rafmacalaba/data-use-mentions-extended huggingface dataset:
Kirigia JM, Kubai PK (2023). Monetary value of disability-adjusted life years and potential productivity losses associated with neglected tropical diseases in the East African community. Res Rep Trop Med. 14: 35-47.

Koch AM, Nilsen RM, Eriksen HM et al (2015). Mortality related to hospital-associated infections in a tertiary hospital; repeated cross-sectional studies between 2004-2011. Antimicrob Resist Infect Control 4(57).

Karagiannidou S, Triantafyllou C, Zaoutis TE, Papaevangelou V, Maniadakis N, Kourlaba G (2020). Length of stay, cost, and mortality of healthcare-acquired bloodstream infections in children and neonates: A systematic review and meta-analysis. Infect Control Hosp Epidemiol. 41(3): 342-354.

Leopold SJ, van Leth F, Tarekegn H, Schultsz C (2014). Antimicrobial drug resistance among clinically relevant bacterial isolates in sub-Saharan Africa: a systematic review. J Antimicrob Chemother. 69(9): 2337-53.

Li G, Bielicki JA, Ahmed ANU et al (2020). Towards understanding global patterns of antimicrobial use and resistance in neonatal sepsis: insights from the NeoAMR network. Arch Dis Child. 105(1): 26–31.

Liu X, Spencer A, Long Y, Greenhalgh C et al (2022). A systematic review and meta-analysis of disease burden of healthcare-associated infections in China: an economic burden perspective from general hospitals. J Hosp Infect. 123: 1-11.

Luangasanatip N, Hongsuwan M, Limmathurotsakul D, Lubell Y et al (2015). Comparative efficacy of interventions to promote hand hygiene in hospital: systematic review and network meta-analysis. BMJ. 351: h3728.

Manoukian S, Stewart S, Dancer S, Graves N et al (2018). Estimating excess length of stay due to healthcare-associated infections: a systematic review and meta-analysis of statistical methodology. J Hosp Infect. 100(2): 222-235.

Mawalla B, Mshana SE, Chalya PL, Imirzalioglu C, Mahalu W (2011). Predictors of surgical site infections among patients undergoing major
.7|0.0|
|18|Insurance, reinsurance<br>and pension funding|41|11|21.2|0.1|23|3|11.1|0.0|
|19|<br>Employment activities|7|0|0.0|0.0|19|2|9.5|0.0|
|20|Manufacture of<br>beverages|7|0|0.0|0.0|37|1|2.6|0.0|
|Total e|mployees (in thousand)|31374|13611|30.3|100|33437|13644|29.0|100|

Source: ILOSTAT database, International Labor Organization 12
shocks<br>Percentage points<br>2 Interquartile range Median<br>1<br>0<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>F. EMDEs: Domestic supply shocks<br>Percentage points<br>3 Interquartile range Median<br>2<br>1<br>0<br>-1<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>Low High Peg. Non-IT IT Exporter Deficits Surpluses<br>Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Float Importer<br>Low High Float<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> Note: One-year impulse responses of the exchange rate to domestic shocks (monetary policy, domestic demand, and domestic supply) from country-specific factor-augmented vector autoregression models estimated for 29 advanced economies and 26 EMDEs over 1998-2017. Bars show the interquartile range and markers represent the median across countries. A positive number indicates an appreciation. EMDEs = emerging market and developing economies; IMF = International Monetary Fund; IT = inflation targeting.

B.D.F. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications (see the Appendix for details). Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

27
br>Figure 6. Key growth drivers in WAEMU Figure 7. Structural drivers of growth in<br>countries (2011-2016) WAEMU countries (2011-2016)<br>4.5 2.5<br>3.5 2.0<br>1.5<br>2.5<br>1.0<br>1.5<br>0.5<br>0.5<br>0.0<br>-0.5<br>-0.5<br>-1.5<br>Residual (unexplained growth) Persistence -1.0<br>External Stabilization Schooling Credit/GDP<br>Trade/GDP Gov Cons./GDP<br>Structural<br>Infrastructure<br>Source: Authors’ estimates based on data described in Section 3. Source: Authors’ estimates based on data described in Section 3.<br>Annualized GDP p.c. growth (in %) Annualized GDP p.c. growth (in %)<br>Annualized GDP p.c. growth (in %)<br>Annualized GDP p.c. growth (in %)<br>Annualized GDP p.c. growth (in %) Annualized GDP p.c. growth (in %)<br><!-- End of picture text --> Furthermore, the baseline sample is re-specified to include only countries that were below the median or mean of GDP per capita in 1995 (Annex Table 2). This is important, as the baseline model includes 11
across the world, including by increasing the availability of relevant statistics and by promoting better transaction-disclosure practices (Maslen and Cigdem 2022).

