rafmacalaba/data-use-mentions-extended huggingface dataset:
the same firms (organizational structures) in our 1991-96 sample.7 The obtained price data allowed us to calculate input and output price indices at the firm level. Those were used in the analysis in the next sections.<sup>8</sup> The interviews covered eighteen manufacturing firms and three former trading companies. The trading companies had turned into private holding companies in 1992 and had managed to buy majority stakes in most of the companies whose products they had marketed including some of the firms on the Ministry list. The interviews with their managers contained information on both the parent holding company and its subsidiaries. While the statistical analysis in the next section is based on the overall financial performance of the holding companies, most restructuring measures (improving quality standards, new product lines, foreign partnerships) are traced back to their subsidiaries.

The case study method normally has significant downsides. Most important is the lack of representativeness of case study findings, i.e., their performance may not be indicative of economywide trends. Fortunately, we have comprehensive data on all large industrial firms in Slovakia and can therefore link the smaller sample of case studies to the broader trends in the manufacturing sector (see Figure 1 above). Another potential problem is the subjective narrative of managers/owners regarding the causes of (and constraints to) restructuring. We have, however, only used the interviews to complement our analysis of the financial performance of firms in getting a better understanding of the many elements of a successful restructuring strategy.

- 7 In two cases, firms were still undergoing a split-up of the former conglomerate in1991. Since both firms were independent plants (located away from other plants in the conglomerate and with their own general managers) even before the split-up, we obtained financial and other (including employment) data from their managers pertaining to their respective plants _only._ For further analysis of the impact of conglomerate split-ups on restructuring in Czechoslovakia, see Lizal et al., (1996).

- 8 The possibility of
The combination of diets heavily reliant on wheat and wheat supplies dependent on imports means that events in global wheat markets play an outsized role for welfare outcomes in the region, and this link between international markets and domestic welfare will likely strengthen with time. Population growth in MENA is projected to be 1.7 percent per year, significantly higher than the world rate of 1.1 percent (World Bank, 2009). At the same time, the potential for expanding supplies on irrigated lands is severely constrained; per capita water availability is projected to fall by half by 2050, with serious consequences for the region’s already stressed aquifers and natural hydrological systems (World Bank, 2007). As a consequence, projections of the region’s food balance indicate that imports will increase by almost 64 percent over the next twenty years (World Bank, 2009).

When price shocks occur, governments often intervene. In MENA, the interventions come in many forms, but consumer subsidies are the most favored instruments (Lampietti et al., 2011). For example, in Tunisia, Jordan, Morocco, Egypt, Syria, and Iraq, where disaggregated data are available, food subsidies averaged 1.6 percent of GDP in 2009 and totaled $8.1 billion (World Bank, 2011c). More recently, and in response to high food commodity prices and popular uprisings, many governments have expanded consumer subsidies, likely contributing to significantly higher costs.

At the same time, the capacity of governments to shore up safety net programs against high food prices differs among countries in the region (World Bank, 2009, 2011c). All else equal, countries with large fiscal deficits and high cereal import dependency are least able to absorb a wheat price shock. These are mostly the oil importers (Jordan and Lebanon) and the region’s developing oil exporters (Yemen, Iraq and Syria). In contrast, the Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi
0 2 4 6 -4 -2 0 2 4 6<br>Georgia Ghana Kenya<br>-4 -2 0 2 4 6 -4 -2 0 2 4 6 -4 -2 0 2 4 6<br>Ukraine Vietnam<br>-4 -2 0 2 4 6 -4 -2 0 2 4 6<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Decision making (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Colombia<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Georgia Ghana Kenya<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Serbia Ukraine Vietnam<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .
# **Figure 8: The Feasibility–Ambitiousness (FAB) matrix**

The FAB shows that setting promising targets requires the consideration of both ambitiousness and feasibility. Neither low-hanging fruits nor long shots are desirable as they may lead to greenwashing allegations (see Section 5.1) The ideal targets are both feasible and ambitious but require additional SLB funding to become realizable.

