rafmacalaba/data-use-mentions-extended huggingface dataset:
and daughters is larger in rural areas, a 50 percent higher intercept for the sons, compared to only a 16 percent higher intercept in the urban areas. Although useful as a benchmark comparable to the existing estimates in the literature, these preliminary estimates are, however, contingent on the maintained assumption that the CEF is linear.

The estimates from the quadratic specification for rural India show that the coefficient of the quadratic term is statistically significant at the 1 percent level, for both the sons and the daughters, providing clear evidence that the null hypothesis of a linear CEF is rejected. The estimated quadratic coefficient is negative, suggesting that both the CEFs are concave, similar to what we observed in urban India (see also the Lowess plots in Figure F2.IR). However, there is an important gender difference between the urban and rural India; the degree of concavity is similar for sons irrespective of location (the estimate of the quadratic coefficient is -0.007 in both rural and urban samples), but the quadratic coefficient is substantially larger in magnitude for daughters in rural areas. In fact, the null hypothesis of equality of quadratic coefficients can be rejected in rural India at the 1 percent level, indicating that the girls face especially strong forces of diminishing returns in the villages.

The estimates of mobility in the bottom panel of Table T2.IR show that the girls face lower mobility, both in terms of absolute and relative measures, when they are born to low educated parents. The expected years of schooling remains consistently lower for girls, even when the father has college education; a girl growing up in rural India can expect a year less schooling on average. For the lowest educated households, the gender gap is almost 2 years of expected schooling. The estimates of relative mobility show that the intergenerational persistence is substantially higher for the daughters born into households with low educated fathers (compare the _IGME_ 0 estimate of 0.52 (sons) with 0.64 (daughters)).<sup>
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discriminated against small, rural nonfarm firms (Haggblade, Liedholm and Mead, 1986; Page, 1979; World Bank, 1982, 1987), together with hisi:oric urban policy biases (World Bank 1981), will need to be redressed if rural nonfarm enterprises are to achieve their potential for income generation and economic decentralization.

* The historical focus on manufacturing, by both policy makers and researchers (Haggblade, Hazell and Brown, 1987) seems now worthy of redirection. Spatial, time series and consumption data uniformly point, not to manufacturing, but rather to commerce and services as key growth sectors over the course of Africa's rural structural transformation. Services, in particular, are the activities best insulated crom urban competition; hence consumption leakages are smallest in this sector. Service and commercial enterprise should not he excluded from assistance programs as has frequently been the practice in the past.

* Rural towns emerge as focal points in the development of the rural nonfarm economy. Cross-section data systematically identify much higher densities of nonfarm activity in rural towns than in dispersed rural settlements. By providing nonfarm enterprises with larger markets, rural towns offer firms the potential to exploit economies of scale. Prospects for sharing equipment as well as the emergence of repair and support facilities induce enterprise establishment in rural towns. Moreover, the emergence of rural towns enables policy makers to provide necessary productive infrastructure at lower cost than would be possible under widely dispersed settlement patterns.

° For rural towns to play their role in a balanced rural development process, it will be essential to assure adequate economic and social infrastructure to support nascent modern nonfarm activities. Physical infrastructure will undoubtedly plav a key role. Anecdotal evidence on the rise of secondary cities in developing countries identifies ground
# **1. Introduction**

Even in the face of rapid global urbanization, agriculture continues to be the mainstay for the majority of households in the developing world. Rapid development of the agricultural sector is one of the most effective mechanisms for reducing poverty and food insecurity (Ligon and Sadoulet 2008, Ravallion and Chen 2007). Still, data on agricultural production in most developing countries are notoriously weak and unreliable. This can greatly undermine the scope to design and fine-tune policies that will fully realize agriculture‟s potential strengths. For example, without high quality data, it will be difficult to assess the effectiveness of interventions to improve access to technology, the extent to which these affect household welfare, and the ways in which gains are distributed among different types of households or members within the same household. All of this will reduce the ability for evidence-based policy making and quick scaling-up or adjustment of policies and other interventions in light of the outcomes they achieve.

