rafmacalaba/data-use-mentions-extended huggingface dataset:
,821|0.9|0.2|Yes|
|Other|15,388|3.7|0.8|Yes|
|Indirect subsidies (off-budget)<sup>c</sup>|2,714|0.7|0.9|Yes|
|Others<br>**_Source:_**_Authors’ calculations; Data from_|40,288<br>_the Ministry of Fina_|9.8<br>_nce, the Ethiopian P_|2<br>_etroleum Suppl_|Yes<br>_y Enterprise, the_|

**_Source:_** _Authors’ calculations; Data from the Ministry of Finance, the Ethiopian Petroleum Supply Enterprise, the Ethiopian Trading Businesses Corporation, and ESS._<sup>_a_</sup> _The value of PSNP for 2018/19 is derived from the ESS data, which has information about PSNP transfers for the previous 12 months._<sup>_b_</sup> _Food security value is also estimated from the ESS data._<sup>_c_</sup> _Data on subsidies are from the Ethiopian Petroleum Supply Enterprise and the Ethiopian Trading Businesses Corporation._ **_Note:_** _ETB = Ethiopian Birr._

# **3.2.  Survey data: Consumption, utilization of services and child poverty**

The survey data are from the 2018/19 Ethiopia Socioeconomic Survey (ESS). ESS is a nationally representative survey implemented by the Central Statistics Agency in collaboration with the World Bank under the LSMS-ISA project. The survey interviewed 6,700 households out of which 4,992 households had at least one household member between 0-17 years old at the time of the interview. A total of 13,820 members in this age group are included in the analyses.

Table 4 presents the descriptive statistics of the children included in this study. The profile shows that both boys and girls have similar demographic and socioeconomic characteristics. The average
from models estimated on past data generate credible estimates, in a variety of country contexts, of poverty headcount and gaps.

The preferred model generates an estimate of 10.0% in urban areas and 16.8% in rural areas, which implies an overall poverty level of 14.6% for 2014-15 (Table 1 and Figure 1). The estimate is consistent with poverty reduction from 2004-05 to 201112 in rural areas and suggests a slight deceleration in poverty reduction in urban areas (Table 2). This in turn implies that the elasticity of poverty with respect to growth of per capita GDP was -2.11, which is within the range of past experience. Reassuringly, when looking at the state level, predicted poverty reduction is greater in states with higher rates of GDP growth.

We use two main tests to validate the survey-to-survey imputation models. The first estimates a model with the same specification, using data from 2004-05 and 2009-10, to predict into 2011-12. These predictions are then compared with actual measured levels of poverty from the national sample survey. In a similar, vein, the second validation test uses data from 2009-10 and 2011-12 to predict backwards to 2004-05. This is a much more challenging exercise because the data from two years apart are being used to extrapolate five years in the past.

While the traditional line-up method performs slightly better than our preferred specification in 2011-12, it gives wildly implausible estimates when projecting back to 2004-05. To be specific, the standard line-up method is off by only a percentage point in 2011-12 but exaggerates poverty by 25 percentage points when predicting back to 2004-05. Our preferred model performs much better on average, as the discrepancy is about 2 percentage points in 2011-12 and about 3 percentage points in 2004-05.

The traditional line-up method happens to do well when projecting forward into 2011-12 because growth in mean reported consumption in the survey was unusually high between 2009-10 and 2011-12 and nearly matched growth in HFCE. This is a sharp break from the pattern observed during the previous period, and
 subjective evaluation of one's own satisfaction. Such empirical tests are based on several European panel data that have been gathered in the database called Consortium of Household Panels for European Socio-Economic Research (CHER).

# **II) The Concept of Generalized Gini index and the Measurement of Relative Deprivation**

Assume that<sup>_y_</sup> _i_<sup>is the income of individual</sup><sup>_i_,</sup> _~~y~~_ the average income in the population, _n_ the size of the population, and rank the individuals in such a way that _y_ 1  ...  _yi_  ...  _yn_ . Berrebi and Silber (1985) then showed that the Gini index could be expressed as  where _si_  ( _yi_ / _~~ny~~_ ) is the share of individual _i_ in total income. Note that, following Donaldson and Weymark (1980), (1) may be rewritten as   where  is the "equally distributed equivalent level of income"<sup>2</sup> corresponding to Gini's index.

> 2  See, Atkinson, 1970, for more details on this concept.

