rafmacalaba/data-use-mentions-extended huggingface dataset:
# Figure 9. ECA: Drivers of potential output growth

A. EBRD state-owned enterprise activity B. EBRD assessment of governance, 2021 and assets <!-- Start of picture text --> Percent of GDP Percent of GDP<br>6 120<br>5 100<br>4 80<br>3 60<br>2 40<br>1 20<br>0 0<br>SOE value added SOE assets (RHS)<br>C. EBRD assessment of transition to a<br>competitive market economy, 2021<br><!-- End of picture text --> <!-- Start of picture text --> Index, 10 = Frontier<br>8<br>Min-max range Median<br>6<br>4<br>2<br>ECA CE WBK CA SCC EE<br><!-- End of picture text --> E. World Bank Enterprise Surveys: Share of firms that met with tax officials <!-- Start of picture text --> Index, 10 = Frontier<br>8 Min-max range Median<br>6<br>4<br>2<br>ECA CE SCC WBK CA EE<br><!-- End of picture text --> D. EBRD assessment of integration, 2021 <!-- Start of picture text --> Index, 10 = Frontier<br>8 Min-max range Median<br>6<br>4<br>2<br>ECA CE SCC WBK EE CA<br><!-- End of picture text --> F. World Bank Enterprise Surveys: Share of firms that introduce process innovation and invest in

# R&D

<!-- Start of picture text --> Percent Percent Manufacturing<br>40 30 Services<br>EU-26<br>25<br>30<br>20<br>20
1 percentage point per year.

Gylfason et al. (1999) postulate that the natural resources sector creates and needs less human capital than other productive sectors, which is similar to the assumption of this paper. A larger primary sector induces an appreciation of the currency, which makes the development of a skill-intensive sector difficult. Thus the model they develop predicts an inverse relationship between real exchange rate volatility and human capital accumulation and, hence, growth. Similarly, they predict a positive relationship between external debt and profitability in the secondary (industrial) sector and growth. However, the evidence they provide regarding these two explanatory variables is at best mixed: exchange rate volatility is not statistically significant, and external debt is statistically significant but has the wrong sign.

According to Gylfason et al. (1999), the share of the labor force in the primary sector can be used as an explanatory variable. However, they find it to be statistically significant only when human capital is excluded from the regressions. This result may be due to multicollinearity, which our model can explain, since the fraction of the labor force (or human capital) employed in the primary sector depends on the level of human capital. Thus Gylfason et al. (1999) find that “an increase in either the share of the primary sector in the labor force or in the share of the primary exports on total exports from 5% to 30% from one country or period to another reduces per capita growth by about 0.5% percent per year, other things being equal.” In short, the model we have presented is consistent with the results found by Gylfason et al. (1999) related to the size of the labor force in the primary sector.

In a multisectoral study, Feenstra et al. (1997) test the hypothesis of semiendogenous growth using data on bilateral trade between the United States and South Korea and between the United States and Taiwan. Their study focuses on sixteen industrial sectors, for which they test whether
next section further discusses the comparability of the IE-LFS 2019/20 and AWMS R3 samples.

> 2 In line with best international practices, the poverty line for Afghanistan is estimated following the Cost of Basic Needs (CBN) methodology. There are no PPP conversion factors available in Afghanistan to convert the value of the national line into international lines.

> 3 In order to correct for these possible sources of selection bias and to preserve the national representativeness of the sample of completed interviews, we implemented a four-step process. First, the sampling weights for all households interviewed in the two IELFS rounds were harmonized so that the consolidated sample could match the population size and distribution coming from the original sampling frame. Second, the new sampling weights for the households that provided a telephone number in the IELFS rounds were adjusted so that this sub-sample could be used to estimate nationally representative estimates. This adjustment was based on the probabilities of each household providing a telephone number in the IELFS rounds, as predicted by their socioeconomic characteristics (such as region, urban/rural, access to electricity, and household assets). Third, a readjustment was conducted on the sample of households with completed interviews accounting for the panel status of the household and the probabilities of completing the interview (again, as predicted by sociodemographic characteristics). Finally, poststratification of the sampling weights by regions trimmed excessively large weights to reduce their impact on estimate standard errors.

