rafmacalaba/data-use-mentions-extended huggingface dataset:
characteristics. However, the household fixed-effects approach may be more appropriate for a panel spanning only a few years—where unobserved community and household characteristics are likely to vary less between project start-up and completion.

The third round, which became available recently, follows the same project and control households from the earlier survey rounds, allowing for a comparison of shorter- and longer-term impacts. In this paper, we use the third survey round from 2005 to examine whether improvements in household outcomes have continued since 1997 or whether certain impacts have diminished over time. An added complexity in our analysis is that some villages designated as control in 1997 ultimately received the project after the second survey round (between 2001 and 2005). With the third survey round, we address these identification issues by instrumenting project status in the dynamic panel model with second-order lagged outcome variables from the first round, and estimating the model via Generalized Method of Moments (GMM) (Arellano and Bond, 1991, Jalan and Ravallion 2002, 1998).

The third round of data allows us to examine the effects of rural road development in the context of long-term changes in community-level outcomes, including expansion of local markets and other institutions. We can also examine the long-term distributional impacts of rural road improvement, given the potential for increased migration between targeted and non-targeted areas several years after road development. The third-round survey also has new data revealing heterogeneity in program implementation that can refine our understanding of the impacts from road development, including differences in the length of road paved to bitumen standard (as a measure of road quality) across project areas.

The paper is organized as follows. Section 2 reviews the research to date that has compared shorter- versus longer-term impacts of rural road investments. Section 3 discusses the dynamics of impacts of rural road investments on rural households, while Section 4 outlines the estimation 4
temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of monthly wages, unconditional on return migration. The predicted logarithm of monthly wages for non-migrants, illegal migrants, and legal migrants are reported in the last row.

43
# **Endnotes**

> 1 The way these subgroups have been defined is described in detail in section 2.1.

2 The Gini index published by the World Bank reaches 40.3% in 2011 (World Development Indicators).

> 3 Momar B. Sylla and Matar Gueye of the Agence Nationale de la Statistique et de la Démographie of Sénégal (ANSD), as well as Philippe De Vreyer (University of Paris-Dauphine and IRD-DIAL), Sylvie Lambert (Paris School of Economics-INRA), and Abla Safir (World Bank), all designed the survey. The data collection was conducted by the ANSD, thanks to the funding of the IDRC (International Development Research Center), INRA Paris, and CEPREMAP. The survey is described in detail in De Vreyer et al., 2008.

4 Details are given in Appendix S1.

5 Vertical extensions refer to the cases where the household includes more than 2 generations of members, i.e. presence of grand-parents or grand-children. Horizontal extensions subsume situations where several family members pertaining to the generation of the head cohabit. It could be for example married siblings of the head or of his wife.

6 Half of the households with 3 cells or more are headed by a polygamous household head.

7 The DQ, « Dépense quotidienne », the name Senegalese give to the amount of money a woman has at her disposal to buy the fresh ingredients for the meals of the day. One of the husband’s duties is to provide the DQ. See Appendix S1 for more details on PSF survey design.

8 According to ANSD (2007), in 2005-2006, more than 80% of households in Senegal were owners of their housing. Less than 16% were renting it, and only 2.6% in rural areas. This leaves a very small number of observations to compute meaningful imputed rents, unless a very large 28
F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on UNPVSR<br>4/87|
|Kenya|Aug 79|15327 F|Bank projection from census,<br>adjusted for SX undercount|1980-85, based on various<br>national surveys; 1985-90,<br>Bank projection|1980-85, U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank<br>projection|
|ribati|May 85|64 F|UNPVSR<br>1/88 (officiat<br>est.)|Based on USBOC<br>1985|Based on USBOC<br>1985|
|Korea, People's|May 44|a F|U.N. 1984 assessment|Based on U.N. 1984|Based on U.N. 1984|
|Dem. Rep. of||||assessment|assessment|
|Korea, Rep. of|Nov 85|40432 F|Official<br>est., Economic|Based on U.N. 1984|Based on U.N. 1984|
||||Planning Board|assessmmnt|assessment|
|Kuwait|Apr 85|1697 F|U.N. 1988 revision (prelim.)|Based on UNPVSR<br>1/88|Based on U.N. 1984|
||||||assessment|
sume that firm _i_ is taxed at a rate _τi_ , and assume that labor and capital are taxed at rates _τL,i_ and _τK,i_ respectively. Both firms have Cobb-Douglas production functions, where the labor share is _θi_ . Let _W_ and _R_ be the wage rate and the capital rent respectively.

