rafmacalaba/datause-encoder-data huggingface dataset:
provide a wide range of information on firms’ production capacity and their BE. While the firms were requested to provide information as of 2004, for many accounting measures, information from up to three years prior was also collected.

The fifth data source concerns intellectual property (IP) cases in China. The data set comes from a collection of nation-wide online IP cases from 2005 to 2013. We use it to construct several measures on IP protection in China at different levels. In particular, we construct a measure on the enforcement quality of IP rights by using the proportion of IP infringement cases won by the plaintiff in the local courts (Ang et al., 2014).<sup>10</sup> To construct provincial level indicators on entrepreneurship and BE, we match most of the data at the provincial level and then average firm-level indicators in the same region.<sup>11</sup> **4. Main results**

# **Evolution of the role of the state and the BE**

Throughout the last two decades, the government has remained crucially important for both setting up the rules and keeping control over resource allocations despite the rapid expansion of market institutions in China, implying a dual-track feature that characterizes China’s economic reforms **.** As shown in Figure 1a, both the shares of state ownership in industrial output and industrial assets have declined substantially since the mid-1990s, thanks to ownership reforms in the state sector (Xu et al. 2005). Nevertheless, state ownership still maintains a considerable share in industrial sector until 2012, accounting for about 30% of industrial output and 40% of industrial assets. Moreover, both the share of foreign ownership in industrial output and industrial assets rose, especially after China's entry into the WTO, and dropped slightly after the global financial crisis in 2008 (Figure 1b). The dual-track development pattern also emerges in the regional distribution of different types of ownership. Compared with more marketized coastal regions, state ownership plays a greater role in inland regions due to the stronger government control of economic resources (Figures 1c and 1d). We now turn to the evolution of the BE. The quality of property rights protection is

> 10 The assumption is made here that cases are randomly distributed across regions in terms of legal strength, which then implies the positive relationship between plaintiff win rate and the IP protection level in the region. 11 Ideally, we would like to make inference based on sampling weights. Unfortunately, these sampling weights are not publicly available. As a result, we rely on simple averages for constructing most of our institutional indexes.

16
18 The table partly reflects that the two countries are at different stages of development. This is hardly surprising because Ghana enjoys a per capita GDP of about $1,800, three times higher than that of Mozambique (World Bank, 2014). Households with expenditure levels below the national poverty line harbor one-quarter of the Ghanaians as compared to more than half of the Mozambicans. By the same token, child malnutrition is much lower in Ghana (28% stunted in 2008 as compared to 44% in Mozambique 2009).

Yet, both countries also share common challenges, as for example the low literacy rates of the household heads indicate. Likewise, the share of food in total expenditures is close to one-half in both countries and reflects food-based economies. Furthermore, the much higher dependence on homegrown foods in Mozambique (61% of the food as compared to 18% in Ghana) reflects the prevalence of rural conditions. Almost 70% of the population of Mozambique lives in rural areas as compared to less than 50% in today’s Ghana.

The differences in food availability are also noteworthy. At an estimated consumption of slightly less than 1,750 Kcal per capita per day, food appears very scarce in Mozambique, even after accounting for underreporting (Alfani et al, 2012). On the contrary, food consumption in Ghana seems rather abundant at an estimated average of slightly more than 3,800 Kcal per capita per day, although here over-reporting may be an issue (GSS, 2014).

The food poverty line in the table is an estimate of the level of expenditure per capita per day that the household needs to meet the food requirements of its members, correcting for equivalence scales. The corresponding calorie cost concerns an average diet at the median price per calorie. Of course, because food consumption baskets are quite diverse in the various population groups and also the prices of foods differ at different locations, the actual cost of the observed diets shows a wide range.

The food poverty line is composed as follows. Letting qk be the amount calories that the k-th food item contributes to given requirements qfood with the corresponding price per calorie pk (Annex 1), the food line equals: where pfood is the calorie cost of the diet (see Table 1).
Assuming that the residential land area in a ring stays constant, the new ring population is found by multiplying the original population by the proportional change in the FAR/ _q_ ratio, which represents the change in density. This proportional change is computed using the FAR values in Figure 6 along with the assumed 7 percent city-wide increase in _q_ . Note that the original level of _q_ is not required for this calculation.

