rafmacalaba/data-use-mentions-extended huggingface dataset:
recorded while the event is ―active‖ (Combs et al. 1998; Jonkman and Kelman 2005). Most events in our sample killed at least one person, as the proportion of events with zero deaths was just 9.9 percent. Table 3 shows that the mean number killed was large, 119, with great variation: the standard deviation was more than 20 times as large. The frequency distribution of this variable shows that the large mean and standard deviation are due to a long right tail, as more than 90 percent of the values are under 100. One particular event, flooding from a 1991 Bangladesh cyclone that had a death toll of 138,000, has an especially large impact on the mean and standard deviation. As a robustness check, we estimate models that exclude events with unusually large death tolls.

As water bodies are not confined to national boundaries, some floods in the archive (less than 10 percent) are regional in scope. For these events, the reported number of deaths is the aggregate figure per event, with no available split between the countries affected. Because of this restriction, our sample excludes multi-country floods from all models.

The DFO reports the magnitude of a flood as the log of the product of three terms: area affected by the flood (in km<sup>2</sup> ) × flood duration (in days) × flood severity. Floods are divided into three severity classes depending on their estimated recurrence interval. Class 1 floods have a 10-20 year-long reported interval between similar events, class 1.5 have a 20100 year recurrence interval, and class 2 have a recurrence interval greater than 100 years. Flood magnitude varies much less than either the number of floods or the number of deaths, with the standard deviation being smaller than the mean.

# _3.2 Exposure: Population in flooded areas_

Each entry in the DFO‘s register of major flood events has an associated GIS polygon representing the area affected by that event. The DFO uses
more than the mean number of voluntary policy councils in a given year and “0” if it does not.<sup>5</sup> The second indicator records whether a municipality has greater than one standard deviation above the mean voluntary council presence and 0 if it does not. Of these observations, 14% are coded “1” and 86% are coded “0”. The third variable records whether municipalities have a mean or greater than mean-level of councils that do carry federal funds with them. Finally, we record the percentage of voluntary councils, out a possible total of 17. The mean percentage is 35 and the standard deviation is 21.

# _Participatory Budgeting (PB), Years PB has been in place_

We updated Boulding and Wampler’s (2008) original database of PB for all Brazilian municipalities with at least 100,000 residents (N = 253). Our information on the adoption of PB programs comes from similar sources as theirs: surveys of Brazilian officials, a non-governmental organization’s records, and secondary sources for the 1989–2016 period (Spada et al. 2016; Wampler and Touchton 2017; Spada et al. 2012; Torres and Grazia 2003; Wampler and Avritzer 2008). We begin with a dichotomous measure, but also move beyond a simple distinction between whether municipalities do or do not adopt PB to capture the significant variation that exists in the quality of participatory budgeting programs (Avritzer and Navarro 2003; Goldfrank 2007; Nylen 2003; Wampler 2007). Our data set records the number of years PB has been used in a municipality- some municipalities have used PB for 20 years where it has become an institution that anchors the budgeting process. In contrast, other municipalities have yet to adopt the policy or have recently adopted it and are still experimenting with its configuration. During 1989–2016, 138 municipalities in our data set adopted a participatory budgeting program for at least four years, although some adopted PB for the entire 20-year period. Accounting for
prevalence of female ownership participation between Africa and the entire sample is only three percentage points (24 percent in Africa versus 27.1 percent in the entire sample). In our multivariate analysis, we will present results for the whole sample as well as for a subsample of sole proprietorships, as this allows us to isolate enterprises that are completely run by a female owner, as opposed to other firms where some

> 5 We cannot compare this to the rest of the world because informal sector surveys where not performed in other regions outside Africa.

7
IDPs are positively associated with employment outcomes in low-income settings (e.g., Verme and Schuettler, 2021).

There are several possible explanations for this link between the presence of displaced people and local economic activity. For example, Alix-Garcia _et al._ (2018) explain it by an increase in the demand for goods and services in the hosting communities. An additional explanation is that social norms and values are different between the displaced population and the host community. When individuals from both groups interact with each other, norms and values of the IDPs may spill over to the host community. To descriptively examine differences between host and IDP households, we use data from the Nigeria IDP Profiling Survey conducted in the northeastern states. We compare the average characteristics of 270 host households with those of 250 IDP households (of which 128 live in host communities and 122 live in camps). Table A7 in the appendix displays these descriptive statistics (compare Columns I and III). IDP and host households are similar in household size (about seven household members, on average), mean age of the household head (around 44–45 years old), and whether the household head is married (87 percent married in the host sample and 90 percent married in the IDP sample). The gender composition is also similar across populations (45 percent women in host populations and 47 percent women in IDP populations). The table also shows that for IDP households, they are on average more educated, more women are head of the household, and more household heads are non-Muslim than for host households. These differences may point to IDP households’ being more liberal than host households (e.g., with respect to the role of women in society). Importantly, we also observe high reported levels of social cohesion. In fact, both IDPs and host-community members report relatively high integration of IDPs. This suggests that conditions are in place for spillover of social norms among IDP households to host households.

