rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_the top 5 percent of exporters, the average number of products and destinations per exporter, and entry and exit rates are explained to an important extent by those characteristics._**

We conduct an ANOVA decomposition of the same set of Database measures shown in Table 2 taken at the country and HS 2-digit sector level –averaged across the 2006-2008 period– to determine the explanatory power of country effects and sector effects.<sup>43</sup> The results are shown in Table 7 and indicate that country and sector effects combined explain an important share of the observed variation in the number of exporters, the average number of products and destinations per exporter, the share of the top 5 percent of exporters, the number of exporters (export base), and in exporter entry and exit rates.<sup>44</sup> In contrast, country and sector effects do very little to explain total exports in the country-sector, the average or median size of an exporter and exporter survival rates in the country-sector. The pattern of total exports across sectors in a country reflects comparative advantage. Since total exports in a country-sector are by definition determined by the number of exporters in the country-sector and their average size, the importance of country and sector effects in determining the number of exporters but not the size of exporters implies that comparative advantage works primarily via firm size.

There are also interesting differences in the importance of sector versus country characteristics in explaining exporter behavior. Sector characteristics do more to explain the share of top 5 percent and the number of products, while country characteristics explain better the number of exporters, the number of destinations, and entry and exit rates. While many of these differences are to be expected, the importance of sector for the share of top 5 percent implies that market structure drives a good part of concentration as opposed to country rules and

> 43 The ANOVA decomposition is a type of statistical hypothesis testing, whereby the observed variance of a given variable is partitioned into components attributable
could be trusted”, and on average, only 6% of the surveyed parents are relatives or close friends of each other, although there seems to be quite a large variation in social ties in the different schools. The third panel of Table 2 shows that 76% of the parents help with homework at least once a week (higher than the 54% generated by the LSMS data), and on average parents had 4 meetings with the head teacher in the previous semester, or 8 meetings in a year, as shown by the LSMS data.

> 22 See the instructions in Appendix for a full description of the examples employed in both versions of the public goods experiment. As we discuss in the next section, the specific example used does not seem to affect the correlation between individuals‟ behavior in the public goods game and their voting behavior. 23 Table A1 in Appendix reports the specific questions used in the two surveys to generate measures of social capital and of parental involvement with their children‟s education. 24 Principal factor analysis is a technique used to summarize information contained in a large number of correlated variables into a smaller number of mutually uncorrelated components. The first factor or component is the linear index of all the observed variables that captures the most common variation among them. The first factor or component in our analysis captures 53% of the common variation among the 14 asset variables. One of the assets included in the wealth index is the ownership of a television, which is likely to be particularly important to convey information about the national elections; we found that 98% of the parents own one.

12
*3.2 Municipal governments and their finances**

Brazil has over 5,000 municipal governments, with remarkable financial and political autonomy. They are led by an elected mayor and an elected city council. Legislative, budgetary, and administrative authority are concentrated in the mayor’s office, and the city council is largely responsible for approving the mayor’s policies (Couto and Abrucio 1995; Wampler 2007).<sup>16</sup>

> 13This is 2,288 calories. See Ferreira _et al._ (2003) for computations of three poverty lines for Brazil using 1996 household survey data. The lowest is the indigence line. The authors also present a set of spatial income deflators. 14This is the 1996-97 Living Standards Measurement Survey, carried out by the Brazilian Institute of Geography and Statistics (in Portuguese, Instituto Brasileiro de Geografia e Estat ́ıstica (IBGE)).

> 15I partial out the effects of child age and gender, household size, municipality fixed effects, and several household head variables including gender, migration status, years of education dummies, race dummies, and age dummies.

> 16For a thorough discussion of local politics in Brazil, see Ames (2001) and Kingstone and Power (2008). For a treatment specific to the politics of education, see Plank (1996).

8
given level of expected quality, uncertainty about quality is likely higher in smaller countries, where donors have obtained relevant information from fewer projects and have undertaken fewer diagnostic and other analytic studies. If donors tend to be risk averse, this higher uncertainty can reduce use of country systems.

