rafmacalaba/data-use-mentions-extended huggingface dataset:
system. Municipalities and beneficiaries were not initially involved in surveys and village programming until 2002 when local commissions were created. Neither were there consultations with municipal distributors, thus exacerbating the issues regarding limits of service and boundaries of intervention areas. From ONEE’s perspective, the electrification effort achieved its goals precisely because it was not slowed-down by excessive consultations and discussions. The MOI and municipal distributors regard ONEE as overreaching in expanding its electricity distribution activity further jeopardizing the municipal distribution model. There is a legal ambiguity surrounding electricity distribution: considered as a public service, it falls within the competence of local government, but ONEE is also involved in distribution in certain municipalities.

2.2.3 Summary Morocco’s success in expanding rural electrification through PERG is largely due to the critical role played by ONEE. PERG was underpinned by a clear vision and political commitment to follow the plan that was set out, a business model inclusive of domestic and international financing, and an effective institutional framework that draws on ONEE’s strengths while leveraging other national and international stakeholders. PERG was based on a least-cost spatially optimized plan that was technologically neutral, choosing the approach suited to meet local demand and using an internal GIS database that captured almost all rural communities in the country. Equally important in achieving these results is that at the outset, Morocco’s high level of urban electrification may have allowed crosssubsidization from urban to rural consumers.<sup>60</sup> By comparison in sub-Saharan Africa, one study finds that demand-side challenges are a greater obstacle to electrification due to low uptake and low household income translating into very low electricity consumption that affects the financial viability of utilities.<sup>61</sup> ONEE’s expertise provided the country with necessary technical and managerial skills to oversee an important expansion of the power infrastructure and achieve universal access to electricity. Importantly, the liberalization of generation gave ONEE the fiscal space to invest and focus on rural electrification.

# 2.3 Utility Efficiency and Financial Viability

##
. Specifically, poverty is defined by the daily expenditure thresholds of $1.90, $3.20, and $5.50.

# **4. Methodology**

## **4.1. Concept of analysis**

To estimate the number of people who are exposed to intense flood risk, this study follows four main steps:

**1. Generate a combined flood hazard map:** For each country and each subnational administrative unit, a single flood hazard layer is created by combining different flood types. The resulting flood map has a 90-meters resolution, with each pixel showing estimated inundation depths in meters. For pixels where different flood types overlap the higher inundation depth estimate is used (e.g. coastal areas near rivers are exposed to both coastal and fluvial flooding). The flood hazard map is then resampled to ensure that pixels perfectly overlay the GHSL population density map.

**2. Define flood risk categories:** While the flood hazard map offers inundation depths along a continuous scale, the values are aggregated into the following risk categories:

|**Flood ri**|**sk classification**|**Inundation depth**|
|---|---|---|
|**Low risk**|No risk|0 meters|
||Low risk|0 – 0.15 meters|
||Moderate risk|0.15 – 0.5 meters|
|**High risk**|High risk|0.5 – 1.5 meters|
||Veryhigh risk|Over 1.5 meters|

7
depends on both energy costs and idiosyncratic state policies, we allow the IV to reflect energy costs, which may have an impact on the outcomes even after energy controls if the controls are not perfect. Pass-through formulas imply that the effect of energy costs on retail prices is just the cost share of energy costs. The 2009 economic census estimates that the share of electricity, purchased fuels, and other utility payments is 5.5% of operating expenses. Given a gross margin of around 30% in the grocery sector based on the Annual Retail Trade Survey, this implies an upper bound of about 0 _._ 055 _×_ 0 _._ 3 = 0 _._ 0165 for the cost share of energy costs. Therefore, we use 0 _._ 02 as our back-of-the-envelope estimate of the direct effect of the IV on retail prices, which is denoted as _δ_ . When _δ_ = 0 _._ 02, our estimated coefficient is 0 _._ 066 and remains statistically significant at the 1% level.

> 63The posted price is actually recorded as a weekly unit value for Saturday-ending weeks. DVG highlight that this feature creates a slight aggregation bias but the bias is relatively small for state-level shocks in their calibrations.

