rafmacalaba/data-use-mentions-extended huggingface dataset:
similar country-level human capital (as captured by the Global Competitiveness Education System measure or the World Bank’s HLO).

_Access to Finance_ . Compared to domestic firms, foreign-owned firms are less sensitive to local financial development (as captured by the Global Competitiveness indicator, Financial Development). This is consistent with our earlier assumption that foreign-owned firms have better access to finance from their headquarters so that they depend less on the host country’s financial system.

_Infrastructure_ . Relatedly, foreign-owned firms are less hampered by the host country’s electricity quality (as captured by the Global Competitiveness Electricity Quality measure), and less helped by the availability of broadband and internet (as captured by the Global Competitiveness Broadband measure).

_Barriers to entry/exit._ Higher barriers to entry, as captured by a lower value of _Doing Business_ Contruction Permit, are associated with relatively higher _Enterprise Survey’s_ Land Access Obstacles for foreign firms.

Overall, we find foreign-owned firms to be less sensitive to access to finance, electricity quality, and internet quality, but more vulnerable to the lack of physical safety and the difficulty of obtaining construction permits. Interestingly, domestic and foreign-owned firms do not differ much in their responses to corruption and human capital.

# **6. Effects of the business environment on firm performance for micro, small, medium, and large firms**

Firms of various sizes respond differently to the underlying business environment (Beck, Demirgüç-Kunt, and Maksimovic 2005; Beck and Demirgüç-Kunt 2006; Knack and Xu 2017), and countries differ in their distribution of firm size. Thus, how countries and policy makers can adopt size-specific BE policies could have important implications for development (Hsieh and Olken 2014). For instance, very small firms likely face stronger financial constraints to grow out of the status of micro firms (McKenzie 2017). To offer more targeted policy advice, we allow the BE effects on firm performance to depend on firm size. Recognizing that micro and small firms account for the vast majority of firms
The analysis using the customs data, which helps validate the results from BPS data, estimates a simpler version of Equation (1) described in Appendix B. The results of this estimation are represented graphically, as before showing marginal effects along with 95% confidence intervals. There are two important differences across the BPS and the customs data analyses. _First_ , monthly customs outcomes are aggregated and the analysis is conducted with data at the country-firmquarterly level. _Second_ , since the customs data comprise only trading firms, only trade-related outcomes from BPS data are compared using the customs data. Further, when drawing comparisons with the customs data, we restrict the sample of BPS data to include only exporters or importers.

To capture the global engagement of firms, we construct several measures using the BPS data. Two standard measures are indicator variables, one for whether the firm exports and one for whether the firm imports before the pandemic in 2019. These two measures capture firms’ direct engagement in global markets. Another measure is an indicator variable for firms that both export _and_ import, that are designated as GVC firms. The final and more novel measure captures firms’ indirect engagement in global markets through an indicator variable for whether the firm sold to domestic exporters or multinationals in 2019 but did not directly export or import. We refer to these firms as suppliers of GVC firms. Since these four measures may not be mutually exclusive, we also construct four mutually exclusive categories of global engagement to contrast with firms that had no engagement at all: only indirect engagement; only direct engagement (export or import); both indirect and direct engagement (with direct engagement being measured with either export or import), and finally firms that are indirectly engaged and are also a GVC firm in 2019. Except for the indicator variable for firms that export, other measures of global engagement are only available for the countries surveyed in round 3 including the trade module. Hence, the analysis using these measures is based on a restricted sample. Appendix Table A2 shows summary
Table D.1 presents results on the origin nationality of foreigners living in Venezuela in 1990 and on the host countries of relatives living abroad as reported by Venezuelan households interviewed in the ENCOVI between 2017 and 2021. The overwhelming majority of foreigners living in Venezuela in 1990 had been living in Colombia five years earlier, drawn by demand for labor during Venezuela’s oil boom that began in the 1970s. Their subsequent return to Colombia, combined with the networks they left behind, provided informational and logistical support that lowered displacement costs for Venezuelans seeking to relocate after 2013. Consistent with this, Venezuelan households reported that most of their relatives abroad were also living in Colombia. Our measure of foreign settlements therefore captures the strength of network connections to Colombia across municipalities, connections that facilitated forced displacement after the crisis began. We validate this mechanism in Section 4 by examining if Venezuelans arriving in Colombia after 2013 were more likely to originate from municipalities in Venezuela with historically higher foreign settlement shares.

