rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Motorbike-based ride-hailing services have grown exponentially in many of the heavily congested cities in the developing world. While this growth has expanded the mobility options of urban residents, it has also prompted fears that the convenience and low prices of these services are undermining the viability of public transport investments, with potentially negative repercussions for traffic congestion, greenhouse gas emissions, local air pollution levels, and, through these, the health and productivity of a city’s residents. Against this, however, it has also been argued that, far from being substitutes for public transport, ridehailing services may, in fact, be complements due to the first- and last-mile connectivity that they provide. A trip from, say, the workplace to home that involves public transport for the main portion of the trip plus use of a ride-hailing service for the first and/or last mile of the trip may offer a more convenient, less costly, and/or less polluting transportation option than was hitherto available.<sup>1</sup> In this paper, we empirically investigate whether ride-hailing services and public transport are substitutes or complements in such a developing country city context. In particular, we examine the impacts of the staggered opening of Jakarta’s mass rapid transit (MRT) system on the use of ride-hailing services in the immediate neighborhoods of newly opened MRT stations. In doing so, we take advantage of (anonymized) spatially very fine-grained daily ride-hailing activity data from _Gojek_ , which, together with _Grab_ , dominates the Indonesian ride-hailing market, as well as daily tap-in and tap-out data from Jakarta MRT.

Jakarta officially opened its first MRT line, which consists of 13 stations, on March 24, 2019. This opening, however, was staggered in the sense that it was immediately preceded by a 12-day limited public trial during which no charge was made for use of the metro, but ridership was capped. This was followed by a further one-week period during which users could continue to ride the
of formal child care. However, the successfulness of such a policy depends crucially on the extent to which increased public supply crowds out existing private supply, which in turn depends on how private preschool providers respond strategically to increased competition from public providers. Do private providers respond by lowering their prices and competing more aggressively in all market segments? Or do they react by increasingly targeting higherincome households that do not find public providers attractive? If an expansion in free-ofcharge public supply simply induces households to switch from high-quality private suppliers to lower-quality public centers, negative impacts on child development cannot be excluded.

In the present paper we analyze these questions empirically by examining the effect of changes in the supply of public child care services on private child care provision in Brazil. We use rich municipal-level panel data covering the period 2000-2006 to analyze the effect of increased availability of public child care centers on the quantity of private supply, as measured by private enrollment rates and number of private centers. We also check whether increased public supply has any impact on the quality of private child care, as measured by group size, teacher qualification and quality of infrastructure. To plausibly identify exogenous variation in public supply, we exploit unique features of the allocation mechanism of federal transfers to municipalities in Brazil, where the transfers received by local governments exhibit a non-linear and non-monotonic relationship with given population estimates. Results from a regression-discontinuity design reveal that larger federal transfers to a given municipality lead to a significant expansion of public preschool services (as measured by the number of municipal centers and enrollment), but show no evidence that such an expansion crowds-out private enrollment. If anything, the results suggest that private enrollment also increases. We also find no evidence of adverse effects on the quality of private provision.

To guide the interpretation of our empirical results, we develop a simple theoretical model of vertical differentiation, analyzing the optimal pricing response of a private child care provider to entry of a public competitor. In the model, public preschool education is free of charge (zero price)
|0.347***|0.004|
||(4.43)|(0.03)|
|SAS|0.368**|0.390***|
||(2.52)|(2.85)|
|SSA|0.493***|0.347**|
||(4.03)|(2.12)|
|Observations|328|162|
|R-squared|0.654|0.663|

Robust t-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies and a constant term were included in all specifications, but not reported. The default region is Middle East & North Africa (MNA); the regional dummies represent East Asia & Pacific (EAP), Europe & Central Asia (ECA), Latin America & Caribbean (LAC), South Asia (SAS) and Sub-Saharan Africa (SSA).

If bonds under U.K. law are indeed mispriced at launch, then it is natural to ask whether the spread difference disappears eventually. In this context, one would expect the coefficient associated with governing law to become insignificant if a similar cross-sectional regression were successively run at 60, 90, 120 and 180 days after launch. The results are shown in Table 6. Using secondary market data from Bloomberg,<sup>10</sup> we tracked each bond spread during the first six months. While some control variables remain constant throughout this period (bond maturity, tranche size, governing law, GDP growth before launch, and year and regional dummies), some change either periodically (S&P ratings) or even daily (VIX). We used the sovereign ratings instead of individual bond ratings on the right-hand side. We can see that the governing law coefficient is not only significant, but also relatively steady. So, the evidence indicates that the initial spread
There is a high correlation between governance quality and per capita income -- OECD countries dominate the top governance category and none are in the two categories with the poorest governance. Conversely, there are no African countries in the top governance category. The correlation between the index of governance quality and per capita GDP appears to be much stronger than its weight of 6.7% (through the HDI) would suggest. This high correlation between governance quality and per capita GDP raises the question of causality. If demand for high governance quality is driven by high per capita income, then an index of governance quality will simply reflect per capita income. Or, if there are necessary pre-conditions for high per capita income, such as outward economic orientation and work force education, then high per capita income will reflect high quality governance.

