rafmacalaba/data-use-mentions-extended huggingface dataset:
(2)|(3)|(4)|(5)|
|C-19_×_Distance|-0.014|-0.001|-0.037***|-0.033***|-0.034***|
||(0.016)|(0.005)|(0.008)|(0.008)|(0.009)|
|C-19_×_pc exports (log)||||0.004<br>(0.003)||
|C-19_×_CARES exposure|||||0.045<br>(0.137)|
|Observations|22354|25600|25600|25600|25600|

**_Notes:_** Table 2 shows robustness checks of the main results. _Panel A_ presents results using alternative measures of distance between the municipality and the US-Mexico border. Column (1) uses a measure of distance in log(km) and column (2) presents results using a north municipality indicator that takes the value of one if the distance between the municipality and the USMexico border is less than 350 km. Column (3) uses the minimum distance to the border or airport measured in log(hr). Columns (4) and (5) present results for restricted samples using log(hr) as in the main model. Column (4) drops observations below or above percentiles one and 99 of the change in biannual remittances distribution and column (5) restricts the sample to municipalities in which less than five percent of households have relatives living in the United States. _Panel B_ summarizes results of placebo tests and after the inclusion of additional controls in the main model that might confound the main effects. In column (1), we restrict the sample to non-north municipalities (distance _≥_ 350 km) and define a north municipality dummy that takes a value of one if its distance is below the median
future investments. Thus, Duflo et al. (2011) find that providing farmers with input price subsidies during harvest times can help overcome this problem. They show that providing time‐limited subsidies to fertilizer just after harvest, when farmers have money, can result in sizeable changes in fertilizer use in Western Kenya. This implies that farmers may require market incentives to overcome behavioral limitations such as present bias.

Overall, the economic literature provides several key lessons in terms of what factors affect technology adoption among smallholder farmers. First, the technology should be easily available and of reasonable quality for the farmer to even try it. Low output price fluctuation and well‐functioning credit and insurance markets are also factors that promote input adoption. Similarly, the availability of information, through extension services and training often results in increased adoption, particularly if paired with social networks. Social networks and ICTs can leverage the knowledge imparted by extension programs alone, and help to increase their impact. For technologies that have been in use, such as fertilizer and improved seeds, opportune subsidies for inputs that encourage purchases when farmers are least cash constrained can also jump‐start adoption. With positive net returns, farmers are more likely to purchase the inputs in subsequent years.

# _4.2 What do we know about farmers who adopt inputs in Uganda?_

In this section, we make use of five rounds of the Uganda National Panel Survey 2005/06‐2013/14 to examine whether some of the relevant factors identified in the literature are associated with input use in Uganda. More specifically, we look at the degree to which access to markets, past weather conditions and prices affect input use. We also assess how usage rates correlate with information and training, credit access and certain plot characteristics. We are unable to consider the role of behavioral factors, due to a lack of good measure of these in the panel data survey. In addition to inorganic fertilizer and hybrid seeds (for which we analyzed the financial returns in Section 4)
LAI, defined as the green leaf area per ground area is closely related to FAPAR, the fraction of radiation in the 400−700 nm spectrum that is absorbed by green elements of the vegetation canopy and thus available for photosynthetic activity by plants. Finally, the LSWI uses the shortwave infrared (SWIR) and near infrared (NIR) bands that are strongly absorbed by liquid water to provide a measure of the total amount of liquid water in vegetation and its soil background that will be relevant to check if excessive extraction of soil water by deep-rooted sunflowers is a plausible be mechanism for sunflower’s negative impact on successor crops’ yield as observed above.