Finally, the international community can continue to support small states through tailored technical assistance. Small states have limited institutional capacity. Like many EMDEs, they may lack the technical know-how required to implement in a timely manner adequate fiscal reforms—including strengthening domestic revenue mobilization, reforming SOEs, establishing a sovereign wealth fund and implementing fiscal rules (Balasundharam and Khadan 2024; Heller 2022). Overall, small states have made good progress in some aspects of climate change adaptation planning. In other areas, however, they appear to be lagging other EMDEs (UNEP 2023). Limited economic data, such as reliable macroeconomic statistics and household surveys, often hinder evidence-based policy making in these countries. A weak civil service and complex bureaucratic processes erode government efficiency and contribute to delays in policy reforms (Lafuente and Molina 2018).

# **5. Conclusion**

The unique structural characteristics of small states constrain their fiscal policy. They include high trade openness, limited economic diversification, narrow tax bases, obstacles to providing public goods and services at scale, and vulnerability to external shocks—particularly those arising from climate-change-related natural disasters and global recessions. Many small states, particularly 21
# **Introduction**

Conflict can affect a country’s development through various channels, including the loss of physical and human capital, the disruption of production processes, and the undermining of markets (Blattman and Miguel 2010). Not surprisingly, the empirical evidence available shows a negative correlation between economic activity and conflict intensity. But the available evidence is diverse in nature. Of the 42 studies reviewed for this paper, 20 assess impacts on aggregate economic activity and 12 focus exclusively on the formal sector of the economy. Only seven of the 42 studies deal with the impact of conflict on illicit activities, while just three focus on the informal sector.

Estimates of the impact of conflict on aggregate economic activity differ in their magnitude, but they are all negative and sizeable. De Groot et al. (2016) claim that global gross domestic product (GDP) would have been 15.7 percent higher in 2007, had there been no conflict since 1960. According to Collier (1999), civil wars result in a 2.2 percent decline in GDP per capita annually, primarily because of slow production but also due to the loss of capital. Similarly, Acemoglu et. al (2011) find that cities that suffered the Holocaust most intensely have grown less since then, and that administrative districts where the Holocaust had the largest impact have lower GDP per capita and lower average wages today. And Singh (2013) reports that Punjab districts experiencing a major terrorist incident saw their long-term fixed investment decline by around 17 percent.

A more recent literature has relied on detailed microeconomic data, rather than on aggregate GDP, average wages or fixed investment. And again, the estimated impact is unambiguously negative. For example, Besley and Mueller (2012) show that house prices in Northern Ireland declined with conflictrelated killings. Berman et al. (2013) find that construction permits decreased in number in the Philippines when rebel groups triggered an escalation of conflict. And Blumenstock et al. (2018) report that mobile phone
|Rdper|R&D personnel|Number of researchers<br>(scientists and engineers)|(including electronic versions available<br>through the World Bank electronic<br>databases)<br> -OECD: Science and Technology<br>Indicators in electronic and printed<br>format<br>-RICYT<sup>7</sup><br>- Taiwan(China)Statistical Data book|
|---|---|---|---|
|Rdfinprod|R&D<br>expenditure<br>financed by the<br>productive<br>sector|Percentage of total R&D<br>expenditure|-UNESCO<br>Statistical<br>Yearbooks<br>(electronic and printed formats)<br>-OECD Main Science and Technology<br>indicators<br>(electronic<br>and<br>printed<br>formats)<br>-RICYT<br>-Taiwan (China) Statistical Data book<br>-Database developed byFurman et al.|
|Rdfinabro|R&D<br>expenditure<br>financed with<br>funds from<br>abroad|Percentage of total R&D<br>expenditure|-UNESCO Statistical Yearbooks<br>(electronic and printed formats)<br>-OECD Main Science and Technology<br>indicators<br>-RICYT<br>- Taiwan(China)Statistical Data book|
|Rdperfprod|R&D<br>expenditure<br>performed by<br>the productive<br>sector|Percentage of total R&D<br>expenditure|-UNESCO Statistical Yearbooks<br>-OECD Main Science and Technology<br>indicators<br>-RICYT<br>-Taiwan(China)Statistical Data book|
|Rdperfhe|R&D<br>expenditure<br>performed by<br>higher<br
and electricity also improved between the two survey rounds: the share of individuals that agree that citizens should pay taxes so local government develops increased from 41% to 50%, and those who think it is wrong not to pay electricity improved by 9 p.p.