It seems likely to us that causality runs both ways -- some components of governance do enhance the likelihood of higher per capita income and higher per capita income does increase the demand for higher quality governance. The challenges for those postulating a relationship between governance quality and income that only runs in one direction are the outliers -- why, for example, is Ireland's per capita income so low given its high governance quality or, if causality runs the other way, why does Czech Republic have such good governance given its per capita income?

The good performance of European countries is not limited to Western Europe. The Central and East European countries (as well as Latin American countries) have combined improvements in citizen participation and economic management with relatively high marks for social development. Strong geographical patterns appear in all parts of the world -- European countries govern well, African and South Asian countries govern poorly, Latin American and East Asian countries are somewhere in the middle. One possibility these patterns raise is that countries' standards for governance quality may be influenced by the performance of their neighbors.

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# **VI. Census Towns and Access to Basic Services**

As mentioned above, one potential advantage of being an urban settlement concerns the funding available with urban local bodies for the development of urban services. The available literature is again limited on the level of urban services in Census Towns and on the capacity to design, operate and maintain networked services. There are a number of papers comparing variations in services from metro-cities to small towns that show a systematic decrease in the level of basic services as one moves down in the size of towns (Dubey and Gangopadhyay, 1999; Raghupathi, 2005) , with marked inequalities in some specific states, two of them (Bihar and Odisha) of interest to this research (Kundu et al., 1999). Small towns are therefore described as deprived of funds and as suffering from a weak governance capacity<sup>5</sup> . Nevertheless, these papers do not differentiate between CTs and STs. The only available paper that addresses the question that the administrative status plays in service provision in a comparative manner is Mukhopadhyay (2017). He uses a unique constructed data set of matched settlements from two census periods and defines indicators related to access to piped water supply, and improved sanitation and finds limited differences between STs and CTs both in the levels of service and in their growth over 2001-11 (Table 6). But, CTs close to larger towns have better improvements in levels of service.

**Table 6:  Comparison of Census Towns and Statutory Towns for Select Services**

||Level of|amenities i<br>In-|n 2001||Change|in Level of a|menities 200|1-11|
|---|---|---|---|---|---|---|---|---|
|Amenity|Tap<br>Water|house<br>Tap<br>Water|In-house<br>
|
|Altman Z-score|8.0|4.6|4.9|4.4|9.1|5.0|1.1|5.9|5.4|
|NumberofObservations*|11|48|24|9|10|7|4|51|164|

Interest coverage shows the ability to cover interest expenses using the current period earnings. Interest coverage is the highest in financial firms, followed by trade and construction. It is lowest in industry- only 2.8 on average, followed by transportation and tourism and hotels.

> 5 In ASE data there is no line item such as total debt. We have calculated total debt as total liabilities minus accounts payables, which represent trade credit.

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bank’s return on assets has to drop below its expected value before equity is depleted and the bank is insolvent (see Roy, 1952, Hannan and Henwick, 1988, Boyd, Graham and Hewitt, 1993 and De Nicolo, 2000). Thus, a higher z-score indicates that the bank is more stable.

While the z-score has been used widely in the financial and non-financial literature, it is subject to several caveats.<sup>10</sup> Specifically, it might underestimate banking risk for several reasons. First, it measures risk in a single period of time and does not capture the probability of a sequence of negative profits. Second, it considers only the first and second moment of the distribution of profits and ignores the potential skewness of the distribution (De Nicolo 2000). On the other hand, this measurement bias is less of a concern if it is uniformly distributed across banks and countries. A third concern is the reliance of the z-score on accounting data whose quality might vary across countries. Specifically, several papers have shown the tendency of firms to smooth reported earnings over time and that the degree of earning smoothing varies with the degree of institutional development (see, for example, Leuz, Nanda and Wysocki (2003)). This, however, should bias our results against finding a significant relationship between measures of deposit insurance and bank failure resolution and bank fragility. As an alternative method, other authors have relied on stock market data to compute bank risk as a put option on the value of the bank’s assets (Laeven, 2002, and Hovakimian, Kane and Laeven, 2003). Relying on stock market data, however, reduces our sample to a small set of large, listed banks in countries with stock markets, and we therefore decide to use z-scores that can be calculated for all banks instead.

