rafmacalaba/data-use-mentions-extended huggingface dataset:
of Congo, Jeandron et al. (2015) link water interruptions to increased cholera incidence rate using time series data combining admission information from a Cholera Treatment Center and daily variations of water supplied by the water treatment plant. They find that the suspected cholera incidence rate typically increases by 155% over the next 12 days following one day of outage, compared to the incidence rate following optimal water provision. Ashraf et al. (2017) find that water outages in Lusaka, Zambia, are associated with increased incidence of a number of diseases, such as diarrhea and respiratory infections.

Case studies of specific water disruptions also find significant impacts of health. Huge diarrhea outbreaks caused by cholera and _Escherichia coli_ infections are observed after floods (Ahern et al., 2005; Qadri et al., 2005). Diarrheal pathogens can spread through direct contact with floodwater or through compromised water sources. However, their impact is temporary and prevalence of diarrhea in exposed and non-exposed groups are equal in the long-run (Joshi et al., 2011). A case study from Alabama, US, looks at the effect of a freeze-related water emergency on diarrheal and respiratory illnesses. The researchers find a significantly higher risk of contracting diarrhea for households experiencing water disruptions and low water pressure and prevalence increases with the length of disruption (Gargano et al., 2015). The diarrhea outbreak in Milwaukee which affected 403,000 individuals, was caused by a filtration system that malfunctioned after a heavy storm releasing the bacteria _Cryptosporidium oocysts_ in the water distribution system (Hoxie et al., 1997; Mac Kenzie et al., 1994).

11
44|
|Skilled work|0.17|0.38|
|Transfer|0.03|0.18|
|Other income source|0.04|0.19|
|Aid/transfers from abroad|0.12|0.32|
|Aid/transfers from Haiti|0.18|0.38|

Source: Own computations using June 2010 (EFSA II) surveys. Note: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. See Table 16 for a complete description of variables in directly affected and non directly affected areas.

# _(e) Pre-earthquake conditions_

Table 5 presents summary statistics by quintile of pre-earthquake wealth. The poorest households lived mostly in the East of the directly affected area (Léogane, Gressier, Jacmel, Petit Goâve, Grand Goâve and Croix-des-Bouquets). They were mostly agricultural households: 66% of them were practicing agriculture, compared to only 5% among the wealthiest, who were mostly concentrated in Port-au-Prince or Pétionville. The poorest households lived in houses with no electricity and no toilets, and do not have access privately to water. They had no car and 15
You don’t trust them_ ”; and “ _(e) You don’t have enough money to use them.”_

> 22 Sartori (2003) points out that the Heckman-type estimator may perform poorly when the same variables are included in both the selection (likelihood of having an account) and the outcome (likelihood of using the account to save) equations.  This is especially problematic when estimations are conducted using small samples. Sartori (2003) proposes an alternative estimator that assumes that errors are near identical across equations to obtain more precise estimates. In unreported regressions, we applied the Sartori method. We found that the results based on the Sartori estimator are almost identical in size and in significance to those we obtain when using the probit Heckman-type model (without weights).  Our challenge is that the Sartori estimator does not allow for the use of weights which is very important when using individual-level survey data. Hence, given the similarity of the results and considering that we have a large sample, we prefer to estimate the Heckman-type model with two binary dependent variables using maximum likelihood.

16
that new data approaches meet minimum requirements to establish their validity and dependability, relative to extant conventional approaches.

Since FPCA was implemented in two (2) discontinuous phases, there were two short gaps (4months and 6 months) in the temporal trend of the crowdsourced data within the 3-year period (Figure 3a), yet the data trajectory reflects major shifts in prices. This comparative assessment provides compelling evidence that suggests both validity and complementarity of crowdsourced and AI-estimated price data under demonstrably limiting geographic context. At the first level, the magnitude of agreement between the prices that were curated through both data sources transcended initial expectation. As new efforts emerge to apply crowdsourcing approach to generate high-frequency and large volume of data (Manners, et al., 2022; Minet, et al., 2017), there remains a lingering and prevalent notion that citizen volunteers are unlikely (or incapable) to submit credible data that can be as reliable as data from trained enumerators (Zeug, et al., 2017). During the period, total of 2,355 and 102,842 individual price datapoints accrued to ground truth enumerators and volunteer crowd, respectively, within the focal region. Both datasets were originally conveyed with different pre-defined local packaging units and market segments, however majority of the datapoints (72%) were submitted from retail-based market sources. The strong agreement between both price dataset obviates doubts regarding the usefulness and validity of high-frequency data sourced directly from citizens or market actors.

