rafmacalaba/data-use-mentions-extended huggingface dataset:
In this paper, we construct cost-of-living indexes for different groups of households in order to quantify differences in the inflation burden across the populations of countries in Europe and Central Asia (ECA). We use microdata on household expenditures to construct consumption baskets for each household and use detailed price data to track the evolution of the price of such baskets. We generate detailed inflation estimates for 19 emerging market and developing economy (EMDE) countries in ECA and provide a limited set of estimates for 25 countries in the European Union (EU) and the European Economic Area (EEA).

Our results show that the high inflation of 2022 had a heterogeneous impact on European populations. The burden of inflation was spread unevenly across households, with poor households appearing to have suffered the most from rising food and energy prices. Households in the bottom decile of the consumption distribution faced inflation, that is, for the average country in EMDE ECA, 2.3 percentage points higher than the inflation faced by households in the top decile, with the difference reaching 5 percentage points in some countries. We also show that poverty and inequality rates and the profiles of the poor derived from household-specific inflation rates differ from those based on the standard CPI approach. In all countries in our sample, the poverty headcount rate, the poverty gap, and the Gini index of inequality are higher when using COL household-specific indexes than when using the CPI.

The rest of the paper is organized as follows. Section 2 reviews the literature. Section 3 presents the methodology for constructing household-specific COL indexes, and section 4 describes the data used for our analysis. Section 5 presents a descriptive analysis of the differences in inflation rates across goods and services and in consumption patterns across households. Section 6 presents the main findings. Section 7 discusses the implications of our approach for the analysis of poverty and inequality. Section 8 draws policy implications.

# **2 Literature review**

Official measurements of the cost of living started in the late 19<sup>th</sup
Policy Research Working Paper 10330

# **Abstract**

The COVID-19 pandemic caused significant disruptions to economies around the world. In response to this, some developing countries offered reliefs such as electricity subsidies while others did not. How did the pandemic affect the electricity consumption of households and firms? Did the utility subsidies enable a quick recovery from the pandemic? And what are the distributional impacts of the utility subsidies? This paper leverages unique administrative billing data on electricity consumption from two African countries, Ghana and Rwanda, with differing policy responses to the pandemic to document the demand response of households and firms to the COVID-19 pandemic, and the role of utility subsidies during the period. Findings from the paper indicate that the pandemic led to higher consumption of electricity in both countries, albeit with variations across countries and sectors. While residential consumption soared, consumption of non-residential customers such as hotels and industries declined during the period. Electricity subsidies in Ghana during the pandemic explain the sharp increase in residential consumption. These findings highlight the potential effects of pandemic relief measures on household welfare.

This paper is a product of the Office of the Chief Economist, Africa 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 jmensah2@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,
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE A much more detailed study of irrigation potential in Côte d’Ivoire was conducted by the Ministry of Agriculture in 2005 and used to develop the country’s national irrigation plan. The plan provides for the rehabilitation of around 3000 hectares of irrigated areas and the creation of 139,000 hectares of new irrigated areas. The total investment cost amounts to $1,657 million with an average investment cost per hectare of $11,600. Interestingly, the extent of irrigated area falls within the range that is identified in the AICD simulations, even if the assumption for the unit cost of irrigation development is several times larger. Nevertheless, the study notes that the benefits of the future irrigation investments depend on the development of the feeder road network.

# Information and communication technologies

## **Achievements**

Like many African countries, Côte d’Ivoire has gone through an ICT revolution during the last decade. By 2005, the country had established a partially competitive mobile market with two operators of roughly equal size and mobile phone penetration had reached9 percent, which was fairly typical for the peer group (table 15). Between 2006 and 2009, the government awarded four more mobile licenses. As a result, competition intensified, and mobile penetration had reached 51 percent by 2008. Nevertheless, the price of a representative basket of mobile services remains comparatively high by both regional and global standards at around $15 per month.

**Table 15 Benchmarking ICT indicators**

||Unit|Low-<br>incomefragile<br>countries|**Côte d’Ivo**|**ire**|Middle-income countries|
|---|---|---|---|---|---|
|||Mid 2000s|**2005**|**2008**|Mid 2000s|
|GSM coverage|% population|62.6|**55
cents||7|18.8|5.0–10.0|
|industrial at 50,000 kWh|U.S. cents||7|14.2||

_Source:_ All sources are AICD unless indicated otherwise. Access to electricity (national, urban and rural) from World Energy Outlook (International Energy Association, 2010); installed power capacity data for 2009 based on presentation from Zimbabwe investor conference (2009); collection rate data based on World Bank staff information based on 2004 and 2009; revenue per unit derived from data based on AICD calculations for 2004 and 2009; system losses are for 2003 and 2009 and are based on World Bank (2008a) and World Bank staff estimates; cost recovery calculated based on World Bank (2010) and are for 2004 and 2009; hidden costs based on data from 2004 and 2009 and derived from AICD calculations; and tariff information is for 2004 and 2009 and derived based on World Bank (2010). _Note:_ MW = megawatts; kWh = kilowatt-hour.

