rafmacalaba/data-use-mentions-extended huggingface dataset:
than 100 vehicles per day), new metrics and methods need to be developed to capture the social gains that an increase in resilient accessibility can bring.

The Institute for Applied Economic Research of Brazil has recently published (in April 2020) a technical note presenting the use of geo-spatial modeling to evaluate transport accessibility to hospitals for critical COVID19 patients in the largest urban area in Brazil. The authors prove the relevance of evaluating the transport gaps on access to hospitals so that the government can target mitigation measures. Hallegatte, Rentschler, and Rozenberg (2019) already connected the concepts of accessibility, transport disruption, and climate impacts. A new method was proposed for the city of Kampala in Uganda (Rentschler et al. 2019) to quantify the loss of accessibility to referral hospitals due to the disruption of the urban roads network. This method serves as the foundation for the analysis described in this paper.

The analysis in this paper, piloted in three provinces in Cambodia (Kampong Cham, Kratie, and Tboung Khmum), quantifies the loss of accessibility to referral hospitals as well as calculates accessibility to schools and markets using methods similar to those described by Rentschler et al. (2019) but goes one step beyond to quantify the benefits of rural roads improvements and to develop a prioritization framework to select those investments that have the largest gains in resilience of rural accessibility. This analytical model was applied for the prioritization of rural roads under the Cambodia Road Connectivity Improvement Project (P169930).

The analysis proposed in this paper comprises three focus areas:

1. **Health emergency preparedness.** The analysis used data on location of hospitals to assess the accessibility of rural people to social services, as a key indicator to ensure effective response to health crisis of rural areas. This analysis detected which areas have lower rural accessibility and what would be the accessibility gains owing to the improvement of the rural road network. Accessibility was measured as the ability of people to reach hospitals in a given time frame. This analysis also includes schools as
/sup> becomes negative, but it is statistically insignificant. The change in the sign of the relationship is primarily driven by the three small richer states that grew relatively slowly.

A valid question is whether the conclusion would have been the same had GDP per capita been used to measure living standards, instead of household expenditure per capita. To answer this question, we repeat the analysis for the growth of GDP per capita across states over 2004‐2011, using official data from MOSPI (2015). But again, we find no evidence of convergence across states. For the largest 21 states, the estimate of the convergence coefficient β<sup>�</sup> is 1.1, very close to the one estimated using household expenditure per capita. Moreover, the coefficient based on GDP per capita is statistically insignificant, which is consistent with previous studies.

In sharp contrast, we find strong evidence of absolute convergence of living standards when the district rather than the state is the unit of observation. The results are not sensitive to the choice of the sample, which is also in contrast to the results of the state‐level analysis. For both large states and all states, the estimate of β<sup>�</sup> is significantly negative (between –2.6 and –2.5). Therefore, a district whose household expenditure per capita in 2004 was 10 percent higher than the average experienced an annual growth rate 0.25 to 0.26 percentage points lower than the average.

The speed of absolute convergence is twice as fast when considering place instead of district as the unit of observation. The estimate of β<sup>�</sup> remains significant and negative, regardless of the choice of sample. Based on these more granular estimates, a place whose household expenditure per capita in 2004 was 18
|417.4|
|Tanzania||-8.6|458|515.6|13533.3|14498.3|
|Uganda|||1182.6||2473.7|3656.3|
|Zimbabwe|||46.5|1967.1|4384.2|6397.9|
|Total|2653.5|-8.1|2063.3|15970.4|26669|47348.2|

Source: Authors’ computations based on UNCTAD (2016) China’s FDI spans a number of countries with the biggest recipients including (as shown in Table 3): The Democratic Republic of Congo, Angola, Ethiopia, Kenya, the Republic of Congo Equatorial Guinea, Chad, and Côte d’Ivoire. Although many of these are resource rich, China’s recent investments are increasingly going to the manufacturing and construction sectors (in both resource and non-resource rich countries). Shen (2015) shows that 44 percent of Chinese FDI (based on data from investment promotion agencies of 6 African countries in 2011, namely, Nigeria, Ethiopia, Zambia, Liberia, Rwanda and Ghana) flows to the manufacturing sector in 17
in developing countries. High-frequency phone surveys can shed light on aspects of the economic impact of the pandemic that are difficult to capture using more traditional data. They can be conducted more quickly than GDP statistics, which often rely on projections in real time and are significantly revised in subsequent years. Moreover, phone surveys can uncover dimensions of heterogeneity, as well as information on coping mechanisms and potential implications on the lives of respondents that are not captured by official economic statistics.