Importantly, the tax rates _τi_ , _τi,L_ and _τi,K_ are to be interpreted more broadly as distortions that prevent the competitive equilibrium allocation. For example, finding that _τi,K_ is higher for some firms does not necessarily imply that these firms are (literally) taxed at a higher rate; rather, we learn that these firms face an effective higher price of capital, which may be a result of incomplete access to credit or other distortions.

Firm _i_ maximizes: Denote the (“pre-tax”) revenue of firm _i_ by: The FOC with respect to labor is: Similarly, the FOC with respect to capital yields: These simple equations allow us to approach two questions: first, are there differences in the effective rates of taxation in the tradable and non-tradable sectors? Second, is capital taxed differently than labor, thereby giving an edge to capital-intensive industries (which may be disproportionately tradable)?

To answer the questions above, we need firm-level or industry-level data that includes the following variables:

1. Labor costs, _WLi_ .

37
migration. As discussed above, all individuals in our sample report their income from the previous year allowing us to also implement a ‘difference-in-differences’ estimator.

Before proceeding with microeconomic non-experimental estimators, it is worth comparing the experimental estimate of the income gains to the cross-country macro estimator. Cross-country studies of the determinants of migration often use differences in per capita national income as proxies for the income gains from migration (e.g. Clark, Hatton and Williamson, 2002). In 2004, New Zealand’s GDP per capita was NZ$30,469, while Tonga’s was NZ$2,044.<sup>17</sup> This difference in GDP per capita therefore equates to NZ$546 per week, or twice as large as the actual gain experienced by the average migrant in our survey.

# **4.1. The Single Difference Estimator**

We begin by examining whether a simple single difference estimate calculated using only information from the migrant group provides a good estimate of the income gains from migration. Several recent surveys of new immigrants (eg. the Longitudinal Immigrant Survey: New Zealand (LisNZ); and the New Immigrant Survey (NIS) in the U.S.) ask about income prior to migration. Thus, one approach to estimating the average income gain from migration is to calculate the mean difference between the migrant’s premigration and post-migration incomes. That is, the estimate is: λ SD = E[Incomei,t – Incomei,t-1 | _i_ migrating between _t_ and _t_ – _1_ ] (8)

> 17 Source: World Bank GDF and WDI Central (August 2005 update) for population and GDP. The exchange rate of 1 pa’anga to 0.729 NZ$ prevailing at the time of our survey was used to convert Tongan GDP per capita to New Zealand dollars. Section 6 below reports calculations of PPP exchange rates which are very close to this market exchange rate.

- 20 -
# **Table 1. Sample of countries in the analysis**

|**Income**<br>**Tercile**|**N**|**GNI per capita**<br>**(PPP) range**|**Countries**|
|---|---|---|---|
|1|22|5,030 - 15,530|Albania, Azerbaijan, Bolivia (Plurinational State of), Brazil, Colombia,<br>Cuba, Ecuador, Egypt (Arab Republic of), Georgia, Guatemala, Iran<br>(Islamic Republic of), Iraq, Kyrgyzstan, Republic of Moldova, Mongolia,<br>Nicaragua, Peru, Paraguay, Tunisia, Ukraine, Uzbekistan, South Africa|
|2|22|16,090 - 33,730|Argentina, Bulgaria, Bosnia and Herzegovina, Chile, Costa Rica, Dominican<br>Republic, Greece, Croatia, Hungary, Kazakhstan, Latvia, Mexico, Oman,<br>Panama, Poland, Romania, Russian Federation, Serbia, Slovak Republic,<br>Thailand, Uruguay, Malaysia|
|3|22|36,330 - 70,150|Australia, Austria, Belgium, Canada, Switzerland, Czechia, Germany, Spain,<br>Estonia, Finland, France, The United Kingdom, Israel, Italy, Japan, Republic<br>of Korea, Kuwait, Lithuania, Netherlands, Portugal, Sweden, United States|

Note: Country sample used from the WHO Global Excess Deaths dataset.

8
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE inefficiencies with reasonable developing-country benchmarks, and executed the full amount of its infrastructure budget.

After adding existing spending to required spending, and subtracting the potential savings from improved efficiency, a substantial funding gap of $460 million per year remains. By far the largest portion of this is to be found in the water supply and sanitation sector.

