rafmacalaba/data-use-mentions-extended huggingface dataset:
enters equation (6) can be written as follows: The quantity of private capital follows the same accumulation process as public capital. But with δ<sup>�</sup> as the private capital depreciation rate, and I�� as private investment. The growth rate of private capital per worker is as follows:

> 10 The measured public capital stock K��� here is G�� in Berg et al (2015). Pritchett (2000) refers to K�� in his Equation (2) as the efficient capital stock.

> 11 The treatment of “new” investment versus maintenance expenditure requires some clarification. For instance, Buffie et al. (2012), in their macroeconomic model of public investment effects, consider infrastructure investment as encompassing net investment, as well as operations and maintenance; and treat the depreciation rate as exogenous. Kalaitzidakis and Kalyvitis (2004), in their infrastructure‐led growth model, specify the accumulation of public capital as a function of new investment, and the depreciation rate depends on maintenance expenditure. Our model is more in line with Buffie et al. (2012), in that

> depreciation is exogenous. Conceptually, I�� in our model could include spending on major repairs, which along with new investment helps offset the capital decumulation effects of depreciation. But practically, we note that maintenance spending is typically subsumed under public consumption data and hence is hard to gauge. (From a national accounts perspective, the SNA (1993) notes “ordinary maintenance and repairs to keep fixed assets in good working order are intermediate consumption. However, major improvements, additions or extensions to fixed assets which improve their performance, increase their capacity or prolong their expected working lives count as gross fixed capital formation. In practice, it is not easy to draw the line...Some analysts...would favor a more “gross” method...all such activities are treated as gross fixed capital formation.”) User concern
_GLOBAL FOOD INSECURITY OUTLOOK_ _11_ Any synthetic data case consists of elements describing both the L.H.S. and R.H.S. of the regression model. A drawback from popular non-parametric sampling approaches that mix observed attributes by creating synthetic cases out of dynamically weighted observed cases is that these procedures may result in combinations of covariate values that are internally inconsistent. For instance, nonparametric sampling methods could generate synthetic cases with higher poverty and lower inequality, or simultaneously increase the shares of all types of land cover, ignoring that there is typically a systematic relationship between such variables. This issue can be circumvented when there are a high number of entries in the data for which covariates are observed and only the dependent variable is missing. In this, case synthetic observations can be generated whose covariate values are consistent with actual observations.<sup>13</sup> Here, synthetic cases are generated stochastically using a historical prediction model applied to historical observed covariates. Recall that the restrictions on the use of covariates in the final model are based on the availability of reliable future values of those covariates. For a historical prediction model used only to impute past observations, those restrictions do not apply. This fact is exploited to generate synthetic cases from historical covariates combined with historical proxies for food insecurity. The observations for the prevalence of severe food insecurity start only in 2015, while other food insecurity proxies go back much further in time. Importantly, these include data on undernourishment rates, child mortality rates, life expectancy, poverty at national lines, but also environmental rents and the percentage of urban population in addition to the full set of covariates used in the main model. This allows designing a more accurate model for the sole task of completing historical data which can be used to draw synthetic cases from. This historical imputation model in vector notation is (2) _Y_ = _g_ ( _X, Z_ ) _._ In this model, _Z_ now includes the additional covariates that are historically predictive but cannot be used for
2010; World Bank, 2009).

Broadening of tax bases has been a high priority for many developing countries (IMF 2011, Bird, 2008, Heady, 2002). Such reforms help improve the business climate by establishing a level playing field for all firms and by allowing to reduce the overall tax rate. Expansion of tax bases also results in greater public revenue, which gives governments space to invest in a number of public goods (such as electricity, infrastructure or education) and contributes to macroeconomic stability essential for investment attraction. Furthermore, tax legislation with similar rules for all taxpayers reduces compliance costs and supports small business growth and formalization (World Bank, 2009).

For corporate income tax, coverage can be expanded through reduction of tax incentives (see for example Zolt, 2013, Bird and Wilkie, 2012). For indirect taxes, a VAT on goods and services with few exemptions has become popular throughout the world in lieu of its advantages over other types of indirect taxes, such as multistage turnover taxes or single stage taxes at the manufacturing/wholesale/retail level. A detailed discussion of the benefits of the broad based VAT is provided in Lent et al (1973), Ebril et al (2001) and Pomeranz (2012). In short, a broad based VAT eliminates cascading associated with turnover taxes and removes incentives for firms to vertically integrate; it can be waived for exports to support international competitiveness and its self‐enforcing mechanism facilitates higher compliance (Heady, 2002). To ensure neutrality and non‐interference with market processes, VAT has to be applied to all sectors of the economy.

