rafmacalaba/data-use-mentions-extended huggingface dataset:
that quintile. Poor and non‐poor correspond to the bottom 20% and the top 80% respectively. ASPIRE at present does not cover developed countries, so we use data from the US Consumer Expenditure Survey (CES, 2015), Canadian National Household Survey (CNHS, 2015), Australian Household Wealth and Wealth Distribution Survey (AWWDS, 2015), and European Union Survey of Income and Living Conditions (EU‐ SILC, 2015).

ASPIRE does not cover all countries in the world. To expand the set of countries in this study, we proceed as follows. First, we estimate an econometric model that explains the share of income from transfers, for poor and nonpoor people in each country by the share of GDP spent on social protection at the national level (from the International Labour Organization), an indicator of government effectiveness (from the Worldwide Governance Indicators), and whether the country used to be in the Soviet Union. (Other explanatory variables we tested include World Bank income category of the country, region, and GINI coefficient, but they turn out to have no explanatory power.) This provides an estimate of income from transfers in 26 additional countries (from 98 using only ASPIRE data).

Diversification also comes from financial inclusion. We use the fraction of the population with savings at a financial institution, from the Global Financial Inclusion Database (FINDEX, 2015). The Findex database provides these values for the bottom 40% and for the top 60%, which we use for poor and nonpoor people respectively. We assume that the fraction of income that is diversified increases by 10% for people who have bank accounts.

Figure 6 shows the diversification of income thanks to remittances and social protection for poor and non‐ poor people as a function of GDP per capita. It shows that generally, poor people receive a large share of their income from transfers (which sounds logical but is not the case in all countries). Here, we see that most countries below $10
on home assignments, can contribute to improved student performance. We do not have reliable measures of time spent by the students in those activities. We do, however, have measures of child labor, that has been shown to have detrimental effects on both education attainments and learning (Ravallion and Wodon, 2000, Beegle et al., 2009, Heady, 2003) School quality - a broad term that refers, among others, to the quality of teachers (Rockoff, 2004, Chetty et al., 2014) principals (Roland G. Fryer, 2017), peers (Duflo et al., 2011b), class size (Angrist and Lavy, 1999) - is a key determinant of learning outcomes. Finally, a growing body of literature shows that parental time investments play a key role in improving child cognitive and socioemotional skills in the early years (Attanasio et al., 2018, Agostinelli and Sorrenti, 2018).

Our data allow us to test the impact of cash and in-kind transfers on each of these inputs

> 18Figlio et al. (2014), using a sample of siblings in Florida, find that birthweight has a positive constant effect on test scores throughout the entire academic life. Bharadwaj et al. (2012) exploit school and birth records from Chile and Norway to find that low-weight children who receive extra medical care at birth have higher test scores.

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7

**Figure 3: Top ten revenue generating countries**

<!-- Start of picture text --> 1990-1999 2000-2003<br>0% 10% 20% 30% 0% 10% 20% 30%<br>Brazil China<br>Argentina Brazil<br>Mexico Poland<br>China Czech Republic<br>Hungary Slovak Republic<br>Poland Saudi Arabia<br>Malaysia Morocco<br>India India<br>Russia Thailand<br>Peru Russia<br>Rest of the World Rest of the World<br><!-- End of picture text --> _Source_ : World Bank Privatization Database 12. Data on foreign vs. domestic privatization revenues is available only for the earlier period, 1990-1999. Foreign revenues comprised 50 percent of total privatization proceeds in developing countries; three-quarters of foreign revenues were raised through direct investment and the balance through portfolio flows (World Bank 2001). The Latin American region represented 56 percent of all foreign investment as a result of sales in telecommunications, banking, and oil and gas, followed by Europe and Central Asia (23 percent) and East Asia (15 percent). China was the main recipient of foreign investment in East Asia and Pacific followed by Thailand, Indonesia, and Malaysia. Despite the decline in total privatization proceeds after their peak 1997 level, foreign investment in privatization remained steady and increased in 1999 due to large transactions in East Asia and Latin America. Data on the FDI share in privatization is not available for the 2000 to 2003 time period. A comparison of general FDI trends and privatization trends (Figure 4) shows, however, that while total FDI declined from 2001 onwards, privatization proceeds began to pick up, albeit modestly--largely as a result of share sales of a few large companies via domestic stock markets.
Policy Research Working Paper 5666