4
(i) higher availability of vaccines for children, mostly by making it possible to have a working fridge, which is essential to store most vaccines; and (ii) higher availability of essential and priority medicines for children and pregnant women, partially through the use of ICT for supply chain management. Finally, access to electricity and ICT use for supply management are positively related to the availability of antenatal care (ANC) tests for pregnant women. Test availability also decreases when facilities are further away from fair or good quality roads. Overall, our results suggest that investments in infrastructure and technology may play an important role in improving the quality of health service delivery, including early childhood and maternal health.

Our contributions to the literature are three. First, we focus on the potential impact of access to infrastructure (i.e., electricity, road quality, cellphone and internet coverage, and use of ICT) on the quality of health service provision in a lower-middle-income setting. This is different from the current literature in that (a) we focus on the supply side while most papers focus on the demand side; (b) we use novel data by merging geospatial data with high-quality health service delivery indicators survey data from different sectors; and (c) while our data does not allow to identify causal impacts, we document the initial gaps in access to infrastructure and control for a wide set of characteristics in our correlational analysis to quantify the potential effect of infrastructure on health service provision. Furthermore, we provide insights for the provision of general health services, as well as for early childhood care and pregnant women. Second, we take advantage of the rich set of questions in the health service delivery indicators survey to provide suggestive evidence of the possible channels through which infrastructure affects the quality of health service delivery. Third, we lay the groundwork for future cross-sectoral studies by reviewing the literature and proposing a simple analytical framework on the channels through which infrastructure affects the quality of health service delivery.

The rest of the paper
with developing countries.<sup>43</sup> Moreover, Schiff et al. (2002) show that developing country trade with technologically advanced countries is very important in technology intensive sectors, but trade with developing countries can be important for productivity spillovers in less technologically complex products in which developing countries have comparative advantage. So on low R&D products like footwear and textiles and apparel, trade with China and Indonesia could be as important for technology diffusion as trade with the EU and the US.

In summary, this literature shows that the purchase of intermediate inputs and FDI from industrialized countries is an important mechanism for the transmission of R&D and productivity growth in developing countries. For small developing countries, trading with large technologically advanced countries is crucial for TFP growth. But for products in which developing countries have a comparative advantage, developing country trade may be important for spillovers.

In our model, the parameter that reflects the ability of a region to increase total factor productivity through the transmission of new technologies is the elasticity of varieties with respect to the price. Schiff _et al_ ., (2002, table 1) have shown that for R&D intensive sectors, trade with industrialized countries contributes significantly to total factor productivity in developing countries, but trade with developing countries does not. Averaging over the industries in Schiff _et al_ .,( 2002, table 3) yields that trade with industrialized countries in R&D intensive products is about eight times more valuable for developing country TFP increases. On the other hand, for sectors that are low in R&D intensity, their results suggest that for technology diffusion trade with developing countries can be as important as trade with industrialized countries.

Based on these considerations, we first classify the increasing returns to scale sectors of our model into low, medium and high technology sectors. The classification is defined by the share of R&D expenditures in total sales, based on U.S. data. For low R&D intensive sectors, we
and quality of skills supply, including the soft skills that many employers say they want?  Do these skill deficits pose significant obstacles to the operation of firms? If so, how are these deficits distributed across firms, and do they vary systematically across firms?

- _Skill Strategies_ : What strategies do firms rely on to address skill gaps – using high-skill expatriate workers, providing company-sponsored training, hiring new workers, or outsourcing professional services – and which ones are most effective?

> 6 A second enterprise skills survey is currently on-going in Zambia, which will also support the preparation of a new World Bank project and contribute to the regional framework of data collection on skills. The regional framework contributes to work being undertaken under the Partnership for Skills in Applied Sciences, Engineering and Technology (PASET). PASET was launched in 2013 to address the critical need to strengthen science, and technology capability in Africa to support its socio-economic transformation. PASET convenes African governments, private sector and educational institutions; development partners; businesses from outside the continent who are investing in Africa; and new country partners from other Regions.

> 7 World Bank’s Skills Toward Employment and Productivity (STEP) framework).

3
2010 (ILO, 2013b). At the same time, multi‐topic household surveys, such as the LSMS(‐ISA)<sup>4</sup> surveys supported by the World Bank, focus primarily on ‘final’ outcomes such as poverty and food consumption, and focus less on ‘intermediate’ outcomes such as employment (Oya, 2013).

Even when household or labor force surveys are readily available, the quality of the employment data remains a concern. Seemingly minor differences in survey features ‐ questionnaire design, the wording of a question, sampling, coverage, the structure and length of the labor module and its implementation in the field – can have substantial effects on labor market statistics. Fox and Pimhidzai (2013), for instance, show how the absence of screening questions in the labor module affects the employment rate in Uganda. Our analyses for Ethiopia and Malawi reveal that the employment rate can vary substantially depending on the survey instruments used to capture this indicator (Figure A in the Appendix).