Of course, HFPS survey data have limitations too. Particular care and effort must be taken when comparing and harmonizing surveys from different countries. Further, the surveys are not nationally representative, as they can only reach phone owners and in many countries greatly over-represented heads. However, we see that measures of stopped work in the phone surveys are more consistent with reductions in household wage income, estimates of demand reduction in firms, and ILO employment estimates than GDP growth projections. Also, the initial round of the phone surveys contained few variables found in traditional face-to-face household surveys implemented before the crisis. As a result, the reweighting procedure could only utilize a few variables and did not have a large effect on the results. Despite these limitations, harmonized data from phone surveys appear to contribute valuable new information on how households in a broad cross-section of developing countries were affected by this severe shock. This is in line with recent findings by Heath et al. (2021) that comparing interviews conducted on the phone and in-person led to differences in measures of employment, hours and days worked for the self-employed in Ghana.

The rapid deployment of phone surveys to measure the socio-economic impacts of COVID19 was only possible because of an extraordinary effort around the globe. To be better prepared for such rapid deployment in future emergencies, National Statistics Offices can invest now to improve the speed of deployment and quality of data. Investments in statistical infrastructure (e.g., the preparation of representative sampling frames for phone surveys), physical infrastructure (e.g., setup of
is reported among those who received credit based on the selection criteria at the time, which was relatively permissive and spanned the distribution of credit histories (including no history at all).

The credit data provide an indicator for whether a particular borrower repaid their obligation (the partner’s definition is 15 days past due). From the phone data we derive various features that may be associated with repayment. In a similar exercise, Blumenstock et al. (2015) generates features from mobile 9
largest cities. Proceedings of the National Academy of Sciences. 2018;115(30):7735–7740.

18. WHO. Data systems: A road safety manual for decision-makers and practitioners. World Health Organization. 2010;.

19. Williams S. Data Action: Using Data for Public Good. Cambridge, MA: MIT Press; 2020.

20. Gu Y, Qian ZS, Chen F. From Twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies. 2016;67:321 – 342. doi:https://doi.org/10.1016/j.trc.2016.02.011.

21. Zhang Z, He Q, Gao J, Ni M. A deep learning approach for detecting traffic accidents from social media data. Transportation research part C: emerging technologies. 2018;86:580–596.

22. Finkel JR, Grenager T, Manning C. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05); 2005.

12
2 This paper provides an illustration of the process of designing subsidy policies in the wider context of water sector reform, drawing on recent experience from Panama. In the early stages of the reform, the Panamanian government made a legal commitment to using direct subsidies as a means of achieving social objectives in the water and sanitation sector. As part of the proposed implementation, an extensive study was commissioned to put forward a detailed proposal of how such a subsidy system might function. The study undertook a systematic empirical investigation of the key design questions posed above. In the event, the implementation of the scheme was put on hold following a change of government in the early months of 1999. Notwithstanding, the lessons learned from the design exercise reveal some important empirical features of direct subsidies with important implications for the design of such schemes in other contexts.

The purpose of the paper is to illustrate the steps involved in the design of a direct subsidy scheme, drawing on concrete examples and illustrations from the Panama case. It is intended that the paper should serve as a practical guide for those interested in introducing such a scheme in other countries. A companion technical paper provides greater detail on the empirical analysis that underpins the present discussion, expanding upon the key data requirements and modelling tools.

The rest of the paper is organized as follows

- Section 2 provides the relevant background on water sector reform in Panama and the historical basis of social policy in the sector.

- Section 3 explains how the need for a direct subsidy system was assessed in the case of Panama.

- Section 4 uses Panamanian data to examine the targeting performance of alternative eligibility criteria for the subsidy.

- Section _5_ presents the analysis that was used to determine the appropriate size of the subsidy for Panama.

- Section 6 describes the legal, institutional and administrative framework for the Panamanian subsidy system.

- Section 7 draws out the main lessons and conclusions from the rest of the paper.
- 40 -

See Adayemo, 1985; Aluko et.al., 1972 and 1973; Child, 1977; Liedholm and Chuta, 1976; Olufokunbi, 1981; Schadler, 1968 and Thomi and Yankson, 1985. Their data are reproduced in Haggblade, Hazell and Brown, 1987.

1/ See Haggblade, Hazell and Brown's (1987) analysis of data from Zambia, (Milimo and Fisseha, 1986) and Sierra Leone (Liedholm and Chuta, 1976).

Kilby (1987) has also begun looking at time series data on nonfarm employment in Kenya. However, his analysis is not readily comparable with the others cited here, because his data track only "informal sector" firms, essentially those employing between one and nine workers. While important on employment and equity grounds, trends in informal nonfarm activity may not be representative of the entire rural nonfarm economy given that the "informal sector" accounts for only about 20 percent of nonfarm activity (Ndua and Ng'ethe, 1984).

2/ Vanvali's data are reproduced in Haggblade, Hazell and Brown (1987).