<!-- Start of picture text --> forward-looking<br>extrapolates business-as-usual<br>Ambitiousness trends with no policy changes<br>Feasibility Low High Very high<br>Highly ambitious<br>backward-looking but low chance<br>based on historical Low Long Shots of success<br>precedents of Target unlikely to<br>relevant peers be achieved given<br>High lack of historical<br>precedence<br>Reachable<br>Low-Hanging Targets<br>Very high Fruits<br>Highly feasible but unambitious<br>Target requires little to no effort and Source: Wang, Gurhy,<br>may have been reached in any case Hanusch, Kollenda (2023)<br><!-- End of picture text --> Source: Authors.

The first type stems from funding unrealistic projects whose targets may never be reached. This is compounded by the second risk, namely that they will need to justify benefiting from a stepup when the issuer fails to meet an unrealistic SPT – especially, if it was known that the SPT’s feasibility was low.

We illustrate how SPTs for reduced deforestation in Brazil’s Legal Amazon could be set using the FAB matrix. The methodology to evaluate feasibility relies on a peer country comparison with other tropical forest countries. For these countries, we use country-level primary forest loss data from the Global Forest Watch rather than INPE’s data, which is only available for Brazil’s Legal Amazon. We set the SPT horizon to four years for this application. From a collection of all tropical forest countries, we selected 19 comparable peer
difference between outstanding tax arrears at the end of period and of its beginning. Fines and penalties are excluded.

Subsidies embedded in non-equivalent barter<sup>50</sup> are evaluated under the assumption that prices of goods or other non-cash instrument used to pay RAO Gazprom and RAO UES in NCS transactions are inflated by 30%, while energy prices are not, because the latter are fixed by the energy commissions. Example: the entry "II.i.b" in Table A-1 in 1995 is calculated as: Implicit subsidy = Amount of non-equivalent barter*0.3/1.3 = 62.0*0.3/1.3 = Rb 14.3 bn.

Implicit subsidy as percent of GDP = Implicit subsidy / GDP = 14.3/1585*100 = 0.9%.

The change in net inter-industry receivables of Gazprom (line II.i.a) is calculated as the increase of stock of overdue unpaid deliveries of gas to other industries after barter and before tax offset operations during each year of the period multiplied by the average annual domestic price of gas deliveries:<sup>51</sup> Line II.i.a. Change in net inter-industry receivables of Gazprom = change in stock of overdue unpaid gas deliveries to other industries after barter and before tax offsets * average annual domestic gas price. Change in net inter-industry receivables of Gazprom as percent of GDP = Change in net interindustry receivables of Gazprom / GDP.

> 49 30% is a sort of average overpricing reported in interviews with subnational authorities.

50 Data on RAO Gazprom and RAO UES are taken from companies' financial reports. **51** Gazprom's own overdue payables to other industries are negligible.
0|
|**Total**||2368104|1147250|7924277|0.144|

_Source:_ Authors’ analysis based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note: Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High education but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and completed Senior High but did not move on to University. _University_ refers to persons who attended and obtained a University Degree. _Nis_ refers to the number of informal workers of the educational levels specified in each row. _Nfs_ refers to the number of formal workers in each of the educational levels. _(1-_  _s)Nis_ denotes the absolute size of the excess supply of workers with that level of education, and _(1-_  _s)Nis/(Nis+Nfs)_ denotes the excess supply of workers relative to the total number of workers of that educational level.

