Washington, DC. Retrieved from <u>http://www.martinot.info/RE2005_Global_Status_Report.pdf,</u> June 16, 2008.

- _____. 2006. “Renewables: Global Status Report: 2006 Update.” Renewable Energy Policy Network for the 21st Century, Paris and Worldwatch Institute, Washington, DC. Retrieved from <u>http://www.ren21.net/globalstatusreport/download/ RE_GSR_2006_Update.pdf, June 16, 2008.</u>

- _____. 2008. “Renewables: Global Status Report: 2007.” Renewable Energy Policy Network for the 21st Century, Paris and Worldwatch Institute, Washington, DC. Retrieved from http://www.ren21.net/pdf/RE2007_Global_Status_Report.pdf, January 08, 2009.

30
# **Figure 6.2: Expenditure per Student and Student Performance over Time**

Association between change in student performance (1994/95 relative to 1970) and change in educational expenditure (average annual rate of change of real educational expenditure per student between 1970 and 1994/95 in percent).

<!-- Start of picture text --> 110Change in student performance<br>(1970=100)<br>Sweden<br>Netherlands<br>Italy<br>100<br>USA<br>Japan Australia<br>Belgium Germany<br>France<br>United Kingdom<br>New Zealand<br>90<br>80<br>70<br>0 1 2 3 4 Change in educational expenditure5 6 7<br>(Percent per year)<br>Source: Wößmann (2002), p. 106.<br><!-- End of picture text --> This evidence, while covering a wide range of countries, is again lightly represented in developing countries, because developing countries have not participated very frequently in the various international tests. This nonparticipation is itself an important policy issue. It is difficult to know what improvements are needed or whether any policy changes are having an impact without accurately measuring student performance.

Comparisons across countries are nonetheless difficult in this area for a number of reasons. First, to compare spending, it is necessary to deal with exchange rate differences and with different earnings actually start schooling with better basic capabilities than ever before. Nonetheless, these issues are better dealt with directly through econometric analyses.

62
ii

# List **of** Figures and **Tables**

|||Paae|
|---|---|---|
|Figure 1|Remittances (US$), Income per Capita in Host Countries (US$),<br>andNumberof Workers Abroad<br>.....<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>|<br>.. 15|
|Figure 2|Remittances and Number of Workers Abroad<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|.<br>16|
|Table1|Remittances, Workers Abroad and TotalPopulationAbroad.<br>.<br>.|.<br>16|
|Table 2A<br>Table 2B<br>Table 2C<br>Table 2D|OLS Estimates of the Remittance Function. Level of<br>Remittances. Stock of Workers<br>..<br>.<br>.<br>.<br>.<br>.<br>. <br>OLS Estimates of the Remittance Function. Per<br>Capita Remittances. Stock of Workers.<br>.<br>.<br>. . . . . . . . . <br>TSLS Estimates of the Remittance Function. Level of<br>Remittances. Stock of Workers<br>.........<br>.<br>.<br>. ..<br>TSLS Estimates of the Remittance Function. Per<br>CapitaRemittances. Stock of Workers ... .<br>.<br>.<br>.. . . . .|.<br>33<br> . 34<br>.<br>35<br>. 36|
|Table 3A<br>Table 3B<br>Table 3C<br>Table 3D|OLS Estimates of the Remittance Function. Level of<br>Remittances. Stock of Population . . . . . . . . . . . . . .<br>OLS Estimates of the Remittance Function. Per<br>Capita Remittances. Stock of Population . . . . . . . . . . <br>TSLS Estimates of the Remittance Function. Level of<br>Remittances.Stock of Population ... . .<br>.....<br>. . . .<br>TSLS Estimates of the Remittance Function. Per<br>CapitaRemittances. Stock of Population.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|. 37<br> . 38<br>.<br>39<br>.<br>40|
|Table 4|Heteroscedasticity Tests<br>.......||
|Table S|Endogenity Tests<br>..|42|
|Table 6|Elasticity of Real Remittances Relative<br>To Its Determinants ....................|43|
|**APPENDIX**<br>**T**|<br>**ABLES**.<br>..<br>.<br>.<br>.<br>.<br>. .|.<br>48|
# Digitalization, Remote Work and Firm Resilience: Evidence from the COVID-19 Shock<sup>∗</sup>

Cristina Constantinescu<sup>†</sup> Arti Grover<sup>‡</sup> and Gaurav Nayyar<sup>§</sup>