IQ/ Table 1 indicates that between 7 and 30 percent of the African rural labor force engages in nonfarm activity as a secondary source of employment, with agriculture presumably their primary occupation. Farm management data indicate this secondary employment may be even higher. The data cited earlier (Section III.A) indicate that 15 to 65 percent of farmers engage in nonfarm activities at certain seasons of the year. Given that farm employment typically accounts for 85 percent of rural employment, this leads to an estimated 13 (15 x .85) to 42 percent (65 x .85) of the rural labor force with secondary nonfarm employment. We must add to that the flip side of the coin, the primarily nonfarm workers who work seasonally in agriculture, to arrive at the total share of the rural labor force moving seasonally between farm and nonfarm activities. Given that as much as 75 percent of rural nonfarm workers may
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 NISR (2015) for estimating poverty rates in EICV3, then the national poverty rate in 2010/11 should be close to 45 percent. If the COLI is not used and nominal expenditures are used, the national poverty rate should be close to 39.7 percent. In both cases, we cannot replicate his result. Since we cannot identify what COLIs he used, we cannot guarantee that condition (ii) is satisfied. In conclusion, we cannot confirm his poverty estimates of 33 percent for EICV3 are comparable with 39 percent for EICV4.

# **VII. Conclusion**

This paper revisits the declining poverty trend published in NISR (2015) and NISR (2016). Findings from this analysis support the official trend published in NISR (2016). We also compare the methodology adopted by NISR (2015) and NISR (2016) and although both produce similar poverty trends, it shows several methodological advantages of the latter. Moreover, we show that poverty rates for Rwanda measured at an international poverty line, $1.90 per day per capita, also show a declining poverty trend if the international measure properly adjusts for spatial price variations across regions and temporal price variations during survey data collection. Given the significance of spatial and temporal price variations, we recommend that the international poverty statistics be updated by properly adjusting for these. Finally, this paper presents possible reasons behind the significant differences between national CPIs and NISR (2016) COLIs. It is important to continue research on these differences to establish a common understanding and agreement on the price index deemed most appropriate for poverty monitoring over time.

20
& Teignier (2016) - Full GEGI<br>S<br>o 3S pes<br>~~ India «oO v7<br>S ag<br>$e MENA as Nf Lh<br>= SA (ex. India) 5 z<br>g @ 8 i b<br>‘2 a °<br>Qo 3 $5 3<br>oN fac 5 Ls<br>g EAP (ex China) So Oe ok " © — CT(2016): LFP<br>co © MGI: LFP + Hours + Sectors ON ME) H° § ° sabia<br>= NA CA © MGI: LEP + Hours hae | wo e CT(2016): LFP+Enterp.<br>s ° © MGI: LFP oeagaSas 8 Sse! ‘4 = =~~~ 45Fitted Degree Line (LFP+Enterp.) Line<br>Full Gender ioe Employment I it GapGap IndIndex< (GEG!) (%)() 6° 0 20 40 60<br>Notes: McKinsey Globlal Institute (2015) data taken from Exhibit 7 (Full-potential scenario) FULL Gender Employment Gap Index (GEGI) (%)<br>multiplied by share due to labor force participation and/or hours components (Exhibit 6) Notes: Adjusted Cuberes & Teignier (2016) data from Appendix Tables B1 and C1<br><!-- End of picture text -->
employed. The unemployment rate among the young population is almost twice as high as compared with the entire working age population.

The disparity in outcomes between men and women is also telling. An estimated 11 percent of working age women are employed as compared to 71 percent for men, the latter itself being low by international standards. The gender dimension in unemployment is significant as well. The unemployment rate among women aged 15-64 years is twice as high as that of men (23 percent versus 12 percent). Education levels among women appear lower as well with the exception of tertiary education, although the tertiary education population is a small fraction of the overall population (less than five percent for both women and men). Thus, from the descriptive statistics, it appears that the youth are objectively deprived compared to the non-youth, and that women are objectively deprived compared to men.

**Table 5: Descriptive statistics**

||<br>|**Entire popu**<br>**(aged 15-**<br>|**lation**<br>**64)**<br>|**Youth popula**<br>**(aged 15-2**<br> <br>|**tion**<br>**9)**<br>|
|---|---|---|---|---|---|
||**All**|**Males**|**Females**|**All**<br>**Males**|**Females**|
|Observations|6451|3112|3339|2429<br>1169|1260|
|Population share|100|48.78|51.22|100<br>47.76|52.24|
|Share employed|40.2|70.6|11.3
# **3.3 Patterns within countries**

How are gender gaps shaped by urbanization? Though we lack panel data or repeated crosssections to explore trends over time, we can compare gender gaps between urban and rural areas. Own land has a central role in rural areas, as the main place to live and produce, but is less relevant in towns and cities where most jobs are outside of agriculture and housing can be rented. In urban areas, housing and residential land play a larger role than agricultural land, but even though city dwellers tend to earn more than their rural compatriots, they are often less likely to own a home (Pendall et al 2016 for the United States; Eurostat 2015 for European Union countries; Sato et al 2011 for China). This reflects partly that land is relatively scarce in urban areas, leading to highpriced real estate, but also better access to financial services, as an alternative means to store wealth. Because of this, some caution must be exercised in comparing ownership of land and housing property across rural and urban areas.