Tables D.2 and D.3 characterize foreigners living in Venezuela in 1990. In addition to predominantly Colombian nationality, these individuals were mostly employed, older, and more educated than Venezuelans (Table D.2). On average, they also had incomes three times greater than those of the Venezuelan population in 1990 (Table D.3).<sup>12</sup>

# **3.2 Remote-Sensing Data**

To create a proxy of Venezuela’s economic growth and inequality, we construct a municipalityyear-level longitudinal dataset of satellite night-light density for 1992–2021. This information is processed by the National Oceanic and Atmospheric Administration (NOAA) using images collected by the U.S. Defense Meteorological Satellite Program Operational Linescan System (1992–2013) and the Visible Infrared Imaging Radiometer Suite remote sensor (2012–

> 12Since the main specification follows a difference-in-difference design, any imbalance in the outcome levels between high and low shares of foreign settlements in municipalities does not threaten the empirical strategy because they are absorbed by the
simulated samples from the experimental data, and then demonstrate how ‘ex ante’ simulations can be used to decide, for example, on the number of experimental waves. Section 7 concludes with a short discussion.

# **2 Using Adaptive Sampling in Experiments for Policy Choice**

This section gives an overview over the use of adaptive sampling for policy choice and the exploration sampling algorithm proposed by Kasy and Sautmann (2021a). We start with the “basic ingredients” for an adaptive experiment: the objective, an algorithm that builds on the data from each wave to adaptively allocate units to treatment arms, the estimation approach, and constraints that determine whether an adaptive experiment is feasible. The corresponding features of the IVR experiment are described in detail in sections 3 and 4.

This section also discusses the gains from adaptive vs. non-adaptive sampling or adding adaptive waves, and how these gains can be calculated in simulations to choose the number of experimental waves. We return to this in section 6, building on the data collected in Kenya and the estimation results in section 5. To begin with, however, we assume that there are _t_ = 1 _, ..., T_ exogenously given consecutive sample draws (waves) of size _Nt_ available for testing.

**Objective.** In the canonical policy choice problem, there are _K >_ 2 policy options – or treatment arms – labeled _k_ = 1 _,_ 2 _, . . . , K_ . Each arm has unobserved (stationary) average outcome _θ_<sup>_k_</sup> , and the policy maker wants to implement the arm with the highest average outcome. Formally, let _k_<sup>(1)</sup> = argmax _k θ_<sup>_k_</sup> be the true best arm, and _k_<sup>_∗_</sup> the arm that is chosen. We call the loss (per unit) from implementing a suboptimal arm _k_ the policy regret
differences between biomass and “clean” fuels. As Table 8 shows, mean PM10 in cleanfuel households is 133 ug/m<sup>3</sup> – little higher than ambient pollution in the urban area where most clean fuels are used. For biomass-using households, on the other hand, the average concentration is 242 ug/m<sup>3</sup> . Among biomass fuels, we fine a statistically significant difference only for jute (about 40 ug/m<sup>3</sup> lower). However, as we noted in Section 7, very few households in our sample use jute as cooking fuel.

For biomass fuels, a cautionary note is introduced by large differences in median and mean exposures recorded by our 24-hour monitors. Very large pollution “spikes” for short periods (e.g., 10,000 ug/m<sup>3</sup> or higher) under some conditions can have large effects on estimated mean concentrations. Median concentrations are often much lower. This difference highlights the importance of better information about the time-structure of the relationship between pollution and respiratory disease. To illustrate the problem, for two houses with identical mean concentrations, is it better to experience two daily PM10 spikes of 5,000 ug/m<sup>3</sup> , with very low levels for the rest of the day, or constant exposure at 150ug/m<sup>3</sup> over the 24-hour cycle? Limited evidence from time series studies has not yet provided a robust answer.<sup>5</sup> Given the intensity of indoor pollution spikes under some conditions, further research on this issue seems warranted.

(4) How important is fuel choice for pollution when we account for other household characteristics? Although fuel choice certainly affects indoor air pollution, our results suggest that its role is secondary to the role of ventilation factors for Bangladeshi

