rafmacalaba/data-use-mentions-extended huggingface dataset:
a tax escrow account, compared to other homeowners with mortgages who make one or two large direct tax payments a year to the government. Areas with higher rates of escrow use also have higher property tax rates, suggesting that the lower salience and automation of collection allows those jurisdictions to collect more revenues.

One contemporary example of how the availability of technology can influence the tax choices of governments in low- and middle-income countries is the emergence of taxes on mobile money transactions in a number of African countries. Digital payments via mobile phones have seen substantial growth in the region, serving as a major driver of financial inclusion (Suri and Jack 2016). For governments, a major justification for taxes on mobile money transactions is to expand the tax net over large informal sectors that are traditionally outside the current reach of tax authorities. A mobile money tax is relatively easy to monitor and can be collected by telecommunication companies and service providers, without the need for a tax authority to make costly investments in identification, detection, and collection capacity. Critics have called it a “lazy tax” (Karombo 2022). Tellingly, the salience of the tax has led to stiff public opposition, with some countries having to reduce the tax rate (for example, Ghana, Tanzania, Uganda) and others to completely abandon it (like Malawi).

Lastly, the decision of whether or not to pursue investments in tax capacity may depend on the current levels of tax collection. In settings with low levels of tax collection, initial investments in tax capacity – such as building a tax database or establishing technology for tax monitoring – 19
The bottom panel in Table 5 shows the correlations between the investment shares predicted by the model and the ones in the data. They are positive and substantially stronger for roads than for electricity. This, in principle, implies a larger degree of misallocation in the electricity infrastructure investments conditional on the available budget.

Looking at roads in more detail, many locations in the data are almost isolated (i.e., have few low-quality connections). When the government connects these locations via additional road segments, trade costs for all locations trading through that road segment fall. Figure 7a shows the degree centrality ( _I_<sup> ̄</sup> _i_ ) for the Fully Optimal scenario against the data.<sup>20</sup> We see that the correlation is positive, but the optimal solution requires substantial reallocation. Because the share of the government budget allocated to roads increases, most locations are above the 45° line. This suggests that the planner reduces investment in roads with less central roles.

Figure 7: Equilibrium Allocations <!-- Start of picture text --> 35<br>-3<br>30<br>-4<br>25<br>-5<br>20<br>-6<br>15<br>-7<br>10<br>-8<br>5<br>-9<br>5 10 15 20 25 30 35 -9 -8 -7 -6 -5 -4 -3<br>Data Data<br>(a) Quality of Roads I ̄ i (b) Log of Population<br>Model Model<br><!-- End of picture text --> <!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>10 20 30 40 50 60 70<br
effect after 1999. Hence we compare FEMA deforestation rates from periods prior to program implementation (1996-97; 1998-99) with post-program FEMA data (2000-01; 2002). Since FEMA enforcement targets private rural properties, our analysis extent was limited primarily to non-protected areas of Mato Grosso. Because the program prioritized enforcement on large landholders (FEMA 2001:21), we compare the change of large-scale deforestation (deforestation clearings greater 200 hectares) versus small-scale deforestation (clearing below 200 hectares)<sup>8</sup> .

# **3.5 Data**

The original and derived data sets (digitally coded maps) that are used in the analysis are summarized in Table 1. All data is projected to Universal Transversal Mercator (UTM), Zone 21 South, South American Datum 1969.

An important variable in our study distinguishes high/low natural vegetation cover. Ideally, we would like to distinguish between deforestation potentially consistent with the RL requirement versus deforestation on properties that are already out of compliance with the requirement. Lacking a map of property boundaries, we constructed a proxy. Using the SEPLAN land cover map, the proportion of natural vegetation cover within a radius of about 1,250 meters – an area of 625 hectares that corresponds to the mean property size in Mato Grosso (IBGE 1998) – was computed for each 100 meter pixel. Based on the biome specific legal reserve requirement, the mean natural vegetation cover in non-protected areas in Mato Grosso was classified into areas of “high” (in concordance with the RL-definition) and “low” (below the RL-requirement) proportion of natural vegetation cover. We defined high versus low cover based on the legal reserve requirement of the revised Forest Code 1965, i.e. 35% in the Cerrado, 80% in Amazon

> 8 Our criterion based on size of _incremental_ clearing corresponds to the criterion of 200 ha _incremental deforestation size_ which distinguishes FEMA oversight (large-scale deforestation) from IBAMA oversight (Mato Grosso – Ordem Serviçio No. 26/00)
final sample includes 61 countries and 21,411 observations after dropping observations with missing values in our variables of interest.<sup>9</sup> In our sample of 61 countries, 28 are classified as more traditional and 33 as less traditional, when using the median of the WVS indicator as the threshold. Table A.1 in the Appendix provides details on variable definitions.

