rafmacalaba/data-use-mentions-extended huggingface dataset:
spatially explicit estimates of forest loss to the country-level to create a panel dataset of estimated annual forest loss for 193 countries from 2001 until 2014. While high-resolution satellite imagederived estimates represent a dramatic improvement over previously used data for country-level forest loss estimates (Steininger _et al._ , 2001; DeFries _et al._ , 2002), there are notable limitations that warrant some discussion.

First, the data only provide estimates of forest _loss_ at the annual level, not forest gain. Because tree growth is a slower process than tree clearing, the data only reports estimates of forest gain over a 12 year period (from 2001 - 2012). Consequently, our empirical work only considers forest loss, not net forest change, from RTAs. While RTAs may lead to shifts in the distribution of forested land over the long run (on the order of decades, at a minimum), we argue that differences in the short run dynamics of deforestation and afforestation will likely still create acute environmental externalities. Hence, given the nature of our setting and question of study, we believe that annual forest loss is the important metric to study. Second, the data do not distinguish between different types of forest loss. The classification of tree cover is any vegetation greater than 5 meters in height. Thus, this classification does not allow us to separately examine deforestation in primary forests from harvesting trees from timber plantations (for example). Finally, the detection algorithm used in our version of the data (version 1.2) changes slightly in 2011 - 2014 relative to the earlier years. At the time of writing this paper, Version 2 of the dataset, which intends to provide the entire sample period with a consistent detection algorithm, is not yet available. We believe this is only a minor issue and the use of year fixed effects should account for year-to-year changes in detection common to the entire sample.

# **3 Methodology**

We develop a matched _agreement-level_ approach that aims to control for the potential endogeneity of environmental
stark patterns in differential response according to recall module, suggests that methods to improve the accuracy of recall surveys should consider a dual-track approach: (1) efforts related to the prompting of households to report any positive consumption because recall modules underreport the consumption incidence associated with almost any food group and (2) efforts aimed at improving the accuracy of consumption value reporting, conditional on the household reporting any consumption.

Regarding efforts to improve the accuracy of consumption incidence data, note that the absolute magnitude of recall error is particularly high for foods that are not commonly consumed. Further prompting in the interview and perhaps the use of locally salient images to aid the memory of survey respondents may therefore represent avenues of exploration, especially for less common food items.

Regarding efforts to improve the accuracy of consumption value reports, note that overreporting of value is a particularly prominent error for short recall periods such as seven days. Unlike the incidence error, we do not have a strong lead on what causes the misreports. If telescoping of high-value consumption items is the driver, then one approach sporadically discussed in the literature is to bracket the recall period with a surveyor visit at the start of the relevant period as a reference for the respondent during the second visit when the recall is reported. The initial visit from a survey team may help delineate the exact period of recall and improve accuracy. However, this approach would need to be validated before being taken up more broadly. It also has consequences for fieldwork structure and cost (in cases where mobile teams rather than resident enumerators are used).

We have extended the basic analytic framework with respect to how food reporting errors across modules interact with the key socioeconomic characteristics of households and with food characteristics such as perishability or the prevalence of home production. With respect to interactions with household characteristics, the bias in consumption incidence is most pronounced among disadvantaged households 16
(Tsivanidis, 2019). I compute these times for four different modes of transportation: walking, car, traditional buses, and the subway. I add five minutes in each station when I compute times for the public transit network, and three minutes when I compute travel times for “car” to capture the time spent in the parking lot. To compute commuting and shopping iceberg costs, I take an average of these times across the different modes. I calculate a matrix across census tracts of approximately 13 million observations.