34
where _y_ represents output per worker, _k_ denotes the capital-labor ratio, _h_ denotes the level of human capital per capita, and _A_ captures the residual, usually attributed to Total Factor Productivity (TFP). Taking the log on both sides decomposes cross-country income differences into three proximate sources: capital-output ratios, total factor productivity, and average human capital. Since we are only interested in the share of income differences that can be explained by variation in human capital, we provide decompositions of our baseline accounting results for the human capital share. In Table II, we provide decompositions as direct analogies to Schoellman (2011) who used inferred measures of quality as well as measures based on education quantity (Hall and Jones 1999; Hendricks 2002). In Table III we include an additional decomposition: #$(&$%)(#$ (&"%) which provides direct comparisons with a literature using various quality measures. #$("$%)(#$ (""%) To measure human capital, we extend the standard Mincer specification that weights education by its micro labor market returns to consider learning as well as schooling: where _S_ is the quantity of schooling and _L_ is a measure of learning, and _r_ and _w_ are their respective returns. For years of schooling, we use Barro-Lee (2010) data. For learning measures, we use the data presented in this paper. We assume rates of return based on the microeconomic literature. We take the value _r_ = .10 for the rate of return per school year, and _w =_ .20 per standard deviation increase in learning.<sup>8</sup> The .20 value is based on U.S. data. However, we might expect that returns to skills will be higher in developing countries, where the supply of skills is lower, as is the case in the returns to schooling literature. Significant work has been done to identify this parameter value. For the purpose of this paper,
import competition. The private costs of trade liberalization take the form of reduced employment and incomes in industries affected by increased competition from imports. They are not the same as the social costs: the latter are likely to be lower than private costs because of the benefits, in the form of employment and income, that accrue to workers in export industries, whose output.is likely to increase following import liberalization (Matusz and Tarr, 1998). But, whatever their size, private costs are likely to exist and influence the political economy of trade reform. It could be argued that prospects for further liberalization could be affected by whether or not developing countries have institutions which can effectively mitigate the costs to firms or to individuals of increased competition resulting from trade liberalization.

The TPRs revealed that only seven of the forty two developing countries had trade-related adjustment assistance programs. In some cases, e.g. Mexico, the programs focused on providing assistance to firms facing increased competition from imports. In others, e.g. Egypt, the focus was on providing a safety net for affected workers. In still others, trade-related adjustment assistance was offered in the context of programs with wider objectives of raising productivity or strengthening the profitability of SMEs. In none of the cases was it possible to form a judgement as to how significant were the private costs associated with trade reform or on the role adjustment assistance programs played in addressing them. In a few cases, e.g. India, it appeared that the program of assistance had little impact on firms affected by import competition. But there was very little information to suggest that developing countries more broadly have the institutional capacity to cope with the adjustment costs associated with trade reform, especially the private costs incurred by displaced workers It may not be necessary or even optimal to design adjustment assistance programs focusing narrowly on adjustment linked to increase competition from imports. More broadly based programs

> ' For a more detailed discussion of these issues see Michalopoulos, 1999.

43
disproportionately involved in retail trade).

## **3.1.3 Differences between the two data sources**

Table 1 summarizes the key differences between the tax administrative and survey data. It is important to keep these differences in mind when considering what analysis will be completed and comparing the findings across the data sources.

Table 1: Key differences between the survey and tax administrative data

||**Tax administrative data**|**Survey data**|
|---|---|---|
|**Coverage**|All frms|Subset of frms|
|**Frequency**|Monthly|Every 6 months|
|**Number**|Around 20,000 frms|601 frms|
|**Location**|All locations|Only 4 largest cities|
|**Size**|All sizes above thresholds|Only frms with 5 or more employees|
|**Sector**|All sectors|Manufacturing, retail and wholesale|
|||trade, other services|
|**Information**|Sales, employment,|Many frm characteristics|
||location, sector and age||

# **3.2 Analysis**

There are two main parts to the analysis. The first focuses on measuring descriptive trends in sales and employment in the tax administrative and survey data over time, and the second examines what characteristics are associated with changes in firm sales and employment throughout the pandemic.