As illustrated in table 6, crop rotation characteristics for maize based on data from 3 seasons explain 30% to 40% of the variation in vegetation indices. Results are consistent with what was obtained for yield in that sunflower is by far the worst predecessor crop for maize with an outsized effect (compared to that of other crops) especially on water availability as measured by the LSWI. The relatively better performance of other <mark>crops as compared to cereals may be attributable to their positive impact (compared to soybean as omitted category with which they may also share N-fixing properties) on water balances both in dry and in average</mark> years. Insignificant differences in terms of water balances between maize and soybean (with a negative <mark>effect only for maize being cultivated three times in a row) may also partly explain that the negative impact of continued maize cultivation is relative limited.</mark> Results for vegetation indices for areas covered with soybean in table 7 reinforce the notion of soybean being a better successor crop than maize for other crops (where coefficients are positive and significant for <mark>all four indices) and itself in dry seasons (all indices for 2016/17 are positive and significant with the</mark> exception of the positive and insignificant LSWI). Cereals as predecessor crop have a positive impact on LSWI as they
taxes and 95 percent of excise duty collected by the Government of India (CMIE) (Goldberg et al., 2010).<sup>14</sup> Prowess records detailed product-level information at the firm level and can track changes in firm scope over the sample period. Prowess is therefore particularly well suited for understanding how firms adjust their product lines over time, for example, in response to changes in import competition. Prowess enables us to track firms’ product mix over time because Indian firms are required by the 1956 Companies Act to disclose product-level information on capacities, production and sales in their annual reports. As discussed extensively in Goldberg et al. (2010), several features of the database give us confidence in its quality. Product-level information is available for 85 percent of the manufacturing firms, which collectively account for more than 90 percent of Prowess’ manufacturing output and exports. More importantly, product-level sales comprise 99 percent of the (independently) reported manufacturing sales.<sup>15</sup> From Prowess, we use the data on firms that were classified as manufacturing firms at any point during the period 1994-2013.<sup>16</sup>

# **_3.3 Estimation Strategy_**

To explore the correlates of servitization, we estimate the following equation: e + Φ + Ψ (1) ii,tt ii,tt ii,tt ii,tt ii tt ii,tt Where ss = αα T is a measure of TT + ββ e sse _Servitization_ ee + γγΠ for firm _i_ at time+ εε _t._ CMIE data provides information on each product offered by a firm. At the extensive margin, servitization is measured as a binary variable, where one of the ii,tt products offered by a manufacturing firm is a services activity.ss<sup>17</sup> At the intensive margin, servitization is measured as the share of revenue from services sales in total sales of the firm. T is _firm productivity_ for firm _i_
Policy Research Working Paper 10014

# **Abstract**

As countries rapidly urbanize, settlements are expanding into hazardous flood zones. This study provides a global analysis of spatial urbanization patterns and the evolution of flood exposure between 1985 and 2015. Using high-resolution annual data, it shows that settlements across the world grew by 85 percent to over 1.28 million square kilometers. In the same period, settlements exposed to the highest flood hazard level increased by 122 percent. In many regions, risky growth is outpacing safe growth, particularly in East Asia, where high-risk settlements have expanded 60 percent faster than safe ones. Developing countries are driving the recent growth of flood exposure: 36,500 square kilometers of settlements were built in the world’s highest-risk zones since 1985–82 percent of which are in low- and middle-income countries. In comparison, recent growth in high-income countries has been relatively slow and safe. These results document a divergence in countries’ exposure to flood hazards. Rather than adapting their exposure to climatic hazards, many countries are actively increasing their exposure.

This paper is a product of the Sustainable Development Chief Economist Office. 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 author may be contacted at jrentschler@worldbank.org, pavner@worldbank.org, shallegatte@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
**Figure 1: Female employment share, exporters vs. non-exporters**

<!-- Start of picture text --> 0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Agriculture, Fishing Mining and Manufacturing<br>hunting and quarrying<br>forestry<br>Exporters Non-exporters<br><!-- End of picture text --> Source: GEOSTAT Statistics Survey of Enterprises for 2006-2017.

It is worth discussing the validity of the instrumental variables. The F-statistics for the first stages (a test of the exclusion restrictions) are all above the desired critical values, with corresponding p- values for the null hypothesis of irrelevant instruments close to zero. However, the estimated coefficient on the instrumental variable is positive and highly statistically significant in all of the specifications, which implies that real appreciations of the exporting country’s currency are associated with increases in firms’ export over sales ratios. We cannot fully test the validity of the exclusion restrictions by conducting over-identification tests because the models are just identified.

Differentiating by export destination, the analysis finds evidence that exporting to ECA and middle-income countries has significant correlations with employment. We consider the following export destinations in separate regressions: EU, Russia, non-EU non-Russia ECA, high-income, upper-middle income, lower-middle income and low-income markets. Although the OLS results suggest a significant correlation with the share of exports to ECA and middle-income countries, the IV results are not significant. Instead, there is a positive and statistically significant relationship with exporting to lower middle-income countries and employment levels. A 10 percentage point increase in the share of sales that are exported causes female employment levels to increase by 0.5 percent.