> 5   The available evidence is for outdoor particulates in industrial
& industries<br>© elasticities (price, (from various sources) =. .<br>income, substitution) + international fuel price projections (IMF, WB, \ & Structure: uses harmonized HH surveys (WB & country), 1O tables (GTAP), and<br>©© ratesother module-specificof improvementp “ . GDPIEA, projectionsBNEF)jecti (WEO, WB) ©&=I changesprice changesin pricesin mitigation(direct| & indirect),moduleuf sectorscash transfers,to: modeltaxeswelfare: and effectsexpendituresof<br>options + elasticities (price, income, substitution; from Sa Outputs: consumption losses less income gains for deciles, urban vs. rural, and<br>various sources) & rates of improvement “” horizontal equity (p25, p75); impact on industrial input and output prices<br>Manual Inputs: *Outputsenergy (all consumption to year 2035): & prices TM<br>An optional tab for advanced users ++ GDPtax revenues effects (via fiscal multipliers) Transportation module<br>+ takes inputs from other models + GHGs (CO2, CH4, SLCPs) & other climate forcing A reduced form model estimating effects on fatalities &<br>(e.g. CGE, DSGE, pollutants (PM2.5, NOx, S02, BC, NMVOCs) vs. congestion from motor fuel price changes<br>macrostructural) or where user NDC Structure: uses econometrically-estimated country-specific<br>nes more Gevalieg knowledge * fossil fuel imports elasticities (accidents & congestion wrt motor fuel prices)<br>9: P ' Outputs: road fatalities & congestion (peak & non-peak)<br><!-- End of picture text -->
cause very few farmers report any challenges related to operations and maintenance over the four years of survey data collection. Second, the government planned to charge farmers in the command area land taxes, which were unconditional on cultivation decisions, to fund O&M in the schemes. To test whether these taxes limit farmers adoption of irrigation, we randomized tax subsidies for farmers. We find no evidence this experiment changed cultivation practices. This result is likely because compliance with the fees was extremely low (4%), so collected fees were too low to plausibly constrain farmers. We discuss these experiments further in Appendix I.2, and conclude here that these issues were not relevant in this context.

Third, we test whether financial and informational constraints limit adoption of irrigation. We assigned horticultural minikits to randomly selected farmers from water user group member lists. Each minikit included horticultural seeds, chemical fertilizer, and insecticide, in sufficient quantities to cultivate 0.02 ha. In principle, these minikits should resolve constraints related to input access, including credit constraints. In addition, they should reduce basis risk which may resolve insurance constraints and facilitate experimentation if information is a constraint. In other contexts, minikits of similar size relative to median landholdings have been shown to increase adoption of new crop varieties or varieties with low levels of adoption (Emerick et al., 2016; Jones et al., 2018). To test for spillovers, water user groups were randomly assigned to 20%, 60%, or 100% minikit saturation, with rerandomization for balance on Zone and O&M treatment status. Minikits were offered to assigned individuals prior to 2017 Rainy 1 and 2017 Dry.<sup>46</sup>

# **6.1 Empirical strategy and results**

We estimate the impact of minikits using the specification

> 46Each of these three interventions exist only in the command area. As such, the effects of irrigation estimated throughout this paper are averages across the experimental treatments. Overall, this concern is
the capital stock_

The official Chinese investment data split investment into two assets: structures and equipment. A growing share of investment in most countries has been in items such as computer software and other items of intellectual property. This type of investment has a very high deprecation rate so, if the growing importance of this type of investment is not allowed for, the depreciation rate for equipment will be understated and the capital stock over-stated.

No official estimates of investment in software and intellectual property are available for China. The authors of the Penn World Tables have estimated the investment flow in such assets by using the commodity flow methodology. Using this approach, for a given product, investment is determined as the sum of production plus net exports less consumption. The authors have also estimated a price index for this type of asset and assumed an average depreciation rate of just over 31% for these assets. On the basis of these assumptions, they find that though the share of this type of asset in the overall investment has grown rapidly, its share of the capital stock of equipment and software is nonetheless quite small (Figure 2). It is, moreover, at 4% much lower than the equivalent share in the United States, which was 34% in 2015. Thus, it would be incorrect to adopt the overall US non-residential depreciation rate for China, but it would be inappropriate to completely ignore the depreciation of the software component of the capital stock in China.

9
This paper expands on this literature by combining data on several cross sections of global input-output (IO) tables, social accounting matrices and exports to construct the labor content of exports, and employment data to construct the jobs content of exports. The first three types of data come from the Global Trade Analysis Project (GTAP), which covers a wide range of countries, including many developing countries. The employment data come from the International Labor Office (ILO) statistics. The combination of these data allows for a wider coverage of developing countries and sectors than previous studies. The labor content of exports data for 2011 cover 57 sectors and 120 countries, 74 of which are developing and comprise over 90% of all developing countries’ population (see Appendix 1 for the sector coverage and Appendix 2 for the country coverage). The sectoral and country coverage is more limited for the jobs content of exports due to the restrictions imposed by employment data availability. The data in this case comprise 73 countries in 2001 but only 66 in 2011 and are available for 11 sectors. The resulting database splits the labor content into skilled and unskilled, as well as into the wages paid and jobs used directly for the production of exports or indirectly via the production of inputs for export production. We also use gross output in place of exports to construct the labor and jobs content of domestic production. (See Appendix 3 for the steps needed to reconstruct the LACEX database from GTAP and Appendix 4 for the list of variables in the LACEX database.<sup>4</sup> ) The resulting LACEX database is a valuable tool to describe the extent to which exports support jobs and wages in an economy, in specific sectors and over time. On the other hand such data are not meant to be used for policy analysis, for example to assess the possible labor market effects of a policy promoting specific sectors’ exports. The data are a representation of equilibrium outcomes, where firms have made input choices, given technology, output prices,
yarn stock, introducing restocking norms for future purchases, and monitoring inventory on a daily basis, the firms reduced their inventories. But this took time as the reduction in inventories primarily arose from lowering stocking norms and using old yarn for new products.