Beyond the previously highlighted limitations of new generation data imputation methods, AI-based algorithms can be constrained by paucity of training data to consistently validate model outputs. Due to limited availability of food price data for model calibration in fragile contexts, it is within reason to assume inherent unstable model behaviors for northern region of Nigeria, subject to intrinsic model robustness, which may lead to imputation of egregious price data outputs, especially over a long timeframe, characterized by multiple seasons. Note that the period was ostensibly defined by a market-disruptive shock of calamitous proportions, the COVID-19 pandemic
calculated under various assumptions. Recall that for all States (non-judicial) stamp duties collections in 20002001 totaled Rs. 100,782.3 million. Assume that the average tax rate applied to these transactions in the different States was 15 percent, so that the total declared value is Rs. 671,882.0 million. Assume also that the fraction of these transactions subject to the capital gains tax ranges from 25 percent to 100 percent. Then, under different assumptions about the extent of undervaluation, the lost capital gains revenues can be calculated. These losses are shown in Table 14.<sup>33</sup> As before, these losses are only suggestive, but they indicate that undervaluation of transaction value has a major impact that cascades beyond its effect on stamp duty collections. They are also likely to considerably underestimate the true revenue loss. An implicit assumption in the calculations is that capital gains taxes are paid on the reported value of the transaction; however, it is unlikely that these transactions are actually declared for capital gains taxation. If these transactions are not in fact reported for capital gains taxes, then the revenue loss is even greater.

|**Table 14. Forego**<br>|**ne Capital Gai**<br>|**ns Revenues, 2000**<br>|**-01 **(in Rs.millions)<br>||
|---|---|---|---|---|
|**Percent**<br>|**Percent of B**<br>|**ase Subject to Capi**<br>|**tal Gains Tax**<br>||
|**Undervaluation**|**25%**|**50%**|**75%**|**100%**|
|10%|3732.7|7465
# **1. Introduction**

On paper, many countries have stringent regulations governing the hiring and firing of workers. For instance, there are almost 90 countries in which a firm is required to pay ten weeks or more of salary as severance pay when dismissing an employee for redundancy (World Bank, 2016). Such regulations are the subject of a growing body of research which, like research on other business regulation, is based largely on _de jure_ measures. However, survey data suggest that the enforcement of business regulations in low and middle income countries is often weak and uncertain (Hallward‐Driemeier and Pritchett, 2011). Is this poor enforcement related to weak enforcement capacity, and does it matter to how firms respond to regulations?

The Russian Federation’s labor code provides a good setting for examining this question. Its provisions governing hiring and layoffs are strict by international standards. While the labor code is the same countrywide, there is evidence that government capacity to enforce it varies across the regions of the Federation. For example, the number of labor inspectors per hundred thousand covered employees varies from a minimum of 11 to a maximum of 50 across regions. Similarly, the capacity of courts to handle labor disputes also varies across regions (Gimpelson et al., 2010; Gimpelson and Kapeliushnikov, 2008). In general, regulatory enforcement is weak in Russia; for example, its score in the World Justice Project’s 5 “Effective Regulatory Enforcement Index” is 0.5, while most high income countries have a score near 0.8.

In order to test if enforcement capacity matters to firm behavior, we draw on a fundamental theoretical prediction about the effect of regulations that raise the cost of hiring and firing workers. The prediction is that such rules should reduce employment adjustment in response to shocks, not only decreasing the number of layoffs during upturns, but also dampening hiring in upturns because of direct hiring costs and the potential cost of having to lay off newly‐hired workers in
23

# **Summary and Concluding Observations**

Stacking of electricity and fuels is common in many countries, resulting in as many as 158 distinct patterns of household energy use identified in one country studied. Because the recall period for expenditures on energy is almost always one month, use of some forms of energy and especially LPG is not necessarily accurately captured. Asking about primary sources of energy for lighting and cooking is important and informative but reveals little about multiple fuel use. To understand the degree of fuel stacking, adding one more question on all forms of energy used may provide valuable information without adding an undue burden on data collection. For example, the question about the primary source of energy for cooking may be followed immediately by a question about which energy items in the list of options for cooking had been used by the household in the recent past, perhaps covering the previous 12 months to catch seasonality.

Almost all LPG users also use electricity. There are several reasons. First, the absence of grid electricity is emblematic of inadequate infrastructure in general. Reliable LPG supply requires connection to paved roads, but those living in areas without grid electricity are less likely to be living near well-connected paved roads and hence less likely to find LPG outlets nearby. Those not using electricity tend to be poorer, again making them less likely to use LPG. In addition, households prioritize electricity consumption over cooking fuels (Kojima 2021a) so that those who can afford LPG are likely to choose to pay for electricity first before adopting LPG. In Sub-Saharan Africa, where electricity and LPG may become two main energy sources for achieving universal access, expansion of access to LPG will likely go hand in hand with expansion of reliable electricity services.