To explore ways that could possibly help improve the quality of information on agricultural production, this paper draws on the large-scale implementation of diaries, aimed to be filled by a household member, assisted by a local person, at the time of harvest, in parallel to Uganda‟s 2005/06 National Household Survey (UNHS). Having the diary administered to households included in the regular survey allows us to compare production estimates by the two methods and allows identification of household characteristics that are associated with higher levels of participation and reporting of production in diaries.

The analysis reveals some useful findings. First, despite a very limited budget, with no pretest and neither central training nor any manual for local monitors, the diary produced encouraging results. Households remained in the sample for an average of 5 months during the reference period and made an average of 115 entries that are fairly evenly distributed over time and do not provide strong evidence of respondent fatigue. While some 20% failed to start filling the diary altogether (many of them because no qualified monitor could be found
**Figure 17: Reform Indicators on Utility Governance and Decision-making and Full-Cost Recovery**

<!-- Start of picture text --> a) Cost Recovery and<br>b) Cost Recovery and Utility Governance Index<br>Private Sector Participation Index<br>70% 100%<br>R2 = 0.4107 R2 = 0.1772<br>60% Philippines 90% Kenya<br>80%<br>50% Peru<br>Philippines<br>Uganda 70% Colombia<br>40% Uganda<br>India -Odisha 60% Pakistan Peru Ukraine<br>Colombia<br>30%<br>India -Andhra Ukraine 50% Egypt, Arab Tanzania<br>20% Pradesh India - Pakistan Rep.<br>Rajasthan Kenya 40% Senegal Morocco<br>India -Odisha<br>10% Senegal Vietnam 30% Dom. Rep.<br>0% Egypt, Arab Rep. TanzaniaTajikistan Morocco 20% Tajikistan Vietnam<br>30% 50% 70% 90% 110% 130% 50% 70% 90% 110%<br>Dominican<br>Republic<br>Full-Cost Recovery Full-Cost Recovery<br>c) Change in Cost Recovery and d) Change in Cost Recovery and<br>Private Sector Participation Index Utility Governance Index<br>70% 100%<br>R2 = 0.0809 R2 = 0.0084<br>60% Philippines* 90% Kenya<br>80%<br>50% Peru<br>Philippines*<br>70% Peru Uganda<br>40% Uganda Colombia<br>Pakistan<br>India -Odisha 60%<br>Colombia India - Ukraine<br>30% India – AP Rajasthan<br>50% Egypt, Arab<br>20% KenyaPakistanRajasthanIndia - Ukraine 40% TanzaniaSenegal TajikistanRep.<br>10% Senegal India –AP Vietnam 30% Morocco India
(2013). The data cover 180 countries, from all World Bank Group country groups: developing East Asia and Pacific (17), developing Europe and Central Asia (19), high-income OECD (31), highincome non-OECD (26), developing Latin America and Caribbean (23), developing Middle East and North Africa (12), South Asia (8), and Sub-Saharan Africa (44). It also has a broad coverage in terms of income levels: high-income OECD (31), high-income non-OECD (26), low-income (29), lower-middle income (46), and upper middle income (48).<sup>16</sup> The world population is growing more slowly and aging at unprecedented speed. While the global population has tripled since the post-war “baby boom” era, population growth is slowing markedly. After increasing for five decades, the proportion of people ages 15 to 64—the typical working-age population—reached a peak of 66 percent of global population in 2012 and is now starting to fall. The rise in the share of dependents is driven mainly by an increase in the share of elderly in highincome and upper-middle income economies. These global trends—slower population growth and population aging—have been shaped by a steady decline in fertility rates and a rapid improvement in life expectancy. In the 1950s, total fertility rates were more than five births per woman, but since then they have steadily declined to 2.45 births per woman in 2015. In parallel, average life expectancy at birth has risen from 47 years in 1950 to 67 years in 2000, while infant mortality has declined.