# **Challenges**

Zimbabwe‘s power infrastructure is starved of new investments, however. Zimbabwe has not seen any investments to expand its generation infrastructure since 1988, when Hwange Power Station added 440 MW. Only around 60 percent of the 1,960 MW of installed capacity is operational. Installed capacity in per capita terms is following a clear downward trend (table 2). Power transmission and distribution networks have been further corroded due to damage and theft. This vandalism produced losses of about $400,000 a month during 2009, of which only 40 percent was recovered later (World Bank 2010).

ZESA,Zimbabwe Electricity Supply Authority,the integrated power utility, faces an unsustainable financial situation that leaves no room for new investments. Sixty-eight percent of Zimbabwe‘s installed capacity is thermal as of 2008, and ZESA uses 2.4 million tonnes of coal per year for thermal power generation. Between over half of its coal comes from
in the formal sector. A 10 percent increase in the minimum wage shrinks employment in the formal sector by 1 percent, but the contraction ranges between 2.5 and 3.5 percent in the informal sector. Thus it can be assumed that during 1982‐1997 the minimum wage policy reduced the number of informal workers, either because they found a better‐paid job in the formal sector or because they dropped out of the labor market entirely. It is also possible that workers who decided to leave the market may have done so because other household members obtained jobs that paid better.

Based on household survey data for 1982‐2000, Lemos (2004) found that the minimum wage had a long‐term adverse effect on employment in both sectors and that the contraction in employment was consistent with wage increases for both formal and informal workers. Although the impact was greater in the informal sector, in both cases the elasticities were lower than 1 percent. On the other hand, Carneiro (2001) found that minimum wage increases during 1982‐ 1999 reduced formal employment, with elasticities ranging between −0.001 and −0.024, and increased informal employment, which is more consistent with the predictions of the dual model.

A study by Terrell (2009) covering a more recent period (2002‐2008) in Costa Rica looked at the 15
tailed survey information to provide direct evidence on the possible channels through which the API _Plus_ program enhance children’s outcomes.

# **3.3** **_Plus_ vs.** **_Original_ : Channels**

We start by evaluating the role of the remedial education sessions. The estimates displayed in Table B-10 suggest that there is no differential effect across the four children’s outcomes ( _p_ - values = 0.766, 0.675, 0.639, and 0.937) in the relative impact of the two training modalities between children who are more or less likely to be eligible for the remedial sessions (see also Figure B-2). Although the design of the second experiment does not allow us to directly isolate the direct effect of the remedial education sessions within each API modality, this evidence suggests that such mediating factor is unlikely to explain the differential impact between the _Plus_ and the _Original_ documented in Table 2.<sup>11</sup> We next consider the role of the pedagogical practices of the community instructors. Table B-11 reports estimates of the effect of the two API modalities using data at the instructorschool level (the sample average number of instructors per school is 1.2 in the school year prior to the start of the second experiment) on four summary measures of pedagogical practices based on GLS-weighted indices across an array of instructor-student interactions (for details, see Appendix A.2). The results show erratic patterns of positive and negative signs with no statistically significant effects of either API modality. The overall index of pedagogical practices reveals a non-negligible negative effect of 0.18 standard deviations for the _Plus_ modality, indicating, if anything, a crowding-out effect of the presence of the mentors on instructors’ job effort. The effects are quantitatively and statistically similar between API modalities across the different pedagogical practices, as shown in the third row of Table B-11.

Finally, we study the role of the mentor/parent interactions during the home visits as a potential mechanism
Table 12: Medium-term impacts of _Mais Educação_ on educational outcomes for schools opting for the Portuguese activity

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.157|–4.370**|–2.955*|
||(0.829)|(1.764)|(1.666)|
|_R_ <sup>2</sup>|0.716|0.829|0.825|
|Observations|1,494|1,494|1,494|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|–0.200|–1.022|0.107|
||(1.350)|(2.223)|(2.461)|
|_R_ <sup>2</sup>|0.766|0.786|0.77|
|Observations|1,200|1,200|1,200|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined Portuguese activity in 2008 and remained in the activity until 2011. Pedagogical monitoring must be chosen by schools as one of the fields of knowledge, but schools can choose for which subjects they offer pedagogical support (Portuguese, mathematics, sciences, history, etc.
phase is labeled as a rapid accumulation episode if an increase in the debt-to-GDP ratio (from trough to peak) exceeds the maximum of ten-year moving standard deviations (over the period t-9 to t) of the debt-to-GDP ratio during the phase (Figure 1). Episodes at the beginning and end of the data series are similarly classified, but the beginning and end of episodes are set at the points where the availability for government and private debt data begins and ends.