4
using rating<br>agencies’ methodologies| Statistical regression<br>model for each type of<br>beneficiary<br> Model calibrated using<br>historic data of<br>beneficiaries performance<br>vis-à-vis Treasury| Industry-specific internal<br>credit rating methodology<br>developed for electric<br>utilities and water systems<br> Score cards based on<br>rating agencies’<br>methodologies|
|**Credit risk**<br>**quantification**| Expected and unexpected<br>losses<br> PDs based on transition<br>matrices and solvency<br>curve<br> LGDs at 75 percent,<br>adjusted for collateral| Expected losses and<br>market values<br> PDs based on rating<br>agency databases<br> LGDs based on rating<br>agency databases<br> Market values based on<br>spreads in borrowing costs| Expected losses as result<br>of regression model<br> PDs estimated using<br>financial ratios of<br>beneficiaries<br> LGDs based on historic<br>performance vis-à-vis<br>Treasury| Expected losses and<br>market values<br> PDs based on rating<br>agency databases<br> LGDs based on. standards<br>and historic performance<br> Market values based on<br>spreads in borrowing costs|
|**Application in**<br>**risk**<br>**management**| Guarantee approval,<br>collateral, guarantee fees,<br>actual contingency reserve<br>account, monitoring and<br>reporting<br> Fees equalexpectedlosses| Guarantee fees, partial<br>guarantee coverage,<br>notional reserve account,<br>monitoring and reporting<br>
2014. The household survey covers a range of topics related to household well-being and socioeconomic status, including individuals’ work history and their fertility decisions. Information on household and individual-level characteristics were also available. Given that the original household members in 1993 were tracked when they moved or later got married and started a new household, the number of individuals grow as we move across surveys due to these new households. For our analysis, we kept households who are observed and tracked in all four waves so that we have a good amount of information on their work history.

Individuals who were at least 15 years old at the time of the survey were asked about their current and previous work status. A person is considered to be currently working if they responded yes to one of the following prompts: (1) identified working as the primary activity, (2) had work for pay for at least one hour in the past week, (3) having a job/business but currently is off work, (4) or working for a family-owned farm/business (unpaid family workers). We use this definition of working as our main outcome of analysis. For the years between the surveys, individuals also reported whether they worked that particular year. For each year including the current one, those who worked also answered additional questions about their job, such as the sector of occupation (10 categories of sectors which we simplified into three - agriculture, industry, and service) and the type of occupation (self-employed workers, government or private employee, unpaid family worker, and agricultural/non-agricultural casual workers).

We restricted our sample of analysis to individuals who exclude individuals based on the timing of first childbirth. We first identify the year when the first child was born. Then, individuals with children born outside the 1993-2014 survey period were excluded.<sup>1</sup> We further restricted our sample to parents who had their first child between ages 20 and 40. The prevalence of individuals who had
# 4. Additional robustness checks

This section provides the results of extensive robustness checks to ensure the reliability and consistency of the analysis. Various parameter configurations for nearest neighbor searches and training data are explored. Additionally, established alternative approaches, including Survey of Well-being via Instant and Frequent Tracking (i.e., SWIFT) and GDP-poverty elasticity methods, are employed. Moreover, Annex 3 presents results from a straightforward trend reconstruction using common items to define alternative poverty lines and consumption aggregates. These comprehensive checks consistently align with the core findings of this study.

## a. Same methodological approach under different options

To conduct additional robustness checks to the proposed methodology, additional simulations have been implemented in three distinct scenarios that relax the default setup. The first scenario eliminates the constraint of restricting the nearest neighbor search to the same decile. The second scenario involves using only the 2022 dataset for model training and then making predictions for both 2022 and 2016 (or 2010), analogous to the more traditional Lasso PMM approach. The final scenario combines elements of both the first and second scenarios.

Poverty and extreme poverty rates for 2016 show no significant differences across the scenarios (Figure 15.a). For 2010, where the removal of the decile-based constraint results in slightly larger differences, these remain statitically insignificant (Figure 15. b). This outcome is anticipated given the longer backcasting period and the associated challenges in satisfying the assumptions underlying the approach.

Figure 15. Poverty and extreme poverty rates under different scenarios <!-- Start of picture text --> a. 2016 b. 2010<br>29.0 13.0 39.0 16.0<br>27.0 26.46 26.39 25.94 26.24 12.0 37.0 37.08 37.08 15.0<br>25.0 11.0 35.0 35.40 35.4314.0<br>9.22 9.22 9.19 8.
0.751***|0.684***|
||(0.0385)|(0.0500)|(0.0815)|(0.0503)|(0.120)|
|Observations|1,266|828|339|437|106|
|R-squared|0.272|0.286|0.202|0.338|0.236|

Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Each coefficient is from a different regression conducted using different samples. Each regression uses growth rates from national accounts (GDP/GNI per capita) and growth rates in average household consumption from survey-data available in PovcalNet to estimate a discount factor that accounts for the difference in growth rates between these two data sources. Different samples are used in each regression. Pooled samples use any survey-based estimate available in PovcalNet and focus on different income groups, welfare measure or region. Comparable samples use only comparable survey-based estimates available in PovcalNet (see PovcalNet’s comparability dataset, available at: <u>https://development-data-hub-s3public.s3.amazonaws.com/ddhfiles/506801/povcalnet_comparability.csv ).</u> 35
We also control for people’s perception of the spread of the virus using high-frequency data on country-level Google searches for the term “death” from Google trends, and define it as variable _Fearc,t_ . Mertens et al. (2020) demonstrate that the COVID-19 pandemic increases fears and anxiety in the population, and the fears are correlated with social media use. These data have been used recently by several researchers to assess people’s attitudes during the COVID-19 pandemic (e.g., Brodeur et al., 2020; van der Wielen and Barrios, 2020). Hence, we assume that frequencies of Google searches for the term “death” reflect the population’s realized perceptions regarding the dangers of the pandemic, which could differ from the information conveyed by official statistics (the number of daily deaths _Pc,t_ ).

# **4. Data**

We use the daily consumption of electricity as a proxy of economic activity in a country. For many countries, electricity data are available with a daily lag and, in some cases, on a sub-regional level, providing an almost real-time picture of economic changes. Cicala (2020) demonstrates that, in the short-run, changes in electricity consumption closely track standard economic indicators.

In our analysis, we use four data sets, the first one is the proxy measure of economic activity, and the remaining covering information on NPIs, the evolution of the pandemic and measures of trust: 1) Electricity consumption. Data are presented as the total daily consumption in megawatts and were obtained from ENTSO-E and national grid operators. Data are available for 37 countries in Europe and Central Asia; the period covered is January 1, 2017, to September 15, 2020.

2) Data on the implementation of non-pharmaceutical interventions from the Oxford Government Response Tracker, World Bank Education Global Practice COVID-19 dashboard, and alternative news sources.

15
pressure when the treatment comes in the form of inventories or equipment, while the di¤erence in treatment is less relevant for male-owned businesses. We now scrutinize this interpretation more in detail.

# 6 Interpretation of the results

## 6.1 Where do the grants go?

Table 5 examines the extent to which the grants are being used to increase the capital stock of the ...rm, to make transfers to non-household members, and for household spending. In panel A we show the results of estimating equation (2) with di¤erent outcomes, while in panel B we show the results of estimating equation (3) for the female sample and the categorization of low and high initial pro...ts groups, since this is where we found large di¤erences in treatment e¤ects. For reasons of space we report the ...xed e¤ects estimates only (with the exception of transfers out which were not measured pre-treatment), but note when the OLS results show large di¤erences.

We begin by looking at the impact of the grants on the capital stock of the ...rms. Column (1) shows this for total capital stock. In order to reduce the in‡uence of large outliers, column (2) truncates capital stock at the 99.5th percentile, which is 6130 cedis. Both speci...cations suggest that capital stock is increasing by more for the in-kind treatments than for the cash treatments, both for men and women. However, the capital stock data is noisy and the standard errors 27
assumes that it is possible to find a type and form of targeting that is perfect (i.e., no errors of inclusion or exclusion).<sup>10</sup> We look at each of these policies separately and jointly because there are strong advocates for one or the other approach. In this case, we find that uptake rates among the poor are all significantly higher. Compared to the results in Table 5 (with positive monthly charges and connection charges), the uptake rates increase by 19‐20 percentage points for poor connected households and by 39‐40 percentage points for poor unconnected households. Hence, large scale subsidization of the piped water service may have a positive impact on the poor gaining access to the service. Yet, despite the massive subsidization a still significant part of the poor population will still not be interested in making the switch from their current source to piped water, suggesting that subsidization can make a difference, but may not be sufficient to achieve universal coverage of piped water service.

# **5. Discussion and Conclusions**

In this paper, we present a case‐study from Sri Lanka, where we surveyed approximately 1,800 households in 2003, to clarify concepts and offer an operational interpretation of how WTP surveys can be used to estimate coverage of piped water connections. WTP surveys can be used to gauge household demand for improved network water and sanitation services by presenting consumers with a contingent scenario in which they can obtain the described service. These surveys are particularly useful as policy analysis tools because behavioral data are typically not available to estimate parameters of demand for service such as improved supply of safe, reliable and sufficient water. In addition to solving the problem of missing ‘behavioral data’, WTP surveys can contribute a valuable participatory element to the development planning process, and they provide an important type of ‘field experiment’ in which the analyst can mix control and realism in a practical way.

> 10 We investigate the options for targeting through geography
# _Additional income, human development, and interaction variables_

In each of the six regressions reported in Table 6, an additional variable is added to the model from the first column of Table 3, using the larger sample of observations and the _ex post_ income measure. The added regressor in each test is indicated in bold in the respective column headings, with the coefficient and t-statistic for the added regressor shown in bold in a row towards the bottom of Table 6.