Table 2 columns (5) to (7) shows the regression results for log of raw material (yarn) inventory. The results are presented for the 18 plants for which we have yarn inventory data (two plants do not maintain yarn stocks on site). In column (5) we present the fixed-effects result which regresses the weekly yarn on the plant level management practices, plant fixed-effects, and a set of weekly time dummies. The coefficient of -0.639 says that increasing management practices adoption rates by 1 percentage point would be associated with a yarn inventory reduction of about 0.6%. In Table 2, column (6), we see the impact of management instrumented with the intervention displays a point estimate of -0.929, somewhat higher than the FE estimates in column (1).<sup>20</sup> Again, the IV estimator is higher than the OLS estimator, suggesting that the adoption of better management practices may be endogenous (or at least downward biased by measurement error). In column (7) we see the intervention causes an average reduction in yarn inventory of (exp(-.179)-1=) 16.4%.

These numbers are substantial but not unprecedented. Japanese automotive firms achieved much greater reductions in inventory levels (as well as quality improvements) from the adoption of lean manufacturing technology. Many firms reduced inventory levels from several months to a few hours by moving to just-in-time production (Womack, Jones and Roos, 1991).

Finally, as with the quality defects estimates, the IM confidence interval for the IV estimator finds the coefficient significant at the 5% level. However, the IV permutation tests cannot exclude zero. Looking at the ITT coefficient, we
and East Asia (Petley, 2012, Petley, 2010).

**5) Rainfall variability** is defined as the 1981‐2010 standard deviation of monthly rainfall levels. The variable is constructed from the global CRU TS3.21 dataset from the University of East Anglia, containing a long‐term time series of monthly rainfall levels at 0.5x0.5 grid resolution, which was produced using statistical interpolation based on data from 4,000 weather stations (Harris et al., 2014). Higher historic rainfall variability indicates higher inter‐annual and intra‐annual variation of precipitation and a higher incidence of extremely wet and dry conditions. In Vietnam excessive rainfall can have negative income effects for poor households, while wealthier households are more negatively affected by the lack of rainfall (Narloch, 2016).

**6) Temperature variability** is measured by the standard deviation of mean annual temperatures across the time period 1981‐2010.  The underlying data also comes from the global CRU TS3.21 dataset including a long‐term time series of monthly mean temperature values at 0.5x0.5 grid resolution. Temperature variability indicates varying temperature conditions between seasons and years and a higher incidence of heat waves and cold spells. In Vietnam hot conditions have generally an income‐reducing effect for rural households (Narloch, 2016).

**7) Flood hazards** are represented by the share of area at risk of a flood event (inundation depth > 0) with a 25 year return period under historical conditions.<sup>4</sup> The measures are based on the inundation depth estimated by state‐of‐the art flood models at a grid cell level of 3 arc‐seconds combining coastal surge hazard

> 3 http://www.fao.org/soils‐portal/soil‐survey/soil‐maps‐and‐databases/harmonized‐world‐soil‐database‐v12/en/

> 4 A 25 year return period corresponds to 0
# **I. Introduction**

In this paper, we focus on three African countries at different rates of industrialization of their economies and assess the different sources of productivity growth in each country. Specifically, we estimate the size and sources of productivity growth for Ethiopia, Côte d’Ivoire, and Tanzania—and then compare these productivity gains to that in an external comparator— Bangladesh. To do this, we compiled establishment-level data from a wide range of sources including manufacturing censuses, business registrars, and economic censuses.

Using these data, we investigate whether differences in the performance of manufacturing establishments can be accounted for by differences in the efficiency with which resources are allocated to their most productive use. We carry out both the static Olley-Pakes (1996) decomposition and the dynamic Olley-Pakes decomposition (DOPD) proposed by Melitz and Polanec (2015). These decomposition methods break down aggregate productivity changes into two components: one component that captures shifts in the productivity distribution (via changes in the unweighted mean of establishment-level productivity); and another component that captures market share reallocations (via changes in the covariance between market shares and establishment-level productivity). This allows us to identify patterns of productivity growth across different types of firms (e.g., by firm size and industry) as well as the efficiency of the factor reallocation in allocating resources to a sector’s most productive firms.

A growing literature has begun to examine the extent of factor misallocation in developing countries (Alfaro, Charlton, and Kanczuk, 2008; Alfaro and Chari, 2014; Barltesman, Haltiwanger, 2