rafmacalaba/data-use-mentions-extended huggingface dataset:
services affiliates. The left panel shows the breakdown based on the number of affiliates and the right panel shows it by share of revenue. This relationship holds for both exporters and non‐exporters. Examples of sectors with high shares of post‐manufacturing services and low productivity include wearing apparel, leather, and manufacturing nec. No relationship is found for establishment services, while back‐office services are positively correlated with productivity. The back‐office finding indicates that sectors that produce more in‐house back‐office services such as chemicals, pharmaceuticals, and coke and petroleum, tend to have higher levels of productivity.

**Figure 3.8: Productivity and Post‐manufacturing affiliates and revenue (2014)**

<!-- Start of picture text --> Productivity and Post-manufacturing Affiliates Productivity and Post-manufacturing Revenue<br>Manufacturing firms Manufacturing firms<br>Basic metals Basic metals<br>TobaccoCoke, petroleum Coke, petroleum Tobacco<br>Chemicals Chemicals<br>ICT & electronics ICT & electronics<br>Paper Paper<br>Motor vehiclesElectircal machineryBeverages Motor vehiclesElectircal machinery Beverages<br>Rubber and plasticsTextiles Rubber and plastics Textiles<br>Pharmaceuticals Pharmaceuticals<br>Fabricated metalsmachinery Wood Leather Fabricated metalsmachineryWood Leather<br>Non-metallic minerals AgribusinessManufacturing nec Non-metallic minerals Agribusiness Manufacturing nec<br>Wearing apparel Wearing apparel<br>Printing, publish Furniture Printing, publishFurniture<br>Other transport eq Other transport eq<br>20 40 60 80 100 0 20 40 60 80<br>Share affiliates from Post-manufacturing Share revenue from Post-manufacturing<br>Source: EIS data from Turkey; author’s calculations.<br>8.5 8.5<br>8 8<br>7.5 7.5<br>TFP ACF TFP ACF<br>7 7<br>6.5 6.5<br><!-- End of picture text -->

**Benchmark regression analysis yields similar results: manufacturing firms with establishment, post
variables: Firms with higher firm-wide productivity confront a lower threshold for the attribute that guarantees positive profitability from the provision of a given product and, hence, are able to supply a wider range of products to the market. Idiosyncratic distortions interfere with this decision. Firms that are taxed at a positive rate would find it harder to supply products to the market, while firms that are subsidized will find it easier. This mechanism of transmission of misallocation frictions to aggregate productivity is new to the literature and is one of the forces that we are interested in characterizing quantitatively.

# **2.4 Distribution and Evolution of Idiosyncratic Productivity**

Firms are endowed, upon entry, with a realization of firm-wide productivity and a continuum of attributes for each of the in the unit mea- product product types sure. We assume that entrants start off at a firm-wide productivity level consistent with the ratio of the average size of entrants to incumbents in the US data, and then fan out over time according to a stochastic process. Attributes for each product, on the other hand, are drawn independently and identically from a known distribution _F_ � _e_<sup>_λ_�</sup> . Unlike firm-wide productivity, we assume that the product attribute is constant throughout the life-cycle of the firm.

Firm dynamics are driven by the stochastic process for firm-wide productivity. I parameterize such process taking a discrete-time random walk approximation to a Brownian motion with drift _μ_ and variance _σ_<sup>2</sup> . Following Stokey (2008), I assume that given current log-productivity _ω_ , next period’s log-productivity could give an upward jump of size _h_ , with probability _α_ , or a downward jump also of size _h_ , with 1 _− α_ probability ( ) . The discrete time approximation of the drift and variance of the process is The appeal of the discrete time random walk approximation of the Brownian motion is that it easily maps into the model of Luttmer (2007), who shows that the resulting stationary distribution from such
the country’s fiscal system in 2019 on poverty and inequality, with an eye on vulnerable populations, such as elders and children. We show that the effects of some fiscal interventions do improve poor people’s lives, while there are still many others that reinforce their vulnerability.

