rafmacalaba/datause-encoder-data huggingface dataset:
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sections have demonstrated that this method results in consumption measures quite divergent from the “benchmark” and may also significantly over-estimate the degree of inequality in the distribution. Given both its poor relative performance and onerous time imposition, this method is not a recommended choice for this study setting.

Turning to dollar costs as a way to assess the resource utilization of the different diary modules with respect to a recall survey, the exact costs involved to administer each of the questionnaires and diaries depends on a number of context-specific factors, such as the price of labor, literacy levels, costs of travel between the sample EAs, printing costs, overheads, and so on. The main drivers of cost differences, however, are relatively easy to pinpoint. They are (i) differential interviewer-days needed to complete a household, and (ii) differences in time needed to enter the data into electronic format.

Table 9 summarizes these differences and their cost implications under a set of assumptions about time to walk between households, review diaries, and implement recall modules. We are not considering a large multi-topic survey, but a short questionnaire with a complete consumption module (either by diary or recall). In the most labor-intensive set-up, for an interviewer doing personal diaries with daily visits, six households can be interviewed in 17 days. The alternatives to the personal diary are household diaries with varying visits in combination with a second enumeration area (to avoid days with no work in the enumeration area, given that the length of the diary is 14 days). For the analysis in Table 9, no distinction is made between the different types of recall questionnaires as they all yielded around four interviews per interviewer per day, which is three times less intensive than in the ‘light’ fieldwork set-up of household diaries with infrequent visits.

Once the diaries and questionnaires come back from the field, they go through several steps before being available in electronic format: administration and filing of forms, coding of items, double blind entry, reconciliation of the two entries, and so forth. We use information from the survey experiment to compute the time for these activities. On a given day, a data entry operator can process 2.86 personal diary households, 4.31 household diary households, or 8.51 recall questionnaire households.

Based on the person days for field work and data processing and with data entry staff in the office at half the cost of an interviewer in the field, we then compute the total costs of field work (Table 9, column 6). With the average cost to administer a recall questionnaire set at US$100 per household (the numeraire to scale the other field costs), the personal diary will fall between US$597 and US$974, depending on whether the household is visited every day or every two days. For a frequently visited household diary, the estimated costs lie between US$442 and US$726; for
exposure potentially leading to further deterioration in food security due to COVID-19 related income losses. Similarly, PSNP transfers, particularly those in-kind, may be more impactful for households with limited access to markets. PSNP cash transfers may also prove useful given rural transactions take different forms, including reciprocities. Conversely, COVID-19 related disruptions in access to food may have limited impact on households who rely less on markets - often the poor and those residing in remote locations. PSNP would bridge the gap in own production that otherwise would need to be filled through purchases from food markets, an option unavailable for non-beneficiaries.

We test this hypothesis using several disaggregations. In Table 3 we split the sample by wealth quintiles: combining the bottom three quintiles into one group (poorest households) and the top two quintiles into another group (“less poor” households). The results in Table 3 suggest that PSNP transfers are more protective for poorer households’ food security. Relatively, poorer PSNP non-beneficiaries report higher increase in food insecurity experience. In Table 4 we split the sample into remotely located and accessible households, using distance to urban centers. Using median distance to urban centers with population of 20,000 or higher, households located in areas with distances above the median are classified as remote. The results in Table 4 show that households living in remote areas and not receiving any PSNP transfers are more likely to experience a significant deterioration in food security.

Social protection and safety nets can also help households adopt effective coping strategies when they face income losses and related shocks. In our phone survey we elicited households’ coping strategies in the last one month. Households are given a long list of coping strategies, options which are not mutually exclusive as households can choose more than one option. The most dominant strategies include: (i) spent savings, (ii) reduced food consumption, (iii) borrowed money from others, (iv) reduced health and education expenditure, (v) reduced expenditure on agricultural inputs, and (vi) sold assets. Some of these coping strategies are likely to have lasting adverse impact on the livelihoods of rural households. For instance, decrease in education and health related investments will adversely affect human capital accumulation and reduce future earnings. Similarly, reduction in agricultural investments may limit households’ production potential.

