rafmacalaba/data-use-mentions-extended huggingface dataset:
tion, in which we estimate differential results for offshore versus onshore, before and after the reform:

## **4.2 Intensive margins**

We complement the sectoral analysis with one at the firm-level, which exploits the differential responses of existing firms to the reform on the basis of their status.

As in the previous part of the analysis, we exploit a discrete absorbing treatment using a model similar to (1), but adjusted as to account for firm-level variation: where _Yfoy_ is an outcome of interest _Y_ for firm _f_ , with offshore status _o ∈{_ 0 _,_ 1 _}_ in year _y_ ; _θf_ are firm fixed-effects, and _γsy_ allows for industry-by-year shocks. Standarderrors are clustered at the firm level. Our coefficients of interest are _δj_ , which estimate the differential outcomes between offshore and onshore firms by within sector-years.

In Table A1 we present descriptive statistics for firms in 2013, before the reform, for the full sample (column 1); for the balanced sample (column 2); for all offshore firms (column 3) and for offshore firms in the balanced sample (column 4). The main takeaway is that firms in the balanced sample are larger, in terms of employment, revenue, export values, etc. This is expected as the balanced sample is composed of firms which have been active in every year of the period while the full sample includes also new entrants and exiters during the period, which tend to be smaller. The sectoral composition is also different for offshore, which are more likely to be in manufacturing in the balanced sample.

# **5 The Effects of the 2014 Offshore Tax Reform**

This section presents the estimated effect of the CIT reform at industry- and firm-level.

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satisfaction with education in LiTS2, the 2009 PISA survey found that 83 percent of 15-year olds were functionally illiterate (they scored below PISA level 2).

# **Complaints and grievance redress**

About 67 percent of respondents who had used health services during the previous 12 months reported at least one problem with service delivery, and for education services the figure was 50 percent. The main problems reported for health were long waiting times, lack of drugs and payment for services that should be free (Figure 5); in the case of education, the main problems were lack of textbooks/supplies, payments for services that should be free, and poor conditions of facilities.

**Figure 5: Problems with public health and education provision in the previous 12 months reported by respondents**

<!-- Start of picture text --> Public health clinic/hospital Public education<br>Long waiting times No textbooks or other supplie s<br>Payments required for services that should<br>No drugs available be free<br>Payments required for services that shouldbe free Facilities in poor conditio n<br>Treated disrespectfully by staf f Poor teaching<br>Frequent / unjustified absence of doctor s Overcrowded classrooms<br>Facilities not clean Frequent / unjustified absence of teacher s<br>0 10 20 30 40 0 5 10 15 20 25<br>Per cent of respondents Per cent of respondents<br><!-- End of picture text --> Source: LiTS2 (2010).

Grievance redress or complaints mechanisms refer to the institutions and channels that people can use to express their preferences and hold providers to account<sup>5</sup> . There are a number of specific objectives associated with the use of complaints handling in the public sector: promoting accountability in the delivery of services; measuring the quality of service provision and the effectiveness of policy; and harnessing experience to improve service delivery (Lister et al., 2008).

Grievance redress
by different methods, such as OLS, fixed effects, and Blundell and Bond (1998) system-GMM.

In the one-step approach, we include output and export experience directly in the production function: _yit_ = β 0 + β _llit_ + β _mmit_ + β _k kit_ + β _S Sit_ + β _WWit_ + β _VVit_ + β _YEYEit_ + β _EE EEit_ + ω _it_ + ε _it_ . (7) As above, we use intermediate inputs to correct for the endogeneity of input choices with respect to productivity. We assume that the plant manager observes its current productivity ω _it_ before making profit-maximizing choices of labor, labor quality, and intermediates to be combined with the quasifixed input capital and its quality and produce output. To obtain the coefficients on production and export experience variables, we modify the LP estimation procedure. The main identifying assumption is that production and export experience are taken by plant managers as state variables like capital; hence their coefficients are obtained in the same stage of the estimation as that of capital. All details on the estimation procedure are provided in Appendix A.

# **4. Data**

The dataset used in this study is constructed from the 1981-1991 annual census of Colombian manufacturing plants conducted by Departamento Administrativo Nacional de Estadística (DANE). The census covers all manufacturing plants with ten or more employees.<sup>7</sup> The variables provided by the census are in current pesos, except for the number of workers and the

> ξ it represents the unexpected part of current productivity to which capital and vintage do not adjust.

