rafmacalaba/data-use-mentions-extended huggingface dataset:
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 10. Benchmarking ICT indicators**

||Unit||Resource-rich<br>countries<br>**Liberia**|Low-income<br>countries|
|---|---|---|---|---|
|GSM coverage|% population||66.9<br>**32.0**|42.4|
|International bandwidth|Mbps/capita||4.1<br>**0.1**|3.0|
|Internet|subscribers/100 people||0.1<br>**1.0**|0.1|
|Landline|subscribers/100 people||19.3<br>**0.1**|7.5|
|Mobile phone|subscribers/100 people||11.4<br>**34.0**|6.4|
||**Liberia***||Countries without access to<br>submarine cables<br>Other|developing regions|
|Price of monthly mobile basket||**5.21**|11.12|9.9|
|Price of monthly fixed-line basket|||13.58|—|
|Price of 20-hour Internet package||**140.00**|67.95|11.0|
|Price of a 3-minute call to the United States|||2.59|2.0|
|Price of inter-Africa calls, mean|||0.72|n.a.|

Source: Minges and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. — = data not available. n.a. = not applicable.

# **Challenges**

Liberia is not currently connected
step from these ideas to the proposition that survey respondents answer questions with reference to their immediate experiences and that this may well be highly localized in some relevant social or geographic dimensions. People will answer subjective questions relative to their personal ―frame-of-reference,‖ which depends on the respondent’s own knowledge and experience, and therefore is likely to vary systematically with the characteristics of that person, including objective measures of economic welfare.

> 29 Reported in Heckman (2011), using data for Germany.

> 30 For example, Frey and Stutzer (2002) note the possibility of heterogeneity in the scales used in selfreported welfare questions but claim that this does not invalidate regression models for such data.

> 31 For more recent discussions and evidence see Ravallion and Lokshin (2000) and Senik (2004).

21
Policy Research Working Paper 8751

# **Abstract**

Based on a survey of households in 10 Central Eastern European and Western Balkan countries, this paper presents new and unique evidence on which households have savings and how they save. The paper shows that the percentage of savers is low, and savings are frequently informal. Formal savings are dominated by bank savings, and participation in contractual and capital market savings is very low in comparison to high-income countries. Poor households are significantly less likely to have any savings; income also has an effect, albeit smaller, on the choice of formal versus informal savings. With a high density of bank branches in Central Eastern European and Western Balkan countries lack of physical access to banks does not explain the lack of formal savings. Lack of trust in banks reduces the probability of formal savings, especially bank savings.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The author may be contacted at elisabeth.beckmann@oenb.at.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 10921

# **Abstract**

This study explores the relationship between perceptions of catastrophic events and beliefs about climate change. Using data from the 2023 Life in Transition Survey, the study finds that contrary to conventional wisdom, more accurate knowledge about past catastrophes is associated with lower concern about climate change. The paper proposes that heightened threat sensitivity may underlie both the tendency to overestimate disaster impacts and increased concern about climate change. The findings challenge the assumption that a more informed citizenry necessarily leads to better climate policy decisions. Instead, they suggest that psychological factors, like anxiety and risk perception, play crucial roles in shaping climate attitudes. Illuminating these dynamics can help societies to foster a more nuanced and constructive public dialogue about the urgent challenges facing our planet and our species.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mlokshin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
declining further to 15% in 1997. The measured FLFP rate remained roughly at that level through 2003, with the urban rate exceeding the rural rate in 2001 and 2002 for the first time since the early 1980s.

In the meantime, the first wave of the Egypt Labor Market Panel Survey (ELMPS) was conducted in 1998 as a collaboration between the Economic Research Forum and CAPMAS (Assaad and Barsoum 2000). That questionnaire made a clear distinction between market and subsistence work and included a series of eight questions about specific activities to detect employment in a short reference period of seven days and long reference period of three months for those who answered that they were not working in the seven-day reference period. As shown in Figure 1, this resulted in a somewhat higher measured FLFP rate overall (16%), and a much higher rural rate of 20% even when explicitly limiting the definition of employment to market work. A similar approach was followed in the 2006 wave of the ELMPS (Assaad 2009), but the number of detection questions was increased to 13. This resulted in an even higher overall rate of 22% and a rural rate of 23%.

Based on the experience gained from the ELMPS surveys, CAPMAS made further efforts to improve the measurement of the FLFP rate. Although the questionnaire used in the LFS did not explicitly change to include the innovations introduced by the ELMPS until the first quarter of 2007, there were substantial increases in the measured FLFP rates in the LFS as early as 2004 as seen in Figure 1. Since 2007, the LFS questionnaire explicitly distinguishes between market work and subsistence work, the distinction subsequently adopted by the 19<sup>th</sup> ICLS, and includes a series of 12 keyword detection questions for those reporting not working in the 7-day reference period.