Table 5 presents single difference estimates as data on coping strategy were only collected in June 2020 and thus should be interpreted cautiously. It shows that households who received 15
population (aged 15 or more) that used the internet to pay bills or make purchases during in the previous 12 months prior to the survey (conducted by Gallop in collaboration with the World Bank). The survey data aggregated at the national level were downloaded from the World Bank Global Findex Database, using the available data from the years 2014 and 2017.<sup>5</sup> The empirics use the average for countries with two data points and the single observation for countries with one data point.

# _C. Other Controls_

To minimize omitted-variable bias (OVB) in the estimated partial correlation between unemployment and the proxies of the incidence of the digital economy, the econometric models discussed below include several control variables. As discussed further below, the evidence indicates that a key control variable is the informality rate.

Data on informality were downloaded from ILOSTAT, where it is defined as the percentage of people who, during a given reference period, were employed in at least one informal sector enterprise, irrespective of their status in employment and whether it was their primary or secondary job. Again, we work with long-term averages of informality computed with data from 2006-2018. It is worth noting that most high-income countries do not report informality, neither in the ILO database nor in their official labor-market statistics. As will become evident in the following section, this omission presents a key challenge for assessing the partial correlation between unemployment and the incidence of the digital economy. Consequently, we present results with and without imputing informality rates in high-income economies, which we set equal to zero in one set of estimations.

> 5 The data were downloaded in June 2019 from https://globalfindex.worldbank.org/.

10
- 65 -

# V. Performance

5.01 The .valuation of vocation&aly-specific education and training should ideally be carried out against a hierarchy of objectives. The ultimate objective is economic efficiency, measured<sup>by</sup> assessing the economic benefits of training (usually productivity<sup>as</sup> reflected in earnings). The next lower objective in the hierarchy<sup>is</sup> placement in employment, a necessary but not sufficient criterion for econ-omic efficiency. At this same level employer satisfaction with graduate performance provides a weaker but useful standard. Next comes course completion or graduation. This in turn rests on a number<sup>of</sup> internal efficiency measures, such as drop out and repeater rates. Enrollments as a proportion of capacity can serve as an initial indicator of institutional relevancy and efficiency. All of these measures can, and ideally should, be assessed against costs, yielding a range of efficiency and cost-effectiveness indices.

5.02 Fundamental to all of these measures, of course, is the actual establishment of a training institution. From the perspective of World Bank project investments this is an implementation criterion, one bound by the initiation and completion dates of a given project.

# ECONOMIC OUTCOMES

5.03 A review of Bank investments based primarily on document analysis is severely limited with respect to the indicators of investment effectiveness that can be used. Economic analyses of the outcomes of Bank investments are absent from project completion reports.

5.04 Reliable unit recurrent cost data is almost entirely lacking in project documentation. Recurrent costs were reported for 11 of the 76 projects in the sample (8 of 34 countries). The figures are most often developed by dividing gross recurrent cost budgets by enrollments across a set of institutions.

5.05 These data, however, generally confirm that vocational secondary schools are more costly on an annual unit recurrent<sup>cost</sup> basis than general secondary schools, with ratios ranging from 1.5:1 to as much as 11:1. Nonformal annual unit recurrent costs are about twice as high as in secondary vocational schools (Table 26).

5.05 In a very few instances collateral studies shed light on rates-of-return to investment. These show relatively high returns to nonformal training in Latin America (Kugler and Reyes, 1978; Jimenez, 1986). An analysis of returns to nonformal and secondary vocational training in Korea indicates somewhat higher returns to nonformal training (Lee, 1985); returns to both equal or exceed returns to general secondary education as measured four years earlier (see Psacharopoulos, 1987).
probability of being connected to the grid and receiving quality electricity supply relative to being in a district with less dense transmission network (Brown and Sedano, 2004; Chakravorty et al., 2014).

Based on results from the above, we proceed in the third part to examine the taxrevenue of the of on implications negative impact persistent electricity shortages firm productivity and growth in Africa. Under conservative assumptions about the effect of electricity shortages on firm productivity and growth, and with the aid of firm-level data from the World Bank Enterprise Surveys, among other secondary data sources, we estimate the tax-revenue losses associated with unreliable power supply in 36 countries. The estimated revenue losses are assumed as the implied tax-revenue gains that will accrue to these economies by fixing supply irregularities in the power sector.