Application of this algorithm results in 256 episodes of rapid government debt accumulation and 263 episodes of rapid private debt accumulation in a sample of 100 EMDEs with available data for 1970-2018.<sup>2</sup> This identification algorithm for rapid debt accumulation episodes closely follows methods used to date the turning points of business cycles (Claessens, Kose, and Terrones (2012); Harding and Pagan (2002); and Mendoza and Terrones (2012)). The headline results are robust to using a definition more closely aligned with the literature on credit booms.

> 2 Small states, as defined by the World Bank, are excluded.

> 45 government debt and 38 private debt accumulation episodes are still ongoing. Appendixes 1 and 2 list government and private debt accumulation episodes.

3
importing inputs for producing items to export to another link, another participant, in another region or another country. But this system of “importing to export” involves more than trade transactions among participating firms, and includes the sharing of blueprints and management practices, through which new ideas and know-how are continuously transferred across national and international borders and regions.

# 2.2 Data

The core of the GVC analysis relates to trends in indicators of linkages to a manufacturing GVC along two dimensions, namely, upstream (or backward) linkages and downstream (or forward) linkages. The indicator of upstream linkages to a GVC is the share of import content or Foreign Value Added (FVA) in the country’s gross exports. What is left after deducting from the gross exports the FVA component constitutes the country’s domestic value-added exports (DVA, for short). The indicator of the country’s downstream (or forward) linkages to a GVC is the share in the total value-added exports of that country that is used as intermediate input to other countries’ exports to third parties (DVX). A country’s overall rate of linkages to a GVC is the sum of the share of FVA in its gross exports and the share of DVX in the same.

In practice, lack of firm-level data has meant that the measurement and analysis of GVC participation generally and for most African and developing economies particularly is mostly based on the use of projected inter-industry trade data to extract the value-added component of official data on country imports and exports.

To compute the GVC indicators, we draw on the latest release of the EORA MIRIO database of the University of Sydney.<sup>2</sup> The database is a set of input-output tables covering 189 countries for the period 1990-2015, providing the largest coverage of countries in Sub-Saharan Africa that we are aware of. It also provides information for 26 sectors distributed across five broad sectors of production, namely, agriculture, mining, manufacturing, construction, and tradable services. The
Table 8: **ASP Attendance and Dropout**

||Attendance<br>(as % of sessions)<br>(1)|Dropped<br>the ASP<br>(2)|Sessions<br>scheduled<br>(3)|
|---|---|---|---|
|_Virtue_|0.195***|-0.110**|0.235|
||(0.048)|(0.046)|(3.548)|
|_Mindful_|0.064<br>(0.043)|-0.058<br>(0.040)|0.034<br>(2.871)|
|Psychology-based curricula|0.131***<br>(0.042)|-0.084**<br>(0.039)|0.137<br>(2.723)|
|P-value for_Virtue_=_Mindful_|0.0046|0.1598|0.9542|
|<br>Mean_Clubs_group|0.6142|0.2258|42.7689|
|<br>Observations|897|897|897|
|height||||

_Notes:_ This table shows the ASP attendance rates. The estimation sample includes all students treated in _Clubs_ , _Virtue_ , or _Mindful_ . It compares the attendance of participants randomly assigned to _Virtue_ or _Mindful_ to those treated in _Clubs_ . Column (1) shows the attendance rate as a percentage of the number of total sessions conducted (column (3)). Column (2) presents the share of students who dropped out of the ASP. “Mean Clubs is the mean of the outcome for the that in _Clubs_ . The co- group” group participated efficients for both the _Virtue_ and _Mindful_ groups are derived from a unified regression the on the broader of cur- analysis. Conversely
# **5. How Well Do Survey Results Correspond to Data-Based Assessments?**

Besides understanding investors’ and financiers’ risk perception, a second motivation for the survey was to ‘reality-check’ existing, data-based assessments of countries’ readiness for investment. Figure 13a below shows that survey respondents’ perception of countries’ readiness correlates very well with Regulatory Indicators for Sustainable Energy<sup>16</sup> (RISE) 2017 scores (R<sup>2</sup> of 0.47 for a sample of 35 countries). Ghana, South Africa, and Zimbabwe score much better on the RISE index than their perception is by investors, while the opposite is true for Madagascar, Mozambique, and Nigeria. But these are outliers, and overall, the strong correlation suggests that the RISE framework corresponds well with the private sector’s priorities. The Power Sector Reform Index (PSRI) also shows a substantial correlation with the survey data, albeit the correlation is slightly lower (R2 of 0.37 for a sample of 35 countries). It should be noted that RISE is intended to be a normative index of investment readiness, whereas the PSRI is a descriptive index that shows the extent to which the 1990s reform model has been adopted within a jurisdiction. Hence, it is therefore not surprising that overall, the RISE index shows a higher correlation with our survey results.