Niger has the potential to close this funding gap by tapping alternate sources of financing or adopting lower-cost technologies. There is plenty of room for private sector participation in Niger’s infrastructure sectors, in particular ICT. Meanwhile, the adoption of alternate lower-cost technologies in the water supply, power, and road sectors would reduce the financing gap by almost a half ($219 million).

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Niger. The results have been presented in reports covering various areas of infrastructure—ICT, irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Niger, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Niger is a poor but stable country, two sets of African benchmarks will be used to evaluate Niger’s situation: low-income countries (LICs) and middle-income countries (MICs). Detailed comparisons will also be made with immediate regional neighbors in West Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, indicators were standardized so that analyses were done
>5</sup> by providing direct support to borrowers in the form of, inter alia, public guarantees for bank loans, state

> 4 In addition to EMDEs, the following jurisdictions are tracked in the database: United States, United Kingdom, Japan, European Union/European Monetary Union (European Central Bank, Single Supervisory Mechanism and other European Union agencies), Standard Setting Bodies (SSBs), Australia, Canada, Germany, France, Italy, and Spain.

> 5 Around 50 percent of firms do not have sufficient cash buffers to cover their debt-servicing and operating costs as a result of the COVID-19 shock (Banerjee et al., 2020). Moreover, a simple balance sheet stress test based on pre-COVID-19 data suggests that non-financial companies in EMDEs may be vulnerable to liquidity and earnings shocks (Feyen et al., 2020).

6
of 49, and lower-middle-income countries with a mean of 44. Workers in low-income countries have an exposure value to AI of 37. At the worker level, education level is strongly associated with AI occupation exposure in middle-income countries. Female workers are more exposed to AI in all country income levels, but the gap is much more pronounced in high- and upper middle-income countries. Urban workers are generally more exposed to AI than rural workers. Overall, we find general support for the notion that advances in AI will disproportionately affect white-collar occupations.

We also overlay the measure of AI exposure at the worker level with household-level data on electricity access. This analysis shows that energy access is a further constraint to AI exposure, principally for rural residents of low-income countries.

The paper is structured as follows: Section 2 provides a literature review on AI and the labor market with a focus on low- and middle-income countries and introduces the data and approach used in the analysis. Section 3 reports stylized facts about cross-country AI occupation exposure for the country sample. Section 4 concludes.

4
on export spillovers, in which proximity to other exporters is assumed to reduce the fixed export cost due to the endogenous formation of informational networks between exporting firms. Most of the literature on this topic is empirical. In a pioneering work, Aitken et al. (1997) show that the export decisions of local firms in Mexico is positively influenced by their proximity to multinational exporters. This result has been confirmed by Kneller and Pisu (2007) for UK data and by Kemme et al. (2009) for India. In contrast, Barrios et al. (2003) do not find clear evidence for these export spillovers from foreign firms in Spain, whereas Ruane and Sutherland (2005) find that the export intensity of foreign-owned enterprises is negatively correlated with the export decision and export intensity of domestic firms in Irish manufacturing, suggesting that no (and even negative) export spillovers derive from third-country export-platform FDI. This prediction bodes ill for China, where foreign firms are primarily engaged in the processing trade (i.e., the assembly of imported inputs, which are then re-exported as a final product). In the context of China, three studies investigate export spillovers emanating from foreign firms (Ma 2006; Swenson 2008; Chen and Swenson (Forthcoming)). These papers relate the probability of exporting (or the number of new export transactions at the city or province level) to the presence of multinational firms. They find evidence of positive foreign export spillovers at the two-digit industry level (approximately 100 SITC or HS sectors).

This paper goes further in developing an understanding of the mechanisms at play in foreign export spillovers in China by exploiting data at a finer level, both in terms of the geography of exports and in terms of activities. In particular, Krautheim (2012) argues in his theoretical paper that the relevant information might be destination specific. For example, technical regulations or specific consumer tastes vary across countries. Koenig (2009) finds evidence of export spillovers from French data only when the destination dimension
also tracks the use of digital technologies to respond to the crisis: whether the business started to use or increased the use of digital platforms since the beginning of the pandemic; whether the business has invested in new equipment, software, or digital solutions since the beginning of the pandemic; whether the share of online sales in total sales increased during the 30 days prior to the interview; and whether the share of employees working from home increased during the 30 days prior to the interview.<sup>6</sup> Our preferred specifications combine these responses in a single binary indicator that equals 1 when the business reports at least one of the four digital adjustments and 0 if the business did not implement any digital response, but we present results for each digital adjustment separately in our robustness checks.

