rafmacalaba/data-use-mentions-extended huggingface dataset:
Reyntjens (2015) stated that “we can do this by starting from the 2010/11 poverty figure of 45%, but then adapting the 2010/11 poverty line in the same proportion (-19 points) as the 2013/14 adjustment. Based on the EICV3 micro data and lowering the poverty line (of the 2013/14) by 19%, the actual poverty figure is then 33% in 2010/11. There is nothing wrong in principle with a working out a new poverty line for EICV4 – as “many changes in the socio-economic structure of the country have taken place [since the first EICV in 2000/1]” – but even if the change is accepted, the 2013/14 report should measure change in poverty using a comparable poverty figure in 2010/11, namely 33% instead of 45%.” cited from Reyntjens (2015).

We now apply Propositions 1 and 2 to examine the claims made in Reyntjens (2015), asking whether the poverty rates (33 percent in EICV3 and 39 percent in EICV4) are comparable. Since Reyntjens (2015) uses a different poverty line for EICV3 and EICV4, we apply Proposition 2 for the comparability assessment.

First, condition (i) of Proposition 2 seems satisfied. Although it is not clear from the blog, we can safely assume that he uses nominal expenditure data available in EICV3 and EICV4, as released by NISR, and if so, we can say that household expenditure data are comparable. Second, condition (iii) is roughly satisfied. He discounts the poverty line of the 2013/14 by 19 percent, which is close to the inflation rate between January 2011 and January 2014 calculated from the monthly CPI published by NISR (monthly CPI provides an inflation rate of 23 percent). It is not however clear whether condition (ii) is satisfied. The problem here is that it is not clear from the blog as to what COLIs are used. If he is using the COLI of
, i.e. firms with an annual growth rate of at least 75% for four consecutive years (between 2001 and 2010), interesting patterns are established. It is clear that firms that perform consistently well over this period share a few common characteristics, depending on their size. Those with smaller export values tend to, on average, concentrate growth in a few products (at the HS8 digit level) but are well diversified in terms of destination markets. In addition, the relatively smaller top-performers also export mostly to other South Asian countries. The larger top-performing firms, on the other hand, seem to diversify their exports across more countries and away from South Asia. They also seem to, on average, export more products over the high-growth period.

The next two sections will explore the entry, exit and survival of new exporters using firm and transaction-level data. Studying rates of entry and exit will provide an assessment of the dynamics of firm movements in and out of markets, whether destination or product. On the other hand, survival rates will provide an overview of how well exporters are able to continue and deepen their trade relations across new markets.

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## methods.

One added advantage of exploiting within country and sector variation is that it allows us to try to disentangle the impact of different types of innovation measured by β in equation (4). As suggested above, a limitation of the framework in (3) is that it does not only include the impact of product innovation of employment, since the relative efficiency between new and old products is also affected by whether firms also complement product innovations with other types of innovation, such as process or organizational innovations. Therefore, while the estimate of β is unbiased, to disentangle the true impact of product from process innovation on employment we need to decompose this coefficient. The main challenge in doing so is the lack of longitudinal data. However, the variance across country-sectors captured by the random coefficients model provides an opportunity to estimate the effects of different types of innovation.

After obtaining the predicted coefficients β<sup>�</sup> from the random-coefficient model, in a second stage regression we investigate how the elasticities obtained vary by country and sector according to the intensity of product, process and organizational innovation in the following equation: where j denotes the country and sector, α is a constant term and wj is a vector of innovation intensity measures. Although imperfect estimates of each of the types of innovation elasticities given the aggregation of the data to the country-sector level, this two stage estimation allows us to explore whether combining different types of innovation matters in the impact of product innovation on employment.