Academics and statistical agencies alike have expressed concerns that the accuracy of labor market statistics of women and – although less often emphasized ‐ youth are more sensitive to survey design than labor market statistics of adult men (Gaddis & Palacios‐Lopez, 2018; Verick, 2018; Walsh, 2018). Compelling theoretical arguments support these concerns. First, women and young people engage more frequently in atypical employment (Barrientos et al., 2004). They are more likely to combine a job with other activities such as domestic work or

> 4 <mark>Living Standards Measurement Study‐Integrated Surveys on Agriculture.</mark>

2
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||||||
|Project Characteristics Index<br>|||0.121**|||||
|for comparison<sup>1</sup>|||(0.057)|||||
|CPIA at Approval||||0.300||||
|||||(0.195)||||
|Change in CPIA over Project|||||-0.294|||
|Lifetime|||||(0.261)|||
|Observations||120|120|120|120|||
|Adjusted R-squared||0.126|0.084|0.062|0.053|||

Note: Explanatory Power of Design index = 5.54, Project Characteristics = 1.14 and Country Characteristics 2.28. This table provides estimates of IEG Outcome Ratings regressed on common design features and quality ratings. Using Principal Component Analysis , an Index is created for Design based on these features and ratings. (1) For comparison, an Index is created for Project Characteristics based on the log of original commitment, preparation cost, first and second half supervision cost, the change in project size from approval to closing, and the time it takes projects to move from one key milestone in the project lifecycle to another. The table also provides the estimates of regressing IEG Outcome on two country variables, CPIA score of project country at approval and the change in the score over the project lifetime.

29
# **2. EVALUATING THE NEED FOR A SOCIAL POLICY**

2.1 There are two basic justifications for social policy intervention in the infrastructure sectors. First, access to basic services is considered essential to achieve a minimum level of household welfare; without adequate sanitation, for example, it is difficult to sustain good health. Second, it is known that access to basic services provides a platform that helps families to escape from poverty; for example, by facilitating labor force participation via improved health or reduced time spent on domestic chores such as collection of water and wood, or by expanding possibilities for household-based microenterprises.

2.2 In order to define the priorities of any social policy for the infrastructure sectors, it is important to begin with an empirical analysis to identify the areas of most pressing social need. In many countries, governments set themselves the objective of providing universal access to infrastructure services, often defined as universal service coverage at affordable prices that enable all citizens to cover their basic consumption requirements. To evaluate the extent to which this objective has been met in today’s Argentina, the study examines household survey data on coverage and expenditure across the income spectrum.

## **ACCESS**

2.3 Household data on service coverage show that access to electricity in Argentina is almost universal, both in the Metropolitan Area and in the provinces (Figure 1). As far as telecommunications are concerned, universal coverage has also been reached, since although only 72% of households have a private telephone, 97% live within 10 blocks of a public telephone service, which is considered to be an adequate substitute.

2.4 A significant coverage deficit, however, still remains for water and sewerage. In the case of water, the deficit is concentrated in the Metropolitan Area where 15% of households currently lack the service, in contrast to the provinces where the service is already virtually universal. In the case of sewerage, 30% of the population nationwide still lack access to this service
exports).

Our paper is structured as follows. Section 2 estimates correlations between various types of trade and employment or labor income within sectors. Section 3 studies the role of job creation in supplying sectors versus export sectors, while section 4 examines job activities in exports across and within sectors. Section 5 explores the role of policy in job creation from exports, while section 6 summarizes and concludes.

> 8 The research by Farole et al. (2018) studied the role of GVC participation for the labor value added, the labor share and the relative demand for skills in a cross-country-sector model for selected years between 2001 and 2014. Our proposed work differs with regards to the chosen models, methodologies, datasets and labor market outcomes.

> 9 See Brown and Christensen (1981) and an application in Feenstra and Hanson (1996), among many others.

> 10 The sample of LMICs only contains employment data and thus prevents us from performing a similar assessment.

4
While LFS, DHS and LSMS surveys typically visit each household only once, some LSMS‐ISA surveys visit each household twice administering the post‐planting questionnaire during the first and the post‐harvest questionnaire during the second visit. However, the labor module is generally only administered once. The exception is the 2015/16 Nigeria LSMS‐ISA survey that administers the labor module during both visits and, thus, allows to estimate the employment rate at two moments in the agricultural year.