## **3.2.1 Descriptive trends in the tax administrative and survey data**

The first and most straightforward part of the descriptive analysis is to calculate the overall trends in terms of sales and employment from pre-pandemic levels until mid-2021 according 13
richer and more reliable the analysis will be. Second, the occupational manuals need to be regularly revised if the green index is to be updated. While Germany has a rich occupational manual developed at the 6-digit occupational classification, most developing countries simply adhere to ISCO (and at best, at the 4-digit occupational classification), missing the opportunity to profit from the richness of text analysis.

# The skills requirements approach

One of the main limitations of the task approach, for both O*NET and text analysis methodologies, is that updating the green classification over time is not easy and time-consuming. The O*NET methodology would require regularly reviewing the literature to evaluate if there are new green tasks and/or new technologies. The text analysis methodology, however, is also subject to this problem since occupational manuals with task statements are usually updated every 10 years, a period that seems too long for the current pace of technological change. Countries with modern labor market information systems, usually with a more disaggregated occupational classification (6-digit ISCO or equivalent), may overcome this limitation by directly working with online vacancy data to identify green occupations.

The skills approach uses green skills required in job postings as a signal of the greening of the economy. LinkedIn, for example, is relying on this approach. Consistent with this note, LinkedIn defines green skills as those that enable the environmental sustainability of economic activities.<sup>42</sup> However, their definition is broader, as these skills are grouped into _core green skills_ (such as recycling) which are most directly related to these sustainability-promoting activities; _ambivalent green skills_ (such as fleet management) which may or may not be used for sustainability, and _adjacent green skills_ (such as biology) which can support the acquisition of core and ambivalent green skills. To our knowledge, their methodology for coding the 38,000 unique skills terms into green and non-green is not publicly available.<sup>43</sup> For LinkedIn, green jobs are occupations
18

# **Table 2. Bank Performance, Regulation, and Government Owned Banks in 1999, Survey I**

|`------------------------------------`<br>`------------------------------------`|`---------------`<br>`Bank`<br>`Development`<br>`---------------`|`------------`<br>`Interest`<br>`Margin`<br>`------------`|`--------------`<br>`Overhead`<br>`Costs`<br>`--------------`|`---------------------`<br>`Nonperforming`<br> <br>`Loans`<br>`---------------------`|
|---|---|---|---|---|
|<br>`Capital Regulatory Index`|<br>`-0.0463`<br>`(0.0316)`|<br>`0.00379`<br>`(0.00497)`|<br>
individual equity deals between country pairs and sectors as tracked by ThomsonReuters‘ Platinum database. We pool equity flows for every host country across all possible origins and recent years to obtain information on the total value of M&A inflows per country and sector. We are able to match data on FDI flows with information on services policy restrictiveness for 93 destination countries and 8 services sectors.<sup>32</sup> Table 5 presents evidence on the effect of policy restrictiveness on the global pattern of M&A activity. In columns 1-3 restrictiveness is measured by the STRI score, whereas in columns 4-6 a set of key individual policy measures are included directly.<sup>33</sup> In each case we report results for a specification with sector fixed effects (columns 1 and 4), with country fixed effects (columns 2 and 5), and both sets of fixed effects (columns 3 and 6), though fixed effects are not reported to conserve space.<sup>34</sup> Whether we control directly for major determinants of FDI inflows such as country size, population density and per capita income or whether we account for unobserved country and sector heterogeneity with fixed effects, we find that restrictive policies are associated with a significantly lower value of M&A inflows.

With respect to the STRI, we do not constrain the discrete index to have a linear effect on FDI inflows but rather include indicator variables for three different levels of restrictiveness, relative to the reference group of ̳open‘ economies.<sup>35</sup> Indeed, the results in columns 1 and 2 show that higher levels of restrictiveness deter investment in a significant and monotonically increasing manner, and the effect is not necessarily linear. In column 3, in which we control fully for country-specific and sector-specific sources of variation, the estimates suggest that over a 7-year period, the expected value of M&A inflow is on average about $2.2bn lower in

> less detailed than in the
Kenya (Dyer & Shapiro, 2023). Generalizing this finding to the context of groundwater irrigation pumps, the study suggests that although high costs may dissuade farmers from using groundwater irrigation, interventions providing information about the costs and benefits of these technologies could enhance productivity through increased adoption. It must be noted, however, that the costs substantially increase beyond 8 m groundwater depth (Sekhri, 2014). Research on groundwater costs across 10 SSA countries indicates the total cost for a pump with a depth of 40-100 meters ranges between USD 6,000 and USD 23,200, on average (Xenarios & Pavelic, 2013). With the expansion of input markets in SSA, these costs could have been substantially reduced over the past decade, and that is why we present some estimates on gains from groundwater access up to 50 m deep in our Appendix.