# **4 The Data**

Bank 2013-2015 Enterprise Survey and its linked innovation modules. This is the most comprehensive set of cross-country surveys on innovation carried out to date. The survey used a Firm size stratified sampling strategy, where firms are stratified by industry, size, and location. levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are micro, small and medium-sized, Enterprise Surveys 12
households depending on the fraction of farmland allocated to coffee. For a household that allocates all its farmland to coffee, LandShareCoffee _i_ = 1, the effect of a percentage change in coffee price is given by _β_ 1. For a household that does not produce any coffee, the effect of the price change is zero. Accordingly, for a household that allocates all its farmland to coffee, a 50% decrease in coffee price results in about a 40% decrease in household adult equivalent food consumption and about a 30% decrease in non-food consumption.

Panel B reports the implied changes in household total, food, and non-food expenditures following a 50% price drop between 2011 and 2013 for households at different points in the distribution of the share of land allocated to coffee. The first row in Panel B reports the implied decrease in consumption of an average farmer in ESS data (rural sample). An average farmer in the ESS data allocates only 5% of its farmland to coffee production (see table 2), and as a result, consumption decreases by only 2.5% following a 50% decrease in coffee price. However, because the share of land allocated to coffee is highly positively skewed (slightly more than three quarters of households do not produce coffee at all), it is important to quantify the loss in 17
show that energy‐intensive sectors (e.g. chemical and textile industries) have a significantly lower share of small firms in countries with frequent outages. In these circumstances, large firms are more likely to be able to mobilize sufficient funds for investing in generators, while smaller firms are pushed out of the market. And unreliable electricity supply also affects entrepreneurship. An analysis of Enterprise Survey data for 23 African countries finds that, power outages diminish the probability that individuals start their own business by 32%, an effect that even rises to 44% when considering only the non‐farm sector (Mensah, 2018).

# 5. Conclusion

The literature reviewed in this section demonstrates the positive effects of provision of electricity, water, transportation, and ICT infrastructure on economic development. It stresses the crucial role that infrastructure quality plays in increasing the productivity of firms, even though further research is needed assessing the impacts of ICT quality upgrades, such as improving internet speeds.

At the same time, there is ample evidence how infrastructure disruptions hurt firms. In analyzing the consequences of infrastructure disruptions, most of the literature focusses on direct effects such as foregone production. Indirect effects have been quantified to a lesser degree. This does not, however, mean that they are less important. Disruptions such as power outages reduce investment through several channels; firm‐level adaptation measures such as procurement of self‐generating capacity crowd out investments that would increase the productivity of capital, and investment in infrastructure‐dependent sectors is more likely to flow to regions with fewer disruptions. Outages also distort the sectoral allocation of firms, leading, for example, to macroeconomic efficiency losses due to a lack of small firms in energy‐ intensive sectors.

The total effect of these and other indirect impacts of infrastructure disruptions on economic growth is unknown. In the long term, they may even exceed the short‐term direct impacts and lead to costs that 16
All-Europe and non-CEE, results are fairly similar: small or insignificant multipliers at the 5% level for OLS and IV specifications. However, at the NUTS2 level for CEE countries the raw spending multipliers are sometimes a little higher, though never close to 1. More specifically, the NUTS2 CEE 2SLS multiplier is now 0.53 and significant at 1% (up from 0.13 and insignificant in Table 1), and the OLS CEE multipliers are now 0.47 (down from 0.67 in Table 1). At the country level, estimates using raw spending are similar for raw and modeled spending for IV specifications — both insignificant — but more volatile for the OLS specifications across samples using raw spending. More specifically, OLS CEE multipliers are now positive and significant, but OLS non-CEE multipliers are negative and significant (both were previously insignificant using modeled spending In Table 1). Positive and negative multipliers are difficult to rationalize and might be due to mismeasurement in the raw spending data.