Table C7: Calibration of speeds using trip data from Google Maps

|Type|Speed<br>_Panel A: Public transit system_|
|---|---|
|Subway Lines|601.24 m/min|
|Metrobus|308.13 m/min|
|Bus|216.67 m/min|
|Walking|90.00 m/min|
||_Panel B: Types of roads for cars_|
|Autopista|752.03 m/min|
|Avenida|266.84 m/min|
|Boulevard|608.12 m/min|
|Calle|198.56 m/min|
|Callejón|69.643 m/min|
|Calzada|169.98 m/min|
|Carretera|623.38 m/min|
|Cerrada|123.39 m/min|
|Circuito|304.69 m/min|
|Corredor|160.75 m/min|
|Eje vial|273.98 m/min|
|Pasaje|240.71 m/min|
|Periférico|673.43 m/min|
|Viaducto|399.99 m/min|

_Notes:_ This table reports the calibration of speeds using trips from Google maps. The calibration uses 4,000 random trips. The information was downloaded with the command _gmapsdistance_ in R that uses the Distance Matrix Api from Google. I computed these times between 8 am - 11 am and 5
LSDVC model to other popular models designed to address dynamic panel data bias, including the popular system GMM model developed by Blundell and Bond (1998). They find that even in the case of moderate endogeneity and serial correlation, the LSDVC emerges as the most accurate methodology. We estimate dynamic model (1) separately for the resource and non-resource sectors and perform a Chow test (Chow, 1960) in order to test whether the coefficients in the resource and non-resource FDI estimations are statistically different from each other. The Chow test is designed to test whether the coefficients of a model estimated over one group are similar to those estimated in another group.

In addition to analyzing the short-term effect of political violence on greenfield FDI inflows, we are also interested in the long-term effect. The dynamic panel model (1) makes it possible to identify the long-term equilibrium effect of political violence on greenfield FDI as follows:

# **Results**

Table 1 shows the baseline results estimated using the Bun and Kiviet LSDVC estimator for three different specifications in which the dependent variable represents total greenfield FDI (columns 1 and 2), resource greenfield FDI (columns 3 and 4), and all other greenfield FDI, also referred to as nonresource FDI (columns 5 and 6). Battle-related deaths are negatively associated with total FDI flows, and the variable is significant at the 10% level. A 10% increase in the number of battle-related deaths decreases total greenfield FDI flows by approximately 0.95%, ceteris paribus. This effect is in addition to the decline in FDI stemming from worsening macroeconomic conditions and restrictions in investment policies; these additional effects are captured by the controls for changes in GDP, exchange rates, inflation, and the regulatory quality <mark>index. The long-term effect of BRD on total</mark> greenfield FDI flows, shown in Table 1, is slightly greater than the short-term effect. We therefore find support for the first hypothesis (H1), namely that political conflict has a negative effect on
to SME access and usage of finance as per the latest WBES available in a country. Our final sample is a cross-section of 153 countries around the world, including both advanced economies and emerging markets and developing countries, spanning 2006-2020.

FCS countries are defined as those countries with a Fund for Peace’s Fragile States Index (FSI) above the cutoff point of 90, as in Assaf et al (2021). The FSI is a composite indicator based on 12 sub-indicators capturing a country’s political and economic strength, internal cohesion, and social stability. Scoring is done with a 10-point scale, with 1 for least fragile and 10 for most fragile. These are then added up to give a total score for the country, which serves to determine country rankings.<sup>2</sup> This approach gives 31 countries classified as FCS (see Table 1 in the Annexes). Our classification of FCS countries is robust to alternative definitions such as the one provided by the World Bank Group.<sup>3</sup> According to our data, SMEs represent an important share of firms and employment in FCS countries. In line with world averages, SMEs in FCS countries account for 85 percent of all registered companies, employing 60 percent of the total labor force, more than in non-FCS countries (Figure 1.a). However, a significant share of SMEs in FCS countries is likely to operate in the informal sector. Based on <u>Elgin et al (2021), informal output in our sample of FCS</u> countries accounts for more than 40 percent of official economic activity on average. For formal firms, access to finance is consistently ranked among the biggest business environment constraints for SMEs in FCS countries, especially for small-sized businesses (Figure 1.b).