More than half of the population in all but five of the 24 countries studied used wood, charcoal, kerosene, and other solid fuels as primary cooking fuels, with wood and charcoal most frequently cited. A recent analysis of the Multiple Indicator Cluster Surveys found that one form of energy dominated clean cooking
grow on average by an additional 2.2 percentage points and East and West African countries by an additional 1.6 percentage points (Calderón and Servén 2010a). Similarly, if each Latin American country would match the average level of infrastructure observed among non-Latin American middle income countries (such as Turkey or Bulgaria), growth in Latin America would rise approximately by 2 percentage points per year (Calderón and Servén 2010b).

35. The Chinese experience illustrates the benefits of infrastructure investments. Between 1990 and 2005, China invested approximately US$600 billion to upgrade its road system. The centerpiece of this investment was the National Expressway Network, which, spanning 41,000 km, was designed to eventually connect all cities with more than 200,000 inhabitants.<sup>23</sup> Only the U.S. Interstate Highway System, with a length of 75,000 km, exceeds its length. Roberts, Deichmann, Fingleton, and Shi (2010) show that aggregate Chinese real income was approximately 6 percent higher than it would have been in 2007 if the expressway network had not been built. Using annual data for the period 1975 to 2007, Sahoo, Dash, and Nataraj (2010) estimate that the output elasticity of infrastructure investment in China is around 0.2 to 0.41 percent. The authors conclude that China’s infrastructure investment strategy was successful since it was embedded in an overall economic policy that focused not only on improving physical infrastructure, but also on enhancing private sector investment and human capital formation.

# **IV. A Global Infrastructure Initiative – For the Benefit of All**

36. The gap between the demand and the supply of infrastructure services is large in developing countries, as it is difficult for developing countries to raise significant amounts of long-term financing, which is required for infrastructure investments. Putting a price tag on this gap requires making heroic assumptions as comprehensive and reliable data, which is required to calculate this gap, is unavailable.<sup
inequality. We have seven available two-year periods: 1987-88, 1989-90, 1991-93, 1995-96, 1997-98, 1999-00, and 2001-02.<sup>31</sup> In all specifications we include the core set of village covariates (controls for initial mean log income, education, land, dependency ratio, composition of village income, geography, and province dummies), but we also add period dummies. We address the dynamic panel data issues through the use of a variety of estimators, and discuss details in Appendix 2.

The key results are presented in Table 5. We vary the estimation in two main dimensions: First, by estimator, and second by the length of the difference. Longer differences may be more robust to highfrequency variation of inequality driven by noise, and may better capture the “long run” effects of inequality on growth.<sup>32</sup> We also show results for both measures of inequality used in the cross-section analysis: the Mean Log Deviation, and the 90-10 ratio. In the first column we report the pooled OLS estimates of Equation (4), with no account for fixed effects but with standard errors clustered at the village-level. The estimated effect of inequality is negative, but very small, and statistically insignificant. Growth from one period to the next is unrelated to previous inequality when we pool growth and inequality from the entire period. In the second column, we take first-differences to sweep out the fixed effects. Whether we take short (one period) or long (up to four period) differences, we find no significant effect of growth on inequality. If anything, the effect is positive, though insignificant, as one might expect if growth caused inequality. Accounting for dynamic panel data issues does nothing to change the conclusion: when we difference to eliminate the village fixed effects, there is no relationship between inequality and growth.

On the surface, this suggests that unobserved village-level heterogeneity may be driving our previous results. However, the evidence is more complicated
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Challenges**

Niger still faces tremendous challenges in extending access to ICT. Barriers include low incomes, relatively high prices, lack of electricity,<sup>15</sup> and a large, landlocked landmass. Niger, however, has a good platform to grow the sector with a partly private incumbent operator, a second national operator, and a competitive mobile market.

**Figure 17. The efficient market gap in Niger—highest in the region**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient market gap Coverage gap<br>Percent of population<br><!-- End of picture text --> _Source:_ Mayer and others (2009) using GSM coverage figures as of 2005. _Note:_ Existing access represents the percentage of the population covered by voice infrastructure as of the third quarter of 2006.

Efficient market gap represents the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets. Coverage gap represents the coverage gap—the percentage of the population for whom services are not viable without a subsidy.