> 16 The Regulatory Indicators for Sustainable Energy (RISE) index measures countries’ regulatory readiness for Sustainable Energy across three different domains: 1) Renewable Energy, 2) Energy Efficiency, and 3) Energy Access. We therefore disaggregate the RISE index to more closely correlate it with the sub-components that are relevant to our survey data.

28
for civil society as an explanatory variable.<sup>24</sup> The Freedom House index, though adequate, is imprecise. It is not clear whether it measures the quality of institutions, of the political participation process or of individual attitudes vis-a-vis public authority. Future research should help to clarify this point, thus facilitating the identification of various channels through freedom affects economic policy (the role of media in influencing public opinion, the importance of political participation, of trust in public institutions, of transparency, etc).

> 24 Campos and Nugent (1998) note a very high Spearman rank correlation coefficient between five indicators related to freedom and political participation: political rights and civil liberties (both from Freedom House), competitiveness and regulation of political participation, and "strong civil society" which is the interaction of the previous two (the latter three indicators from the Polity III data set. See Jaggers and Gurr 1995)

25
incorrectly attribute a much larger variation in the dependent variables to the variables we include as regressors than is true. Similarly, in (22), it is possible that the unobservable climate, soil and other topographical conditions that render a village unsuitable for forest growth also make it unsuitable for agriculture which would in turn limit human settlement and give rise to low population densities. On the other hand, villages endowed with a more benign geography in the form of fertile soil, adequate rainfall and easier access to local markets are likely to have higher population densities, higher levels of agricultural technology and thus, ceteris paribus, higher incomes and higher land prices.

In order to control for these time invariant village characteristics that could bias our estimates, we exploit the fact that we have panel data which allow us to observe villages at two points in time. Particularly, by differencing both equations across the two time periods, we can sweep out the fixed effects and end up with, and where _D_ is the operator.

Although the fixed-effect specification eliminates time-invariant characteristics of villages that may bias our coefficients, it is still imaginable that the _change_ in village unobservables between the years of our analysis are correlated with the change in the regressors during the same period. For example in (23), if we interpret _D￿zt_ as an adverse weather shock then this shock could be negatively correlated with village population as households may send out more migrants. By the same token, households that already have migrants working abroad may receive a larger remittance in response to this shock. This is in fact one of the major endogeneities that we have to contend with even after controlling for fixed effects. Similarly a positive weather shock in the first period could independently increase forest area while at the same time increasing village income by triggering adoption of better agricultural technology.

In order to control for contemporaneous effects of weather shocks, we include a village level rainfall variable in all of our specifications. Additionally, to account for the possibility that these first period shocks could have
power of the upstream firm, the larger is its share of the RSI-induced surplus. It also stands to lose more if it does not make the appropriate level of RSI and thus the disciplining effect of its large share of future surplus makes trade credit less needed as a commitment device. Thus, _ceteris paribus_ , Dass et al. (2015) predict less trade credit from suppliers with greater bargaining power but for a different reason than in other papers. And, if trade credit is used as a commitment mechanism when there is uncertainty about the level of RSI, its use should be less prevalent when information frictions between supplier and customer are less severe.

Because RSI are not easily observable or measurable within a large sample of firms, Dass et al. (2015) follow previous literature which measures RSI using proxies such as firms’ research and development expenses (R&D), patent citations and advertising expenses. The intuition is that a firm that conducts research that yields well-cited patents is more likely to be making RSI and producing output that is specialized input for its downstream customer. Previous research also suggests that firms that make RSI and produce unique products are also more likely to advertise than firms that produce commodities (Titman and Wessels, 1988).

Using data on all available firms in Compustat over the period 1997–2008, Dass et al. (2015) find a significant positive relationship between R&D expenditures and the amount of trade credit extended by upstream suppliers, one which is stronger if the supplier patents its research or cites patents of downstream industries in its own patent applications. In addition, they confirm that trade credit is less prevalent when information frictions between supplier and customer are less severe and thus credit is less necessary as a commitment device. Specifically, suppliers extend less trade credit when their relationship with the customer is longer, they are in closer geographic proximity to the customer, or they have a listing on the New York Stock Exchange (and thus information about them is readily available). Moreover