Leveraging the CEQ methodology and both household survey and administrative data, we find that both poverty and income inequality decrease from the pre-fiscal income to different concepts of post-fiscal income. Extreme poverty reduces by 5.1 p.p. and moderate poverty by 0.6 p.p. Income inequality, in turn, declines by 6.4 Gini points from MIPP to consumable income. Despite the relatively low reduction in the poverty headcount ratio, we demonstrate that the Brazilian fiscal policies do alleviate the situation of the poor, as both the poverty gap and the squared poverty gap reduce by a third and a fifth in terms of extreme poverty, respectively. Notwithstanding the large impacts of fiscal policies, Brazil remains one of the most unequal countries with a Gini coefficient of 0.521.

Our results also indicate that direct transfers and public services benefit mostly the individuals from the first quintile of the pre-fiscal income distribution. Programs such as BPC, Bolsa Familia, and rural pensions are key examples of such direct transfers that mainly go to the poorest. We also show that the Brazilian fiscal system tackles more poverty among households with elderly people, while seems to have the opposite effect on those with children. Importantly, we also document that the current policies lead to a higher poverty reduction for Afro-Brazilians than whites among the elderly. However, poverty remains high among Afro-Brazilians at 41 percent (compared to 19 percent among whites).

Such analyses hold the promise of providing actionable information for policy makers and fostering datadriven insights for researchers on possible reforms to the fiscal system that could improve its capacity of shortening gaps and promoting welfare-enhancing interventions. The recent move towards increases in transfers to children residing in low-income households seems like a step in the right direction. Coupled
3,945 total), and analyze the change in the share that experience any of the IPV events often, the estimates are small in magnitude, imprecisely estimated, and change signs between specifications.<sup>34</sup> Thus, even among women that experience multiple IPV incidents each year, there was little change in the severity of the IPV.

Second, it is possible that the time period covered by the questionnaire- one year- is too long and might also capture events from before the disaster, which would contribute to noise in the measure and the less precise estimates. However, this is unlikely to be the case. The surveys in disaster-affected regions were delayed until November and December of 2018, which already is six to seven months after the disaster and limits the time period covered that is prior to the disaster. Additionally illustrating that this is likely not the entire explanation, the food security questions

> 34The result is the same when estimated with the full sample of women, where the indicator for experiencing IPV often for the excluded part of the sample is zero.

18
|Yes|
|Receiving FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|F-test|344.58|||
|p-value|0.0000|||
|Hansen's J chi2||3.00468|6.13152|
|p-value||0.3909|0.1054|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800. Instruments are centered around their mean, and they are as follows: (i) the size of the bilateral diaspora between countries i and j in the 1960s—and its square, and (ii) the size of the unskilled diaspora original from country i residing in country j in 1990 (migrants with only primary or lower levels of education)—and its square.

One way to see whether my interpretation of the different effects of inventor diaspora is plausible is to include tertiary educated stocks of migrants as extracted from census data. The data come from Docquier et al. (2009) and were originally built from two census rounds, 1990 and 2000, in
* p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1959 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

66
2100]. These estimates, plotted in Figure 1, suggest that there exist a positive relationship between SLR and the increase in global mean surface temperature, but also a time component, related to the substantial inertia of the physical processes involved.

<!-- Start of picture text --> 4<br>3.5<br>3<br>2.5 Avg increase in<br>temp 2055<br>2<br>Avg increase in<br>1.5<br>temp 2090<br>1<br>0.5<br>0<br>0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7<br>SLR (in meters)<br>Figure 1 . Global mean surface temperature change (°C) and global mean sea level rise (m)<br>Increase in Temperature (°C)<br><!-- End of picture text --> To better understand the nature of the relationship between the global mean SLR, the increase in the mean global temperature and time, we ran a series of regressions, finding that the following equation provides a satisfactory fit for the relationship:

# _SLR_ =[( _α_ + _βΔt_ )( _T −_ 2000)]

where Δ _t_ is the change in average global temperature with respect to the baseline [1985-2005], and _T_ is the year period. A panel estimation of equation (1) gives a value for the α coefficient of 0.000954281, whereas the corresponding value for β is 0.003421296.