> 7 More specifically, DANE requires a plant to have more than ten employees to enter the census for the first time, but then continues to cover the plant regardless of its employment levels.  As a result, plants with less than ten employees are included in the sample in almost all years.

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# (see the Appendix for details).

In the overall assessment of the novel intervention examined in our paper, we aim to take a quite nuanced perspective. On the one hand, our results show that extending contraceptive provision to community health workers has the potential to effectively increase adoption of the latest generation of injectables in rural villages. By educating and training community health workers to administer injections locally, the scale-up of the new family planning services can be expedited. These results underscore the promising potential of the self-administration attribute of the injectable. On the other hand, the intervention did not significantly increase overall contraceptive coverage, meaning it failed to better match contraceptive options with the preferences of women in the study. While the rise in new contraceptive injections suggests an improved fit of the menu of contraceptive services for women in the area, the decline in the use of longer-acting methods is a concern for public health and economic development, especially when follow-up injections are missed.<sup>17</sup> Our findings highlight the need for further research. First, individual-level data will be required to identify which sub-populations are gaining protection coverage and which are losing it as a result of the intervention. Second, a more comprehensive intervention should be tested that exploits the potential associated with the self-administration attribute of the new injectable method while ensuring that the adoption of longer-acting contraceptive methods is not penalized. This may involve a better integration of service delivery models and associated health worker incentives, i.e., actively offering the entire contraceptive choice set and measuring health center performance based on women-months of coverage. A promising component is educational campaigns that highlight the benefits of the various contraceptive methods with an emphasis of the importance of maintaining continuous protection. Third, future research should aim at separately exploring the relevance of the various barriers to contraceptive utilization. This knowledge will likely stimulate development of tailored contraceptive technologies and research on effective service delivery strategies.

> 17 This becomes even more critical considering evidence that injectables’ discontinuation rates are roughly double those of implants
technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, indicators were standardized so that analyses were done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why does infrastructure matter?

Niger’s economic growth is below the desired level for reducing poverty. Between 2001 and 2009 Niger’s grew at 4.8 percent per year, below the annual Sub Saharan growth at 6.2 percent. Niger’s economic performance is below the 7 percent mark needed to make a significant impact on poverty reduction.

The poor condition of Niger’s infrastructure hardly contributed to economic growth. The overall contribution of telecommunications, electricity, and roads infrastructure to Niger’s per capita growth between 2000 and 2005 was only 0.3 percentage points, among the lowest on the continent. At this level, Niger—along with Chad—is one of the worst performers in infrastructure development in the region.

2
The approach proposed in this paper can be applied to other countries across the Europe and Central Asia (ECA) region and beyond. The method can help survey implementation in countries where a reliable national sampling frame is unavailable which often causes the World Bank, UN, and other international organizations to face critical challenges in preparing socioeconomic surveys. In those countries, the National Statistical Offices (NSOs) are often unwilling to share their census sampling frame with international organizations although the sample frame might be available. Even if the census sampling frame is available, census EAs are often large and cover large population areas which require substantial resources and time to conduct the household listing. The resource and time-efficient approach we propose thus can save a significant amount of budget, which could be invested in other areas, in all sampling stages.

# **5 Discussion**

The national sampling frame based on pre-EAs provides several benefits that the existing and accessible potential sampling frames cannot offer. However, it could also introduce some practical challenges and methodological limitations. This section thus now discusses the additional benefits and potential concerns and proposes alternative solutions that have been previously tested to resolve some of those challenges in other developing countries where pre-EAs have been employed, such as Somalia (Qader et al., 2021) and the Democratic Republic of the Congo (Qader et al., 2023).

**Population Estimates as a Measure of EA Size:** The main challenge of using the proposed national sampling frame for household surveys is that the size of the pre-EA is measured by population estimates, which can differ from the actual population and thus introduce bias in the probability of EAs being selected in the first stage of the two-stage design.

Although it has been made clear that this study does not address the validation of the gridded population estimates, it is important to provide some clarity to understand the limitations of the data. The automatic creation of a national sampling frame requires granular information about the population to ensure that the output sampling units are manageable. This
for each country-pair separately. In a world where production chains spread many countries, a third country can be a key component in the GDP synchronization of any pair. Hence, it is important to include a “network” variable in the analysis in order to account for such a mechanism, which is consistent with the importance of trade in input in the rise of cross-country inter-dependence.