Use of country systems may be higher in more aid-dependent countries for two reasons. First, delivering higher volumes of aid may be accomplished in part by disbursing a greater percentage of it in the form of direct budget support, which by definition uses country PFM systems. Second, the impact of donor decisions to use (or bypass) country systems on strengthening (or undermining) government capacity and accountability is likely to be greater where aid volumes are higher. These potential impacts may also be greater in lower-income countries, perhaps explaining in part why governments of many middle-income countries receiving modest levels of aid show relatively little concern over donors’ choices of aid management practices (Wood et al., 2011). The net effect of income on use of country systems is therefore ambiguous.

Use of country systems is expected to increase with the quality of recipient countries’ national development strategies, as measured by PD Indicator 1. These qualitative assessments were based on three criteria (OECD, 2008; World Bank, 2006): (1) existence of an authoritative country-wide development policy; (2) realism of the development policy with clearly-identified priorities; and (3) explicit links with policies in the budget.<sup>8</sup> This “Strategy” indicator is scored on an A-E scale. Nearly half of the observations in the sample receive the middle score of C; only 2% have the highest grade of A and 1% have the lowest grade of E.

> 8 Scores were assigned by World Bank staff based in part on information provided by government officials responding to a PDMS questionnaire.  These assessments are not done by the same Bank staff responsible for the CPIA.  Clist, Isopi and Morrissey (2012) show that countries
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effect of a higher intensity of PROSPERA’s treatment on the youth’s intergenerational occupational mobility?

2. What are the determinants of the occupational attainment of the rural youth beneficiaries? Do differences by sex, ethnic background or migratory condition exist?

Addressing the first set of questions will enable us to identify how much the social stratum of the rural beneficiary youths of PROSPERA has changed relative to their providers (usually their parents), as well as the strength and pattern of the association between their occupational origins and destinations. Furthermore, we will explore if this varies by sex, ethnic background or migratory status, as well as if a higher intensity of PROSPERA’s treatment increases intergenerational mobility and reduces inequality of opportunities.<sup>5</sup> The analysis of these questions is carried out through the construction of intergenerational occupational mobility tables, the calculation of the absolute mobility rates and the application of log-linear models to analyze relative mobility or inequality of opportunities.

The analysis of the second set of questions will identify some of the factors, and the strength to which they do, that affect the occupational attainment of rural beneficiary youths of PROSPERA and that may help to have a better understanding of the results of the first set of questions. It is important to identify how much weight some ascribed factors related to their social origin (the constructs of social origin and the provider’s cognitive abilities) and non-ascribed factors that act as mediators between social origin and occupational attainment (the constructs of the youth’s cognitive abilities, education and first occupation) have on the youths’ occupational attainment. Likewise, it will allow us to know if other ascribed characteristics such as sex and ethnic background shape this process and whether migration from their locality of origin can reduce the weight of the ascribed factors.

This document is structured into seven sections, beginning with this introduction. Section II contextualizes the design, impacts and limitations of PROSPERA within the macroeconomic and social development context of Mexico during the past two decades. Section III provides
)|<br>so|so|
|Imports||||||
|Industrialized<br>countries|<br>68.0|6.0|71.6|3.0|62.1|
|Developing countries|32.0|5.1|28.4|6.0|37.9|
|Upper middle-incomec|<br>(17.4)|<br>5.8|17.7|..|g|
|Lower middle-income|<br>(8.0)|<br>4.1|5.9|..|..|
|Low-income|(7.2)|<br>3.6|4.8|..|..|
|Memosandum items:||||||
|NICs,|4.5|7.9|6.7|10.0|15.6|
|Asia-Pacific regione|13.6|7.6|19.3|7.0|29.6|

______________________________________________________________________________

- a. Excluding World Bank non-member countries.

- b. At 1985 prices.

- c. Including high-income oil exporters.

- d. Brazil, Hong-Kong, Mexico, Singapore and South Korea.

- e. ASEAN countries, Australia, China, Hong-Kong, Japan, New Zealand and South Korea.

- f. Same countries as above, plus tentative estimates for the GNP of the US Pacific Region and of British Columbia, based on personal income data for these areas.

Sources: World Bank, 1987, and author's estimates.
reports showing a distribution of 59.6% individuals residing in urban areas and 40.4% in rural areas. Regarding province of residency, 44.2% individuals reside in the Coastal region, 31.4% in the Interior region and 23.9% in the Western region according to estimates using the CFPS. These shares are also comparable to the respective shares in official data of 45.2%, 33.1% and 21.9%.