SMEs in FCS countries lag their peers in non-FCS countries in terms of both access and usage of financial services. While SMEs in FCS countries perform relatively well in terms of access to accounts (
purchaser of the highest reported transaction. Providing evidence of third-party reported transactions and transaction partners clearly demonstrates the tax authority's possession of third-party reports and thus makes the message highly credible. In addition, providing examples rather than the total amount of third-party reported transactions, as in Carrillo, Pomeranz, and Singhal (2016), allows us to apply this treatment to small or marginally formal rms with very small third-party reports.

> 21As the message style and content used in past communication campaigns with non-lers varied from year to year, relied on a diverse set of communication methods (emails, SMS, phone calls, invitations for personal interview), and messages were not systematically sent to all late lers, we consider that a treatment eect compared to the control group cannot simply be due to sending a dierent message.

> 22This mention of your client is added to make the message suitable for being sent directly to the rm or the rm's legal representative.

12
# **6.2 Employment Upgrading for Skilled Migrants**

Our empirical analysis has shown that over-qualification is a pervasive problem among Venezuelan migrants in Ecuador. As a result, their productivity and wage levels are well below those of Ecuadorans in the same region of residence and with the same education level. This is a waste of productive skills that lowers GDP and shifts the burden of the labor-market adjustment toward low-skill natives (as documented in Olivieri et al. (2020)).

A natural policy to consider is to facilitate the access of highly educated Venezuelans to skilled jobs. The main goal of this section is to conduct a simulation of the potential effects of such a policy. As a first step, we begin by analyzing some relevant information in the EPEC survey. In practical terms, a barrier that may be preventing skilled Venezuelan workers from applying to skilled jobs may be the lack of transferability of education credentials or even the lack of credentials altogether.

We estimate that around 51,000 Venezuelan migrants in Ecuador (completed) college education prior to migration. Among these, 76% report having received a college degree certification. Presumably, the remaining 24% left the country prior to receiving the certificate.<sup>19</sup> Our data reveal that only 17% of the 51,000 Venezuelan college graduates have registered their college certificate with the corresponding government agency in Ecuador (SENESCYT). The survey also reveals that among those graduates who have not registered their degree, about 90% state that they are interested in carrying out the registration but are unable to do so because they lack the required documents (47%), are not familiar with the procedure (35%), or lack the funds to pay for the associated fees (7%). In contrast, only 1.4% are currently in the process of registering their degree in Ecuador.<sup>20</sup>

## **6.2.1 Upgrading in Terms of Education**
in 2011-15 and 2016-20, respectively. The lack of a visible response to the 2008-09 commodity price boom indicates that domestic institutions have an important role in translating exogenous shocks into effective demand by investors with the means and know how to improve agricultural performance. To probe if the nature of large farm establishment in Eastern Province differs significantly from that in the rest of the country, we aggregate data on the number of farms and the area they cultivate from the Zambia Integrated Land Management Information System (ZILMIS), the country’s (as of yet incomplete) electronic registry by district and year of farm establishment for the period 2000-2017 inclusive and, in addition to an indicator variable for the post-2008 period, include an interaction term between Eastern Province and post-2008 dummies to test for significant differences in the post-2008 trend in either of these variables between Eastern Province and the rest of the country. Results, as reported in appendix table 1, point towards a positive trend post-2008 in both variables presumably due to greater use of the digital system, but they do not allow us to reject equality of trends in Eastern Province and the rest of the country based on existing registry data. We thus have no basis to assume that the time pattern of large farm establishment outside of Eastern Province is different from that found here. With only 3% of farms reporting to have acquired more land (through rental or sale) after

> 21 While there are some discrepancies between own estimate and mapped area, the fact that the software used did not allow immediate crosschecking of polygons caution us against over-interpretating these.

> 22 Central Government institutions administer land that had been converted to state land long time ago (e.g., under resettlement schemes) whereas land received from chief implies a recent conversion.