This paper provides an analysis of competitiveness‐related issues surrounding the design and administration of corporate and consumption taxes in four South Asian countries – Bangladesh, India, Pakistan and Sri Lanka. Whenever possible, tax regimes in South Asia are compared with the prevailing international practices. The paper is based largely on the analysis of tax legislation. Additionally, data from 4
that was 75% below average, while the one with the highest value scored 85 percent higher than the average. The Border Waiting Time indicator is constructed by combining Hummel’s (2001) estimate of willingness-to-pay for saving time for shipment and WBES on typical border waiting time.

# _<u>International Road Transport Union (IRU)-Indices on road transport performance</u>_

Border delays are a costly handicap for trade and tourism. IRU provides a web application with up-to-date information on delays for coaches and trucks at principle border crossings in Central and Eastern Europe, which is designed to provide road transport operators with a practical planning aid. It is updated daily from Monday to Friday, using information supplied by IRU national Member Associations.

IRU indices on road transport performance give estimates of Western European countries’ economic growth, truck registration and transport volumes - indicators that are closely followed in the specialized transport and economic press and by transporters who need to follow the various national trends affecting their operations.

For 17 European countries, the IRU compiles road transport indices that give estimates of Western European countries’ economic growth, truck registration and transport volumes 19 ____________________________________________________________________________________________________________
and conflict data when analyzing FDI in fragile developing countries. Second, institutions offering investment guarantees must recognize the differential exposure and sensitivity of MNEs to conflict when pricing risk. Third, FDI to resource-rich, conflict-affected countries can hamper rather than facilitate countries’ efforts to escape the conflict-resource trap. FDI flows to fragile countries are mostly financing resource projects, deepening resource dependence and thus threatening prospects for successful peace building (Doyle and Sambanis, 2000). Hence, the efforts of fragile developing countries to diversify and attract FDI into manufacturing and services sectors must be accompanied by efforts to improve political stability, governance, transparency, and institutional quality.

The rest of the paper is organized as follows. Section 2 presents a review of the previous literature, a theoretical framework building on the risk and economic geography literature, and several hypotheses, which are tested in section 4. Section 3 presents the econometric framework and the data. Section 4 discusses estimation issues and the main empirical results, and section 5 presents additional analysis exploring the robustness of the results. Section 6 discusses the mechanisms at play in the case of the oil and gas sector. Section 7 provides a summary of the findings and suggestions for future research.

# 2. LITERATURE REVIEW AND THEORETICAL FRAMEWORK

A large strand of the IB and economics literature has analyzed the role of risk in internationalization decisions (e.g., Agarwal & Ramaswami, 1992; Kogut & Chang, 1996; Pennings & Sleuwaegen, 2004; Rivoli & Salorio, 1996). Most of the theory explaining and classifying risk and uncertainty can be traced back to the work of Frank Knight (1921). According to Knight, risk applies to situations in which an informed agent can make a reasonable judgment on the probability of the event occurring; as such, risk differs from pure uncertainty in which these probabilities are unknown. Miller (1992) distinguishes three main sources of business risk: the general environment, the industry, and the firm itself. A large body of literature is focused on one specific
entrapment, lowered self-esteem, loss of autonomy and diminished functional capacity (Smith et al. 1995, Counts et al. 1992, Landenburger 1989, Kirkwood 1992, Walker 1994, Ellsberg et al. 2000).

Dependence on abusers created powerful disincentives for women to act or reach out to any form of support. This dependence was often both economic and emotional, and reinforced by the fact that abusers had cut women off from their networks and support, isolating them from options external to the relationship – way before the escalation of violence. This behavior is in line with the findings of GarciaMoreno et al. (2005) that show that violent partners often keep women isolated from potential sources of help. Many women had never had financial autonomy and had difficulty envisioning their ability to provide for themselves and children. In these cases, women chose to remain in violent relationships for lack of choice.

Women’s hope that the violent partner would change also created personal barriers to help-seeking. Women described cyclical patterns of abuse – violence followed with contrition – making it difficult for them to leave relationships of emotional dependence.<sup>15</sup> In Manguinhos, one woman, age 38, described this cycle: “I was without him for a period, right? But then he asked me back, he said he wouldn’t do anything bad to me, he came here, cried, and I believed it. In the first week, it was great, then he started to get worse and worse.” Risks associated with the exit of a relationship led women to “try again” with the hope that the partner’s violent behavior will cease.