> 10 Hong Kong SAR, Macao SAR, and Taiwan, China are not covered. For more details on the CFPS survey, visit: <u>https://www.isss.pku.edu.cn/cfps/en/index.htm.</u>

11
nation (such as the standard of living the migrant would expect to achieve at destination) and the relation between the individual and the location (such as distance to destination or the interaction between the individual’s wealth level and the destination’s wealth level).

Our data setup has 78 observations per individual, which allows us to address observed and unobserved individual characteristics of the individual by including an individual fixed effect _αi_ in the regression. Another econometric concern is whether the structure of the error terms is influenced by the baseline survey’s two-stage sampling design. We therefore report standard errors that account for clustering within each of the 51 KHDS villages of origin, which were the primary sampling units in the baseline survey (Abadie et al., 2017).

The key independent variables of interest include the characteristics of the destination district in vector **D** _d_ and relational variables in vector **R** _id_ that are specific to the _i − d_ pair, such as distance of _d_ to _i_ ’s baseline village and how the effect of destination characteristics changes with individual characteristics such as education interacted with destination characteristics. We do not include any individual, household or community level effects. These are controlled for by the inclusion of the individual fixed effect _αi_ , which helps protect our estimated coefficients on the destination and relational variables of interest from bias due to unobserved individual heterogeneity (selection).

We then estimate the following equation: where _εid_ is an error term.

In our most basic regression setup we populate **D** _d_ average wealth at destination and **R** _id_ with the natural logarithm of the km distance of the destination district to the baseline community.

11
definition of national administrative boundaries follows the standard World Bank global administrative map. However, national boundaries are further disaggregated into subnational units for all countries where World Bank household surveys are available with subnational representativeness. These subnational units are typically provinces or states (i.e. admin1) but can also include custom groupings of subnational regions determined by the sampling strategy of household surveys. Overall, this study covers 211 countries, which are disaggregated into 2,183 subnational units.

6
in import demand directed to the labor market as a source of variation in exports, we document a positive causal effect of positive export shocks on employment, residual wages, and job turnover rates in the corresponding labor market.

Motivated by this reduced-form evidence, we develop a dynamic general equilibrium model of labor mobility and trade, featuring the endogenous number of job options as well as frictional labor mobility between and within labor markets. Our model characterizes the mechanism through which trade shocks affect the number of job options, labor mobility, and workers’ welfare. We provide a sufficient statistic result showing that the effects of a trade shock on relative welfares are fully embedded in the gross flows between labor markets. This is a powerful result that greatly simplifies the analysis of the welfare impacts of trade shocks. We then structurally estimate the model using the same administrative data from Brazil and the same IV strategy used in our reduced-form analyses. Armed with the estimates of the structural parameters and the sufficient statistic result, we find that a 10% change in exports during the sample period increases the lifetime welfare of a median worker in the formal labor market by 2.99% of the annual wage.

40
Policy Research Working Paper 11036

# **Abstract**

This paper uncovers new stylized facts on the emergence and diffusion of green technologies across countries, sectors, and firms. It draws on the textual analysis of patents and corporate earnings calls matched with multi-country, firm-level panel data for 2012–2021. The paper documents the growing importance of green technologies after 2019, as revealed by a rapid increase in the share of earnings calls transcripts mentioning keywords associated with them. Among initially high-emissions firms, those that mentioned green technologies in earnings calls tended to observe a decline in carbon emissions in subsequent years. Buyer-supplier relationships and innovation partnerships with these firms, especially when they had high-emissions intensity, are systematically linked with the diffusion of green technologies.

This paper is a product of the Development Research Group, Development Economics and the Prosperity Vertical. 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 pbastos@worldbank.org and lcastro@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 the governments they represent._ Produced by the Research Support Team
Given the larger number of remarried widows and the fact that 60 percent of current widows identify themselves as heads of households, while a majority of the rest reside either with their parents or brothers, the following analysis assumes that widows who accepted the levirate are classified as currently married by the DHS.