The key findings of the paper are summarized as follows: We find a strong and positive impact on electricity access and reliability on attitudes toward taxes. The impact is not restricted to household access to electricity but community access as well. More importantly, we also document that the level of impact rises with the quality of supply. In other words, moving from a lower tier (quality) of access improves attitudes toward taxes significantly. This underscores the importance and urgency of resolving the challenges of reliability in the electricity sector in Africa.

from the reveal the of in the Additionally, findings paper presence spillover effects impact of electrification on attitudes toward taxes. Extending grid connection into a community improves the willingness to pay taxes even among individuals without household connection to electricity. This finding supports that notion that the salience of infrastructural services plays a crucial role in enforcing the social pact between citizens and governments in developing countries.

Further, we document national identity as an important mechanism through which electricity provision affects attitudes toward taxes. Our findings show a positive relationship between electricity connectivity and high levels of reported national identity. Combined, these pieces of evidence suggest that a fiscal pact pertains in SSA. That is, when citizens enjoy reliable access to social infrastructure– such as electricity– they are in turn more likely to report relatively higher levels of tax morale and national identity.

Our findings have substantial implications for revenue mobilization in Africa. Results from the simulations show significant losses in tax revenue – more than $9.5 billion dollars (4.3 percent of total tax revenue) – in 36 African countries. The losses are highest in South Africa, Angola, Nigeria, Kenya, and Ghana. In sum, our findings provide suggestive evidence that provision of reliable electricity supply in SSA can generate significant financial returns to economies in the region – enough to make such investments fiscally sustainable 3
19

||All|TP|STP|SMP|MP|P-value|
|---|---|---|---|---|---|---|
|Numbers of person-days/ha (Men)|3.97|9.06|4.39|2.60|1.86|<0.001|
|Numbers of person-days/ha (Women)|1.19|4.80|1.28|0.37|0.32|<0.001|
|Numbers of person-days/ha (Children)|0.28|0.00|0.38|0.28|0.00|0.802|
|Total person-days/ha (Household)|5.44|13.86|6.05|3.25|2.17|<0.001|
|Use of hired labor (1=Yes)|0.18|0.20|0.18|0.17|0.18|0.886|
|Person-days/ha of hired labor|1.82|6.63|1.72|0.93|0.36|<0.001|
|Person-days/ha of total labor|7.27|20.49|7.77|4.19|2.54|<0.001|
|**5. Harvesting**|||||||
|Use of household labor (1=Yes)|0.91|0.95|0.88|0.93|0.89|0.033|
|Numbers of person-days/ha (Men)|8.93|21.90|8.17|7.04|3.79|<0.001|
|Numbers of person-days/ha (Women)|6.62|16.31|5.26|5.82|3.66|<0.001|
|Numbers of person-days/ha (Children)|1.92|1.44|1.81|2.16|1.44|0.264|
|Total person-days/ha (Household)|17.47|39.65|15.24|15.02|8.89|<0.001|
|Use of hired labor (1=Yes)|0.87|0.83|0.91|0.84|0.92|0.010|
|Person-days/ha of hired labor|20.25|27.61|21.35|17.40|22.90|<0.001|
|**6. Labor allocation**|||||||
|Total household labor (Men)|29.31|70.78|29.75|20.98|10.11|<0.001|
|Total household labor (Women)|15.01|35.51|13.32|12.54|5.82|<0.001|
|Total household labor (Children)|5.32|3.09|5.54|5.87|2.39|0.315|
|Total household labor|49.65|109.38|48.62|39.39|18.32|<0.001|
|Total hired labor|48.23|85.98|61.12|29.39|35.61|<0.001|
|Total labor(HL + HH)|97.87|195.36|109.74|68.78|53.93|<0.001|
|Observations|1039|109|415|477|38||

Notes: Mean values are shown with standard deviations in parentheses. P-values of continuous variables are obtained by one way anova test for equality of mean values and P-values of binary variables are obtained by Pearson’s _χ_<sup>2</sup> -tests for equal proportions. All labor data are expressed in person-days/ha.