# **2.3 The severity of the COVID-19 shock**

Data for the FAT survey was collected in face-to-face interviews between August and December of 2019 in Cear ́a-Brazil; between August 2019 and February 2020 in Senegal; and between November 2019 and February 2020 in Vietnam. Data for the COV-BPS was collected in phone interviews in the early months of the COVID-19 pandemic: between the end of May and the end of July of 2020 in Cear ́a-Brazil; between the end of April and beginning of May 2020 in Senegal; and between mid-June and early July 2020 in Vietnam. Figure A2 in the appendix shows the exact collection period for the COV-BPS in each country with daily average mobility trends around transit stations, which we compute using the data collected by Google in their COVID-19 Community Mobility Reports ( _19_ ).<sup>7</sup> Mobility trends likely reflect both, lockdown restrictions and voluntary reductions in interactions out of fear of the virus, and help proxy for the severity of the COVID-19 shock on economic activity ( _1_ ). In Senegal, average mobility in the period covered in the COV-BPS reached the lowest level recorded for 2020; in Brazil, mobility around transit stations was increasing after a
**Table 15.  Microfinance Program of the Promoter Agencies, 2002**

|_Particulars_|_DHAN_|_SERP_|_CDF_|
|---|---|---|---|
|Clientele||||
|Members|164,552|67,226|87,500|
|Primary Groups|10,544|5000|400|
|Average membership in primary|16|13|219|
|group||||
|Apex federations|32|20|41|
|Savings||||
|Savings (million)|283|84|192|
|Average Savings|1,720|1250|2,194|
|Reserves and surplus (million)|81|12|47|
|Loans (million)||||
|External loans to date|520|52|None|
|Total credit generated|1,010|794|n.a.|
|Loanportfolio inprimary groups|552|n.a.|180|

n.a.: Not Available Notes: Data for SERP for 2000 Currency in Rupees

- 43 -
This structural model includes five latent variables (provider’s cognitive abilities, as well as youth’s social origin, cognitive abilities, education and first occupation) and a manifest dependent variable (youth’s occupational attainment). Each latent variable is constructed from the manifest variables based on a confirmatory factor analysis (Brown, 2015). Each latent variable and the model’s response variable are described below. Appendix E will provide more details on the construction of latent variables (and on the manifest variables), as well as the calculation of the composite reliability  .

 The _social origin_ is constructed from the manifest variables, such as the ISEI of the provider’s occupation, the schooling of the father and the mother when the youth was 14 years old and an index of assets and services in the household in 1997. The composite reliability is  = 0.60.

 The _youth’s and provider’s cognitive abilities_ are measured with manifest variables created by two cognitive tests that are affected by education: 1) Raven’s progressive matrices (Alderton and Larson, 1990; Bilker, et. al., 2012), that are used to measure inductive reasoning and are considered predictors of academic and labor performance, and 2) digit span, which is a test frequently used to measure short-term memory and attention span (Ostrosky-Solís and Lozano, 2006). The measurements of both youths and their providers were performed using data from 2017. Nonetheless, both are granted a place in the model preceding the current occupational attainment. This is because the cognitive abilities are essentially developed during childhood and are determined to a large extent by family socioeconomic status (Evans and Schamberg, 2009; Hackman, et. al., 2015). Therefore, it assumes that the measurements performed on youths reflect their cognitive state prior to the current labor insertion and the ones performed on the providers may closely reflect their abilities when the youths were 14 years old. The composite reliability is  = 0.46
Policy Research Working Paper 10116

# **Abstract**

During the 2020–2022 period, the COVID Pandemic affected, directly or indirectly, every economic sector globally. While the effects on some sectors, such as travel and tourism, were highly visible, those on other sectors, such as utility services, require investigation. Whether COVID19 affected total electricity generation and the electricity supply mix is an empirical question, which this study attempts to answer using panel data of daily electricity generation between January 2018 and September 2021 from 26 countries. The study finds that a 1-unit increase in the stringency measure of COVID-19 is linked with a 463 MWh decrease in the total daily electricity generation. The study does not find a significant change in the electricity generation mix due to COVID-19 responses, although there is some evidence of some substitution of wind with solar and coal with natural gas.

This paper is a product of the Development Research Group, Development Economics. 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 cshen1@terpmail.umd.edu, aalberin@umd.edu, and gtimilsina@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