Table 5 shows the sample of women interviewed in the 2006 DHS by urban-rural sector and marital status. Around 16 percent of this sample have been widowed or divorced at some time, with a rate that is higher in urban than rural areas. Out of 14,583 surveyed women between 15 and 49 years of age, near to 5 percent (655) are previously widowed but currently remarried (430 or 2.95%), or currently widowed (225 or 1.54%). Many more have ever been divorced (12%) and most of these have remarried.<sup>18</sup> The data also reveal that in rural (urban) areas 57 (74) percent of once married women have no co-wives while this is true only for 26 (31) percent and 44 (62) percent of remarriages of prior widows and divorcees, respectively. An examination of the spousal rank of women in polygamous unions confirms that wives who have previously been married are less likely to be ranked as first wife. In urban Mali, 44 percent of once married women are first wives compared to 6 and 11 percent for previously widowed and divorced wives, respectively. In rural areas, the figures are 41 percent for first marriages versus 16 and 23 percent, for those whose first husband passed away or who have divorced.

In sum then, remarriages are much more likely to be into polygamous unions as lower ranked ─ and hence lower status ─ wives.

The 2006 DHS also contains information on a number of individual welfare indicators for women as well as for their children. The first of these I examine is women‘s body mass index (BMI), a measure of health. Low BMI
UN (2019), and Biemer (2010)). We make sure to compare our framework with such other relevant frameworks.

Other collections of case studies of the impact of data on development exist as well, and we will draw examples from some of these other collections. Among these are the Data Impact Case Studies gathered by Open Data Watch (2021), the Value of Data Case Studies gathered by the Global Partnership for Sustainable Development Data (2021), Open Data Impact Map’s collection of use cases of open data (Center for Open Data Enterprise 2021), GovLab’s collection on the same topic (Verhulst and Young, 2016), a World Bank collection on the same topic (World Bank 2015), data2x’s collection of examples related to gender data (data2x 2018 and data2x 2019a), and the World Bank’s collection of examples related to digital identification in the health sector (World Bank 2018a), the public sector (World Bank 2018b), and the private sector (2018c).

We aim to contribute to the literature by directly tying the theoretical framework of data attributes to case studies, that is, to show how certain attributes of the data can help generate value for development. We make no attempt to estimate the net social value of government data, in part because many of the benefits occur in dimensions for which prices do not exist (e.g. improved health from drinking clean water), but this also reflects our view that there is no satisfactory way to assign a monetary value to an inexhaustible, or nonrival, good. The limitless scope for data to be used and reused to address new and unexpected problems, as well as the potential for data to be misused for public harm, suggests to us that any estimate of the expected net social value of government data is one that will be very imprecise. Rather by reviewing a series of examples where public data has been used to improve well-being, we provide evidence of the many ways
(Ancey, 1974; Anderson and Leiserson, 1980; Chernicovsky *t al., 1985; Delgado, 1979; ILO, 1985b; Liedhola and Chuta, 1976; Hatlon at al., 1979; Mueller and Zevering, 1970; Norman, Simmons and Hays, 1982; Okafor, 1983; Thomi and Yankson, 1983). Focusing exclusively on agricultural households, farm management surveys in numerous ; :tcan countLies have found 15 to 65 percent of farmers with secondary employment in nonfarm enterpr.ses, and time allocation studies of farm households have found 15 to 40 percent of total family working hours devoted to income generating nonfarm activities (Anthony et al., 1979; Cleave, 1974; Hill, 1977; ILO, 1985b and 1985c; Luning, 1967; Norman, 1972; Okafor, 1983).

B. Income Africa's rural inhabitants typically derive between 25 and 30 percent of their income from nonfarm sources (Table 2). And because nonfarm activities are monetized to a much larger extent than is agricultural production, nonfarm earnings constitute an even larger share of cash income. Panel B of Table 2 indicates that nonagricultural income regularly accounts for 30 to 50 percent of rural cash incomes.

C. Income Distribution Available data on African rural nonfarm earnings do not show the clear pattern of equity enhancement that analysts have regularly reported in Asian studies (e.g., Bell, Hazell and Slade, 1982; Kilby and Liedholm, 1986). Researchers in Northern Nigeria (Matlon, 1979) and Lesotho (Van der Weil, cited in ILO 1982h) have found that high-income rural dwellers derive a greater share of their earnings from nonfarm sources than do the poor. Less detailed evidence from rural Tanzania (Collier, Radwan and Wangwe, 1986) and from farm families in Zambia (Ghai and Radwan, 1984), Uganda (ILO, 1985d) and Zimbabwe (Weinrich, 1975) suggest similarly inequitable trends. But support for the equity enhancing view of rural nonfarm incomes does emerge from some studies