4 The 19th ICLS held in 2013 further limited the definition of “employment” to work for pay or profit (or market work) and defined other forms of work, which include “own-use production work
by relieving taxpayers of the duty to submit aggregate summary lists of invoices and store invoice records for five years. The tax authorities also introduced KRW200 in sunset tax credit for ETI issuance to compensate for the cost of the Internet or outsourcing ETI issuing. The NTS also took various measures to facilitate e- invoicing. It launched _e-sero_ , an ETI-issuing website, and a telephone automatic response system to help small companies and individual businesses lacking their own IT systems issue ETIs for free.

Such concerted efforts ultimately paved the way for the successful implementation of compulsory ETI. The ETI adoption rate in terms of transaction value reached 99.8 percent during the first year of introduction in 2011 and 99.9 percent by 2013, which compares to 15 percent before the compulsory ETI. According to estimates by the NTS and Korea Institute of Public Finance, ETI cut taxpayers’ compliance cost by KRW978 billion (approximately USD890 million) after incurring KRW263 billion (approximately USD240 million) for initial transition costs including the purchase of new IT systems and employee training. Investment cost to the NTS for various ETI support systems and their operational cost from 2009 to 2014 was estimated at approximately KRW27 billion. Therefore, ETI has helped taxpayers and the tax authority achieve substantial cost savings since its introduction.

A survey of tax practitioners and employees of business taxpayers also showed that compulsory ETI had a significant effect on tax compliance, the transparency of business transactions, and taxpayer service. 69.4 percent of the respondents agreed or strongly agreed that tax evasion would decline as a result of greater transaction transparency brought about by ETI. 72.9 percent of the respondents agreed that taxpayer service improved after the introduction of ETI as a result of the convenience of e-invoicing and e-tax filing. Although 52.4 percent of the respondents agreed that taxpayers’ burden increased following the introduction of ETI, only 19.9 percent of the respondents were against the idea of subjecting more businesses to ETI by lowering the threshold for the compulsory ETI, which is currently set at the annual turnover
reference week). Not surprisingly, under the revised standards, the measured share of agricultural employment in overall rural employment is significantly smaller than under the previous standards. This also highlights the need for household surveys to collect data on multiple, potentially overlapping, activities.

The remainder of this paper is organized as follows. Section 2 provides some background on the 19<sup>th</sup> ICLS standards. Section 3 describes the data sources. Section 4 uses multi-topic household survey data from Ghana, Malawi and Nigeria to illustrate how male and female farmers respond to questions about the intended uses of their agricultural produce and the share of farmers considered to be employed by the revised standards under different measurement approaches, paying specific attention to gender differences. In addition, the section explores the over-time variability in reports about the intended use of output. Section 5 compares male and female employment-to-population ratios under the previous (1982) and revised (2013) standards. Section 6 discusses implications for labor statistics in developing countries and concludes the paper.

# **2 The 19**<sup>**th**</sup> **ICLS resolution and its operationalization in household surveys 2.1 Background**

# **_Own-use production in national accounts and labor statistics_**

The 19<sup>th</sup> ICLS standards are rooted in a century-old debate on the appropriate delineations and statistical treatment of market and non-market production in the context of national accounts and labor force statistics. Historically, national income accounting emphasized the monetary economy (Benería 1999), but the 1953 United Nations systems of national accounts (SNA) made an exception for primary producers (such as farmers). Such production was recommended to be included irrespective of whether it was exchanged or not. This position was formalized in the 1993 SNA, which specified that household production of agricultural goods for own-use falls within the SNA production boundary, and hence, ought to be included in GDP – at least in countries where it is thought
of refugees or host communities. A few studies on poverty among FDPs appeared before 2010, including a few studies on the well-being of Palestinian refugees (Hejoj 2007), and occasional studies on refugees in upper income countries (Bollinger and Hagstrom, 2004, Kriechbaum 2006), but these studies were extremely rare and relegated to specialized journals, or specialized agencies’ reports. This changed after 2010 with tens of studies now published on refugee poverty and household well-being of host populations in top economics journals (Verme and Schuettler, 2021).