The added variable in the first column is (the log of) PPP-adjusted per-capita income. If PPP-adjusted income is a better measure of living standards than income based on exchange rates, it may provide additional explanatory power in our graduation tests. Results show it is in fact positively and significantly related to the probability of being a graduate. Moreover, with its inclusion the coefficient on GNI per capita (based on exchange rates) falls and is no longer significant.

The second and third columns add human development outcome indicators, as proxies for the broad concept of “economic and social institutions.”<sup>23</sup> Column (ii) adds a measure of educational attainment – average years of completed education for the population age 25 and over– from Barro and Lee (2010).<sup>24</sup> Column (iii) adds instead a health outcome indicator, infant mortality, that has reasonably good data coverage.<sup>25</sup> Neither of these human development outcome proxies for economic and social institutions turns out to be significantly related to the probability of graduation. Column (iv) adds instead a gender equality indicator, the female labor force participation rate. This variable is also not significant.

Column (v) adds income inequality, as measured by the Gini coefficient.<sup>26</sup> For a given level of average income, a higher Gini will tend to be associated with higher poverty rates. Higher poverty in turn may be associated with a greater need for continued access
stronger upward mobility both in the medium term and the long term.<sup>20</sup> These results provide further supportive evidence that our estimation results are robust to attrition.

# _Other Robustness Checks_

We examine a battery of other robustness checks and offer a brief summary of the main findings here. First, we investigate whether estimation results change if we analyze the household panel data instead of the individual panel data. We use two different definitions of a household panel: one whereby any household member remains in the panel data over time, and another whereby half or more household members remain the same over time.<sup>21</sup> Using both definitions offers qualitatively similar results (Appendix 1, Table 1.5). Second, we examine whether adjusting for household equivalence scale may affect our estimates. We employ two different scale adjustment methods, one by the OECD (2009) and another with a different scale parameter (i.e., using an economy-of-size parameter of 0.8).<sup>22</sup> Estimation results are also similar, especially for mobility in the long term (Appendix 1, Table 1.6). Third, following Gorodnichenko _et al._ (2010), who showed that accounting for differences in the cost of living between regions could reduce consumption inequality, we also make a similar adjustment. Estimation results suggest somewhat higher upward mobility in the medium term but are generally similar (Appendix 1, Table 1.7). Fourth, instead of dividing the income distributions into three terciles, we use an alternative

> 20 In fact, even if we only rely on the adjustments offered by Wooldridge’s method, the finding that there was more upward conditional mobility than downward mobility in both medium-term periods is not very different from our finding that this finding holds for the long term and mostly for the short term.

> 21 More precisely speaking, we analyze the panel of household heads, where we define heads according to the
area of influence of an urban center, essentially relaxing the requirement for strong social and economic interaction of an FEA. It emerges endogenously based on an urban center’s size and where other urban centers are located.

URCAs are defined by matching all rural locations to their urban center of reference based on the time needed to reach it. A hierarchy of urban centers by population size (largest to smallest) is used to determine which center is the point of reference for a given rural location: proximity to a larger center dominates over a smaller one in the same travel time category. Each rural location is allocated to one defined category: less than one hour, one to two hours, and two to three hours travel time to one of seven urban center size categories. If needed, this information can be used to develop urban-rural continuum indicators of the kind presented above. Figure 1 presents, in a stylized manner, the steps to estimate these URCAs based on travel time.

# **Figure 1. Illustration of the URCA classification by city size and proximate areas of different minimum travel times to these cities**

8
effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Characteristics of<br>household & head|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling<br>characteristics|Yes|Yes|Yes|Yes|Yes|Yes|
|Drought affected<br>status|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,241|2,241|2,241|2,241|2,234|2,167|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from an IV regression. Characteristics of household refer to size and receiving remittances, while those from household head refer to age, sex and literacy. Dwelling characteristics include tenure, roof and floor material, water source and sanitation type. Humanitarian assistance is not included due to collinearity with the region fixed effects. Standard errors considering sampling weights in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

The estimated coefficient for the effect on the poverty gap is positive and larger with the IV approach (Table 4), compared to the DiD result (Table 3). Despite this, the IV estimate is not statistically significant. Contrary to this result, the IV coefficient of hunger is positive and statistically significant, unlike the DiD estimate. For the perception of police competence, the IV result also suggests a negative effect, as our DiD estimate, but the coefficient is not significant. Even though the IV and DiD coefficients are estimated from different samples of households, the differences in results seem to be related to larger standard errors from the IV approach. For the effect of incidents on the poverty gap and police competence, the 95% confidence interval of the DiD coefficient lies within the confidence interval of the respective IV estimate. The imprecision of IV estimates, combined with the fact that the point estimates are consistent