Figure 5 plots gender gaps in property ownership by country, separately for rural and urban areas. Gender gaps in sole ownership are almost always larger in rural than in urban areas. This pattern holds for the most part, though less pronounced, also for gender gaps in sole and joint ownership, though there are some exceptions. This, however, does _not_ mean that women are more likely to own property if they live in urban areas. It rather reflects that urban men are much _less_ likely to own property than their rural counterparts.

To further investigate distributional patterns, we estimate gender gaps by quintile, using the DHS household-level wealth index. As shown in Figure 6, gender gaps in sole property ownership are in most countries larger for the poorest than for the richest quintile, though there are some countries where the opposite pattern holds (Burundi, Jordan, and Mozambique). This income gradient is weaker for joint ownership, where nine out
our results, although the possibility cannot be ruled out completely. There are several examples in the literature to support our claim. For instance, Pounov (2016) uses ES data to analyze the effect of corruption on firm’s decision to obtain quality certificates and patents. For corruption, it uses an aggregate (country-level) measure – the proportion of firms in the country that were asked to pay to bribe to get a business license. The study notes that using the aggregate measure of corruption helps to avoid the endogeneity concerns that would arise with a firm-level measure of corruption; it also helps to control for potential measurement error if some firms choose not to respond.

The omitted variable bias problem is a more serious concern for us. To address this potential problem, we include several firm-level and country-level variables as controls in our regressions.

The various controls can be divided into macro-level and micro-level controls. Consider the controls at the micro or firm-level. The data source for all these controls is ES. First, we consider the tendency for men and women to be segregated by industry (Juhn et al. 2014, Amin and Islam 2014, Do et al. 2011). Some of the reasons suggested for this in the literature include the brawn vs. brains content of jobs, the ease with which work can be combined with family responsibility, and culture. Such segregation may cause spurious correlation for our main results if the share of segregated industries in total employment varies systematically with the length of maternity leave across countries. Thus, we control for dummy variables indicating the industry to which the firm belongs (Industry fixed effects). There are 36 industries in our sample defined at the 2-digit ISIC (Rev. 3.1) level.<sup>4</sup>

> 4 Some services industries with very few observations in the data are grouped together into one single category.

9
balanced repeated replication (BRR) weights provided in the data to obtain sampling errors. In addition, we estimate measurement errors by estimating variation among point estimates obtained for different plausible values. As up to PISA 2018 databases contain only five plausible values and 10 since 2018, we use only the first five for 2018 and 2022 data. The final standard errors are obtained by the so-called Rubin’s formula, the same way as in the official OECD (2022) publication.

# **4. Results**

The overall learning loss estimates are presented in Table 2 (column 1), along with estimates depending on weeks of closures for four different measures of the length of school closures (columns 2-5). The overall decline in mathematics associated with pandemic-era school closures is 12 points. In 2022, the average within-country SD of mathematics is 86.2. Thus, the decline in achievement is equal to 14.2 percent of a SD. Considering that one year of learning is equivalent to roughly 20 points on the PISA scale (Avvisati and Givord 2023), then this means that on average across around 70 countries students lost an equivalent of more than 7 months of learning.

Results in columns (2) to (5) show that one week of school closures is associated with an additional decline in student achievement. The estimates per week of closures vary across the measures of the length of closures and are not directly comparable as they reflect different definitions of closures. The clearest definition is the full closure defined by UNESCO; thus, this parameter (-0.44) will be used further to estimate the global welfare losses. We compare results calculating the learning loss for different percentiles of each measure of the length of closures. Figure 1 compares the estimated learning loss for the countries with the shortest (10<sup>th</sup> percentile of the length of closures in each of the four measures), short (25<sup>th</sup> ), average (
capital, and the various infrastructure indicators, for the cross-section corresponding to the year 2000. Output and the capital stock are expressed in PPP US dollars at international 2000 prices, while infrastructure variables are expressed in per worker terms.

> 16 One caveat regarding these data, as noted by Canning (1999), is that they may exhibit significant variations in quality. In particular, they do not reflect the width of the roads nor their condition. 17 The railways database can be found at http://go.worldbank.org/13EP3YJVV0

> 18 Before applying principal component analysis, the underlying variables are standardized in order to abstract from units of measurement.

> 19 As shown below, the individual infrastructure measures display stochastic trends, and hence we obtain the first principal component by computing the weights from the (stationary) first-differenced series.

8