10
Our comprehensive characterization of the weighted firm size distribution shows that not only is the average business size smaller in LDCs, but that the most outstanding contrast with AEs is an extreme skewness towards tiny productive units. While 70% of the workforce in AEs is engaged in firms of 10 or more employees, in LATAM, that fraction is only 32%. Roughly half of the (68%) remaining workers in the region are self-employed without employees, while the other half work at (or own) micro-businesses with fewer than 10 employees. Firm-level databases, even economic censuses, neglect a significant fraction of these two groups either as a deliberate design decision or due to the difficulties in identifying these businesses and recording their information. For instance, the Mexican Economic Census, the region’s most comprehensive dataset on businesses, arguably covering all private businesses outside agriculture, covers only 60% of workers in the private non-agricultural sector. Most of the omission has to do with workers in self-employment and in businesses with fewer than five workers (Busso et al., 2012). Much of the activity left out in LDCs is informal because own-account workers and micro-firms in these countries most frequently do not make social security contributions or register their businesses (three-quarters of this segment in our LATAM data).<sup>2</sup> Still, the omission occurs even in datasets that also intend to cover informal firms.

The data also show a tight correlation between individuals’ earnings and business size in LATAM. As an extreme manifestation of this close relationship, the share of individuals in LATAM’s bottom income decile working at firms of 10 or more employees is only 3%, while over 60% of individuals in that income decile are own-account workers without employees. Firm size and individual earnings are also correlated in AEs, which is consistent with models of firms that are heterogeneous in productivity and pay wages correlated with the marginal productivity of their workers, but the gradient is much weaker. The allocation of workers across size categories,
**Figure 10. The long-run impact of the indexation of minimum wage by inflation (Scenario 1) across regions (NUTS-3) of the country.**

<!-- Start of picture text --> 25 2.5<br>20 2<br>15 1.5<br>10 1<br>5 0.5<br>0 0<br>Impacted (%) Job losses (%) Wage increase (%)<br>Impacted (%)<br>Job Losses and Wage Increase (%)<br>Suceava Constanca Buzau Valcea Braila Botosani Harghita Bihor Ialomita Neamt Calarasi Vaslui Teleorman Tulcea Caras-Severin Galati Hunedoara Olt Giurgiu Mehdinti Maramures Bacau Satu Mare Alba Gorj Dambovita Vrancea Arad Salaj Bistrita-Nasaud Prahova Iasi Arges Dolj Mures Covasna Ilfov Cluj Brasov Sibiu Timis Bucharest<br><!-- End of picture text --> Source: Own estimates based on administrative tax data and microsimulation model Note: Sample is restricted to wage earners, assuming all dismissed workers move to unemployment or inactivi 46
7.955|315|
|5. Regulation|5.928|1.123|2.722|8.678|312|

_Source_ : Gwartney, Lawson, and Hall (2017) Economic Freedom of the World (EFW) report. _Note_ : Summary statistics for the Gwartney, Lawson, and Hall (2017) EFW index and its components, 1990–2015.
do not work with VAT data directly, we simulate taxable value-added as sales minus material inputs.<sup>12</sup> Since firms adjust their material costs proportionally to the shock, our estimated drop in value-added is exactly proportional to the size and duration of the revenue shock. Annual VAT revenue drops on average by 12% (19%) of its baseline value. This leads to a 0.7% (1.1%) drop in tax revenue as a percentage of GDP on average across countries.<sup>13</sup> The fourth row of table 2 shows the increase in losses accumulated by firms over the year. The absolute

> 12In some countries, a share of the fixed costs—for example machinery—can be deducted from the VAT. Therefore our simulated value added might be too large. To account for this, we obtain the effective VAT rate as the tax rate which, when multiplied by our simulated value-added, equals the official VAT collection at baseline. The average effective VAT rate in our sample is 19%.

> 13The VAT loss calculation assumes that, when sales minus materials costs _<_ 0 in a particular month, the firm pays no VAT that month, but does not “carry forward” the difference between sales and material costs in month _t_ as a deduction in month _t_ + 1. As firms are normally allowed to carry forward negative value-added for some time, this is a simplification which may lead us to under-estimate the true VAT loss.