Similar improvements were observed in the survey of government officials, presented in Table 7 and Table 8. The share of respondents affirming the LGA has a plan that is up to date improved from 27% to 44%; the share reporting a plan approved in the last 2 years increases by 26 p.p.; and the share of LGAs with formal capacity building plans increased by 30 p.p. Improvements were also observed in responses about budget delays and execution, agreements with independence of internal audits, and responses on both staff capacity and staff morale.

# **4.4 The impact of the project on household responses and government official reports of institutional quality**

In order to estimate the causal effect of the project on areas that received it, as discussed above, we use areas that did not receive the program as counterfactuals. Under the assumption that these areas are valid counterfactuals (i.e., they would have followed similar trajectories in the absence of the program), assessing whether the project had an effect on the outcomes of interest is equivalent to examining whether areas that received the program had a differential change in outcomes, when compared to the comparison areas. The simplest (and most transparent) way to make this difference-in-differences estimation is to compare columns (5) and (6) in Tables 4 through 8: were the changes in project areas larger than those in comparison areas? Simple visual inspection suggests that, overall, this is not the case: project LGAs do not seem to consistently outperform comparison areas, which often presented larger improvements.

To formally test whether changes in project areas were different from changes in comparison areas, we estimate equation 1 above, including a series of individual level controls.<sup
particular households that may have been excluded due to scoring higher on one or two important predictors. For example, smaller households are less likely to be poor and therefore more likely to receive a higher PMT score. However, this may miss smaller vulnerable households like those headed by elderly people caring for orphaned grandchildren. A grievance process can help address specific cases of exclusion like these.

# _Data and Variables_

The HIES is a multi‐topic survey conducted every three years by Sri Lanka’s Department of Census and Statistics (DCS). The survey includes modules on consumption, education, health, income, household characteristics, assets, and information on access to social safety net programs. The nationally representative survey included 21,756 households from all regions of Sri Lanka, including areas of the North and East that were excluded from previous rounds due to conflict.

In this study, we aim to develop a PMT formula that is consistent across different definitions of a household. The HIES includes data collected at the household level, while eligibility for Samurdhi is determined at the family level. Households are defined as those in a dwelling that share a cooking pot, while families are 6
While this is difficult to test, empirically, we provide some checks to assuage this concern. We use census data on migration rates at the municipality level and sequentially trim our sample by dropping observations in municipalities with high migration rates.<sup>15</sup> Specifically, we estimate the baseline specifications under the three sets of fixed effects in Table 2 while systematically dropping observations in municipalities where the migration rates exceed the following thresholds: 60%, 50%, 40%, 30%, 20%, 10%, 6% and 3% respectively. The results are shown in Figure 7. Across the various specifications, the results for the respective subject groupings remain positive and statistically significant, regardless of the migration threshold that is applied. Thus, we can rule out the potential of selective migration confounding our results.

# **5.3.4 Alternative Measure of Catchment Area**

So far, we have used a very conservative measure of treated locations using a 2.5km radius around the coffee mills. However, as shown in Macchiavello and Morjaria (2021), the size of the catchment area of coffee mills could extend to about 5km. Thus, if indeed the true benefits of coffee mills extend beyond 2.5km, then our baseline estimates may underestimate the true effect of the coffee mills on students’ outcomes.

In this section, we redefine the catchment area to include communities within a 5km radius of a coffee mill and replicate the analysis forgoing analysis. The corresponding results are shown in Tables A3 - A7 (Section B.2) in the appendix. Reassuringly, the results align with the baseline results, albeit the effect sizes are relatively low compared to the latter. Overall, both sets of results point to a positive impact of exposure to coffee mills in early life on educational performance in the medium term.