Demographic change has a profound impact on the share of the global working age population that lives in developing countries, particularly lower-income countries. In 1950, 33 percent of the global working age population lived in high-income countries. Developing East Asia and the Pacific—the region with some of the most rapid fertility declines and life expectancy improvements in recent years—accounted for
# _Industry-level data set_

Industry-level data was used to estimate the model’s production function. This data was derived from aggregating the individual-level skills and industrial emissions dataset to the industry level after adding industry level data on employment and fixed assets from EUROSTAT (2021c, 2021d), mapped to ISIC in the same way as described above. The aggregates were averages for each ISIC 2 digit industry weighted by the PIAAC sample weights and converted to per worker terms. Summary statistics for this data set are presented in Table 2.

# _OECD’s PISA 2000 and 2012 data sets_

The final data sets used in this study were the OECD PISA data sets for 2000 and 2012. OECD PISA is a nationally representative, school-based assessment of 15 year-old students’ reading, mathematics, and science skills. The 2012 data set was used to estimate the baseline steady-state of the model while the 2000 data set was used in one of the counterfactuals.

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|
|17.72<br>Have target sale for next year|0.38|378|17.91|0.38|134|
|% of firmst that prepare the following at least annually:||||||
|60.42<br>Profit and loss statement|0.49|379|62.69|0.49|134|
|57.78<br>59.63<br>Balance sheet<br>Statement of cash flow|0.50<br>0.49|379<br>379|58.96<br>64.18|0.49<br>0.48|134<br>134|
|Income/Expenditure sheet<br>72.29|0.45|379|67.91|0.47|134|
|_Panel E: Other ac_|_tivities_|||||
|16.55<br>Hours spent looking after dependents|10.90|317|14.88|10.54|73|
|12.85<br>Hours spent doing household chores|9.87|317|7.29|9.64|73|
|12.63<br>Hours spentpracticinghobbies andpersonalpursuits<br>Note: The table shows the mean, standard deviation, and number of observations from MGES 2015. Age o<br>chores and doing hobbies refer to weekly averages. All the variables used in the analysis are describes in mo|7.51<br>f firm is reported in n<br>re detail in Appendix|317<br>umber of year<br>A1.|14.34<br>s, hours spent looking|9.12<br>after dependents, doin|73<br>g household|

17
school notice board. The school committee developed an expenditure plan with the assistance of the facilitators supported by the project, who coached school committees on how they might address problems at the school with the block grant (but only those that could be implemented in two years, or the life of the experiment), approved expenditure proposals from school committees, authorized transfer of the block grant (once they approved expenditure proposals), ensured transfer of the grant to school committees‟ bank accounts, and monitored the use of the block grant. On average, one facilitator was assigned to ten schools, and visited each school committee 13 times.<sup>10</sup> Using these estimates on the number of visits, time facilitators spent in the schools, and staff salaries to break down the facilitation costs, and considering other treatmentspecific costs, we estimate that the cost of implementing the grant was about US$321 (excluding the grant itself) per school.

The eight million Rupiah (US$870) block grant was transferred directly from the MoNE into a bank account held by the school committee, in two tranches, with the first tranche amounting to three million Rupiah (US$326). This first tranche was disbursed in January 2008, three months later than planned, due to budgeting problems at the MoNE. The second tranche also confronted Government budgeting delays. It was to be disbursed to the schools subject to sufficient progress achieved by the school committee in using the first tranche of the grant; but, in practice, all schools received the second tranche, and received it ten months late, in December 2008, after the endline survey.<sup>11</sup> Thus, these results measure the impact of the first tranche, and the anticipation of getting remaining funding.

# _Intervention 2: Training_

Other factors hypothesized to be holding back school communities from realizing their role was information, such as their lack of knowledge about the decree; and capacity, such as how to engage the community
pressure from organized interest groups, incomplete vertical unbundling and no horizontal unbundling of the sector.