# **Abstract**

Using a new tax database for 28 countries and a variety of econometric methods, this paper contributes to the debate on the effects of fiscal policy on economic activity in a number of ways. The analysis finds that tax cuts have a stimulative effect on economic growth in developing countries. Lowering the personal income tax rate by 1 percentage point, or cutting revenues by 1 gross domestic product of gross domestic product increases gross domestic product by 0.3–0.4 percent on impact and 0.8 percent in the long run. The author finds that cuts in personal income taxes are more effective in stimulating growth than cuts in corporate or valued added tax rates. The author incorporates debt dynamics into a fiscal vector autoregression model for a number of developing countries. Existing estimates of the effects of fiscal policy on growth use linear time-series methods, which may assess the effects of fiscal policy along a debtpath that is unsustainable. Incorporating the non-linear relationship between government expenditure, taxes, and debt alters estimates of the impact of fiscal policy on gross domestic product in several countries. In Brazil, for example, conventional time-series methods may overstate the effects of fiscal policy on gross domestic product, by ignoring the detrimental effects of debt accumulation.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at e.ilzetzki@lse.ac.uk.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited
# **Figure 14 Non-residential capital-output ratios in the OECD area and China**

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1986 1991 1996 2001 2006 2011 2016<br>Average OECD country China<br>Source: Author estimates and OECD Economic Outlook Database<br><!-- End of picture text -->

# **_Infrastructure and government and residential sector capital-output ratios_**

Across countries, there is much less information available that would permit a comparison of the capital stock in the government and infrastructure sectors. Information is available for investment in the general government sector but not all infrastructure is undertaken by the general government sector. Moreover, at the industry level, few countries publish detailed capital stock data that would enable an exact comparison to be made with these estimates for China. Cross-country comparisons are complicated by the differences in the classification of infrastructure investment across countries. One country, the United States, does publish estimates for the non-residential capital stock in the private sector by industry and for the total government sector. However, some of the sectors shown as infrastructure in China are in the private sector in the United States. Moreover, even in the United States, no industrial breakdown of the private sector capital stock has been published for the years after 2001.

An approximate comparison between China and the United States of the combined capital stock in the government and infrastructure sectors can be made. It requires an assumption that private infrastructure has remained a constant share of private non-residential investment since 2001. On this basis, the capital-output ratio for the infrastructure and government sector in China had reached the same level as in the United States by the beginning of this decade (Figure 15, Panel A). Since then, it has moved steadily above the level in the United
4.2**|

_Source_ : Author‘s calculations based on data from MxFLS panel database.

In the cases of Chile and Peru the data shows only the occurrence of health shocks, so that a cross-country comparison is only possible for those shocks requiring hospitalization. While the occurrence of health shocks is stable for all five quintiles in Mexico, in Chile and Peru it shows a growing pattern through the income distribution. Although this pattern suggests a higher use of hospitalization for upper quintiles according to the capacity to pay, these results evidence a generalized exposure to risks in all three countries (see table 2).

Given that insurance markets are actually imperfect or inexistent, households insure against risks through less efficient strategies (e.g. investing resources in activities with low returns, but which assure a minimum level of consumption), making the efficient cost of insuring against risk both variable and problematic to realistically define. We argue that it is possible to find the income level associated with a set of assets and socioeconomic characteristics that would allow the households to be less vulnerable to fall into poverty due to idiosyncratic and asymmetric shocks, and interpret it as an ―augmented‖ poverty line in the spirit of Cafiero and Vakis. From that perspective, and based on the notion discussed in Goldthorpe and McKnight, we explore the link between income and vulnerability to poverty to define the lower threshold of

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, Lanzi, E., and Magné, B. (2017). Long-term economic growth projections in the Shared Socioeconomic Pathways. Global Environmental Change _42_ , 200–214.

Dercon, S., Gilligan, D., Hoddinott, J., and Woldehanna, T. (2008). The Impact of Agricultural Extension and Roads on Poverty and Consumption Growth in Fifteen Ethiopian Villages. American Journal of Agricultural Economics 91 (2009) 4 _91_ .

Espinet Alegre, X., Rozenberg, J., Rao, K.S., and Ogita, S. (2018). Piloting the use of network analysis and decision-making under uncertainty in transport operations : preparation and appraisal of a rural roads project in Mozambique under changing flood risk and other deep uncertainties (The World Bank).

Fay, M., Han, S., Lee, H.I., Mastruzzi, M., and Cho, M. (2019). Hitting the Trillion Mark -- A Look at How Much Countries Are Spending on Infrastructure (The World Bank).

Gao, J. (2017). Downscaling Global Spatial Population Projections from 1/8-degree to 1-km Grid Cells.

Iimi, A., and Rao, K. (2018). Spatial Analysis of Liberia’s Transport Connectivity and Potential Growth (Washington DC, USA: The World Bank).

Iimi, A., Ahmed, A.K.F., Anderson, E.C., Diehl, A.S., Maiyo, L., Peralta Quiros, T., and Rao, K.S. (2016). New rural access index : main determinants and correlation to poverty (The World Bank).