54
|0.352|0.010|
|Dummy, All Members>59|0.016|0.003|0.037|0.011|0.022|0.003|
|Dummy, HH Member Disabled|0.261|0.010|0.211|0.024|0.257|0.009|
|**Wealth**|||||||
|Wealth Index|0.254|0.005|0.212|0.009|0.276|0.004|
|# Mobile Phones<br>**Notes:**The table is constructed across all h|0.902<br>ousehold observat|0.027<br>ions used in|0.746<br>the analysis.|0.050<br>†**Weather**|1.081<br>**Shock**is a di|0.025<br>chotomous|

**Notes:** The table is constructed across all household observations used in the analysis. † **Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

20
potential correlation of residuals across local markets with similar industry composition by calculating shift-share errors (Adao et al. (2019)).

For the plant level analysis, the main challenge is that we do not observe robots’ adoption at the plant level. Instead, we follow Autor et al. (2003) and assume that industrial robots are best suited to perform manual routine tasks. Since we observe the educational attainments of employment at the plant level, we calculate the typical level of education of routine task-intensive occupations based on the Indonesian labor force survey. This turns out to be either Junior or Senior High School Diploma (“secondary education” hereafter).<sup>4</sup> We measure plant level exposure to robots by interacting yearly imports of robots by 2-digit industry to plants’ share of secondary education workers in the base year. Under the assumption that within robot-adopting industries, plants with larger shares of secondary education have greater opportunities for automation, our exposure measure can be used to estimate the impact of robots by comparing the outcomes of firms with large and small shares of employment with secondary education.

We perform a battery of tests to assess the validity of our exposure measure and we show that it is strongly correlated with plant level investments in machinery and equipment. Our empirical methodology addresses concerns related to unobserved firm heterogeneity and time-varying industry characteristics that may be related with robot imports and firm outcomes. We also provide robustness tests instrumenting Indonesia’s robot imports with OECD imports, an approach similar to Acemoglu and Restrepo (2017), Dauth et al. (2017), Faber (2018) and Giuntella and Wang (2019).

Our paper is related to the growing literature on the impact of industrial automation on advanced economies at the industry and local labor market-level (Graetz and Michaels (2018); Acemoglu and Restrepo (2017); Dauth et al. (2017); Giuntella and Wang (2019); Artuc et al. (2019); Kugler et al. (2020))
Nguyen-Hong, D. (2000), “Restrictions on Trade in Professional Services”, Productivity Commission Staff Research Paper, Ausinfo, Canberra. Available at: http://www.pc.gov.au/research/staffresearch/rotips World Bank Regulatory Survey in East Africa conducted in the context of the Project “Trade in Professional Services in East Africa” in 2009.

World Bank Survey on Applied Policies in Services conducted by DECRG in 2008-2009.

118
# Abstract

This paper complements the existing knowledge in the renegotiation literature on infrastructure concessions by analyzing government-led renegotiations. We first propose a multiple-period theoretical framework in which both Pareto improving and rent shifting renegotiations at the initiative of the government can occur. We then perform an empirical analysis based on a sample of 307 water and transport projects in five Latin American countries between 1989 and 2000. While some of the main insights from the previous literature are unchanged, for example concerning the importance of having a regulator in place when awarding concessions and the fragility of price cap regulatory schemes, there are also significant differences as predicted by the model, in particular with respect to the effect of investment and financing, as well as the corruption variables. We provide additional evidence showing that a good regulatory framework is especially important in contexts with weak governance and political opportunism.

1
alone, but needs permission,<br>2=Woman is involved in joint decision-making,<br>1=Others decide, but need the woman’s permission,<br>0=Woman is not involved.<br>I create dummy variables for each ordinal category and decision, and<br>then compute a standardized index following Anderson (2008) and<br>O’Brien(1984).|

> 83For the selected or nominated women who had been offered a promotion in the wave 2 survey, these questions refer to the decision-making about the actual offer instead of the generic question.