- The link between trade proximity and GDP comovement is very strong for country-pairs including at least one high income country. For low income countries, this results means that integrating in Global Value Chains with high income economies could potentially lead to an increase in business cycle synchronization with those trade partners.

- The association is also linked to GDP volatility: country-pairs including at least one low volatility country are also those where the relationship between trade proximity and GDP correlation is significant. This is related to the second point, since high income countries also have less volatile GDP fluctuations (i.e. GDP level and GDP volatility are strongly negatively correlated).

- Using recent decomposition of trade flows based on precise value added accounting from Borin and Mancini (2019), we show that various indices of GVC participation are strongly related to GDP correlation across countries. The association between trade in input and GDP comovement holds using both gross trade and value added trade data. Moreover, intermediate input that are absorbed by the destination country seem to play a more important role for GDP fluctuation than inputs that are re-exported further.<sup>4</sup>

- Results are _robust to a number of controls_ : we included constructed third country indices to control for common exposures to other trade partners, indices of similarity in the sectoral composition of GDP for each country-pair, as well as financial proximity indices such as foreign direct investment and bilateral financial interconnection indexes. In all cases, the main message emerging from the analysis does not change and cross-country production linkages, as proxied by trade in intermediate input, is associated with higher GDP co-movement, especially for all country-pairs that include
investigate the interest-related side of the business, where NIM is defined as net interest income as a fraction of total assets. We further investigate NIM by dissecting this variable into its two drivers – Interest Income to Assets and Interest Costs to Assets.

Second, we explore _efficiency_ variables. To capture bank efficiency we use the ratios of total Overhead Costs to Assets, and Personnel Costs to Assets. Further, we break the two cost ratios between the underlying quantities and prices by computing the number of Employees per unit of Asset and the related average wages.

Third, we study _asset allocation_ to understand how banks allocate their resources between lending and non-lending activities. We use the Securities to Assets ratio which encapsulates many types of securities but is mostly driven by government securities. As such this ratio also measures private sector crowding out effects to some extent.

Fourth, we examine _asset quality_ and its impact on profitability. Aggregate country level data show that countries that have a large share of state banks also have a high ratio of NPLs to total loans (Figure 6). Ideally, we would use this indicator as a measure of asset quality, but we could not obtain sufficient bank level data on non-performing loans (NPLs), especially for state banks, due to deficiencies in financial disclosure. In order to capture differences in asset quality and its impact on profitability we use the ratio of loan-loss provisions to gross loans. This indicator captures the extent to which banks’ loan portfolios are being contaminated by non-performing loans and having an adverse impact on profitability.

Our explanatory variables include ownership variables which we compiled by using a variety of sources including Bankscope, Bankers’ Almanac and individual bank websites. We classify equity holders as being either public or private, or domestic or foreign). Our public ownership dummy assumes a value of 1 if the bank is majority government-owned and 0 otherwise. Similarly, our foreign ownership dummy has a value 1 if the private bank is majority foreignowned and 0 otherwise. We do not differentiate between foreign banks (i
measure capturing the delayed container shipping capacity across global maritime ports. This stress metric, which has been published in the World Bank’s Trade Watch since late 2021, is inherently port-centric and derived from analyzing container shipping traffic flows. The index is computed monthly and can be aggregated from port-level data into regional or global indices. It focuses specifically on container vessels of the Panamax class and larger operating on intercontinental liner services. Feeder loops exhibit, by nature, higher volatility and have not been included in the initial scope. Therefore, the index does not yet fully capture port stress across the entire shipping network as ports being solely called by ships of lower capacity than Panamax are not included.

Measuring and understanding the impacts and propagation of supply chain disruptions has gained significant attention, especially since the 2021-2022 supply chain crisis. Most efforts in this domain have originated from private sector initiatives and publications (e.g., shipping lines and operators, as well as maritime research consultancies), with only a few predating the crisis. The increasing availability of vessel tracking data and schedule information has facilitated these endeavors since this type of information recently became available. However, in contrast, economic literature tackling these topics or providing an empirically testable theoretical framework remains limited, with few exceptions (e.g. Bai, et al. 2024). Notably, a fundamental question as central as the evident connection between freight rates and supply chain disruptions has not been comprehensively covered in prior literature (Notteboom et al., 2021; Rodrigue, 2022).