> 10 Note that this z-score differs from z-score developed by Altman (1968). The Altman z-score is a predictor of corporate financial distress based on financial
transition perspective, both in terms of financial risk and in the potential for CGSs to support those sectors through the energy transition: We furthermore compare the transition risk of individual CGSs _k_ by summing the sectoral transition risk score over all _n_ sectors:

**To assess the vulnerability of CGSs to physical risks, we map spatial exposure data at the second administrative level to vulnerability data from Think Hazard.** We obtain the full data set from Think Hazard, covering wildfire, coastal floods, river floods, extreme heat, drought, earthquakes, and landslides. We exclude earthquake risk since it is not climate related. We then map our data to NUTS 2 by combining administrative regions in the Think Hazard data set. For those regions where there is more than one administrative region per NUTS 2 region, we average the Think Hazard score. Next, we sum the regional exposures in our sample per risk category in Think Hazard (high, medium, low, and very low risk). For missing Think Hazard data we use the no data label. For coastal flood we furthermore introduce a “non-coastal” category representing zero risk.

**We also map sectoral exposures to a set of physical risk sensitive sectors.** We use two classifications for sectoral climate change sensitivity. The first is based on Lazo et al. (2011) who analyze the weather sensitivity of US economic sectors. Agriculture, mining, manufacturing, finance and insurance, and real estate are identified as being highly impacted by annual weather variability. For these sectors year-to-year variations in weather characteristics are found to affect economic performance by 10 percent or more. A second classification is based on a set of sectors identified by Skougaard et al. (2017), who capture sectors that are vulnerable specifically in the European context. Vulnerable sectors include agriculture, mining, utilities, transportation and storage, accommodation and food, professional, scientific and technical activities, and human health. The detailed mapping can be found in Annex 3.

**As a final
are the estimated control-group mean and standard deviation for outcome _k_ in family _K_ . Our estimates for these families represent standard deviation changes relative to the control group.

Following Kling et al. (2007), in case _yik_ is missing but another sub-component of the family is measured, we impute the mean from the same treatment arm and survey wave. Some sub-components, e.g., those that relate to childbirth, are only defined for a fraction of respondents. As such, we do not impute values when estimating treatment effects for individual sub-components. To demonstrate the imputation is innocuous when looking at effects on families, we follow Kling et al. (2004) and Casey et al. (2012) and aggregate treatment effects across the sub-components of each family using seemingly-unrelated regressions (SUR). These results (reported in Appendix Section D.2 generate nearly identical inferences.

Below we describe each family; Appendix Section B.1 provides additional detail on each family’s constituent outcomes.

1. **General utilization** measures the number of episodes in which individuals seek care at a western clinic, including in response to four of the most common health needs relevant for primary health units — childbirth in the past year, antenatal or postnatal care, vaccination, or any illness or injury in the past month — as well as a residual category of any other type of consultation in the past month. The residual category helps generate a comprehensive measure of utilization; we note, however, that regular health check-ups are not common in our setting, so nearly all utilization occurs in response to specific health needs. Utilization of western clinics reflects the decision to seek care at western-style medical facility (overwhelmingly at a government-run clinic given the lack of NGO-run or private clinics), rather than visiting a traditional healer or spiritual leader or avoiding any type of care.

2. **Maternal utilization** is measured among women who gave birth in the year before the endline survey. The family includes two outcomes: an
the benefits in line with the 2020 income data, which is part of the CEQ fiscal incidence analysis. We consider the existing MSA rules and replicate the total number of beneficiaries based on administrative data. This scenario illustrates that the MSA benefit reaches only 11.5% of households in the first decile (Figure 5, Panel a) and extends to just 2.9% of poor households. Regarding benefit incidence, the MSA benefits are concentrated in the first decile (Figure 5, Panel b). When looking at adequacy, the benefits provided by the MSA account for just 4.4% of the pre-fiscal income (Market Income plus pensions) for the first decile, indicating a relatively low level of generosity (Figure 5, Panel c). In terms of its impact, the MSA shows no significant effect on income inequality, and its marginal impact on poverty reduction is relatively modest, with a noticeable reduction in the poverty gap by only 0.11 percentage points.

**Comparing these findings to previous assessments, the MSA's coverage for the first quintile in 2020 mirrors the previous estimate.** Although the level of generosity is relatively low, it does exhibit a slight improvement compared to estimates from 2013/2014 (World Bank, 2020). These results underscore the need for further evaluation and potential adjustments to the MSA scheme to enhance its effectiveness in addressing poverty and income inequality.

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firms. We use the unique microdata to study how the firm credit distribution aggregates into macroeconomic outcomes.