# **5.3.5 Spillovers and Geographic Distribution of Impacts**

Next, we address two potential threats to our assignment of treatment and
16 that is, very imprecisely _(intermediate information case 1)._ We have implemented this possible scenario using Census data for the second largest city of Argentina"<sup>3</sup> at the level of _the.ftacci6n,_ which is a collection of _radios,_ which in turn is a collection of _manzanas,_ the smallest reporting unit available.'<sup>4</sup> We have also computed costs when information is available at the level of the _radio._ The results of this thought experiment can be seen in Table 1.

Another intermediate information example _(intermediate information case 2)_ might be one, as in Argentina, in which the wire center locations are known, line totals are known, and location data are known at the level of the _manzana._ The relatively disaggregated location data are used to impute a more uneven customer location distribution by assigning the lines in the wire center proportionally to the populations of each _manzana._ The results of the computations for this particular case can also be seen in Table 1 below.

**Table 1**

||**Total I**|**nvestment (in**|**dollars)**|
|---|---|---|---|
|**[Total lines**|**Manzana**|**Radio**|**Fracci6n**|
|**1**320,403|**215,032,106**|**216,802,535**|233,801,440|

The total investment shown in the table is the sum of feeder costs, distribution costs, drop costs and some other costs (e.g., terminals, interfaces).<sup>15</sup> As can be readily seen from the table, the use of relatively aggregated Census data does not markedly bias the results with
Figure 2 shows the distribution of health care systems by income decile, differentiating all such contributory health system workers with incomes below 10 monthly minimum wages and their beneficiaries, from those with incomes above 10 monthly minimum wages.

<!-- Start of picture text --> FIGURE 2: DISTRIBUTION OF HEALTHCARE SYSTEMS IN COLOMBIA BY INCOME DECILE<br>100<br>Scheme: No information<br>80<br>Scheme: Special<br>60<br>Scheme: Contributory (more than<br>40<br>10 SMMLV)<br>20 Scheme: Contributory (less than 10<br>SMMLV)<br>0<br>Scheme: Subsidized<br>1 2 3 4 5 6 8 7 9 10<br>Decile<br>Source: World Bank Group and staff estimates based on the 2017 ENPH<br>Note: The deciles were put together using market income + per-capita pensions<br><!-- End of picture text --> Given the particularity of the Colombian health care system, there are three scenarios for the treatment of transfers in kind for health services. In the first scenario, it is assumed that only workers belonging to the subsidized health care system** receive a transfer in kind for health services. The following two scenarios consider that given the CREE tax (tax with specific allocation) was eliminated, and currently, the national government uses resources from the National General Budget to make transfers of money shortfalls to the contributory healthcare system. Therefore, these funds must be treated as a national government transfer, similar to pensions.

Thus, in the second scenario, in addition to considering the persons belonging to the subsidized health care system, all employees who belong to the contributory health care system but who have incomes below 10 monthly minimum wage are included. In other words, it is assumed that all such employers of workers with incomes below 10 smmlv [monthly minimum wage] do not make any direct contribution
world price of maize was 38% above trend<sup>8</sup> . However, there was an export ban in place which limited the country’s ability to capitalize on this market development. This was finally lifted at the end of 1996, allowing Tanzania to export at greater-than-trend world maize prices till 1998, after which maize prices dropped below trend again. All of this suggests the need to systematically investigate the role of Tanzanian trade policy in the context of volatile grains production nationally, regionally and globally.

> 6 1972-1974, 1976, 1977, 1981-1986, 1994-1996

> 7 Please see Anderson and Valenzuela (2008), Chapoto and Jayne (2010), Morrissey and Leyaro (2007), and World Bank (2009) for detailed reviews.

> 8 World maize prices are obtained from the World Bank’s Development Prospect Group’s GEM database. The price series for maize is in constant 2000 USD/MT. The time series is de-trended with maize prices explained by a quadratic function of time. The world price percent deviations from trend are then estimated from the ratio of the estimated residuals to the predicted prices. The world maize price time series and the percent deviations from trend are available in Appendix A.