Indicators quantifying supply chain disruptions generally fall into three broad categories (Table 1): (1) surveys of supply chain professionals, (2) indicators derived from tracking or schedule data, and (3) metaindicators aggregating existing data series, based on expert knowledge. Other noteworthy tools are those visualizing vessel movements, including the IMF Port Watch and similar tools by maritime consultancies.

2
19 cases and deaths, the logarithmic transformation would make the interpretation of estimated coefficients more convenient. However, in the weeks preceding December 2019, when the first COVID-19 cases were reported, and for part of the COVID-19 period, the number of cases/deaths was zero. Thus, the weekly cases and deaths are transformed using inverse hyperbolic sine transformation, while the Google search data are transformed using logarithmic transformation. The estimated coefficients from these transformed data can be interpreted as elasticities. Panel (a) presents results for hotels and restaurants, transport and tourism, while Panel (b) presents results for ICT solutions, delivery services, and retail trade sectors.

The results in Panel (a) of Table 1 show that demand for hotels and restaurants, transport and tourism have contracted due to COVID-19. A 1 percent increase in confirmed weekly cases leads to a 0.09 percent decrease in demand for hotels and restaurants, a 0.04-0.07 percent decrease in demand for transport, and a 0.01-0.04 percent decrease in demand for tourism. Panel B shows that a similar rise in weekly cases is associated with a 0.03-0.09 percent decrease in demand for retail services. On the other hand, ICT (Zoom and Skype) services have enjoyed a positive demand shock because of the pandemic. A 1 percent increase in weekly confirmed cases induces a 0.050.07 percent increase in demand for Zoom and Skype. As we show in our next sections, the demand for delivery services also jumped, but only in countries that implemented lockdown measures. This is intuitive as delivery services are expected to increase in the presence of restrictions of movements. We find similar results using the number of confirmed deaths (see Table B.5 in Appendix B).

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Baseline data also revealed awareness of handwashing before eating and after defecation among a majority of the adult population in Punjab. Almost 80 percent of the respondents were aware of the importance of handwashing at the baseline. Large improvements were noticed across treatment and control groups after project implementation. In the weighted data, awareness of the importance of handwashing after defecation increased to 91 percent of the adult respondents for the control group and between 95 and 96 percent for the three treatment arms. Similarly, 88 percent of the respondents from the control arm and between 92 to 95 percent of the respondents from the treatment arms reported awareness of the importance of handwashing before eating at the midline.

The use of soap before eating in both treatment and control arms was relatively low at the baseline and improved considerably post implementation. Use of soap in the control arm increased from 56 percent of adult respondents at baseline to 71 percent at midline—an improvement of 15 percentage points. The corresponding improvements in the three treatment arms were between 22 to 23 percentage points post implementation. In contrast, use of soap after defecation was relatively high at the baseline. Ninety-two percent of respondents in the control arm and 84 to 88 percent of respondents in the treatment arms reported using soap after defecation at the baseline. Increases in the use of soap after defecation were modest post implementation. While the use of soap increased by 2 percentage points in the control arm and by around 4 percentage points in arm 3, improvements in arms 1 and 2 were relatively higher, at 9 percentage points and 7 percentage points, respectively. The unweighted summary statistics relating to hygiene awareness and hygienic behaviors among adults were very similar to the weighted ones.

In the baseline survey, around 18 to 20 percent of households from the treatment and the control arms reported the treatment of drinking water, which increased by 2 to 5 percentage points post implementation. So, the impact of the project on households’ decision to treat drinking water was limited. Moreover, safe-disposal practices of child feces in households with
trainers within walking distance.”<sup>9</sup> Apprentices were then randomly assigned to a provider in their feasible set. Training began in late 2013 and lasted between two and three years.

# **3.2 Data and Balance**

We collected baseline data from applicants while they waited to be interviewed by the district selection committees. The baseline apprentice survey covered a broad range of information, including educational attainment, family background, cognitive and noncognitive assessments, and labor market outcomes. Baseline data on trainers were

> 9Apprentices could also list trainers who were further away as long as they had reliable means of transportation.

9