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government capacity was lower. Finally, an additional finding of the evaluation is that clients in middle-income countries (MICs) prefer non-lending to lending services, and clients in all countries prefer TA to ESW.‖ (IEG, 2008) Furthermore, the World Bank has conducted research on its own research—that is, the publication record of the World Bank and its use by scholars. Ravallion and Wagstaff (2010) use bibliometric data to analyze the World Bank publications. They find that the sheer volume of publications on development surpasses any other academic institution. In terms of indicators of quality and relevance, the Bank‘s scholarly publications are on a level with, or superior to, the top universities. One somewhat negative finding, though, was that there is a fair share of publications that receive no citations.

The above discussion refers to scholarly use of the Bank‘s publications, but what about Bank staff themselves: do they use the Bank‘s research? Ravallion (2011) analyzes data from a recent survey of World Bank staff to find that the results are mixed. Most Bank senior staff value the Bank‘s research and know it well. On the other hand, a substantial minority find the research difficult to access. Others find little value in the research for their daily work. Poverty, human development and economic policy oriented staff seem to value and use the research more than staff in the more ―lending-intensive‖ sectors of agriculture, rural development, energy and mining, and transport and urban development.<sup>70</sup> The Bank‘s own Independent Evaluation Group (IEG) implicitly admitted that the measurement of development effectiveness of knowledge activities is weak when it put this topic on the agenda of their ―evaluation week‖ at the World Bank offices in the fall of 2011. At the center of the discussion was the first Knowledge Report produced by the Bank.<sup>71</sup> The Knowledge Report lays out a new knowledge strategy for the Bank
vastly different approaches to taxing small businesses, even though there are somewhat similar economic circumstances, and this contributes to variation in inequality across countries. While existing studies have shown how STRs have been shown to produce regressive outcomes in high-income countries (Aghion _et al._ (2024)), this paper is among the first (along with Tourek (2022)) to show inequitable outcomes of an STR in lower-income settings. These inequitable outcomes manifest in three ways: minimum thresholds that are not binding in practice, taxpayers’ responses to changes in taxation contribute to inequality, and efforts to simplify the tax paying process only increase tax payments made by taxpayers below the median of the distribution.

This paper is organized as follows. Section 2 provides a comprehensive overview of STRs, describing three key design features and how they vary across Sub-Saharan African countries. Section 3 provides details about the TOT regime in Kenya and the tax administrative and survey data that this study draws on. Section 4 presents the findings of the analysis of taxpayer behavior in Kenya, including the results of an event study and randomized experiment. Section 5 discusses the implications of this analysis of the trade-offs faced by policy makers when designing STRs.

# **2 Review of Simplified Tax Regimes**

## **2.1 Design Features of Simplified Tax Regimes**

When designing an STR, policy makers will first need to determine the regime’s objectives. These objectives typically involve revenue collection, simplicity, and equity while minimizing real distortions. They can be, however, in conflict with one another. For example, a more progressive (i.e., vertically equitable) tax regime is typically less simple, and too high a tax burden may undermine the functioning of a simplified tax regime, resulting in inequitable outcomes. The key design features of STRs that influence the trade-offs between equity, simplicity, and revenue collection can roughly be divided into three categories: i) the level of eligibility thresholds; ii) whether a set fee or a
the countries at the end of 2015.<sup>10</sup> For the OECD countries, we relied on publicly available information and policy information from the OECD STRI database (Geloso Grosso et al., 2015).

This paper focuses on five major services sectors and modes, covered by the Database. The five major services sectors are disaggregated into further subsector-modes (as shown in Annex Table 1): financial (banking and insurance), telecommunications, retail distribution, transportation (international maritime shipping, maritime auxiliary, road freight, and rail freight services) and professional services (accounting, auditing and legal). Within each subsector, the database covers the most relevant modes of supplying the respective service: establishing commercial presence or FDI (mode 3 in WTO parlance) in every subsector; cross-border trade in services (mode 1) in financial, transportation and some professional services; and the presence of service supplying individuals (mode 4) in professional services.

Among the TPP countries, there are 29 regional and bilateral trade agreements with services components. Among the 66 country pairs within the TPP region, 43 country pairs have PTAs covering services (Annex Table 2). Chile has the most number of PTAs (with all 11 TPP partners), while Canada has only four PTAs (with the US, Mexico, Peru and Chile). We reviewed these agreements and their annexes for reservations made for the 27 sub-sector modes covered by the database (for the sector list see Annex Table 1). For each subsector-mode, we identified the measures and commitments made under the PTAs, and the PTA with the most open regime was used as the benchmark.<sup>11</sup> It is hard to measure precisely policies affecting services trade because of their variety and complexity (see, for example, Hoekman (1996) and the overview by Deardoff and Stern (2008)). We rely on a measure of the restrictiveness of a country’s policy regime for any subsector-mode, the “
**Figure 2: 2006 PISA Scores of Jordanian Students, by Type of School**