# **3.2 Effect on Investment**

One possible explanation for the modest or null effects of EU funds on regional GDP growth is that EU funds do not affect investment spending.<sup>47</sup> This could be because of measurement issues, or perhaps EU funds are used to finance projects that would have been implemented anyway. To test these hypotheses, Table 2 presents the results of a different regression specification, where on the LHS, the growth rate of GDP (from Table 1) is replaced by the change in total investment as a share of GDP (Table 2). The RHS and IV approach stays unchanged.<sup>48</sup> The regression now has an “investment multiplier” specification, namely the € total investment generated by an extra 1€ of EU funds. We focus on total investment (rather than public investment) as public investment is not available at the NUTS2 level.

46 We tried weighting the country-level regressions by the number of NUTS2
**PCR:** We report PCR at school level using EMIS data. We report PCR rather than PpCR, with no restrictions on the type of classroom.<sup>12</sup> We report PCR school-wide and at lower primary level. For overall PCR, we sum the total enrollment at each school and divide by the total number of classrooms available for use at the school. For lower primary PCR, we sum the total enrollment in Grades 1-4 and divide by the total number of classrooms at the school which are used entirely or partially for these grades.<sup>13</sup>

Our _ultimate_ outcomes of interest are student repetition rates and test scores.

**Repetition:** We report repetition rates at school level, both the overall rate across grades and the lower primary rate across grades 1-4. We calculate repetition rates from EMIS data. The rate is calculated using the number of students in each grade repeating a grade in the most recently completed school year, divided by the total number of students enrolled in that grade for that year; averaged across all grades offered in a school for overall repetition, or across Grades 1-4 for lower primary repetition.

**Student test scores:** We report student test scores at student level, using data from the MLSS learning assessments, which are norm referenced tests that cover competencies from Grade 1 through Grade 6. We employ data from a longitudinal sample of students who were in Grade 4 at baseline and progressed to upper primary grades during the course of MESIP (see Sample). We report student scores in English, Chichewa and Mathematics. Percentage scores in each subject are adjusted using Item Response Theory and converted to knowledge scores mean-centered at 500 points with a standard deviation of 100 points. For more on the MLSS learning assessments, see Appendix A.

- Mathematics Knowledge Score: This is measured by the knowledge score of each student in the mathematics test for each school.

- English Knowledge Score: This is measured the knowledge
variables that have relatively low levels of missing values (less than 20 percent).

As expected, student characteristics and socioeconomic background variables are important determinants of student learning outcomes. In all four years, girls get significantly better reading results than boys, averaging close to a quarter of a standard deviation, which confirms the international evidence on gender differences in learning outcomes (Guiso et al., 2008). Grade-forage is also significantly correlated with learning outcomes across all years and with very large effects: 15-year-old test takers who are not yet in grade 10 (the accurate grade-for-age), capturing either late entry into the education system or, more likely, repetition of grades, achieve learning outcomes that are close to three quarters of a standard deviation lower than 15-year-olds who attend 10<sup>th</sup> grade.

In terms of family background, mother’s education is correlated with better results but only in 2009 and 2012, and father’s education is not statistically significant in most years. In both 2006 and 2009, family wealth is a significant predictor, but insignificant in 2000 and 2012. With regards to household factors, having books at home is a consistently important predictor of students’ performance, in line with many other studies in the literature. There is a substantial degree of multicolinearity between the number of books at home, wealth index, and parents’ education. Therefore it is not surprising that, contrary to what was expected _ex-ante_ , the wealth index or mothers’ education is for some years not statistically significant. We do not attempt to address the issue of multicollinearity, since the variables included in students’ characteristics and socioeconomic background are simple controls of exogenous inputs, whereas the paper’s main focus lies on the effects on learning of school characteristics and institutional settings.

> 9 The parameters of the production function were estimated in Stata using the user-written command pv developed by MacDonald (2008, revised in 2014).

15
These results show that the problem of missing SDG gender measures is, in part, a problem with processing existing data rather than the lack of the primary data collection, since the underlying sources (typically household surveys) almost always (if not always) collect the sex of household members. If countries with a population estimate also reported data by sex, the SDG gender coverage rate would rise from 31% to 43%.