This exercise is carried out using data on the existing ring populations for Bangalore, which are shown by the dark columns in Figure 7. Applying the proportional adjustment from above yields the new ring populations, which are shown by the lighter columns in the figure. Populations in the last few rings in the city involve a further adjustment, which is explained momentarily.

Ring populations rise near the center of the city, falling farther from the center, and this pattern is easily understood. The substantial rise in the FAR values in rings 1−4 dominates the slight rise in _q_ , leading to an increase in FAR/ _q_ and thus in density. These density changes, which mirror the simulation results shown in Figure 5, lead to higher populations in rings 1−4. In the city's outer rings, the decline in FAR and the rise in _q_ reduce densities, again mirroring Figure 5, and ring populations fall. However, the population gain near the center dominates the declines farther out, so that the city's land area shrinks. While the edge resident lives in ring 12 in the existing city, the edge resident in the restricted city lives in ring 9. Thus, the edge resident is 3 km closer to the center following removal of the FAR restriction. This predicted reduction in _~~x~~_ provides the key to computing the welfare cost of the FAR restriction, a task that is carried out in the next section.

A population adjustment is needed in the outer rings, as mentioned above, because the growth pattern of real-world cities differs from the idealized model. Instead of filling in a uniform circle as population grows, development at the outer edges of cities is “ragged,” with growth 17
**Table 5.2 Growth of Annual Throughput, by Region**

|**Region**|**Growth**<br>**1999–2010**<br>**(%)**|**Compound**<br>**Annual**<br>**Growth Rate**<br>**1999–2010**<br>**(%)**|**Growth**<br>**2007–10**<br>**(%)**|**Compound**<br>**Annual Growth**<br>**Rate 2007–10**<br>**(%)**|
|---|---|---|---|---|
|Central America|302|12|-1|0|
|Caribbean|53|4|-24|-9|
|OECS|166|8|-18|-6|
|South America|542|17|4|1|
|**Total**|**306**|**12**|**-2**|**-1**|

_Source:_ Adapted from Sarriera and others (2013), based on authors’ primary data collection. _Note:_ OECS = Organization of Eastern Caribbean States.

# **The OECS’s average throughput of 10.6 TEU/hour in 2013 compares relatively well with the overall**

**Caribbean average of 11.4** , considering that, on average, Caribbean ports are much larger and have more capacity than the OECS ports. This average, however, hides a wide range of variability across countries. Grenada has the lowest average throughput per hour when compared with the other OECS ports, with a maximum TEU per hour that is half that observed in Vieux Fort (figure 5.1). Such averages also hide fluctuations in hourly throughput.

# **Throughput (as measured in TEU per hour) is highly volatile, not just in the OECS but in the region**

**in general, making averages difficult.** The reasons for this are not quite clear. In some cases, such as Vieux Fort, throughput variations can be attributed to crane and spreader outages; the delays in St. Lucia are attributable to unions. Throughput performance also points to other incongruences: the top two performers with the highest average TEU per hour are equipped with mobile cranes, but so is the one with the third-lowest average.

**What such averages do not capture is the inefficiency embedded in these ports.** Efficiency in the OECS is admittedly very low; for example, a typical container ship operated by ZIM will arrive at 6 a.m. and not leave until ten hours later, at 4 p.m. Because of variability, one measure of port performance may be the maximum monthly average container output achieved during the year. This would put Vieux Fort, a transshipment port, in the same league as other transshipment ports in the Caribbean (the Bahamas and Jamaica), but Castries, Kingstown, and St. George’s would clearly fall into the slow category, with just over 10 TEU/hour maximum throughput.