Another constraint to groundwater access stems from the lack of access to the electricity grid. A proposed solution that reduces the variable cost of electricity is the provision of photovoltaic- (or solar-) powered drip irrigation pumps. Although these pumps may help reduce the variable cost, regulation of their use is crucial to prevent over-exploitation of groundwater resources. Finally, subsidizing the fixed costs of groundwater irrigation technologies (while regulating sustainable use) is another avenue of inquiry for policymakers and future research.

# **9 Conclusions**

Rural poverty in Sub-Saharan Africa is closely linked to the lack of substantial growth in agricultural productivity. Enhancing agriculture productivity could stimulate wider economic growth, promote inclusive development, structural transformation, and poverty reduction. Agricultural productivity growth worldwide has been driven by investments in high return rural public goods, improved policies, and institutions. However, Sub-Saharan Africa significantly under-invests in these areas. This paper explores one untapped growth source—the sustainable use of groundwater for irrigation. We measure potential agricultural production gains in Sub-Saharan Africa through groundwater irrigation.

To do so, we construct a novel dataset by merging gridded agricultural data with local shallow aquifer data and their corresponding details.
# **5.   Conclusions**

In  this  paper,  we  use  a  linked  employer–employee  database  from  Brazil  to  examine  the impact  of  trade  reform  on  the  wages  of  workers  employed  at  heterogeneous  firms.   Our analysis  of  the  data  at  the  firm-­‐level  confirms  earlier  findings  of  a  differential  positive  effect of  trade  liberalization  on  the  average  wages  at  exporting  firms  relative  to  non-­‐exporting firms.  However,  this  analysis  of  average  firm-­‐level  wages  is  incomplete  along  several dimensions.  First,  it  cannot  fully  account  for  the  impact  of  a  change  in  trade  barriers  on workforce  composition  especially  in  terms  of  unobservable  (time-­‐invariant)  characteristics of  workers  (innate  ability)  and  any  additional  productivity  that  obtains  in  the  context  of employment  in  the  specific  firm  (match  specific  ability).  Furthermore,  the  firm-­‐level analysis  is  undertaken  under  the  assumption  that  the  assignment  of  workers  to  firms  is random.  This  ignores  the  sorting  of  worker  into  firms  and  leads  to  a  bias  in  estimates  of  the differential  impact  of  trade  on  workers  at  exporting  firms  relative  to  non-­‐exporting  firms. Using  detailed  information  on  worker  and  firm  characteristics  to  control  for  compositional effects  and  using  firm-­‐worker  match  specific  effects  to  account  for  the  endogenous  mobility of  workers,  we  find  the  differential  effect  of  trade  openness  on  wages  in  exporting  firms relative  to  domestic  firms  to  be  insignificant.  Consistent  with  the  models  of  Helpman, Itskhoki,  and  Redding  (2010)  and  Davidson,  Matusz  and  Schevchenko  (2008),  we  also  find that  the  workforce  composition  improves  systematically  in  exporting  firms  in  terms  of innate  (time  invariant)  worker  ability  and  in  terms  the  quality  of  the  worker-­‐firm  matches.

28
sup>35</sup> yet it can be a marker of progress.

In our constantly changing world, as Coyle accentuates, “we now have an economy dominated by services and intangibles, with _much_ greater variety of products and closely linked across national boundaries.”<sup>36</sup> In the cultural context, according to the United Nations Conference on Trade and Development, global trade in creative goods and services reached a record 624 billion in 2011.<sup>37</sup> What is more, these billions do not include informal trade activities that directly and indirectly support livelihoods across the world.

Again, the CTI will not answer to all we need to know about cultural trade. But it may help explain what we understand and what we do not. This, among other benefits, is likely to encourage debate and inform policies that advance cultural activities in economic diversification and meaningful development. It may also help organizations and countries see the need to allocate scarce resources to develop national cultural statistics. On that end, sufficient resources have to not only be made available to collect data. They also have to be made available to analyze and to effectively use the data.<sup>38</sup>

> 33 Stiglitz, Sen, and Fitoussi 2010, p. xxvii.

> 34 See Kabanda 2016a.  This exercise could also lead to the development of the Cultural Commerce Index (CCI), or the Cultural Domestic Product (CDP).  Unlike the CTI, the CCI (or the CDP) would account for not only the cultural exports and imports, but also for the country’s overall cultural economy.  Think of it as GDP minus everything else except the commerce of cultural goods and services.  The International Trade Index (ITI) may also be considered.

> 35 For related discussions See Stiglitz, Sen, and Fitoussi 2010, p. 9; Coyle 2015, pp. 35, 130, 145; and Mwabu 2016. 36 Coyle 2015, p