## **JEL classification** : D22, F14, L20, L25, O10.

**Keywords** : Digital, Remote work, Firms, Resilience, COVID-19, Trade

∗The authors are immensely grateful to the many World Bank Group colleagues and counterpart institutions that have supported the Business Pulse Survey (BPS) data collection, without whom this work would not have been possible. Likewise, we thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. We thank Gene Grossman, Mona Haddad, Denis Medvedev, Antonio Nucifora, Marcelo Olarreaga and Daria Taglioni for their guidance and strategic support of the BPS initiative, and the Umbrella Trade Trust Fund for financial support. We thank Yewon Choi, Shwetha Eapen, and Mauricio Latorre for their research assistance. The authors are also grateful to the following colleagues for their feedback at the concept stage of this project: Asya Akhlaque, Ana Fernandes, Caroline Freund, Mary Hallward-Driemeier, Leonardo Iacovone, Michele Ruta, Daria Taglioni, Gonzalo Varela and Erik von Uexkull. We thank Edgar Avalos and Jesica Torres for their contributions in the initial stages of the project and Arthur Johannes Giesberts for his generosity in sharing his tool for converting text variables into 4-digit industry codes. We thank Alvaro Espitia and co-authors of Espitia et al. (2021) for kindly sharing the remote work flexibility indicator and the output share of durable products by industry. The views expressed in this paper are solely those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

> †The World Bank; email: ineagu@worldbank.org

> ‡The World Bank; Corresponding author, email: agrover1@ifc.org

> §The World Bank; email: gnayyar@worldbank.org
Policy Research Working Paper 10452

# **Abstract**

This study investigates the effects of public transfers and taxes on the wellbeing of children in Ethiopia. It applies the Commitment to Equity for Children methodology to examine the burdens of taxation and the benefits from government transfers and spending, and their differential wellbeing impacts on children. The study integrates data from the 2018/19 Ethiopia Socioeconomic Survey, which also collected data on taxes and transfers, with administrative data. Measuring its distribution by child monetary and multidimensional wellbeing, the study finds, on average, a progressive, poverty-reducing and equalizing fiscal system. However, there are important differences in the distribution of some of its elements. Indirect taxes, comprising of value-added and excise taxes, are regressive. Similarly, primary education spending, the largest of in-kind transfers, is only progressive in urban areas. On poverty and inequality, the fiscal system reduced the monetary child poverty headcount by 21 percent and the poverty gap by 33 percent. The effect is stronger for girls and children in rural areas than for boys and children in urban areas, therefore reducing inequalities in poverty rates. However, this is only the case when in-kind transfers for education and health are considered. Without the inclusion of in-kind transfers, the study finds that the fiscal system is not well calibrated to reduce poverty. This highlights the essential role of public services, not only in delivering fundamental child rights, but also in reducing poverty among children.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at aambel@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
# **Table A7.1. The Effect of PMT on Probability of Stunting Among Children 6 to 12 Months:  Using Binary Indicator for Program Exposure**

||Poole<br>|d Linear<br>|Linear<br>|Probability<br>|
|---|---|---|---|---|
||Pro|ability|w/Com|munityFEs|
|Year (2000=1)|0.084|0.101|0.231|0.286|
||[0.106]|[0.107]|[0.200]|[0.206]|
|Program since ‘98 (Yes=1)*Year|-0.130|-0.151|-0.330*|-0.395*|
||[0.103]|[0.105]|[0.198]|[0.206]|
|Gender (boy=1)|-0.025|-0.026|0.019|0.021|
||[0.033]|[0.034]|[0.054]|[0.056]|
|Age|0.023**|0.023**|0.019|0.020|
||[0.010]|[0.010]|[0.016]|[0.017]|
|Mother education (years)|0.002|0.002|0.009|0.009|
||[0.004]|[0.004]|[0.007]|[0.007]|
|Father education (years)|-0.000|-0.000|-0.002|-0.001|
||[0.004]|[0.004]|[0.006]|[0.006]|
|Mother height (cm)|-0.011***|-0.011***|-0.011**|-0.011*|
||[0.003]|[0.003]|[0.005]|[0.005]|
|Father height (cm)|-0.014***|-0.014***|-0.021***|-0.020***|
||[0.003]|[0.003]|[0.005]|[0.005]|
|Head of HH (male=1)|0.010|0.006|-0.000|-0.008|
||[0.060]|[0.060]|[0.116]|[0.119]|
|Number of children 5-14 yo in HH|0.011|0.012|0.010|0.009|
||[0.017]|[0.017]|[0.025]|[0.025]|
|HH owns private toilet (Yes=1)|-0.051|-0.045|-0.084|-0.086|
||[0.036]|[0.037]|[0.058]|[0.059]|
|HH has sanitation (Yes=1)|0.018|0.008|0.012|-0.014|
||[0.046]|[0.053]|[0.110]|[0.131]|
|Type of community (urban=1), C|-|-0.025|-|-0.011|
|||[0.046]||[0.129]|
|Distance to district capital (km), C|-|0.000|-|0.001|
|||[0.001]||[0.002]|
|Distance to bus station (km), C|-|-0.005*|-|-0.002|
|||[0.003]||[0.006]|
|Land w/ technical irrigation (%), C|-|-0.058|-|-0.341|
|||[0.091]||[0.216]|
|Comm. has asphalt road (Yes=1), C|-|0.030|-|-0.006|
|||[0.044]||[0.115]|
|C h Y=1 C||0041||0050|
|omm. as sewerage system (es),|-|-.|-|.|
|||[0.038]||[0.097]|
|Comm. has piped water (Yes=1), C|-|<br>0.031|-|<br>-0.009|
|||[0.041]||[0.089]|
|Constant|3.902***|3.939***|5.125***|4.887***|
||[0.663]|[0.669]|[1.080]|[1.135]|
|R-squared|0.09|0.11|0.53|0.53|