To account for the vertical land movement ( _V_ ), equation (1) can be modified as follows:

# _aSLR_ =[( _α_ + _βΔt −V_ )( _T −_ 2000)]

where _aSLR_ is the _adjusted_ sea level rise. Data on VLM by country have been retrieved from the
provided by IMF-WEO and IMF-WCE, respectively.<sup>28</sup> Likewise, non-resource GDP is computed as the difference between GDP and resource GDP. The baseline world annual real interest rate, , is set to two percent, which is in line with the 10-year inflation-indexed US Treasury bond yields averaged over 2000-2019 from WW ff the St Louis Federal Reserve Bank Economic Data (Series: WLTIIT). The baseline debt-elasticity of the interest spread is set to , which implies that a ten percent of GDI increase in the external debt leads to a one ψψ= 0.1 percentage point increase in the country’s interest rate.<sup>29</sup> Finally, we set , which is sufficient to prevent any explosive paths for public debt as . φφ= 0.05 **Initial conditions.** GDP for 2020 is taken from World Bank’s World WW φφ> ff

**Initial conditions.** Development Indicators (WB-WDI), in constant 2010 U.S. Dollars.<sup>30</sup> In the absence of a data set containing comprehensive information on GDP at the industry level for several commodity-exporting countries, we proxy GDP in resource industry by exports of the resource good . More specifically, GDP in industry is set to match the average value of exports as a share of GDP.<sup>31</sup> The export data is ii ii ii taken from the UN-Comtrade Database (UN-CT), which provides information on export value for all 11 commodities and all 56 countries pre-loaded in the LTGMNR, with a time series that usually starts in 2002.<sup>32</sup>

28 As a complementary data set for government revenues (total, resource and non-resource), we use ICTD/UN-WIDER Government Revenue Dataset (UN-GRD). 29 The range of estimates for in literature varies widely across countries and papers. For example, while Schmitt-Grohe and Uribe (2003) set to match the volatility of the observed current-account-to-GDP
and the private sector virtually disappeared (Bahar et al., 2021). Since 2015, social protests and political violence have become rampant, and Venezuelans have been forced to migrate on a scale never seen before in Latin America (Ib ́a ̃nez et al., 2021).

By 2020, the Venezuelan exodus had become one of the world’s largest humanitarian crises with more than five million people having fled the country. Colombia is the primary host of these forced migrants. That same year, the Colombian government reported that more than 1.72 million Venezuelans had registered with the United Nations Refugee Agency to stay in the country. Nonetheless, the real number of Venezuelan migrants in Colombia is likely larger due to irregular migration.

Colombia is also one of the most generous countries in terms of the support it has offered these migrants. In 2018, the government regularized approximately half a million Venezuelans and in 2020, it granted all irregular migrants access to complete health services. Moreover, in 2021, it also offered a temporal regularization (for up to 10 years) to all irregular migrants living there.<sup>7</sup> As in other countries that have experienced large migration inflows in a short period of time, the Venezuelan migration has sparked strong reactions from Colombian natives (Rozo and Vargas, 2021). Some Colombians have welcomed the migrants, but others resent them and blame them for current socioeconomic problems.

# **III THE SURVEY EXPERIMENT**

We conducted an online survey experiment in Bogot ́a with 3,400 Colombian Facebook users. Bogot ́a has a population of 10.7 million, of which 7.8 million use Facebook. As noted above, we recruited respondents through Facebook ads and stratified the experiment by age group and gender. Table A.1 of the Appendix compares sociodemographic characteristics of the survey respondents with those of the Bogot ́a population (as collected by the population census conducted by the Colombian Statistics Agency in 2018). As the
- Betti, G., Dabalen, A., Ferré, C., Neri, L. (2013), Updating poverty maps between Censuses: a case study of Albania, in Laderchi C.R., Savastano S. (eds.), _Poverty and Exclusion in the Western Balkans_ , Economic Studies in Inequality, Social Exclusion and Well-Being 8, Springer Science+Business Media, New York.

Betti, G., Pavelesku, D. (2019), Calculation of the design effect and JRR standard errors in the 2015 Household Budget Survey in Tunisia, Report to The World Bank. Corral, P., Molina, I., Nguyen, M.C. (2020), Pull your small area estimates up by the bootstraps. World Bank Policy Research Working Paper No 9256. Washington DC: The World Bank.

21
sunflower to the detriment of dairy, horticulture, and agri-processing, all of which would require landattached investment. To address tenure security constraints, the government recently passed legislation that would allow farmers to sell or buy agricultural land.<sup>7</sup> Subsidy programs to promote diversification of crops and a move towards higher value addition have also been put in place. Partly due to lack of data to underpin appropriate designs, these have been ill-targeted and ineffective.