4.3 Econometric results

# **_4.3.1 Factors explaining the adoption of modern farm technologies_**

Table 5 presents the estimated results from the first stage of the multinomial endogenous treatment effects model. The following insights regarding the correlates of the adoption of different technology packages emerge. First, those with access to credit and wealthier households are more likely to adopt improved farm technologies in rice production, highlighting the continuing importance of access to financial markets and the ability to take risks. Second, contact with extension services and the number of plot visits by extension agents is also positively correlated
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Road standards match traffic levels in Burkina Faso**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Underengineered Correctly engineered Overengineered<br>Nigeria Benin South Africa Rwanda Uganda Chad Ethiopia Senegal Tanzania Ghana Malawi Cote d'Ivoire Cameroon Burkina Faso Kenya Niger Lesotho Mozambique Madagascar Namibia Zambia Average<br><!-- End of picture text --> _Source:_ Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data) using RONET.

# **Challenges**

Burkina Faso established a system for funding road maintenance when it adopted a secondgeneration road fund in 2007. This was an important and significant step toward effectively provisioning for road maintenance. But the level of spending is 50 percent short of the estimated requirements, if we take into consideration the size,

**Figure 7. Maintenance and rehabilitation needs of the main road network are underfunded**

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>-<br>(10) Senegal Burkina FasoCote d'Ivoire Liberia Niger Mali Ghana<br>(20)<br>(30)<br>(40)<br>(50)<br>(60)<br>(70)<br>requirements<br>Spending as percentage of<br><!-- End of picture text --> _Source:_ Gwillliam and others 2008.

conditions, and standards of the existing networks (figure 7). Not surprisingly, the country faces a huge rehabilitation backlog that must be addressed before the trunk network can be considered to be in sustainable condition. This seems to contradict the apparent good condition of the network, which reflects the significant investment made through development assistance rather than through the enforcement of sustainable maintenance (World Bank 2007).

There are two factors that make appropriate road maintenance an uphill challenge. The first factor, exogenous to Burkina’s institutions, is the disruption (due to the crisis in Côte d’Ivoire) of the historic corridor providing Burkina Faso access to the port of Abidjan, shifting traffic from the AbidjanOuagadougou Corridor to the Ouagadougou Tema, Lome, and Cotonou corridors. These corridors were not designed for the subsequent increase in traffic, which has led to an accelerated deterioration of roads and further maintenance and even rehabilitation.

15
**<u>Table 1B: Cross-country regressions explaining variation in returns to schooling and returns to experience, by gender and region</u>**