56
. abolished, the resulting effect would be approximated by our results. Nonetheless, we do riot think that there are very large differences between the "gross" and "net" estimates of FONCODES investments. The Ministry of Education does not have a policy whereby schc ol s which receive FONCODES funding receive priority attention for other educational inputs as well. We also experimentecl with including district-level measures of iNFES expenditures in our regressions. The results (not reported) are virtually identical to those which follow, sugges zing that INFES and FON(CODES investments are orthogonal to each other once appropriate dis tri ctlevel controls are included in the model.

Finally, we were unable to use the 1996 INEI survey to construct cluster-level meastires of FONCODES "treatment" because of the low level of agreement amongst likely FONCODE S "beneficiaries" in a given community. The inability to accurately determine which childrer ial a community have and have not been treated, and the fact that most districts included in the I:iEI and LSMS surveys received some FONCODES education projects, precludes the use of sta:tistical matching as an estimation strategy (see, for example, Heckman, Ichimura, and Todd, 1997; Jalan and Ravallion, 1998).

15
We next analyze periods of under- and overestimation separately. While statistical capacity reduces both, voice and accountability only reduces overestimation. The nighttime light-adjusted measure suggests a smaller decline in economic activity than reported in the national accounts for countries with a higher share of value added by industry and especially services (and a lower share of value added by agriculture). COVID-19 hit economies with a large service sector especially hard, because the sector relies heavily on human-to-human contact and it was partially shut down to prevent the transmission of the virus. Table I.1 in the appendix shows that both reported as well as nighttime light-adjusted GDP growth during the pandemic declined more with higher shares of services. However, the deviations between the two measures suggest that these countries may have overestimated the impact of COVID-19 (and hence underestimated GDP growth more).

It might seem that the result could reflect a different elasticity of value added in manufacturing and services compared with agriculture. However, we showed in table 5 that the elasticity between countries with above and below median agriculture is nearly the same. Moreover, the elasticity was higher for countries with an above-median share of services, suggesting that a possible bias would not weaken but strengthen the finding.<sup>24</sup>

# **7 Heterogeneous Subnational COVID-19 Impacts**

With our estimated elasticity, we can quantify different impacts of COVID-19 at the subnational level. Subnational data on economic activity are not often available for EMDEs, especially not at quarterly frequency. Nighttime lights provide an alternative way of measuring spatially disaggregated economic activity (see Section 5). Our elasticity estimate from Section 5.2 allows us to translate changes in nighttime lights into changes in economic activity.

We regress regional changes in nighttime lights at quarterly frequency on population density and a region’s average nighttime lights per capita, which is a proxy for its level of development. We estimate the following regression model:

> 24To make the argument work would require arguing that the elasticity between nighttime lights and value added in industry
27 Sahasakul, C. (1986). “The U.S. Evidence on Optimal Taxation over Time.” Journal of Monetary <u>Economics. Vol. 18, pp. 251-275.</u> Schick, Allen (1983). “The Distributive Congress.” in Schick, Allen (ed.) Making Economic Policy <u>in Congress. Washington D.C. American Enterprise Institute, pp. 257-74.</u> Vaughn, Karen I. and Richard E. Wagner (1992). Public Debt Controversies : An Essay in Reconciliation." Kyklos. Vol. 45, Fasc. 1, pp 37-49.

# BOOKS

Chen, Derek Hung Chiat (1995). <u>Ageing and Intergenerational Transfers : The Issues.</u> (Unpublished) Master's dissertation. National University of Singapore, Republic of Singapore. Chen, Derek Hung Chiat (2001). <u>Intertemporal Excess Burdens, Bequest Motives, Informal Support and the Budget Deficit. (Unpublished) Doctoral dissertation. Univeristy of California at</u> Davis.

OECD (1988). Ageing Populations : The Social Policy Implications. OECD, Paris, 1988.

O’Sullivan, Arther and Steven M. Sheffrin (1998). Macroeconomics : Principles and Tools. Prentice-Hall, Inc., New Jersey.

Rosen, Harvey S. (1999). Public Finance. (5<sup>th</sup> Edition) Irwin / McGraw-Hill, U.S.A.