32
# **Table A5: Panel B – Nested Logit Regressions, 2005**

Log Odds ratio between Owning with and without a Mortgage

|**Country**|**parttime**|**unemp**|**retired**|**urban**|**suburban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|
|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_||||||||||
|Czech Republic|-0.04|0.06|0.24 ***|-0.01|||-0.03||-0.11|
|Estonia|-1.14|0.75|-0.27|-0.20|||0.22||-0.60 **|
|Hungary|-0.33|0.07|0.62 ***|0.10|-0.04|-0.68|-0.12|0.35|0.49 *|
|Latvia|1.74|1.21|0.67|-0.32||0.28|0.12|-1.04 ***|-0.66 *|
|Lithuania||-0.21||-0.06|||-0.52||-0.48|
|Poland|0.08|-1.21 ***|-0.15|-0.16|0.05|-0.26|0.39|-0.30 *|-0.07|
|Slovak Republic|-0.09|0.01|-0.15 *|-0.16 *|-0.14 **|-0.04||-0.01||
|_EU-15 and Cyprus_||||||||||
|Austria|-0.51 *|-0.27|0.59 **|-0.08|||0.32||0.16 **|
|Belgium|-0.59 **|-0.03|0.84 ***|0.08|0.23|0.71|0.51|1.03 ***|0.97 ***|
|Cyprus|-0.29|-0.55|0.19|-0.53 ***|-0.45 ***|-0.74|-1.12 *|0.30|0.65 **|
|Finland|-0.36|0.01|-0.41|0.13|0.11|3.05|2.99|0.93|0.88|
|France|-0.25|-0.03|0.10|-0.06|-0.24 *|1.11 ***|2.25 ***|0.44|0.56 ***|
|Greece|-0.04|-0.52|-0.18|0.00|0.01|1.35 ***|2.05 ***|0.50 ***|0.60 **|
|Ireland|0.44|0.03|-0.22|-0.27 *|-3.93 ***|-1.36 ***|-1.11 **|-0.27|0.00|
|Italy|-0.38 *|-0.54 *|1.27 ***|-0.87 ***|-0.76 ***|-0.07|0.73 **|-0.15|0.15|
|Luxembourg|-0.49 **|0.09|-0.04|-0.45 ***|-0.48 ***|-1.01 ***|-0.80 **|0.39|0.71 ***|
|Portugal|-0.47|0.34|-0.08|-0.36 *|-0.34|2.25 ***|2.75 ***|0.95 ***|1.14 ***|
|Spain|-0.27|-0.16|0.35 *|-0.33 ***|||0.37||0.95 ***|
|Sweden|0.16|-1.37 ***|-0.50|-0.35 ***|0.07|-0.79 ***|0.27|-0.05|0.03|
|United Kingdom|-0.68 ***|-0.51 **|0.18|-0.28 ***|-0.27 ***|-0.02|-0.26|0.30 ***|0.48 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2005 38
_-50 -_

# ANIx 3L BASc EARNINGS FuNcrIoNs

# BoUvia 1989

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.071|0.073|0.077|0.087|0.06i|
|Experience (EX)|0.040|0.046|0.028|0.053|0.032|
|Experience-squared (EX<sup>2</sup>)|-0.0005|-0.0006|-0.0004|-0.0007|-0.0003|
|Log Hours (H) Worked|0.514|0.301|0.594|0.234|0.726|
|Constant Term|3.278|3.726|2.994|3.656|2.919|
|R<sup>2</sup>|0.17|0.18|0.18|0.19|0.19|
|N|5,356|3,823|1,533|2,989|2,367|
|Mans||||||
|Earnings (Y)|364|402|270|361|369|
|S|10.1|9.5|11.5|8.7|11.7|
|EX|18|19|16|17|20|
|H|8.2|8.7|7.1|8.7|7.6|
|Var (Ln Y)|0.645|0.659|0.539|0.697|0.573|

Source: _Encuesta Integrada de Hogares (EIH), CEPAL._ Notes: The earnings variable (Y) is in bolivianos per month. H is the daily hours worked.

All coefficients are statistically significant at the 5% level or better.
# **Box 2: Findings for the Budgetary Dimension of Implementation Efficiency**

|**STRENGTHS**<br>|**CHALLENGES**<br>BudgetaryDimension<br>|**RECOMMENDATIONS**<br>|
|---|---|---|
|The insurance system is the main<br>funding source of HIV/AIDS care<br>services (both personal and<br>collective) for the whole population.<br>Despite its complexity, the<br>mechanism is well stipulated and<br>regulated.<br>The sustainability of the HIV/AIDS<br>response is dependent upon the<br>financial sustainability of the<br>insurance system.<br>The existence of a single funding<br>source avoids problems of<br>overlapping and/or under-financing.|Lack of differentiated budgets for<br>HIV/AIDS (funds are part of broader<br>SRH programs).<br>Lack of a system of financial<br>accounting that makes it possible to<br>track the flow of money in either<br>the budgetary flow or the<br>expenditure flow for the HIV/AIDS<br>response.<br>It cannot be determined<br>conclusively whether any specific<br>increase or reduction of funds is<br>needed.<br>It cannot be determined<br>conclusively whether the budgeted<br>money is devoted to the expected<br>services, or expenditures match<br>budgets.|Establish a system of financial<br>accounting for the health insurance<br>system that allows for a<br>disaggregated analysis of the<br>HIV/AIDS response, trough:<br>In the medium-term, establish the<br>capacity to single out HIV/AIDS<br>financial flows and costs.<br>In the short-term, conduct a new<br>study to measure expenditures on<br>HIV/AIDS (MEGAS: Medición del<br>Gasto en VIH/SIDA).|