# **2.4 Descriptive stylized facts**

In this subsection, we report some interesting stylized facts, using unconditional means, that emerge from our sample comprising 61 countries.

**Stylized Fact #1** : The share of female-led firms is significantly smaller than male-led ones, and more so in traditional countries. Using the gender of the firm’s top manager as a criterion for measuring female leadership, 14% of the firms in our sample are led by women, with this percentage varying from 12% in more traditional countries to 17% in less traditional ones. By comparison, in the United States, 23% of firms have a female active owner (Morazzoni and Sy, 2022),<sup>10</sup> while 21% of firms have a female solo entrepreneur or a female decision maker in Spain (de Andrés et al., 2021) and Italy (Basiglio et al., 2023) respectively.<sup>11</sup> In India, data drawn from the fourth round of the Micro,

> 7In addition to this, we also considered the following questions: “Being a housewife is just as fulfilling as having a paid job”; “A woman has to have children to be fulfilled”; “University is more important for a boy than for a girl”; and “A pre-school child suffers with working mother”, but they are constrained by the overlap with WBES country coverage.

> 8We define the sample of interest following Ranasinghe (2024). By selecting firms with fewer than 1,000 full-time employees, we drop 240
outlined above to correct for potential endogeneity problems. To this purpose, we take advantage of the panel nature of the data in order to employ a fixed effect estimator methodology (FE, hereafter).<sup>16</sup> In this way, the effect of a variation of the specified covariates is the same for all individuals and over all periods of time, but the average effect for each individual _i_ can be different from individual _j_ . The FE estimator allows us to wipe out all time-invariant unobserved characteristics, which may be correlated with the conflict measure and with our dependent variable. Therefore, we take advantage of the longitudinal design of our ̳ _ad hoc’_ panel data in order to eliminate individual unobserved heterogeneity. The FE model does not account for time-variant omitted variables. Our specification includes thus year dummies that allow us to control for this unobserved time-variant heterogeneity.

In addition to potential sample biases, the results may also contain identification biases. One potential threat to our identification strategy is that trends in education before the 1999 violence were not ̳ _parallel’_ between affected and not affected group. The violation of this assumption would lead to wrong conclusions because pre-existing trends could then be driving post-conflict education outcomes. We have examined the average educational grade achieved before the 1999 violence of affected and not affected groups of individuals. Figures 6a and 6b show that trends in education levels of affected and not affected groups were parallel for cohorts which were not exposed to the 1999 conflict during primary school years and were old enough in 1999 to have completed at least their primary school level.<sup>17</sup> The graphs are done by estimating separate kernel-weighted local polynomial regressions of attained grade of education against age using an Epanechnikov kernel. Given the in figures 6a and 6b, it is unlikely that pre-existing trends may be driving our postconflict outcomes.

# **4.2. Identification strategy: The impact of violence on primary school completion in 2007**

The second aim of this paper is to
and implement health and safety upgrades. Importantly, these conglomerates had made strategic investments over more than 20 years aimed at ensuring access to major global markets, adapting to market trends and changing supply conditions, and upgrading their capabilities. These capabilities’ upgrades were supported by investments in genetic development, global land acquisition, and the automation and digitalization of processes, creating also comprehensive commercial networks to support their expansive operations.

Motivated by these insights, the ensuing analysis will assess if the fresh produce sector’s anecdotal evidence reflects broader trends across other segments of the agri-food industry, as emerging from statistical evidence. This focused quantitative analysis utilizes detailed data to examine agri-food-related firms-to-firm linkages and how these affect resilience and economic performance in the sector. The analysis will probe the key aspects from the fresh the role of cor- emerging produce experience, notably: large agri-food porations, patterns and significance of firm-to-firm linkages for economic performance, and whether the observed long-term strategy of building backward and forward linkages across different industries and geographies constitutes a model that other segments within the agri-food industry are also adopting.

# **3 Data and Methodology**

## **3.1 Data**

**_Data on Firm-to-Firm Links_** The study uses firm-to-firm link data from the FactSet Revere Supply Chain Relationships database, recognized as one of the most comprehensive sources for global firm supply chain information (Huang et al., 2023). This dataset integrates information on a firm’s supply chain from various sources, including official firm filings such as 10-K reports submitted to the Securities and Exchange Commission (SEC), along with other filings (8-K, 10-Q forms), as well as investor presentations, press releases,

7
would be worth much less than the time spent. This tendency for girls to be excluded from certain fields in all kinds of vocational training including apprenticeships is well known in SSA (Adams, 2010). Hick et al, 2011 provided new evidence of this problem in

> 11 Field work in Tanzania reported in Kweka and Fox (2011), found that certain services are performed almost exclusively by men (e.g. butchery, shoe shining ) while charcoal sellers were usually women and women’s hair dressing was an exclusively female activity.