14
sample for the analysis of intergenerational mobility consists of children 18-35 years of age in 2016, and the estimation samples for the investment equation are 6-22 and 8-23 years in the 2010 round of the panel. This age range is chosen so that the age cohorts in the investment equation have substantial overlap with the age cohorts in the mobility analysis. In CFPS-2016 for the 18-35 age cohorts, the mean schooling of fathers is 7.65 in the sons sub-sample and 7.67 in the daughters sub-sample in the urban areas according to Table T1.U. The average schooling of sons is 11.17 years, and 11.18 years for daughters. However, there is a gender gap in the average schooling attainment in rural China: 9.2 (sons) years and 8.7 (daughters) (see Table T1.R, upper panel). The average education expenditure is _higher for daughters_ in the urban estimation samples, but favors the sons in the rural samples.

# **(4.2) Data for India: IHDS (2012, 2005) and NSS (1995)**

The data for estimating the intergenerational mobility of education in India come from the India Human Development Survey (IHDS)-2012. The construction of matched children and parents pairs are slightly different between sons and daughters. To generate matched son-father pairs, we follow Azam and Bhatt (2015) closely (see the Table 8 in Azam and Bhatt (2015)). The only difference is that we are using wave 2 (2012) while Azam and Bhatt (2015) use wave 1 (2005) of IHDS panel data. For all household heads, their father’s education information is available directly in the household module, irrespective of a father’s residency status at the time of the survey. The comparison between our sample using IHDS2012 to Azam and Bhatt’s sample using IHDS-2005 is documented in the online appendix Table A1.S. To generate matched daughter-father pairs, we follow Azam (2016
quality (captured by the _Doing Business_ Getting Electricity indicator) by one standard deviation is associated with a 9-percentage point increase in labor productivity. The Global Competitiveness indicator of Electricity Quality, which is perhaps a narrower measure of the quality of the power supply system, is not significantly related to firm performance. Overall, the complementary findings from the Enterprise Survey and _Doing Business_ bolster the case for the importance of the power supply system for firm performance, for both productivity and growth.

Modern infrastructure, as proxied by the adoption of internet technology, is robustly associated with both productivity and firm growth. The Enterprise Survey’s Web indicator, capturing the local prevalence of internet usage for firm businesses, is positively and

> 16 A higher value of the measure indicates a lower level of corruption.

15
As a proxy for the spatial dispersion of economic activity beyond simple shares attributed to major cities, we compute Pareto exponents ( _α_ ) (Gabaix, 2009; Soo, 2005). The third column in Table 2 shows that while county-level GDP and population show relatively even distributions ( _α_ close to one), measures of economic activity derived from the VAT data exhibit much higher inequality. The Pareto exponents for employment, value added, and trade flows are 57-76% lower than for overall economic activity, indicating higher spatial concentration.

Is this spatial concentration driven by firm locations and number of firm-to-firm relationships (i.e. the extensive margin of trade) or trade volumes (i.e. the intensive margin of trade)? In Appendix A.5, we show that the spatial concentration of both trade out- and inflows is primarily driven by firm locations and the number of firm-to-firm relationships. The number of transactions and average trade volumes per transaction only play a minor role in explaining spatial variation in trade patterns. This insight later informs our choice to focus on a model that allows us to predict the structure of the production network along the extensive margins.

To assess whether the observed spatial concentration and urban bias are idiosyncratic features of VAT data or reflect patterns innate to formal activity, we also compute the spatial concentration for a range of other indicators for economic activity. These range from the least formal to increasingly formalized economic activities and are included in Table 2. We observe a clear pattern: activities that capture very little formal activity such as the number of micro-, small-, and medium-size enterprises (MSMEs) and employment in MSMEs exhibit relatively little spatial concentration ( _α_ = 0.86 and 0.78, respectively). As we focus on more and more formalized activity, e.g. licensed businesses from micro to medium sized, all the way to firms in the census of industrial production, the degree of concentration increases. This suggests that the spatial concentration observed in the VAT data represents