# **Box 3: Findings for the Service delivery Dimension of Implementation Efficiency**

|**STRENGTHS**|**CHALLENGES**|**RECOMMENDATIONS**|
|---|---|---|
||Service DeliveryDimension||
|There is a well-structured network<br>of preventive, diagnostic,<br>treatment, palliative and support<br>services for people living with<br>HIV/AIDS.|The complexity of the insurance<br>system determines the national<br>response in terms of:<br>Geographical inequities and by type<br>of insurance regime.|Tackle the inequity problems and<br>barriers to access by simplifying and<br>improving the efficiency of the<br>social insurance system, promoting<br>the delivery of HIV/AIDS care at the<br>primary care level, and improving|
|The coverage level and the quality<br>of services have improved over the<br>years.|Problems of availability, access and<br>quality of medicine for human use<br>(poorly developed regulatory<br>framework).<br>Continuity of care within the<br>subsidized regime (it is especially<br>problematic).|the regulatory framework for<br>medicines for human use, trough:<br>Clearly identify and resolve the<br>fractures in the insurance system<br>that de facto cause problems of<br>access to services and<br>discontinuities in treatments, as<br>well as economic, family- and work-<br>related, and life disruptions.<br>Complete the transfer of the<br>associated insurance scheme to the<br>subsidized regime – ensuring the<br>sustainability and quality of the<br>latter.<br>Reorganize and transfer prevention,<br>diagnostic and treatment services<br>for HIV/AIDS towards the primary<br>care level. Make the most of the|

9
are surveyed each year. Thus, while plants in India may not be growing at the same rate as in developed countries such as the US, especially during their early life cycle, there is clear evidence that older plants in the formal manufacturing sector are larger than younger plants.

Third, we find that technological differences across industries shape establishment life cycles in India. We use the identification strategy introduced by Rajan and Zingales (1998) for classifying industries into financially dependent and financially independent depending on the extent to which firms can support their capital expenditures using cash flow from operations, based on the experiences of US firms in the same industries. We find that firms in financially dependent industries are larger at all stages of their life cycle. Thus, there is no evidence that financial dependence affects growth rates of established Indian firms relative to firms in industries that are not financially dependent.<sup>5</sup> Fourth, we find stark differences in firm life cycles in the formal and informal sectors: We find that older firms in the unorganized manufacturing sector in India employ fewer people than firms younger than 5 years old. This is consistent with recent findings in La Porta and Shleifer (2008) who show that informal firms look very different from formal firms in terms of size, productivity, and education level of managers and find little evidence that growth occurs by informal firms eventually becoming large formal establishments.

Fifth, we study the impact of institutional differences on firm life cycles in India. Despite considerable differences in financial depth across Indian states, we find the role of state-level financial development to be marginal in explaining firm life cycles in the broad population of firms and in most of the sub-samples we analyze. These results are robust to a number of checks including looking at just the firms in the right tail of the size distributions, across states with

> 5 Rajan and Zingales (1998) show that financially dependent industries grow _less fast_ than financially independent industries using a sample of developed and developing countries.

5
4 Second, though it is widely accepted that hand hygiene is important for reducing the spread of respiratory illnesses, and that it is important for reducing transmission of SARS-CoV-2, there have been limited efforts to map out the pathways for how handwashing can impact a reduction in severe COVID-19 cases. This is despite the use of hand hygiene scenarios that reduce transmission being incorporated into epidemiological models (Winskill _et al._ , 2020). In this study we seek to build on existing literature that sought to quantify the impact of handwashing on reducing the transmission of respiratory pathogens, and examine how that could have knock-on implications for the extent of severe COVID-19 prevalence.