The surge in general interest on the topic, the media exposure of the Syrian and European crises and the growing preoccupation of donor countries with the political and economic implications of refugees led development organizations and poverty specialists to start considering FDPs more closely and collaborate with humanitarian organizations. The UNHCR and the World Bank, for example, started to collaborate very closely around 2014 with the first joint study on the welfare of Syrian refugees in Jordan and Lebanon (Verme et al, 2016). This study used UNHCR’s administrative and survey data to measure poverty among refugees, contributing to improving the targeting system of the UNHRC’s cash assistance program. It found that - using the international extreme poverty line of 1.9 USD/day in purchasing power parity (PPP) – 7 in 10 refugees were poor, a figure that rose to 9 in 10 refugees if the national poverty lines of the host countries were used.

This study led the World Bank to reconsider its role in working with refugee populations and encouraged this organization to strengthen cooperation with the UNHCR, leading to the establishment of a joint research program (“Building the Evidence on Forced Displacement”), country-level cooperation on data collection in several countries, a first round of analytical studies on refugees’ well-being (Pape et al., 2018, Pape et al., 2019a, Cuevas et al., 2019, Beltramo et al., 2021), the establishment of a Joint Data Centre between the two organizations, and
, Kathmandu.

CEN, 2009. Baseline Study of Brick Kilns in Nepal. Clean Energy Nepal (CEN), Kathmandu.

- Chapagain, Y.P., Sapkota, S., Ghale, D.B., Bohara, N.B., Duwal, N., Bhattarai, J., 2020. A case study on mineralogy and physico-mechanical properties of commercial bricks produced in Nepal. SN Appl. Sci. 2, 14.

- Deore, M., Maudgal, M., Maithel, S., Kumar, S., 2019. Market Transformation towards Energy Efficiency in Brick Sector: A Strategic Blueprint, From Vision to Mission. Bureau of Energy Efficiency (BEE), Indo-German Energy Programme (GIZ), Greentech Knowledge Solutions Private Limited (GKSPL), New Delhi.

- DOI, 2022. Micro, Cottage and Small Industry Statistics (in Nepali). Department of Industry (DOI), Government of Nepal (GON), Kathmandu.

- DOMG, 2022. Annual Report of Department of Mines and Geology. Annual Report No. 13 DMG. Department of Mines and Geology (DOMG), Kathmandu.

- DUDBC, 2017. Design Catalogue for Reconstruction of Earthquake Resistance Houses: Volume II. Catalog for Alternative Construction Materials and Technologies. Department of Urban Development and Building Construction (DUDBC), Ministry of Urban Development, Government of Nepal, Kathmandu.

36
modules can improve our understanding of (i) how time is allocated, among men and women, across unpaid and paid activities, domestic and care work, and leisure; (ii) the extent of time allocation to simultaneous activities; and (iii) the links between health and time use, including in the form of leisure and rest. The resulting data can shed light on how individuals may respond to policies aimed at raising economic opportunities and mobility (Floro and Komatsu, 2011). Nationally representative, multi-topic household surveys that collect time use information alongside data on labor, wealth, demographics, education, and health, can help reveal the range of factors affecting men’s and women’s time use and in turn provide direct inputs into national policy making around employment, child and elderly care, health seeking, and social well-being.

The Cambodia LSMS+ time use module was structured as a 24-hour time diary, where men and women aged 18 and older were asked to report their activities over the last day. The structure of the module, the list of activities and the implementation protocols drew heavily from the time use module developed as part of the Women’s Empowerment in Agriculture (WEAI) index. The reporting was set up in 15-minute increments across 26 different activity categories, including primary activities as well as secondary activities conducted simultaneously within each interval (Table 1).<sup>13</sup> Broadly, as detailed in Table 1 as well, these activity categories fell into (1) unpaid work (across household chores; collecting firewood/water; and care national household surveys include the Cambodia LSMS+ Survey 2019/20, Ethiopia Socioeconomic Survey (ESS) 2018/19, Malawi Integrated Household Panel Survey (IHPS) 2016, Sudan Labor Market Panel Survey (SLMPS) 2021, and Tanzania National Panel Survey (TZNPS) 2018/19. In each survey, the LSMS+ has supported the respective national statistical office to operationalize the latest international recommendations for individual-disaggregated survey data collection
narrowed (Figure 10).<sup>15</sup> In the late 1990s, manufacturing sector returns to capital in SOEs lagged far behind private firms. Over time, the difference in returns declined to about 15 percentage points in 2011. This was still a sizable gap, which widened again after 2011, likely indicating continued SOE preferential access to capital.<sup>16</sup> The difference in the returns to labor between SOEs and private firms in the late 1990s was smaller than the one for capital—it declined over time and even turned positive in 2011–13.