9
# _1. INTRODUCTION_

Historically, developing countries have adopted various industrial policies that regulate entry and output capacity of firms with the goal of addressing inadequacies across industries and regions. Regulations imposing artificial constraints on firm entry and capacity can distort efficiency by misallocating resources. In this paper, we focus on the effects of removing such regulations on investment. Specifically, we analyze the impact of deregulatory reforms in India during the 1990s, which eliminated compulsory industrial licensing (known as “license raj”), on the manufacturing firms’ investment decisions. To the best of our knowledge, this is the first paper to study the impact of deregulation on firm-level investment in the context of a developing economy.

The dismantling of the license system in India provides a natural environment to study the effects of deregulation on firms’ investment decisions. Under this system, a license was required to establish a plant, introduce a new product or expand capacity. Through allotments of input and import licenses, the government also controlled the flow of inputs and imported machinery.<sup>1</sup> Following the balance of payments crisis in 1991, the IMF agreed to provide financial assistance to India conditional upon economic reforms. These unanticipated reforms included delicensing, which eliminated the entry and capacity restrictions across industries.<sup>2</sup> We utilize this arguably exogenous shock (see Aghion et al., 2008) in order to identify the effects of deregulation on firm-level investment. To do so, we use data on Indian manufacturing firms for the 1989-2000 period and we estimate an investment equation to uncover a significant positive effect of delicensing on investment. We also show that the increase in investment was predominantly driven by smaller firms, and that firms in states with better credit conditions benefitted more from the deregulation.

Our paper contributes to the growing literature on deregulation and investment. Using industrylevel data for 21 OECD countries, Alesina et al. (2005) show that regulatory reforms are associated with an

> 1 The allotment of input and import licenses were
# **4 Results**

# **4.1 Predicting time use activities**

We present the results for the two utilized predictive models, SGB and Random Forest. We find that our SGB model results in a macro average F1-score of 76 percent. Our Random Forest model results in an F1-score of 68 percent. As a basis for comparison, a random guess would result in a prediction accuracy and an F1-score of merely 8.33 percent since there are 12 balanced classes.

From our findings, we can conclusively assert the following:

1. Machine learning effectively utilizes accelerometer data to forecast time use.

2. The SGB model outperforms the Random Forest model.

Consequently, we have selected the SGB model as our best performing model, and all subsequent model results presented will pertain exclusively to the SGB model. Detailed prediction scores for the SGB machine learning model can be found in **Table 2** .

# **Table 2**

SGB model prediction scores and test set characteristics for the twelve main activities.

|**Activity**|**Set size**|<sup>**Participant**</sup><br>**s**|**Precisio**<br>**n**|**Recall**|**F1-score**|
|---|---|---|---|---|---|
|1. Social or religious activities and hobbies|2036|333|68 %|54 %|61 %|
|2. Farming|2036|199|75 %|77 %|76 %|
|3. Cook orprepare food or drinks|2036|239|56 %|69 %|62 %|
|4. Travellingand commuting|2036|316|80 %|55 %|65 %|
|5. Eatingand drinking|2036|380|68 %|55 %|61 %
|Source<br>W D I<br>UNCTAD<br>CEPII<br>World G overnance Indicators<br>W D I<br>ILO<br>ILO<br>W D I<br>W D I<br>Penn World Tables 9.0, W D I<br>W D I<br>Content of D eep Trade Agreem ents<br>Content of D eep Trade Agreem ents<br>Content of D eep Trade Agreem ents<br>Content of D eep Trade Agreem ents<br>Content of D eep Trade Agreem ents<br>Content of D eep Trade Agreem ents<br>CEPII|W D I<br>D oing Business database<br>LPI database|W D I<br>M elitz and Toubal (2014)<br>W D I<br>K ee, Nicita, and O larreaga (2008)<br>W D I<br>Tax Foundation (2019)<br>IM F (2000)<br>ILO<br>Penn World Tables 9.0<br>World G overnance Indicators<br>NBER-CES M anufacturing Industry D atabase<br>NBER-CES M anufacturing Industry D atabase<br>Braun (2003)<br><br>Nunn (2007)<br>UNID O<br>W ITS, TRAINS and Felb erm ayr,<br>Teti and Yalcin (2019)<br>W ITS, TRAINS and Felb erm ayr,<br>Teti and Yalcin (2019)<br>W ITS, TRAINS and Felb erm ayr,<br>Teti and Yalcin (2019)|
|---|---|---|
|Variable nam e<br>D e...nition<br>Avg. tari¤ rate<br>Applied tari¤ rate to m anufactured products, weighted m ean (in % )<br>