Third, this paper relies on available data to map underlying risk factors of severe COVID-19 disease and potential impact of handwashing interventions in a way that can be used for guiding policy and planning interventions. This approach is particularly useful in data scarce environments, common to developing countries.

# Data and Methods

## Ethics Statement

<mark>This study was approved by the Medical Research Council of Zimbabwe. The approval number is MRCZ/E/302 and it was assessed as exempt. All data obtained and herein reported were anonymized prior to analysis.</mark>

## Study Area

In this paper we focus on Zimbabwe as a case study for the methodology being presented. Though there is an emerging collection of COVID-19 related studies within the African continent (Guleid _et al._ , 2021), the majority of studies thus far have been concentrated in Asia, North America, and Europe, while Africa focused studies are in the minority (Wang and Tian, 2021). With lower rates of vaccination on average than in other parts of the world, vulnerability to severe disease remains an important concern in countries like Zimbabwe. Spatial analysis methods of this nature can be taken as a useful model for similar contexts where vaccine prioritization may be important in public health planning. It is also a data scarce environment when compared to some of the countries studied more intensively during the pandemic, and thus can provide a useful example of how similar contexts can be studied under such circumstances.

## Data

The main data sets used in this analysis are: the Demographic and Health Survey (DHS) from 2015 in Zimbabwe; population density, night-time light intensity for 2015 and distance to nearest Open Street Map (OSM) road obtained via WorldPop (www.worldpop.org); and poverty Small Area Estimations (SMEs) from the World Bank at ward level (Masaki _et al._ , 2020).

The 2015 DHS was used to inform the creation of a hand-washing risk index drawing from data on access to hand-washing facilities with soap and water at the household level. Respiratory illness data was taken from the same DHS survey but only available for children under five years of age. Studies have indicated that children can often be the source of respiratory illness transmission to
Research Release (Tierney et al. 2011).<sup>8</sup> For project outcome ratings, we used the Project Performance Dataset (PPD), a consistent six-point project outcome score based on donor-reported outcome data (Honig, Lall, and Parks 2022). We aggregated from AidData to produce measures of total ODA per country-year, and used the PPD in combination with AidData to construct simple and sizeweighted averages of ratings for projects completed in a given year.

For World Bank projects we use a scraper to download the three main documents of every project - the project information document (PID), the project appraisal document (PAD), and the implementation completion report (ICR). Respectively, they contain the information available at the beginning and end of project preparation, and at project closure. The documents had already been subject to plain text extraction by the World Bank, and we performed a minimal amount of post-processing to clean the files.<sup>9</sup> For project characteristics, we used the data compiled for Ashton et al. (2023). This includes much that is publicly available via the World Bank’s project portal, as well as some internal data on team and management characteristics, and project preparation and supervision steps.

We concentrate on projects in five sectors: health, education, water and sanitation (WASH), energy and fiscal management, due to their combination of prior literature, data availability and size. Together, they account for 35% of aid flows, and one third of PPD projects. When combining sector aid flows and projects with outcomes and country characteristics from the WDI we conducted standard smoothing for noise and minimal interpolation for variables with high missingness.

# _3.3. Linear Methods: Replication, Sectoral Extension and Ratings_

We first replicate the specifications in health, education, and WASH from Mishra and Newhouse (2009) for health, Birchler and Michaelowa (2016) for education, and Ndikumana and Pickbourn (2017) for WASH. Using identical model forms, we are able to reproduce the results in each paper closely (see Appendix A). Since our primary interest is in replicating these models as a baseline, we do not extend them through instrumentation or other techniques, nor do we make more than associative claims based on them.

We then extend this prior analysis by varying controls to check for robustness and adding two sectors: energy, and fiscal management. For energy, we use as a baseline the same controls as in WASH. For fiscal management, we use a limited set of controls, for income level and institutional quality. The general form of the regression equations was as follows:

> 8The AidData is based on the Organisation for Economic Co-operation and Development (OECD) Creditor Reporting System (CRS) donor-reported data, with added granularity on purpose and activity coding.

> 9The scrapped dataset of public documents related to World Bank development projects is openly available at HuggingFace (https://huggingface.co/datasets/lukesjordan/worldbankproject-documentS).

6