Taylor, John B. (1998). Principles of Macroeconomics. (2nd Edition) Houghton Mifflin Company, U.S.A.

Todaro, Michael P. (1997) Economic Development. (6th Edition) Addison-Wesley Publishing Company, Inc., U.S.A.

# DATA SOURCES

Deininger, Klaus and Lyn Squire (1996). A New Data Set Measuring Income Inequality. World Bank Economic Review. Vol. 10, pp. 565-591.

Easterly,
equation (5), in which the dependent variable is the inflation adjusted hourly wage and the main independent variable of interest is a dummy variable indicator for using a computer that is connected to the Internet, while controlling for the general use of computer at the workplace. The vector i includes the same set of time-varying control variables as those reported in equation (3). The regressions also include individual and time fixed effects. Individual fixed XX effects capture all time-invariant unobservable characteristics, while the time fixed effects capture all aggregate factors that likely affect digitalization and labor market outcomes. i 1 i 2 i i (5) ii ii HHEEHHIIE wwssLLEE = θθ CCE EEHHIIEEII ssII wwEEIIww + θθ XX + θθ + θθ + εε

# **4. Results and robustness checks**

# **4.1 Impact of Internet job search on employment and heterogenous effects**

First, I examine the impact of Internet job search on the probability of employment within 5 years following an unemployment spell in 2012. In the ELMPS 2012, individuals were asked about the purposes of their Internet use, which included searching for a job. Internet job search is therefore 10
9 advanced countries and countries with a population smaller than 200,000 persons as of 2002<sup>11</sup> , we are left with a set of about 110 emerging countries and developing economies (Table A2).

As pointed out in the introduction, the descriptive evidence one wants to see when dealing with migrants’ transfers relates to three main characteristics: their increasingly large size, their limited volatility and their low correlation with the economic cycle in the receiving economy. In what follows, we mostly focus on workers’ remittances, which is not only the largest item among the three relating to migrations, but also the one with the largest coverage in terms of countries. For a better assessment of the above properties, we compare workers’ remittances to net FDI inflows that, excluding workers’ remittances themselves, have always been the largest and the least volatile item among capital inflows to emerging countries (Lipsey, 1999).

In our sample, workers’ remittances in 2003 totaled 65.2 billion US dollars, compensation of employees a lower $18.3 billion, migrants’ capital transfers only $1.3 billion. Overall, the transfers of funds from migrants reached about $85 billion<sup>12</sup> . In the same year, FDI inflows towards the same set of countries for which workers’ remittances data are available amounted to $111.9 billion.

In the past 30 years workers’ remittances have grown continuously, starting from 3.6 billion in 1976 (fig.1). This increase partly reflects an expansion of the set of countries for which workers’ remittances data are available: 23 in 1976, 47 at the end of the 1980s, up to 77 at the beginning of the current decade. However, it also reflects a widespread increase of the relevance of workers’ remittances towards most of the countries for which long time series are available.

Comparing workers’ remittances and net FDI inflows over time, having again restricted the sample to the
would be less than commensurate. While multiple rounds of district-representative household sample surveys and private school censuses are available over the study observation period, we are unable to construct panels at the household or school levels using these data to examine this question.<sup>18</sup> Estimates of local randomization-based RD program effects using single cross-sections would be biased.<sup>19</sup> We would also not be able to apply local randomization-based RD if these data are used to construct a panel at the district level—with 34 districts—but must rely on parametric RD estimation which potentially introduces bias as discussed in section 5.

Notwithstanding, we consider the extent to which the estimated program effects are driven by displacement or diversion of students from private schools to government schools to be contained by the somewhat low share of private secondary school enrollment in the RD districts. Household sample survey data for 2014 show that only one of every five female secondary students from poorer backgrounds attended private schools in the RD districts.<sup>20</sup>

# _Program cost-effectiveness_

We assess the program’s cost-effectiveness in relation to girls’ secondary school enrollment in the RD program district, following Dhaliwal et al. (2013) and costing guidelines from the <mark>Abdul Latif Jameel Poverty Action Lab (</mark> J-PAL n.d.). Per beneficiary, apart from the cost of the benefit transfer (PRs 2,400 a year), the government paid a fee to the government post service of PRs 50 per money order (which amounts to PRs 200 a year given quarterly benefit transfers) and what the government refers to as an “incidental expense” of 1 percent of the

> 18 The various rounds of the private school censuses lack a common school ID system.