Notes: n is 525. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

40
Policy Research Working Paper 9076

# **Abstract**

The increasing growth of forced displacement worldwide has led to the stronger interest of various stakeholders in measuring poverty among refugee populations. However, refugee data remain scarce, particularly in relation to the measurement of income, consumption, or expenditure. This paper offers a first attempt to measure poverty among refugees using cross-survey imputations and administrative and survey data collected by the United Nations High Commissioner for Refugees in Jordan. Employing a small number of predictors currently available in the United Nations High Commissioner for Refugees registration system, the proposed methodology offers out-of-sample predicted poverty rates. These estimates are not statistically different from the actual poverty rates. The estimates are robust to different poverty lines, they are more accurate than those based on asset indexes or proxy means tests, and they perform well according to targeting indicators. They can also be obtained with relatively small samples. Despite these preliminary encouraging results, it is essential to replicate this experiment across countries using different data sets and welfare aggregates before validating the proposed method.

This paper is a product of the Development Data Group, Development Economics and the Fragility, Conflict and Violence Global Theme. 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:// www.worldbank.org/prwp. The authors may be contacted at hdang@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
# **1 Context and key concepts**

- **1.1 Introduction: The importance of addressing gender equality in the investment policy discourse**

Around the world, women face persistent barriers in seeking to enter the labor force, attain leadership positions, and launch businesses. Globally, women comprise only 40 percent of the working population and are under‐represented in leadership roles in business in all countries.<sup>2</sup> In OECD countries, 62 percent of women work, compared to almost 80 percent of men.<sup>3</sup> When women are employed, they are more likely to work part‐time, in the public sector, or in lower‐ skilled jobs. The pay is often lower and there are fewer opportunities for advancement. In the European Union (EU), only 3 percent of CEOs and 18 percent of board members are female.<sup>4</sup> On average, women earn around half of what men do, globally ( **Figure 1** ).

**Figure 1. Globally, female earnings are approximately half of male earnings**<sup>56</sup>

<!-- Start of picture text --> $21,000<br>$12,000<br>$11,000<br>$6,000<br>2006 2017<br>Average female earnings Average male earnings<br><!-- End of picture text --> When it comes to entrepreneurship, women also fall behind. In the EU, only 25 percent of business owners with employees are women (a percentage similar to that in Canada and the United States), and survival rates of female‐owned businesses are lower than those for male‐owned businesses. The gaps are similarly pronounced in the developing world. Globally, on average, only 18 percent of firms have a female top manager and 15 percent of firms of any size have majority female ownership ( **Figure 2** ). These figures differ significantly by region, likely reflecting in part, differing labor force participation, as well as attitudes and cultural norms surrounding the role of women in professional leadership.

> 2 Naoko Otobe, “Promoting Women’s Economic Empowerment through Productive Employment and Social Protection,” _World Institute for Development Economics Research_ , May 2014.

> 3 William Adema et al., “Enhancing Women’s Economic Empowerment through Entrepreneurship and Business Leadership in OECD Countries,” _OECD/CDRF_ , 2014, http://www.oecd.org/gender/Enhancing%20Women%20Economic%20Empowerment_Fin_1_Oct_2014.pdf. 4 Adema et al.

> 5 “The Global Gender Gap Report 2015” (World Economic Forum, 2015), http://wef.ch/1MkmweN.

> 6 Briony Harris, “What the Pay Gap between Men and Women Really Looks like,” World Economic Forum, November 6, 2017, https://www.weforum.org/agenda/2017/11/pay‐equality‐men‐women‐gender‐gap‐report‐2017/.

2
TabRe 2. Observations in the Sanmpile

||_NumberPercentagefsales_|
|---|---|
|Firms inourfinalsample|308<br>97.21|
|Firmsthat suppliedincomplete information|25<br>1.04|
|Firmsmergedandkeepnoindependent records|19<br>0.12|
|Firmsthat were liquidatedannolonger exist|22<br>0.78|
|Firmsthat deniedorrefused togiveinformation|26<br>0.85|
|AllPrivatizod Firms(1982-200|400<br>100.00|

_Note:_ This table breaks our world samnple between 1982 and 2000 into two groups. For each group we provide the number of firms and the percentage of pre-privatization sales in the total. Source: Data collected by authors.

26