> 12 See World Bank, 2011, for discussion of this point, including cross-country evidence.

> 13 This was the conclusion of Quinn and Teal (2008) using urban labor market data.

22
**Table 5. Savings regressions, Listed Firms data**

|LABELS|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Log of net sales|0.001<br>(0.156)||||||
|Volatility of Sales||0.10***<br>(0.005)|||||
|Total Debt to Assets|||-0.01<br>(0.789)||||
|Interest Rate||||0.24***<br>(0.000)|||
|Gross Margin|||||-0.01<br>(0.748)||
|Net Income||||||0.08<br>(0.324)|
|year==2002|0.05|0.09*|0.07|0.07|0.05|0.05|
||(0.304)|(0.088)|(0.103)|(0.277)|(0.342)|(0.302)|
|year==2003|0.06|0.10*|0.09*|0.07|0.05|0.06|
||(0.210)|(0.055)|(0.051)|(0.209)|(0.268)|(0.212)|
|year==2004|0.06|0.11**|0.10**|0.10*|0.06|0.06|
||(0.174)|(0.030)|
we felt qualified validators could manage logistically.<sup>26</sup> Post hoc calculations of statistical power confirm the estimates we made at the design stage. With a sample of 240, we can detect general over- or under-reporting greater than 17% of the survey mean (14% of the “true” validated mean).<sup>27</sup> Because each treatment arm is a subsample, however, we cannot precisely measure the effect of treatment on misreporting—it is difficult to detect effects greater than 33% of the survey mean (28% of the validated mean). Thus we are principally interested in the sign and magnitude of the treatment effect on misreporting by treatment group.

# **F.5 Coding validated data**

Validators were unaware of the respondents’ survey responses, and formed their own opinions (based on the evidence collected) about whether respondents engaged in the six activities during the time period captured by the quantitative survey. Every coding recommendation was then discussed with and vetted by one of the authors.

A core part of the validator training included logical reasoning, supporting reasoning with evidence, and writing this down in a clear and structured manner. After each visit, validators made written notes about the relevant data collected, including evidence to support their conclusions, on a standardized form. At the conclusion of the four visits, the validator coded six indicators, one for each behavior, where “1” meant that he had relatively direct evidence that the respondent engaged in the behavior during the recall period, and “0” otherwise.<sup>28</sup> Validators recorded an average of 1.35 “major” pieces of evidence per respondent per behavior to support their coding decision sheets. This was typically the most persuasive piece or pieces of

> 26In general, the validation sample was a balanced subsample of the full sample. Power calculations, based on roughly the first 60 validator interviews, indicated that there was a modest degree of
21 requirements, requirement tables, and comparison of the individual and shared nutrient requirements can be found in (Schneider et al. 2021) and Schneider (2021).

# <u>Cost of Nutrient Adequacy (CoNA) Index Construction</u>

Using linear programming, we attempt to identify a diet that meets all the specified nutrient requirements at the lowest total cost. For the individual indicators, upper and lower nutrient constraints correspond to the individual nutrient requirement as scientifically defined, and the household indicators correspond to the shared requirement defined per above. Formally, the linear optimization model (solved using the R package “lpSolve” by Buttrey (2005) minimizes total cost over all foods (f) within upper and lower bounds for all nutrients (j) and meets the specified energy budget (E). Adding data on price (pf) for each food item (f) and its nutrient contents (afj) yields: Equation (4) is solved for each individual (with individualized requirements) and household (with shared requirements, i.e., replacing Lowerj, Upperj and E by equations (1), (2) and (3) respectively) every month, using the foods and prices in the market of the household’s district of residence. We compute least-cost diets at the monthly level based on the household composition observed at the two points in time the household was surveyed. Nutrient requirements
year variable is missing, we assume it as survey-year minus 1 or refer to the corresponding questionnaires if they indicated the actual last completed fiscal year. We use the sampling weight according to median eligibility from WBES. To avoid biases from extreme values, we censor the age of the firm at 100 and the years of managerial experience in a sector at 35.<sup>5</sup> Half of the firms in our sample perform some kind of innovation (Table 2). Process innovation is more widely adopted (40% of firms) than product innovation (20% of firms). Only 10% of firms invest in R&D. Similarly, the use of technologies licensed by foreign-owned companies is limited: only10% of firms do it. Firms tend to be small, located in cities with at least 1 million people. Concerning internal capabilities, firms are relatively young and on average 14 years old. The typical worker has 10 years of education (human capital). Only 10% of firms belong to a large group and receive 10% or more foreign ownership (FDI), respectively. Diversification of own products is also quite low as about three-quarters of firms have more than 90% of their sales come from their main products (diversification index).