# 3.3.2 Barriers created by bystanders

People to whom women have a close relation play a critical role in facilitating or discouraging access to formal support services. Data from Brazil shows that women who seek help are much more likely to do so from family and friends than from formal institutions. Research from Foundation Perseu Abramo (2010)
(Leamer 1988) or the trade share (Frankel and Romer 1999).<sup>8</sup> The ratio is an elegant measure of trade for three reasons. According to Tang (2011) normalizing trade with GDP controls for economic size and focuses the analysis on trade instead of the size of the economy. In addition, data on this metric is readily available (Fujii 2019).

Although the normalization of trade with GDP is advantageous, it introduces another challenge — the dominance of variability in GDP. According to Fujii (2019), the variability of trade openness can be decomposed into: the variability of trade, the variability of GDP, and the co-variability between trade and GDP. His variance decomposition reveals that heterogeneity in country size or size of GDP can be too influential in explaining observed variation in trade intensity, i.e., variations in the trade ratio from country to country can be attributed more to variations in GDP than trade.

Trade intensity also creates another challenge, the domestic economy size bias. According to Squalli and Wilson (2011), this bias emanates from the ratio’s singular focus on the relative position of a country’s trade performance compared with its domestic economy, imposing a ‘relative size penalty’ on larger economies and ranking them as relatively closed (see Figure A1 in the Appendix). Similarly, Tang (2011) observed that due to the size bias, the trade ratio tends to understate the degree of openness of large economies relative to small ones. For example, this measure ranks the U.S. way below Eswatini and Tajikistan, while China is similarly ranked behind Cambodia and the Lao People’s Democratic Republic — suggesting that the US and China are relatively closed economies. Tang (2011) associated the country size bias with the Balassa-Samuelson effect. He argued that the Balassa-Samuelson effect exaggerates the size of large economies through the prices of non-tradable goods and services which tend to be higher in high income countries than in low-income countries. The
The challenge with comparing results from various studies covering different countries or regions calls for the use of standardized datasets that are comparable across countries. Our second contribution is to offer new, global estimates for the impacts of hotter temperature on inequality and poverty dynamics (including chronic and transient poverty) at the subnational level. We analyze the Subnational Poverty and Inequality Database (SPID), a recently constructed global database derived from official national household income and consumption surveys, incorporating over 1,594 subnational units from 134 countries, and spanning the period from 2003 to 2019 (Nguyen _et al._ , 2023).

Using this database, we obtain new global headcount poverty rates at the subnational unit level, using different poverty lines (from the extreme poverty line at $2.15/day to the higher poverty line at $6.85/day).<sup>2</sup> We further analyze richer poverty outcomes by classifying each subnational unit as being in chronic poverty (i.e., poverty extending over multiple consecutive periods), or transient poverty (i.e., temporary poverty lasting over non-consecutive periods). To our knowledge, these estimates on chronic and transient poverty at a global scale were not available before. The policies to address transient poverty are distinctly different from those for chronic poverty. Specifically, strong social protection programs would most effectively address transient poverty (e.g., as they help prevent the non-poor but vulnerable households from falling into poverty), but longer-term investments in human capital and infrastructure would better tackle chronic poverty.<sup>3</sup> Our analysis suggests that change in temperature has a large impact on poverty at the subnational unit level, with larger impacts over the short-term than over the long-term,

> 2 Unless otherwise noted, all our estimates in this paper are at the subnational unit level. Our poverty measures are adjusted to real values using 2017 PPP dollars.

> 3 See, e.g., Barret (2005) and
- **<mark>Phase 1-- Structural Changes</mark>** <mark>: Budget reform (1); Intellectual Property Rights regime (5); Technology commercialization training (6); Enterprise survey (10)</mark>

- **<mark>Phase 2 – Capturing the Benefits of Knowledge and Innovation</mark>** <mark>: Millennium Science Initiative (2); Matchmaking database (4); Technology transfer center (7); MSTQ system (13); Skills development center (14)</mark>

- **<mark>Phase 3 – Integration and Consolidation</mark>** <mark>:  Matching grants for foreign links (3); SBIR program (8); Fund of funds (9); Matching grants for local industry (11); Market development initiatives (15).</mark>

<mark>As this hierarchy indicates, we propose focusing first on structural changes, next on establishing the institutional infrastructure and finally on implementing various matching grant schemes. However, many activities can be developed and implemented in parallel. For example, various matching grant programs can be discussed and studied in the context of the foresight and budget exercises proposed for Phase 1 and designed during Phase 2 so that they are ready for implementation during Phase 3.</mark> <mark>Finally, many of the recommendations can be categorized according to the eligible activities specified in Priority 2 of the Government’s draft proposal for using EU Structural funds. This would yield the following mapping of recommendations to eligible activities:</mark>

- <mark>Supporting linkages between companies and research institutes (3, 4)</mark>

- <mark>Support new and existing enterprises in the area of innovation (7, 8, 9)</mark>