We find that the programs give different incentives to firms. Riskier firms are more likely to obtain a credit guarantee loan, while risk is not associated with the likelihood of utilizing the employment program. On the other hand, firms facing negative sales growth are more likely to use the employment program. Likewise, firms subject to lockdowns are more likely to use the employment program, whereas lockdowns seem irrelevant for the credit program. Overall, this evidence suggests that firms internalize the opportunity cost of using the employment program, which covers the salaries of employees who cannot work due to declining sales or restricted local mobility. Sending employees home imposes a cost on the firm because it has to reduce its operations. In contrast, the credit program has no such opportunity cost, leading to a much broader adoption. However, its low interest rates and government guarantees appear to be related to riskier firms using the credit program.

The fact that the credit and employment programs coexist has consequences for their adoption. Both programs are used jointly, i.e., increasing the probability of using one program increases the chances of using the other. Firms facing both positive and negative sales growth and riskier firms are more likely to use both programs, while the effect is stronger for firms facing a negative sales growth shock. Also, using both programs mitigates the increase in firm indebtedness as firms need less credit when they receive employment benefits. In addition, using the employment program might signal firms that are in distress or that need to scale down operations. As a result, the availability of the employment program increases the quality of borrowers by helping banks screen firms for the credit program.

In terms of risk and selection at the micro level, both the demand and supply sides play a role in the credit allocation in equilibrium, with demand factors driving the expansion of indebtedness. The allocation of credit is characterized by a shift in lending toward riskier firms
the RDP and later the BNG program, delivered at a rate of approximately 10,000 per year in the 1990s and early 2000s, declining to about 5,000 per year by the late-2000s due to budget constraints and diversification to in situ upgrading of informal settlements.<sup>38</sup> We consider three scenarios for future public housing provision. In the first scenario (entitled ’business as usual’ or BAU), we assume that the provision of public subsidized housing follows the current pace of 5,000 dwellings per year. In the ’low’ scenario, we assume that construction of public housing is slowed down to a pace of 2,500 per year from 2019 onward. In the ’high’ scenario, we assume that construction of public housing is accelerated to a pace of 10,000 dwellings per year starting in 2019. The sites for future public housing replicate the pipeline of projects known to the City of Cape Town starting with ’short-term projects’, before considering ’long-term projects’ (see map of Figure F.13).<sup>39</sup>

> 37The demographic growth projections used in the model correspond to the base projection used by the Cape Town metropolitan authority as of 2019. The 1.77 million households in 2040 will correspond to a total population of 5.3 million inhabitants.

> 38Although government housing estates have featured in Cape Town since at least the 1920s (Wilkinson 2000), today, apartheid-era "Council housing" only includes 43,500 rental units, 21,000 homeownership dwellings, 11,000 hostel beds and 11 old-age home complexes (City of Cape Town Integrated Human Settlements 5-year Strategic Plan 2013/2014 Review).

> 39The information on projects is extracted from a 2015 data set provided by the City of Cape Town, which gives the location and number of dwellings of future RDP/BNG projects, corresponding to a total of 255 _,_ 000 dwellings to be built. We assume that projects indicated as
and economic development in Sierra Leone. The specific objectives are to: (i) Ensure an increase in the sustainable productivity and production of rice in Sierra Leone; (ii) Promote appropriate post-harvest handling, processing and marketing of rice; (iii) Develop appropriate infrastructure for rice production and marketing; and (iv) Improve the capacity of stakeholders and institutions involved in rice sector.

The strategy for increasing rice production is two-pronged: (a) increase in area cultivated, mainly in the lowlands where there is much underutilized capacity, and (b) increases in productivity per unit area in all ecosystems. Area expansion will mainly be in the IVS due to its existence in all parts of the country coupled with its potential for sustainable production. The government’s goal is to achieve rice self-sufficiency. This strategy targets a land area of 830,000 hectares and an increase in the average rice yield/ha to 2 mt/ha to realize the government’s self-sufficiency goal.

# **V. Results**

The primary purpose of the rice sector model is to simulate the effects of policy interventions, most notably the impact of the removal of the ECOWAS tariff waiver. It is therefore important to ensure that the model captures the salient features of the Sierra Leone rice sector and that the base year results are validated against the actual data generated from surveys and other sources for the selected base year. Since 2016 is the most recent year for which there are yield and production data across the five ecologies, it is adopted as the base year. Also, since it is possible that rice farmers may not behave optimally in their choice of production pattern and the use of inputs (see Kutcher and Scandizzo, 1981) and the fact that rice price has substantially increased between 2016 and 2018, the model is also run for 2018 in a more normative vein to simulate the effect of using more lands in the riverain and mangrove ecologies, given 19