# **3. Data, descriptive statistics, and econometric approach**

While national statistical data suggest that the diversity of the country’s agricultural output mix has decreased over time, they provide no information on mono-cropping that would allow to assess the incidence and impact of crop rotations. To generate such data, we combine a crop cover map generated from freely available satellite imagery via machine learning with statistical data for 2016, 2017, and 2018. With some 24% of area in maize, 17% in soybeans, 10% in sunflower, and 20% in cereals having been cropped in at least two consecutive seasons on the same field, detailed study of associated yield effects seems warranted and we describe the econometric approach for doing so.

# **3.1 Data sources and key outcome variables**

Remotely sensed data provide objective information at low cost and the number, resolution and revisit frequency of optical or radar sensors to provide such data have expanded significantly. Imagery covering the entire world at 10 m spatial resolution every 5 days is available freely from the European Space Agency Sentinel constellation since 2013. Access to cloud computing platforms to process these at scale implies that location-specific training data are rapidly emerging as a binding constraint to generation of crop cover maps at field-scale. To generate such training data for Ukraine, _in-situ_ data collection along main roads was undertaken every year during the 2016-18 period along different routes as displayed in figure 1. Experts covered these routes two times each year to capture images of winter as well as summer crops to serve as
**Chart 3. Per capita consumption and real GDP growth, average 1984-93**

<!-- Start of picture text --> 6<br>2 -<br>0<br>]] JEhl:j<br>l Per capita consumption (in US$ X I00 GDP Rge anual in %<br>4<br>&~~~<br><!-- End of picture text --> Source: World Bank. IMF.

- Consumption figures include only 28 countries; GDP growth figures include 30 countries.

- **Growth rate ofthis region is negative due to its economic transformation.

In measuring the benefits of ODA debt relief we include the change in international reserves even though it is not directly linked to higher consumption and investment. In other words we consider the accumulation of international reserves to be one desirable use of foreign exchange (which needs to be financed) instead of it being a source of funds to finance other needs-such as imports of goods and services. We justify this approach by the low level of international reserves held by the countries considered during the sample period (Table 4). In fact, developing countries tend to maintain an average stock of foreign reserves in the range of 4-5 months of imports, mainly as a buffer to protect them against short-term liquidity shocks. Assuming that the average stock of foreign reserves held by other developing countries is near the optimum, it appears that African countries need to increase their own holdings, to make themselves less vulnerable to short-term liquidity shocks. Thus increasing African countries' stock of foreign reserves is a desirable (welfare-enhancing) outcome.

The use of the import bill capacity as defined above as an indicator of the benefits that accrue to the recipient countries when getting grants does not deny the existence of several other potential (although less straightforward) benefits, such as the reduction in indebtedness and its effects on creditworthiness, the strengthening of the fiscal stance (if public debt is being written off) and
statistics across seasons, gender gaps in farm work that varies through the agricultural calendar, and self/proxy discrepancies in child labor statistics that vary according to the seasonal timing of the surveys. As informational constraints may be present in contexts where farms are mostly operated by families and monitoring is costly (Bharadwaj 2015), reporting of children’s activities by proxy respondents could be affected, for instance, by the degree of complementarity of child’s effort to adult labor activities in particular seasons.

Third, this study talks to the stream of literature that shows that women’s work is poorly measured in developing settings (e.g., Reynolds and Wagner 2012, Mata and Greenwood 2000, Anker 1983), particularly in agricultural households where proxy respondents tend to rely on their general assumption about the state of the world in their search for answers to survey questions given the absence of recall-and-count information (Beegle et al. 2012). Our study shows the use of proxies becomes a source of gender bias in the measurement of child labor for girls in settings characterized by patterns of gender stratification of intrahousehold time allocation.

Fourth, this study may carry implications for the current debate on the measurement of agricultural productivity (e.g., Gollin et al. 2014, McCullough 2017), wherein child labor is used as an input in the production function. Our results suggest that eliciting labor information on child labor from a proxy respondent may affect the measured value-added per worker, and therefore productivity gaps by gender, or between agricultural and non-agricultural sectors in countries where child labor has a high prevalence.

This study is organized as follows. Section 2 describes the context of this study, the sampling procedure, and survey design intervention. Section 3 presents the empirical strategy, while Section 4 presents the main results. Section 5 assesses further impacts and potential channels that explain the main results. Finally, Section 6 presents the conclusions.

7
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