Evidence from other sources underscore the problem of available data not being reported. In a review of national statistics for 12 countries related to sex-disaggregated data on asset ownership, employment, and entrepreneurship, Bonfert et al (2023) find that such data exist but are not made available on average for 9 out of 24 indicators. Examining gender statistics from the UK, when comparing the Office of National Statistics (ONS) website for gender data to the reporting in the UN SDG site, we find that about 31% of gender-related SDG indicators (16 out of 50) are on the ONS website but not in the UN SDG site. On the other hand, only 4 of the 50 are in the UN SDG site but not on the ONS website. The remaining 30 SDG gender indicators are in both (21) or neither (9).

Turning to regional patterns in coverage, South Asia has the highest coverage rate of SDG gender indicators at 36% availability, about 5% higher than the global average (31%). There are no clear patterns in terms of which groups or types of indicators have higher or lower indicator coverage by region.

Notably, the high-income countries do not have higher coverage of gender-related SDG indicators (Figure 4). GDP per capita is not associated with better coverage of gender statistics in the UN SDG database (Figure 5). This is also the case for SDG indicators overall: high income countries do not have higher rates of reporting of all 181 SDG indicators (rates are high income 64%, upper middle income 72%, lower middle income
and N. Ryan (2018). The value of regulatory discretion: Estimates from environmental inspections in India. _Econometrica 86_ (6), 2123–2160.

- Dzansi, J., S. Puller, B. Street, and B. Yebuah-Dwamena (2018). The vicious circle of blackouts and revenue collection in developing economies: Evidence from Ghana.

- Foster, T., S. Furey, B. Banks, and J. Willetts (2020). Functionality of handpump water supplies: a review of data from sub-Saharan Africa and the Asia-Pacific region. _International Journal of Water Resources Development 36_ (5), 855–869.

- Galiani, S., P. Gertler, and E. Schargrodsky (2005). Water for life: The impact of the privatization of water services on child mortality. _Journal of political economy 113_ (1), 83–120.

- Glaeser, E. L. and A. Shleifer (2002). Legal origins. _The Quarterly Journal of Economics 117_ (4), 1193–1229.

- Golumbeanu, R. and D. Barnes (2013). Connection charges and electricity access in subSaharan Africa.

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in 2021, five times higher than the level prior to the COVID crisis. Road transport costs also increased substantially. The harmonized consumer price index for transport in Europe jumped from 106.7 in 2020 to 127.6 in 2022 (Eurostat). No doubt transport connectivity is now more important than ever.

10. Regional integration and transport connectivity are of particular importance for the Caucasus and Central Asia region, which is landlocked and located at a strategic place between Europe and East Asia, and between the Russian Federation and South Asia (e.g., Incaltarau et al., 2022). After the collapse of the Soviet Union in 1991, the region experienced long economic stagnation and transition as well as regional disintegration. In recent years, the region has started recovering with relatively robust growth (Pomfret, 2010, 2021). As pointed out by Cheong and Turakulov (2022), trade facilitation and regional reintegration are of vital importance to sustain growth in the region. Since the early 2010s, a number of large transport investments have been made to reestablish the regional connectivity. For instance, Kazakhstan embarked upon a massive road and rail investment program, Nurly Zhol, to connect major cities, logistics centers and free-trade zones to the regional market, i.e., border crossings, including ports on the Caspian Sea dry ports. The country spent about US$37 billion over five years: 2013-2017 (UNECE, 2019).

11. Due to such efforts, the regional connectivity seems to have been improved. According to the ADB CAREC Corridor Performance Measurement and Monitoring (CPMM) program, average transportation costs on the regional corridors were halved from US$1,400 per 20-ton cargo per 500 km in 2014 to US$700 in 2019 ( **Figure 1** ). Average speed also increased gradually. Note that the recorded travel times include not only driving time but also various delays and roadside stops, such as loading and offloading, and security checks. With such
vaccination. There is overwhelming willingness among those vaccinated to do so: In Malawi (80.7%), Tanzania (76.4%), and Nigeria (72.1%), more than 7 out of 10 vaccinated individuals would be “very likely” to encourage others to get vaccinated. In Burkina Faso, most are either very likely (22.9%) or somewhat likely (58.4%).