> 19 This also applies to the examination of school attainment (years of schooling) using data from the household sample surveys.

> 20 Based on data from the 2014 Punjab Multiple Indictor Cluster Survey.

19
outcomes are missing in many studies and measured in very inconsistent ways. We therefore limit our analysis to the amount of cultivated area, fertilizers, pesticides, seeds, and a risk index – whose components are available for all studies and measured in fairly consistent ways. We note that these outcomes are not identical from a theoretical point of view. Assuming that financial constraints are not too binding and that farmer preferences are characterized by decreasing absolute risk aversion, the provision of insurance should affect positively cultivated area, fertilizers, seeds, and crop riskiness, whereas it is not obvious that more insurance would foster the use of pesticides (more pesticides increase production costs but tend to decrease production risks).

Most of these outcomes have straightforward definitions. For the risk index, we draw on Gehrke (2019) and define it for household _i_ in study _k_ as _Rik_ = ∑ _rmkdimk_ / ∑ _dimk_ , where _rmk_ is the coefficient of variation of the yield of crop _m_ in study _k_ , and _dimk_ is the amount of land devoted by household _i_ to the cultivation of crop _m_ in study _k_ .<sup>19</sup> We derive _rmk_ using time series of agricultural yields provided by the FAO at the country level.<sup>20</sup> For India, given the possibly large differences in crop riskiness across regions, we use sub-national data on crop yields from the Directorate of Economics and Statistics, Ministry of Agriculture.<sup>21</sup> For studies in which the insurance products are based on rainfall exclusively (see Table 2), we enrich the index with the term _fmk_ to take into account the rainfall sensitivity of crop _m_ in study _k_ . More specifically, we use _fmk_ to multiply _rmk_ and deflate the contribution to the index of crops which are less sensitive to rainfall. The idea is to prevent the influence of other production risks, such as pests or diseases, which are not covered by rainfall insurance. We use monthly rainfall data drawn from the Climatic Research Unit
they need _permission_ from before exercising their land rights and the degree of tenure security over the land. Finally, each respondent was asked whether he/she had any _decision-making power_ over planting time and crop choice on each parcel. These surveys have been conducted under the World Bank Living Standards Measurement Study-Plus (LSMS+) program,<sup>7</sup> using cross-country comparable survey methods and tools, and in accordance with the international guidelines (United Nations, 2019) for individual-disaggregated survey data collection on asset ownership and control.<sup>8</sup> Compared to the work by Slavchevska et al. (2021) based on household survey data on land ownership and rights as reported by most knowledgeable household members, our analysis relies on self-reported survey data and a wider range of land ownership and rights constructs.

The second contribution of our paper is to introduce the use of unsupervised machine learning (ML) techniques in the analysis of high-dimensional survey data on land ownership and rights – elicited from individuals, at the parcel-level, and based on survey questions on a range of constructs related to self-reported rights to and control over land. The cross-country comparability in questionnaires and respondent selection protocols permits a robust analysis of how machine learning techniques perform in different countries with different institutions and norms around land ownership and rights. The unsupervised ML algorithms are useful for analyzing data without a definite label – in our case, the true “land ownership” type or label is unknown to the researcher. We use multiple correspondence analysis (MCA) and hierarchical clustering to understand how much certain land rights variables matter in distinguishing between different types of reported ownership. Once clusters of different types of reported ownership are identified based on selfreported land rights, we conduct additional analyses that profile landowners within each cluster and across countries as a function of their individual and household attributes. Our methods facilitate a more convenient and data-driven, as opposed to subjective, profiling of landowners based on their self-reported rights (the latter of which often involves, for example