**Figure 15: Reform Indicators on Price Formation and Full-Cost Recovery (C3)**

<!-- Start of picture text --> a) Cost Recovery and b) Cost Recovery and<br>De Facto Tariff Regulation Index De Facto Tariff Regulation Index<br>120% 110%<br>R2 = 0.2186 Peru R2 = 0.104<br>Peru 100%<br>Colombia<br>100% Colombia<br>Senegal Pakistan Uganda Ukraine 90%Philippines* Senegal Pakistan Uganda Ukraine<br>80% Egypt, Arab Philippines 80%<br>Rep. Tanzania Vietnam Tanzania Egypt, Arab<br>70% Rep.<br>60%<br>Kenya Kenya<br>60% Vietnam<br>India -Odisha India -Andhra India -Odisha<br>40% Tajikistan 50% Pradesh<br>Dominican India -<br>Republic Morocco 40% Rajasthan<br>20%<br>Tajikistan<br>30%<br>Morocco<br>0%<br>20%<br>50% 60% 70% 80% 90% 100% 110%<br>-50% 0% 50% 100%<br>Full Cost Recovery Change in Full Cost Recovery<br>De Facto Tariff Regulation De Facto Tariff Regulation<br><!-- End of picture text --> _Notes:_ *The Philippines saw a decline in cost recovery, but post-reform cost recovery was still close to 100 percent on average. _Source:_ World Bank staff.

# **#8: Countries that have made more progress on utility governance and decision-making perform, on average, better on cost recovery**

59. The second determinant of cost-recovery levels investigated in this analysis is reform progress relating to utility governance and decision-making. 6 out of the 17 case studies
results are supported by the empirical literature cited in section 1 that indicates that access to a diverse set of service providers with a domestic presence is crucial for growth.

# **4. Poverty Effects of Russia’s WTO Accession:**

**Modeling “Real” Households with FDI in Services and Endogenous Productivity Effects**

# **4.1 Introduction**<sup>**39**</sup>

The work discussed in this section originated from a request by the Government of Russia to the World Bank for development of a methodology to assess the impact of WTO accession on poverty and social issues in Russia. In Russia, as in many countries, policy-makers are concerned with not only the aggregate effects and impacts on productive sectors of the economy, but they are also concerned with impacts on the poor and other distributional effects.

We extend the model of Russia discussed above to assess the poverty and distribution effects of Russia‘s WTO accession. In particular, the model again incorporates foreign direct investment in imperfectly competitive business service sectors; and we adopt the Dixit-Stiglitz framework in both business services and imperfectly competitive goods sectors, which implies that we have endogenous productivity effects from additional varieties in these sectors. We show that these features are crucial to the distributional and aggregate results. What is innovative in this model is that we integrate all the 55,098 households of the Russian Household Budget Survey (HBS) as agents in the model. Thus, our model is a ―real household‖ model.

Interest in the impact of trade policy on poverty has dramatically increased in recent years. Some modellers have assessed impacts with a computable general equilibrium multiple representative

> 39 This section is based on Rutherford and Tarr (2008) and Rutherford, Tarr and Shepotylo (2005).

61
contagion of the virus from young to old and providing a modest economic downturn (see Alon et al., 2020).

Our first contribution to the literature is to provide an empirical analysis of the impact of Covid19 beyond aggregated economic indicators. It presents a robust causal empirical analysis of the Covid-19 lockdown measures on household welfare in a developing country based on micro data on household expenditure and labor market outcomes. It further informs on the economic cost of lockdowns for households, which can be used as a yardstick in measuring the impact of macrolevel policies against micro-level welfare consequences.

Evaluations of past pandemics like HIV have shown negative impacts on economic growth and labor market outcomes (see Dixon, 2002 and Arndt and Lewis, 2001). The emerging literature has begun to investigate the effects of the Covid-19 pandemic on the economic livelihoods of households in developing countries. The research has heavily evaluated the economic lives of the poor using phone surveys on retrospective household welfare indicators (see Ceballos et al., 2020, Egger et al., 2021 and Schotte et al., 2021, among others). In extension, the empirical estimation has focused on the poor, agricultural or rural areas to understand the impact of the Covid-19 lockdown on the economic livelihoods and global food system (see Gupta et al., 2021; Janssen et al., 2021; Rönkkö, Rutherford and Sen 2021; Swinnen and Vos 2021). Gupta et al. (2021) evaluate the impact of the Covid-19 pandemic on economic outcomes of the poor and vulnerable households living in rural areas in India. They used a micro-level survey on weekly financial data for households in the high remittance regions and found a negative impact on household income. The adverse effect was exacerbated by the increasing interest rate on cash loans and reduction in remittances.