_Figure 6: Decisionmaker about vaccine uptake among adult household members._ <!-- Start of picture text --> Burkina Faso Malawi<br>0.1% 0.6%<br>42.3% 39.4% 42.0%<br>50.9%<br>17.9%<br>6.6%<br>Nigeria Tanzania<br>0.3%<br>6.0%<br>18.3%<br>37.1%<br>6.6% 44.4%<br>69.0%<br>18.2%<br>Each adult for themselves All adults together Household head Other household member<br><!-- End of picture text -->

# **Discussion**

## **Contribution**

Since the start of efforts to vaccinate the world population for COVID-19, vaccine coverage in Sub-Saharan Africa has trailed the rest of the world. In this study, we use data from five national phone surveys conducted by each country’s national statistical agency to provide comprehensive, cross-country comparable, and timely insights into the factors that hold back the progress of vaccination campaigns. Our findings on vaccine hesitancy, uptake, last-mile delivery barriers, and possible promoters of vaccine demand come at a critical moment for vaccination campaigns: vaccines are now more widely available in the region but have generally failed to reach beyond a fraction of the population.

Our findings supersede potentially outdated results from
each cluster has a weight _wc_ which is normalized so that<sup>�</sup> _c_<sup>_wc_=1.Recallherethat</sup> the number of children under five in one household is usually small and can be 1 for some households.

As with the ELL study, we allow for location, or cluster-specific, effect and heteroskedastic household-specific effect. In addition, we have multiple indicators and the individual effect correlated across the indicators. Hence, _u_<sup>(</sup> _chi_<sup>_k_)=</sup><sup>_η_</sup> _c_<sup>(</sup><sup>_k_)</sup> + _ε_<sup>(</sup> _ch_<sup>_k_)+</sup><sup>_δ_</sup> _chi_<sup>(</sup><sup>_k_),where</sup><sup>_η_</sup> _c_<sup>(</sup><sup>_k_),</sup><sup>_ε_(</sup> _ch_<sup>_k_),and</sup> _δ_<sup>(</sup><sup>_k_)respectivelythelocation,householdandindividualeffect.Inprinciple,eachofthe</sup> _chi_<sup>are</sup> three components of _u_<sup>(</sup> _chi_<sup>_k_)couldbeheteroskedasticandcorrelatedacrosstheindicators.The</sup> particular choice we make is driven by the insights from previous studies and also by the limitation imposed by the data. For example, given that **I** _ch_ = 1 for many households in the CDHS data, it is extremely difficult to distinguish household effects from individual effects if both are heteroskedastic. Cluster-level heteroskedasticity is also difficult to estimate because of the limited number of clusters. We allow for flexibility in the correlational structure of the disturbance terms where such flexibility seems most crucial
et al. (2021) examine if the investment in renewable energy technologies promotes TFP. They find that a higher share of renewable energy in the production process significantly promotes TFP in the long run. This is not necessarily the case in the short run. The increased human capital, innovations, and trade openness augment the positive relationship between renewable energy investment and TFP. However, the paper does not explain the channels through which increased renewable energy improves TFP. Similar effects of renewable energy on TFP are also reported by Rath et al. (2019) when they examine the relationship between the growth of renewable energy and TFP using panel data of 36 countries for the 1981–2013 period. They show that an increased TFP is associated with an increased consumption of renewable energy, whereas the consumption of fossil fuels is negatively associated with TFP. The findings are, however, vary across different regions. Since increased consumption of renewable energy reflects increased investment in renewable energy technologies, the findings 11