The regression results for TFP show that the productivity gap between public and private manufacturing firms has also narrowed and become slightly positive in recent years. However, we interpret the differences in firm TFP with caution. Due to the lack of firm-level price data for outputs and inputs, only “revenue” TFP can be estimated (see the appendix). In revenue-based TFP measures, output is calculated using firm revenues deflated by industry-specific price indexes. Thus, revenue TFP combines quantity-based productivity and prices. Hence, differences in productivity between firms reflect changes in either efficiency or a firm’s market power (i.e., firm markups). For example, the efficiency of SOEs may decline relative to non-state firms, but a larger increase in mark-ups will result in a rise in their revenue productivity.

# **Figure 10 Relative efficiency of SOEs**

(percentage points relative to private firms) <!-- Start of picture text --> Return on capital Return on labor TFP<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> Source: Authors’ regression estimates based on NBS’s Industrial Enterprise Survey. Note: The figure plots the coefficient on the
included to capture the impact of the changes in the terms of external finance (or the combined impact of the balance sheet and lending channel effects) on economic activity (see Box 2). It should be noted that the variable SPR understates the actual magnitude of this effect since the average lending rate would not entirely capture the increase in cost to the typical bank borrower, as fewer borrowers qualifv for this rate during tight money episodes.

The VARs are estimated using monthly data from 1992:01 to 1998:12, including 12 lags of the relevant quantitative variables.<sup>2 8</sup> The estimated VAR coefficients are not in themselves very interesting and thus are not reported here. Instead, the impulse response functions and the variance decompositions are reported. In order to assess the overall quantitative impact of a monetary tightening and the credit channel effects on production of SMIs relative to LMFs, Figures 16 and 17 plot the impulse response functions of **ySME** and yL with respect to innovations in the interest rate and the spread residuals over a horizon of 36 months (Appendix 2 reports the overall results of this exercise). Standard errors are calculated by the Monte Carlo method, with 1000 repetitions and standard error bands (of ± 2 standard deviations) are also included.<sup>29</sup>

- 28 All data are obtained from the Central Bank, Department of Statistics, and JP Morgan. In terms of lag selection, a sequence of (likelihood ratio) exclusions tests favored 12 lags against 10 and 8 lags.

> 29 The Monte Carlo standard errors are computed as follows. At each repetition, a random sample from the asymptotic distribution of the VAR coefficients is drawn. The asymptotic distribution of the VAR coefficients is given in Hamilton (1994). From these simulated coefficients, the impulse response functions are computed. After repeating this process 1000 times, the 95% confidence interval, by the percentile method, is constructed. The standard errors reported are the standard deviations
growth. In the past, when such investment‐less credit booms unwound, output contracted more than when the credit boom had been accompanied by an investment surge.

# **4. Factors associated with the investment slowdown**

A series of econometric exercises is conducted to estimate the relative importance of these external and domestic factors to investment growth. First, in a panel regression, investment in 73 EMDEs and 26 AEs for 1998‐2015 is modelled following the standard framework implying that the level of investment is chosen such that the marginal return on capital matches the risk‐adjusted cost of capital. Specifically, the regression model includes as explanatory variables the proxies for the drivers of investment, including the marginal return to capital (e.g., output growth and terms of trade growth) and the risk‐adjusted cost of capital (e.g., measures of uncertainty, FDI inflows, and the private credit‐to‐GDP ratio).<sup>5</sup> These also are the factors that have shaped the macroeconomic backdrop as previously discussed.

Second, the analysis drills down into the short‐term effects of uncertainty and weak activity in major advanced economies on EMDE investment growth using time‐series methods. This is done in two sets of vector autoregressions tailored to examine each factor in detail. The need for quarterly data restricts the cross‐country dimension of the sample (to 18 EMDEs) used in these exercises.

> 5 A large cross‐country dataset for investment growth is only available for aggregate gross fixed capital formation, which includes both private and public investment. The correlates of investment modelled here are mainly those relating to private investment whereas public investment is assumed to be mostly subject to discretionary policy decisions. In EMDEs, private investment on average constitutes about 70 percent of total investment. To mitigate concerns about endogeneity, output growth prospects are proxied by lagged output growth, in line with other studies (see Annex 1).

8
None|Require multiple visits,<br>improve fieldworker training|Ravallion<br>(2022)|
||Questionnaire<br>design<br>improvement|Item non-<br>response|None|Design questionnaire to<br>obtain at least some income<br>information for those who<br>would otherwise not provide<br>any.|Lepkowski<br>(2005)|
|||||Use PSU or regional|Korinek et|
|**Ex Post**|Reweighting|Unit non-<br>response|survey data only|response rates plus an<br>econometric method to|al. (2006),<br>Ravallion|
|||||adjust sample design surveys|(2022)|

11