_Table 2 Weighted summary statistics of the estimation sample of the first stage of our CDM model_

||N|Mean|SD|Min|p25|p75|Max|
|---|---|---|---|---|---|---|---|
|**Innovation**|321997|0.5|0.5|0.0|0.0|1.0|1.0|
|**Product innovation**|323101|0.2|0.4|0.0|0.0|0.0|1.0|
|**Process innovation*
education or occupation. However, the _Industrial Survey_ (GSO, 2000) breaks down employees of 17 provinces into production workers, technicians and administrative workers for the year 1998. The “unskilled-labor intensive industries” here refer to the industries whose proportion of production workers in total labor force is above median (85.7 percent) in 1998.

> 19 Applying the Smith-Myint model of “vent for surplus” to China, Fu and Balasubramanyam (2005) find that the expansion of exports from labor-intensive manufacturing has accelerated the transfer of surplus labor from the agricultural to the export sector. Using a panel of 92 developing countries in the period 1960-2000, Dodzin and Vamvakidis (2004) find that an increase in openness to trade leads to an increase in the industrial value added share of production at the expense of the agricultural share suggesting that trade leads developing countries to industrialization.

11
time periods in which we are able to detect a statistically significant effect of Fonden on night lights. For example, figure 5b indicates that Fonden has a statistically significant effect between 7 and 18 months, while figure 5d indicates that the impact of Fonden could be detected for up to two years after the disaster.

In figures 6a and 6b we verify that our composite processing choices have had no bearing on the results. Specifically, in figure 6a instead of using pixel level averages that use information from overlapping satellites when available, we limit ourselves to information derived solely from the F16 and F18 satellites. In figure 6b we additionally address the concern that our estimates might be biased by the presence of top coded pixels, and exclude these very bright, but upper truncated pixels from the analysis. While, as expected, these estimates are less precisely estimated than those of figure 5c, they are of similar magnitudes and they further confirm the time period over which the impact of Fonden can be confidently established.

All in all, these figures consistently indicate that Fonden has a statistically significant impact on night lights between 7 and 17 months after a disaster has taken place. The coefficients also suggest that the implied economic impact of Fonden is of considerable magnitude. In figure 5a the 12 month point estimate is 0.57 log points (t=1.93). This coefficient implies that municipalities with access to Fonden grew 6.5% more than those without Fonden in the year following the disaster.<sup>8</sup> This estimate of the impact of Fonden is not directly comparable with the ones derived from the annual light composites, because it is derived from a different sample.<sup>9</sup> Nonetheless, taken at face value it does suggest that the estimates derived from annual composites are likely to be lower bounds of the impact of Fonden.

The key difference between the estimates, is that the estimates derived from annual composites are naturally constrained by the calendar year. Accordingly, these estimates
# **2.1 Data Sources**

The empirical analysis in this paper combines and examines several sources of panel data from Brazil spanning the period 2003-2015. We provide a brief description of each data source in this section and give further details in Appendix A.3.

The main source of data is _Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais_ (RAIS), a labor census gathering longitudinal data on the universe of workers and firms in formal sectors of Brazil. RAIS is a highquality administrative census of formal employers and employees, collected every year by the Brazilian Ministry of Labor. These records are used by the government to administer several government benefits programs. Workers are required to be in RAIS in order to receive payments of these programs, and firms face fines for failure to report.

RAIS covers virtually all formal workers in Brazil and provides yearly information on their demographics (age, gender, and schooling), job characteristics (detailed 6-digit occupation, wage, hours worked), as well as hiring and termination dates. For each job, the RAIS annual record reports average yearly earnings, as well as the monthly wage in December. We use the information on the December wage, so as to ensure that all labor market outcomes are measured at the same time and avoid potential mismeasurement for workers that did not work a full year. RAIS also includes information on a number of establishment-level characteristics, notably the number of employees, geographical location (municipality), and industry code (according to the 5-digit level of the Brazilian National Classification of Economic Activities). Unique identifiers (tax identification numbers) for workers and establishments make it possible to follow them over time. The establishment identifier contains 12 digits, and the first 8 digits make it possible to uniquely identify the firm.

We use the detailed classification of occupations to identify those who switch jobs. The Brazilian Classification of Occupations changed in 2002 (CBO-2) and has been reported consistently since 2003. Although the RAIS data are available for