Khandker, S.R., and Koolwal, G.B. (2011). Estimating the Long-Term Impacts of Rural Roads: A Dynamic Panel Approach (Rochester, NY: Social Science Research Network).

Laurance, W.F., Clements, G.R.
separate account with commercial banks to safeguard user funds.<sup>77</sup> In countries such as Colombia, Kenya, and Mexico, payment service providers need to ensure that funds received are placed in a ring-fenced account at commercial banks exclusively.

Governments have to strike a balance between risk control and business efficiency so that they do not impose additional burdens on business operations. Ten countries such as Kenya and Korea in the Digital Business Indicators sample set a limit on the amount of a single payment transaction. Such a limit prevents money laundering, terrorist financing, and other digital payment criminal activities. Although there is no global census on the ideal level of a limit, an excessively low amount could pose an extra operational burden on businesses.

# _Logistics_

The digital economy is not constrained to businesses operating in a virtual environment; it also brings traditional businesses closer to markets thanks to e-commerce. E-commerce, defined as “the process of buying and selling products or services using electronic data transmission via the Internet,” has been booming.<sup>78</sup> Globally, e-commerce is expected to become the largest retail channel by 2021. Because physical products must be shipped to customers using conventional transport after online purchases,<sup>79</sup> logistics is a critical link connecting online transactions to offline production.<sup>80</sup> The rise of e-commerce has increased the flow of small parcels processed by third-party logistics and postal services. In 2013, 86 percent of Amazon products weighed 5 pounds or less. International parcel post grew by an impressive 29.7 percent from 2016 to 2017, with 0.18 billion items shipped globally in 2017; domestic parcel post grew by 8.6 percent, with 13.7 billion items shipped.<sup>81</sup> Customers’ expectations about efficient logistics are also growing. According to a 2018 study, 91 percent of U.S. online shoppers said they will leave a retail website if critical services such
endogenous regressor _LogDistGRs_ but independent from the error _εs_ . We can argue that old main roads would have been maintained to transport armies, and later on these roads would also have higher quality.

# **6 Results**

The results of the two separate datasets and methods are summarized in different subsections below.

## **6.1 ILCS**

First, I show the results from the analysis of the repeated cross-sectional data of Integrated Living Conditions Survey (ILCS). The survey has collected information on perception of respondents on road quality in the region and locally - within the village. The road quality indicators include “good”, “average”, and “poor” (“good” is a reference category in the following results tables).

Table 2 shows the marginal effects of the Probit regression. It shows the probability of a respondent being employed in non-agricultural sector (columns (1) and (2)), doing seasonal work (columns (3) and (4)), and whether there is anyone from the household working outside of village (column (5)). Since the distance variables were not included in the questionnaires in 2007 and 2008, the regression is done with and without the distance variables. The first column shows a clear negative association between the household living in a region with average or poor road quality and non-agricultural employment. Individuals reporting poor road quality are 5.4 percentage points less likely to be working in non-agricultural sector than the individuals reporting good quality roads in their regions. Controlling for the distance to the nearest market, kindergarten, and health center lowers the marginal effects, but still holds statistically significant 3.5 percentage points lower probability of working in non-agricultural sector (column (2)). As expected, average roads show lower marginal effects; individuals reporting quality of roads in their region leading to towns and markets as average, are 1.9 percentage points less likely to work in nonagricultural sector than the individuals reporting good quality roads in
AP operates [117] implies AP is more likely to shape local conflict dynamics than regional conflict dynamics. Absent a high degree of centralization, it seems implausible that one contingent of an armed group operating near an AP site could recuperate lost revenue resulting from AP’s activities by coordinating with another contingent to increase the looting of civilians elsewhere.

We test how the relationship between AP and conflict changes when we alter the buffer area used to define our unit of observation (Figure S18). Removing the buffer area altogether largely produces similar results to our main analysis: transference to AP is not significantly related to the probability of protests and riots, increases the probability of civilian targeting (now, at the ten-percent level), and increases the probability of battles (now, at the five-percent level). For the probability of battles and the probability of protests and riots, increasing the buffer radius to 50 kilometers or 75 kilometers produces similar results to our main analysis. However, the effect of transference to AP on the probability of civilian targeting becomes statistically indistinguishable from zero when we increase the buffer radius to 50 kilometers, and then becomes negatively signed and statistically significant when we increase the buffer radius to 75 kilometers. For the reasons described above, we caution against attributing the 75 kilometer decrease in civilian targeting to AP management. The downward shift in these estimates may reflect how larger buffers include in our analysis urban areas where violence against civilians in some African countries is more likely, all else equal [118].