With a per capita income of $300 in 2008, Niger is the sixth-poorest country in the world.<sup>16</sup> Although the mobile market is competitive, prices have not dropped as sharply as expected, and around half of rural communities are still without access to telecommunications services (Adamou 2009). Prices for fixed, mobile, and Internet services are all above the regional average (table 1). Estimations suggest that to achieve universal mobile coverage, around 14 percent of the population would need to be subsidized (figure 17
data source as the Longest Reasonable Series (LRS). In case the maximum CDP come from more than one data sources, I pick the one that corresponds to the most recent data. The LRS serves as the skeletal for including or excluding data points from other sources.

Next, I define an objective criterion to determine whether to include a specific data point or not. The motivation behind designing this criterion comes from that the fact that the movement of workers across sectors is a relatively slow process and it is unlikely that there would be large variations in sectoral shares year over year. Hence, inclusion of data points that amount to large deviations should be avoided. Yet, the process may be faster in some countries compared to others with the speed of transformation depending on several factors including where the country is at with respect to its development path. This means that using a common threshold for deviation across countries may not be the best strategy. To overcome this hurdle, I use different thresholds across countries where these thresholds are selected based on changes observed in the LRS. Specifically, the steps that determine the inclusion of a data point are as follows: 44
rates and micro level indicators based on information from labor surveys (ILO 2008). The ILO put forward profiles on several countries reporting these indicators.<sup>5</sup> The OECD also offers a valuable proposal based on microdata with some indicators presented at the aggregate level, such as average earnings (Cazes, Hijzen, and Saint-Martin 2016). More recently, the Inter-American Development Bank has developed a methodology to measure job quality in the Latin America and Caribbean region that relies solely on macro level indicators (Busso et al. 2017). Leschke and Watt (2014) propose a JQM for 27 countries in the European Union using a combination of indicators defined at the individual level and aggregate measures on wages, collective bargaining coverage, and trade union density. One of the challenges with these measures is their inability to uncover heterogeneity in job quality across sectors and groups. They thus cannot help identify vulnerable subpopulations or study determinants of job quality in developing countries.<sup>6</sup> Researchers have addressed these challenges by constructing measures of job quality based exclusively on microdata from labor force and household surveys. Many of these studies focus on a single country or on a selected group of countries. Studies on developed countries include Australia (Ribar and Wooden 2020), Canada (Chen and Mehdi 2019), Spain (Díaz-Chao, Ficapal-Cusí, and Torrent-Sellens 2016), and the United States (Horowitz 2016). Olsen, Kalleberg, and Nesheim (2010) explore perceived job quality in four dimensions in West Germany, Norway, the United Kingdom, and the United States. The literature on developing countries includes Poland (Lewandowski, Góra, and Lis 2017), Türkiye (Del Carpio, Gruen, and Levin 2017), and several papers on Latin American countries, including Brazil (Huneeus et al. 2015), Chile (Huneeus, Landerretche, and Puentes 2012; Sehnbruch 2006), Colombia (Gómez-Salcedo, Galvis-Aponte, and Royuela 2016), Ecuador (Villacís and Reis 2016),
demonstrated, collective rural-urban water management is indeed possible, but there are many other instances where the use of water is not collectively governed.

Tourism and recreation are other activities linking urban and rural communities, whereby the sites of outdoor recreation are often located in rural areas and small towns while the demand for leisure is concentrated in urban counterparts. Without collaborative governance, rural areas would have little incentive to protect the environment for urban demand for recreation, creating a potentially large environmental inefficiency. Even if the regulation of these activities is developed at higher levels of government implementation is local.

A research agenda that examines shared interests and collaboration among places is an important basis for development. Future research should focus on the institutions providing services, employment, and civic participation. It should examine how relationships between places develop over time, how power is distributed, and how inter-place spaces are governed (including the roles of both public and private actors). A major question is whether multi-scalar governance is democratic. Since governance typically adheres to municipalities, the multi-scalar model embedded in the rural-urban continuum transcends such boundaries raising the question of responsiveness and accountability. Research should explore how the “soft space” approach may obscure power inequalities within and between places that undermine democracy.

A variety of methodological approaches will be needed including grounded case studies with a mix of qualitative and quantitative data, and analysis of secondary data aggregated into substantively meaningful spatial categories. For example, the development of the RUCC in the US informs the design and implementation of a wide range of social and economic programs. In the institutional realm, for example several scholars have used the RUCC framework to examine ideological and political differences across the rural-urban continuum (Kelly & Lobao, 2019; Scala & Johnson, 2017). The URCA global data set facilitates this type of analysis in other countries where data of this kind are not readily available.

The challenge of a new research agenda lies in avoiding the trap of going from one oversimplified categorization to another. The URCA approach provides a broader classification that