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Guatemalan and Indian children who were near the 50 ' percentile of the reference growth charts prior to puberty, ended up near the 25th percentile by the end of adolescence.

The World Health Organization has long recommended the use of a reference population for the assessment of the nutritional status of children (Waterlow _et al._ 1977; WHO 1979; WHO 1983; WHO Working Group 1986). The emergence of ethnic differences in growth attainment during adolescence justifies the upper limit of 10 years fixed by the WHO on the age for appropriate comparisons with the WHO/NCHS/CDC growth charts.

# **2.3 Standard Deviations Scores**

In order to be able to compare the growth attainment of children of different ages by sex, we converted the EGSF anthropometric measurements into three indexes: height-for-age, weight-for-age, and weight-for-height. Using the WHO/NCHS/CDC curves we then expressed the growth attainment of each as standard deviations from the median (z-scores) (Waterlow _et al._ 1977; WHO Working Group 1986). The standardizing calculations were made with ANTHRO (Software for Calculating Pediatric Anthropometry), Version 1.01, provided by the Division of Nutrition of the Centers for Disease Control and the Nutrition Unit of the World Health Organization.

The z-score measures the degree to which a child's measurements deviate from what is expected for that child, based on a reference population. The formula for the calculation of the height-for-age z- score is: Z, = (yis,a - H s,a)/,s,a where zi is the z-score for child i; **y.S,a** is the measured height (in cm) for child i of sex s and age a; H sa _iS_ the median height (in cm) for children of sex s and age a in the reference population; and as7a is the standard deviation in height (in cm) for children of sex s and age a in the reference population.

# III. *
the school-age population (7-18 years) in the selected districts of Sampang and Bangkalan are both 1.0 percent, whereas in Banjarbaru and Banjarmasin the figures are 1.47 percent and less than 0.1 percent, respectively. As the reported prevalence rate is very low, the contribution of disabled children to the SER as a whole is very small, at less than 1.0 percent in Bangkalan, Sampang and Banjarmasin, whereas in Banjarbaru the contribution is slightly higher, at 1.15 percent, in line with the higher prevalence rate reported in the census.

From the perspective of disabled children, the percentage of disabled children who participate in school (SER of disabled children) in selected districts is high. With the exception of Banjarmasin, with a percentage of nearly 60 percent, about 70 percent of disabled children in the selected districts attend school. Except for Banjar Baru where the SER of disabled females is higher than that of males (GPI of 1.14), the other three districts show that the SER of disabled males is higher than for females, following a similar pattern to their non-disabled peers.

> 34 UNICEF (2018) Progress in measuring global school enrollment gaps for children with disabilities: <u>https://blogs.unicef.org/evidence-for-action/progress-in-measuring-global-school-enrollment-gaps-for-children-withdisabilities/</u> 35 For rare cases such as prevalence of disability, Susenas cannot be calculated at the district level, as the standard error is normally high. Therefore, we rely on the more comprehensive Population Census 2010 for disability figures at the district level.

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. Offers in each arm of the experiment include: (1) nothing – control group, (2) 50 somoni (T-Arm1), (3) 100 somoni (T-Arm2), (4) 200 somoni (T-Arm3), and (5) 400 somoni (T-Arm4).

As additional validation, all respondents were asked whether they “support giving people financial incentives for vaccination” following the experiment questions. The results are consistent with findings from the survey experiment. In Uzbekistan, 72 percent of respondents said they disagree with paying incentives for people to be vaccinated. In Kazakhstan, 58 percent said they disagreed with paying incentives. Only in Tajikistan, where the experiment failed to find any systematic effect on vaccination intentions, just 18 percent said they disagreed with paying incentives.

In table 9, using the same dependent variable, responses are disaggregated by reason for non-vaccination among those who stated they would not be vaccinated in a separate question during the same interview. These highlight that much of the negative response to incentive payments comes from respondents who stated they would be willing to be vaccinated changing their response to refusal in the case that they were offered an incentive. Among those who said they would not be vaccinated, very few changed their response following the offer of an incentive. However, small but significant differences in some cases were apparent with respect to suspicion of vaccines and their risks (table 9 columns 1, 3 and 4).

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