# **A.7 Mechanisms and Management Effectiveness Tracking Tool (METT) data (Table 1)**

METT data contain 3,999 self-assessments of management effectiveness for 2,577 protected areas between 1999 and 2016 [74]. Six protected areas currently managed by AP have METT data for multiple years (treatment group), as do 27 protected areas identified by AP as anchor areas meeting their criteria for future management (control group). The six treatment group protected areas with multiple observations are Pendjari
that T2A has a higher probability optimal than T2B in both the balanced and unbalanced panel, highlighting that the arm with the highest probability optimal may not always have the lowest policy regret. This can occur if some “unlikely” states of the world have very high regret realizations and occurs more often when the best arm is fairly uncertain.

Overall, based on these results there is significant uncertainty about which arm has the highest ORF gains. There is no strong evidence that choosing a policy based on maximal call engagement is systematically at a tension with also increasing oral reading fluency, but we can also not conclude that the two outcomes are definitely aligned. If the implementer would like to revise the decision to target engagement only and learn which call format maximizes ORF gains, additional testing would likely be needed.

# **5.3 Correcting for Sampling Bias and Winner’s Curse**

While most of our analysis is Bayesian, researchers may also be interested in conducting frequentist inference with the data obtained from a policy choice experiment to draw broader conclusions about the interventions tested, and this requires correcting sampling and winner’s curse biases.

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firms (such as manufacturers of personal protective equipment, who need workers). This evidence corroborates the findings from the GFC, in that although globally engaged firms are intrinsically more sensitive to trade shocks (Claessens et al., 2012), they managed to save more jobs, stay more productive and were more likely to survive than non-exporters (Eppinger et al., 2018). Our study enriches this literature by providing clear evidence of stronger resilience of globally engaged firms in the form of faster recovery of their sales and improvement in financial position.

Several mechanisms can be hypothesized for why globally engaged firms may have been more resilient to the COVID-19 pandemic. _First_ , globally engaged firms benefit from a more diversified portfolio of markets beyond the domestic market which allows them to buffer negative shocks (Esposito, 2022; Hyun et al., 2020; Borin et al., 2021). _Second_ , globally engaged firms have better capabilities such as better management practices (Grover and Torre, 2019; Tanaka, 2020) and stronger technology adoption (Bustos, 2011) and are more productive (Melitz, 2003), which helps them cope with negative shocks (though causality likely runs both ways). Our study confirms these patterns for firms in the BPS data. Emerging evidence reveals that better management practices lead firms to more sound decisions during the COVID-19 crisis (Grover and Karplus, 2021) and multinationals have turned to digital technologies during the pandemic to optimize capacity and improve logistics (Saurav et al., 2021).

Nevertheless, most globally engaged firms have less market power in global trade and their products are more easily substitutable across competitors worldwide, making them more susceptible to negative shocks (Hyun et al., 2020). In this regard, public policy support can play a key role in helping such firms mitigate the adverse impacts of recessions and economic shocks and contribute to a more robust recovery. Export promotion policies helped firms in Belgium and Peru weather the GFC (Van Biesebroeck et al., 2016): supported
lack of market discipline as the government can cover potential losses; (3) managerial corruption, illegal behavior and rent extraction by politicians; and (4) conflicting objectives and impacts of short-term political goals. Such inefficiencies might also affect the innovation behavior of public firms. Bortolotti et al. (2019) investigate the impact of state ownership on the innovativeness of firms, measured by the number, quality and value of patents produced within a sample of roughly 5,000 listed European firms in 1999-2016. They find less innovativeness under government control (fewer patents per dollar invested) and no difference in patent quality (measured by citations in other patent filings).

Part of the literature also focuses on whether developing countries actively invest in green innovations and what are the alternative options to foster a worldwide diffusion of green technologies. Based on international patent data, many studies find that frontier green innovations are concentrated in high-income countries (Dechezleprêtre et al., 2011, Dutz and Sharma, 2012, and Napolitano et al., 2022). While Dechezleprêtre et al. (2011) and Dutz and Sharma (2012) explore historical patterns and difference across countries and regions, Napolitano et al. (2022) investigate the relationship between those patterns and income inequality. However, the three studies find the existence of limitations in the capacity and incentives for the developing countries to undertake frontier and relatively complex green innovation. This may also open opportunities to develop those green technologies in countries that have intermediate levels of per capita income (Napolitano et al., 2022).

As an alternative to frontier green innovations, many studies explore different ways of bringing green innovation to local and poor communities (Dutz and Sharma, 2012). Some studies focus on making the green technologies meet the needs of poor consumers with a dramatic reduction of prices. This strand of literature uses different name such as "bottom-of-the-pyramid (BoP)"(Prahalad, 2012), "inclusive innovation" (Foster and Heeks, 2013), "below-the-radar innovation" (