The Malawi General Household Panel survey adopted a variant of this approach. Like other LSMS‐ISA surveys, each household is visited twice, and the labor module is only administered once. In contrast to other LSMS‐ISA surveys, the sample is randomly divided in two groups. The labor module is administered to the first group during the first visit and to the second group during the second visit. Hence, the employment rate can be estimated at two moments in time, while the labor module has only been administered once to each household. In this sense, this approach resembles a continuous survey. Results, not reported here, but available upon request, show that the employment rate is nearly the same in the post‐planting and post‐ harvest period, suggesting that seasonality is either not a major issue in Malawi or plays only a role in‐between the planting and harvesting period.

# 4.3.3. The reference period

The drawback of continuous surveys or surveys with multiple visits are the costs and the logistics required to implement them. For this reason, these types of surveys are often not feasible. An alternative approach is complementing the standard 7‐day reference period with a 12‐month reference period. While this strategy does not allow estimating the annual average employment rate – which relies on a 7‐day reference period ‐, it does provide some insights in the seasonality of labor demand and the occurrence of engaging in multiple economic activities throughout the year.

Since the 1980s and 1990s, several authors have supported
and corruption. However, Goel and Nelson (2010) find the opposite. They argue that corrupt practices are easier to detect and stigmatized in urban areas. Notwithstanding these contrasting effects of urbanization, the possibility of a systematic relationship between urbanization and corruption cannot be ruled out. If the degree of urbanization also varies systematically with country size, our main results could suffer from omitted variable bias. Thus, we control for the percentage of people living in urban areas (Urbanization). The data source for the variable is WDI.

Government size can also impact corruption. One view is that since a larger government promotes a system of checks and balances, strengthens accountability and enforcement measures, an increase in the government size should reduce corruption. This viewpoint is inferred from the fact that developed countries generally have bigger governments and are less corrupt than developing countries. Empirical studies confirming this view include for example, La Porta et al. (1999), Adsera et al. (2003), Billger and Goel (2009), and Goel and Budak (2006). The alternative view is that an increase in government size provides more opportunity for political rent-seeking, causing the politicians and bureaucrats to become more corrupt (e.g., Rose-Ackerman 1978, 1999; Alesina and Angeletos 2005, Goel and Nelson 1998, Arvate et al. 2010, and Bergh et al. 2017). The direction of the relationship between corruption and government size depends on the net of the contrasting effects discussed above. If the size of the government also varies systematically with our measure of country size, our main results could suffer from omitted variable bias. Thus, we control for the government size using Freedom from Government Size indicator from Fraser Institute’s Economic Freedom of the World database. Note that higher values of the variable imply smaller government size.

Corruption has also been linked to trade openness. Theory suggests that greater trade openness lowers corruption due to fewer and less stringent trade restrictions (Krueger, 1974; Gatti, 15
etc.) using appropriately defined recall length variables for each activity (Table 1, Table 3). Of the activities, most person-days are used in planting and maintenance activities, with fewer person-days used in harvest (Table 2). We also assess whether the recall effect differs by type of worker (household, hired). Labor inputs are summed to the plot-level, rather than assessed separately for each worker on each plot, because one respondent reports all labor inputs for a given plot, so that this measure likely best captures the recall effect.

Finally, input use is assessed through incidence of organic fertilizer application per plot and incidence and quantity of inorganic fertilizer application per plot. This analysis is possible because the three surveys record fertilizer use at the plot-level. Fertilizer use is considerably more common in Malawi than in Tanzania, for which Malawi’s inorganic fertilizer subsidy program is likely responsible. Organic fertilizer is applied on 19 and 20 percent of plots in Malawi IHS4 and panel, respectively, and on 10 percent of plots in Tanzania NPS 2012/13. The difference is larger for inorganic fertilizer, which 53 and 61 percent of plots receive in Malawi IHS4 and IHPS 2016/17, respectively, compared to 9 percent in Tanzania. Among those plots to which inorganic fertilizer is applied, the applied quantities are similar across all three data sets at 66 kg per plot in Tanzania NPS 2012/13, 62 kg per plot in Malawi IHS4 cross-section, and 58 kg per plot in IHPS 2016/17 (Table 2). Application intensity (inorganic fertilizer applied per hectare) is again lower in Tanzania, owing to larger plot sizes, while fertilizer intensity is generally decreasing in plot size across all three data sets (Figure 2, row 3).

We apply the double Median Absolute Deviation method for outlier detection and correction to all constructed continuous variables in this analysis, that is, harvest quantity, storage and sales quantity, total, family, and hired labor person-days, organic and inorganic fertilizer quantity applied (