<!-- Start of picture text --> Science<br>Non-Discovery<br>Reading<br>Discovery<br>Math<br>0 100 200 300 400 500<br><!-- End of picture text --> Source: Authors’ calculations Data from the National Assessment for Knowledge Economy Skills (NAfKE)—which measures Jordanian student achievement in the subjects of math, science, and the Arabic language—sheds further light on the impact of JEI, particularly as the NAfKE assessment complements achievement data with surveys of participating students, teachers and principals. The NAfKE data set offers information on student achievement at two points in time, 2006 and 2008, in five types of schools in Jordan: public non-Discovery schools, public Discovery Schools, Directorate of Military Culture schools, schools of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), and private schools.

The analysis in this chapter concentrates on a sample of students attending public Discovery and non-Discovery schools in the 5th, 9th, and 11th grades in urban areas in Amman who participated in NAfKE in 2006 and 2008. A simple analysis of the characteristics of this sample shows differences in average characteristics between students in public Discovery and public non-Discovery schools (Table 4). When data is grouped by student, family, and school characteristics, it is notable that a larger proportion of students in Discovery Schools attended preschool; however, this difference becomes much smaller in the cohort of students tested in 2008. On the other hand, the difference in the proportion of students that repeated classes was almost negligible between Discovery and non-Discovery schools, favoring non-Discovery students in the 5th and 9th grades in 2006 and those in 5th grade in 2008.

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remains unchanged, overall economic activity is negatively impacted by conflict-related violent events.

**Threats to identification** The firm-level fixed effects in our baseline specification account for all time-invariant firm, sector, and geographical characteristics. However, our estimates may still capture some factors that vary over time at the local level and are simultaneously correlated with the number of conflict events as well as the firm’s economic performance. To address this concern, we augment model 1 with a full set of area-time interacted fixed effects to also control for all time-varying, location-specific determinants of firm performance, such as infrastructure availability and local labor market characteristics.<sup>18</sup>

> 15Previous studies find that conflict typically raises consumer and input prices (Amodio and Di Maio, 2018; Utar, 2024; Del Prete et al., 2023; Couttenier et al., 2022).

> 16Due to the coverage of the WBES, the null effect on profits applies only to surviving firms that remain formal—that is, those observable in subsequent survey waves. However, not all firms exiting the sample necessarily go bankrupt. As discussed in Section 4.2.3, conflict may also induce firms to shift to the informal sector, causing them to disappear from our sample (which includes only formal firms) while continuing to operate in the economy.

> 17While the average effect of conflict exposure on profits is null, in Section 4.3, we discuss the firm and country characteristics for which profits decrease with conflict exposure.

> 18 _Area_ is the most granular geographical unit of observation in the WBES and represents the sampling region. There are 314 areas, and they are typically larger than the 20 km radius buffer used to construct our conflict exposure measure.

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for us to make comparisons across owners. Only 5.14% of the firms in our survey are publicly listed and over 67% are sole proprietorships. Only 8 observations are foreign owned firms and 26 observations are government owned enterprises. This lack of variation in ownership type limits our ability to analyze how different ownership types affect firm’s experiences with corruption.

While the limited variation in the types of firms operating in Bangladesh constrains our ability to examine certain questions, such has the differences in foreign versus domestic firm experiences with corruption, Bangladesh has other advantages for the study of how corruption affects firm activities. Bangladesh ranks 134 out of 178 in 2010 Transparency International Corruption Perception Index and is thus one of the most corrupt countries in the world. Bangladesh ranks in the bottom third in global governance indices such as the Doing Business rankings, the World Bank Institute (WBI) indicators for government effectiveness, rule of law, and control of corruption. Reviews of the country’s governance, most notably by Bangladeshi experts such as BRAC University’s State of Governance Reports, have noted entrenched problems in many key public institutions (IGS 2009).

Our research on corruption and investment in Bangladesh complements existing cross-national and single country studies of corruption. Researchers attempting to measure firm level corruption have the options of directly asking respondents on the level of corruption, or ask firms about their overall perceptions of corruption. The first strategy requires firms to potentially incriminate themselves, while the second strategy requires meaningful subjective assessments.<sup>7</sup>

> 7 Olken (2009) finds that individual perceptions of corruption, while containing some information, is highly noisy and subject to bias.

8