- <mark>Financial support for companies with new product innovation capabilities in the priority areas of the economy (10,
Policy Research Working Paper 9071

# **Abstract**

This paper combines remote-sensed data and individual child-, mother-, and household-level data from the Demographic and Health Surveys for five countries in Sub-Saharan Africa (Malawi, Tanzania, Mozambique, Zambia, and Zimbabwe) to design a prototype drought-contingent targeting framework that may be used in scarce-data contexts. To accomplish this, the paper: (i) develops simple and easy-to-communicate measures of drought shocks; (ii) shows that droughts have a large impact on child stunting in these five countries—comparable, in size, to the effects of mother’s illiteracy and a fall to a lower wealth quintile; and (iii) shows that, in this context, decision trees and logistic regressions predict stunting as accurately (out-of-sample) as machine learning methods that are not interpretable. Taken together, the analysis lends support to the idea that a datadriven approach may contribute to the design of policies that mitigate the impact of climate change on the world’s most vulnerable populations.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at eskoufias@worldbank.org.

_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 accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or
communities on rivers, open defecation by any household member is 6 percentage points lower in treatment communities than in control communities. The declines in open defecation are driven by declines in open defecation by people who have access to a toilet. Twenty-four percent of households that have access to a toilet reported sometimes defecating in the open; this figure is 4 percentage points lower in treatment communities than in control communities (26 versus 22 percent).

# _8.3 Handwashing_

Improvements in good hygiene practices, especially handwashing with soap at critical times (before handling food and after defecating) can also help prevent childhood diarrhea (Ejemot et al., 2008) and other illnesses such as acute lower respiratory infection (Aiello et al., 2008). Although TSSM’s message focuses on improvements in sanitation as the main vehicle for improving child health, the benefits of handwashing with soap were also discussed. The endline survey collected information on households’ handwashing facilities and behavior.

Table 8a shows that almost 100 percent of respondents reported that they wash their hands after defecating. Of these, 67 percent reported having a handwashing station where they usually wash their hands. Observation by the survey enumerators of the handwashing station revealed that water was available at about 96 percent of the stations, and soap and water were available at about 58 percent of stations. These figures had increased from the baseline but there was no significant difference between treatment and control communities.

# _8.4 Drinking Water_

Drinking water quality is another important determinant of child diarrhea and parasitic infection. The WHO defines improved drinking water sources as tap water inside the house and outside the house, protected dug well, boreholes (includes tube wells), protected springs, and rainwater collection (WHO, 2012). Table 8a shows that a large majority (more than 87 percent) of households had access to improved sources of water in both the baseline and the endline. There is no difference between treatment and control communities with respect to access to improved sources of drinking water. Note that
WEO database.

48 GRP is a comprehensive dataset that contains data on resource revenue for 69 countries from five different sources: OECD Revenue Statistics, OECD Latin American Tax Statistics, IMF Government Finance Statistics, IMF Article IV Staff Reports, and CEPALSTAT Revenue Statistics in Latin America. The WCED is a collection of key macro-fiscal indicators covering 51 countries that are exporters of oil, gas, and metals (such as copper, gold, iron, and silver), where these commodities represent a large share of exports (20% or more of total exports) or fiscal revenues. The dataset is compiled from the following sources: International Financial Statistics (IFS), Balance of Payments Statistics, Direction of Trade Statistics, World Economic Outlook, and FAD’s fiscal rules database. The resource revenue data from the WCED is broadly consistent with GRD, although there are substantially divergent information for specific years and countries. Appendix Figure 9 displays the dispersion of resource revenues data across the WCED and GRD. In subsection I.B, we discuss the robustness of our main results to potential measurement errors present in the revenue dataset.

49 Columns (6)–(8) in Appendix Table 5 summarize the data source and sample size for expenditure and resource revenue. For nine countries, mixed data from the GRD and WCED are used.

35
Strengths and comparisons with other studies_

This study has several advantages over the existing literature. It is based on a very large sample of respondents from national surveys in 53 developing countries, a part of the world that is underrepresented in the literature. Over half of the surveys used random digit dialing, typically in upper middleincome countries in which a large share of the population uses mobile phones. In lower income contexts, most surveys, 35 percent overall, sampled based on previous face-to-face surveys which were in turn drawn from a census frame. In the remaining 10 percent of cases, sampling was carried out from lists provided by mobile phone operators. In each case, surveys drawn from pre-existing face-to-face surveys were reweighted using the baseline data to become more representative, while many random digit dialing surveys were also reweighted to make those samples more representative of the universe of phone numbers. While it is impossible to eliminate issues of representativeness in phone surveys, the high17