||**To**|**tal**|**M**|**ale**|**Fe**|**male**|**Ur**|**ban**|**Ru**|**ral**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Institutions**|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|
|Economic Freedom|0.073**|0.038|0.065*|0.051|0.063*|-0.010|0.069**|0.035|0.043|0.060|
|Index|(2.30)|(1.23)|(1.95)|(1.36)|(1.74)|(0.24)|(2.08)|(1.16)|(1.06)|(1.57)|
|Glblii Id|0.009|0.011|0.014|0.019|-0.001|0.001|0.028|-0.011|0.016|0.043|
|oazaton nex|(0.22)|(0.34)|(0.34)|(0.47)|(0.01)|(0.01)|(0.72)|(0.30)|(0.30)|(1.20)|
|Empowerment rights|0.259|0.043|0.171|0.021|0.271|0.322|0.234|0.124|0.440*|0.009|
|index|(1.44)|(0.28)|(1.07)|(0.13)|(1.26)|(0.94)|(1.32)|(0.66)|(1.74)|(0.05)|
|Democracy (Freedom|0.081|-0.050|0.170|-0.054|0.009|-0.194|0.156|-0.187|-0.160|-0.088|
|House/Imputed Polity)|(0.43)|(0.29)|(0.93)|(0.31)|(0.04)|(0.60)|(0.88)|(0.94)|(0.63)|(0.36)|
|W i Lilt|0.087**|-0.021|0.071*|-0.003|0.080*|-0.052|0.105**|0.006|0.090*|-0.013|
|omen n egsaure|(2.13)|(0.93)|(1.76)|(0.12)|(1.92)|(1.61)|(2.44)|(0.21)|(1.91)|(-0.55)|
|**Controls**|||||||||||
|Log of Real Aid per<br>Capita|-0.240|-0.402*|-0.173|-0.444*|-0.210|-0.599**|-0.091|-0.334|-0.508*|-0.444|
||(0.94)|(1.75)|(0.69)|(1.71)|(0.75)|(2.03)|(0.35)|(0.92)|(1.75)|(1.58)|
|Log of Life Expectancy|-12.56**|-5.63**|-12.17**|-4.66|-13.10**|-6.94**|-13.43**|-5.39*|-12.23**|-4.68|
|at Birth, Total (Years)|(-4.71)|(2.67)|(-4.47)|(1.59)|(4.88)|(3.19)|(4.95)|(1.93)|(3.95)|(1.32)|
|Log of real GDP per|0.904*|0.515|0.858*|0.361|1.069*|0.501|0.893*|0.482|0.606|0.434|
|capita|(1.74)|(1.00)|(1.72)|(0.58)|(1.85)|(0.96)|(1.71)|(0.86)|(0.95)|(0.702)|
|Growth Rate of Real|-0.146**|-0.066|-0.126**|-0.083|-0.159**|-0.011|-0.161**|-0.073|-0.188**|-0.084|
|GDP per Capita (%)|(2.58)|(1.27)|(2.28)|(1.46)|(2.19)|(0.14)|(2.69)|(1.33)|(2.99)|(1.44)|
|f li|0.156|0.082|0.187|0.068|0.227|0.019|0.180|0.339|0.363|-0.018|
|Log o Popuaton|(0.69)|(0.41)|(0.97)|(0.30)|(0.80)|(0.07)|(0.82)|(1.53)|(1.34)|(0.07)|
|Employment Share with|0.011|-0.041**|0.007|-0.040**|0.001|-0.047**|0.008|-0.042**|0.021|-0.043**|
|<br>8+ years of Schooling|(0.79)|(3.41)|(0.53)|(2.63)|(0.061)|(3.30)|(0.53)|(3.20)|(1.08)|(3.07)|
||43.58**|22.30**|41.54**|18.19|47.20**|31.14**|46.16**|19.51*|41.22**|17.32|
|Constant|(3.54)|(2.51)|(3.41)|(1.52)|(3.65)|(3.75)|(3.59)|(1.83)|(3.37)|(1.04)|
|R2|0.41|0.42|0.40|0.35|0.36|0.25|0.44|0.42|0.32|0.28|
|Number of observations|122|122|122|122|122|122|122|122|113|113|

20
Lass and Weiss (2004), Hanson and Robertson (2009) and Freund and Ozden (2009). Lall and Weiss (2004) compare Latin American and Chinese exports, product by product at the 3-digit SITC level and identify categories where China is gaining market share at the expense of Latin America between 1990 and 2002. They find that in 1990, 30% of trade was in industries where Chinese exports were increasing and Latin American exports were decreasing. On the other hand, they find that China’s threat has been decreasing gradually: By 2002, only 11% of Latin American exports seemed to be negatively affected by China. They conclude that, over time Latin American export structures became relatively complementary to China’s exports.

Similarly, Freund and Ozden (2009) find that China’s export growth had only a small negative effect on overall Latin American exports. They show that China was affecting Mexico’s industrial exports negatively, but for the rest of Latin America, they did not find any significant impact. For Mexico, they found that a 10% increase in China’s industrial exports reduced Mexico’s industrial export growth by 7.9%. Moreover, they conclude that China’s continuing export growth might be affecting the wage distribution since it is concentrated in high wage industries.

Hanson and Robertson (2009) analyzed the effect of China’s export growth on Latin America using a gravity model, where exporters produce differentiated goods and compete with Chinese exporters under monopolistic competition. First, they estimate the changes in exporter fixed effects associated with export growth. Then, they simulate a counter-factual scenario for Latin America’s manufacturing export growth setting China’s export growth rate to zero. They find that China impeded growth of manufacturing exports in Argentina by 1.1%, Brazil by 1.4%, Chile by 2.3%, and Mexico by 3.1%.

Autor, Dorn and Hanson (2013) studied the implications of the rise of China on local labor markets (defined as “Commuting Zones”) within the United States through imports of Chinese goods. The authors argue that changes in Chinese imports by other high-income countries (used as an “instrumental variable”) caused higher unemployment, reduced labor-force participation and reduced wages in U.S. local labor markets that “house[d] import-competing manufacturing 5