These results can be interpreted as follows: the counterfactual earnings distribution presented captures the effects that, from the perspective of workers, would be observed if they were all employed by formal firms (table 9 column 4). On the other hand, formal firms would not be willing to employ all of them in the counterfactual distribution (table 11). More precisely, firms would be least willing to employ workers with fewer years of schooling: 45 percent of workers with

> 15 Of course, if we had normalized equation (3) with, say,  1 1 , the results would show excess demand for workers of all other educational levels except, by construction
to distinguish between migration and other channels. If migration cost is the main mechanism driving the intersectoral reallocation of labor, then our model predicts lower wages in areas with higher incidence of land restrictions. In contrast, wage rate will remain largely unaffected by land restrictions if either credit constraint or insecurity of property requiring guard labor is important (assuming no change in migration costs, and no policy restrictions on rural-urban labor mobility).

To provide evidence on the effects of land market restrictions on occupational choices, we follow Emran and Shilpi (forthcoming, 2015) and exploit a historical quasi-experiment in Sri Lanka where the crosssection variations in the incidence of land restrictions across different sub-districts (i.e., proportion of land under policy restrictions) were primarily determined by historical malaria prevalence through its effects on ‘crown land’.<sup>12</sup> Historical malaria caused exodus from the affected areas, and the land abandoned by the households was nationalized by the colonial government and designated as crown land. The crown land was later distributed through settlements after independence, and restrictions on sales, mortgage and rental were imposed by the government.<sup>13</sup> This created a positive correlation between historical malaria incidence and the incidence of land restrictions in an area through the availability of crown land. We use this positive correlation between historical malaria and incidence of land restrictions in a sub-district to identify the causal effects of land restrictions on structural change in the rural economy. As noted in Emran and Shilpi (forthcoming), the strength of the identification strategy derives partly from the following observations: (i) a successful nationwide malaria eradication program was implemented in Sri Lanka in 1947; malaria endemicity fell close to zero by 1950-51.<sup>14</sup> Historical malaria estimate from 1937-41 is used in this paper to identify the effects of land market restrictions in 2002 (survey year for our data), (ii) the timing of the malaria eradication program was determined by technological breakthrough (DDT),
Table 3: Magnitude of the Difference between Natives and NonNatives (percentages)

||(1)<br>Difference<br>/ SD Total|(2)<br>Difference<br>/[Decil 9 - Decil 1]|
|---|---|---|
|Precarious housing|34.28|45.00|
|Ownership land & house|8.77|22.22|
|Years of education [24+)|16.89|19.89|
|Use computer [14+)|5.56|8.11|
|Legally married [18+)|15.95|36.36|
|Economically active [18-65]|3.36|4.76|
|Formal employment [18-65]|8.97|14.29|
|Unemployment [18-65]|4.99|20.00|
|Health insurance|24.15|33.33|
|Number of disabilities|7.53|29.41|
|Teen pregnancy [14-19]|5.29|25.00|
|Average|12.34|23.49|

_Source_ : REDATAM INDEC Censo Argentina 2010. _Notes_ : Difference corresponds to the columns without FE in Table 2. In Column (1), the difference is divided by the standard deviation of each outcome. In Column (2), the difference is divided by the difference between the top and bottom decile of each outcome (calculated at the department level).

four outcomes.

Table A3 in the Appendix allows us to compare the magnitudes of these disadvantages in terms of standard deviations and relative to the difference between counties in the first and ninth deciles. In terms of standard deviations, the disadvantage faced by Natives is greater in the US than in Argentina in all outcomes but precarious housing. And relative to the difference across counties, the magnitude of the disadvantage of Natives is greater in the US than
April 2nd 2018 until March 3rd 2020, and the median assessment date used as a cut-off is December 15th 2018. Outcome 1 takes the value of 1 if respondent borrowed formally or informally and 0 otherwise. Outcome 2 takes the value of 1 if the respondent borrowed any money for their business from Banks, Micro-Finance Institutions (MFI), or Credit Unions. Outcome 3 takes the value of 1 if the respondent borrowed any money for their business from their spouse, friends and relatives, and informal savings groups. Outcome 4 includes both formal and informal borrowing amounts reported by the respondent, conditional on borrowing. Outcome 5 takes the value of 1 if business was operating at the time of the endline survey (September 2021). Outcomes 6-7 are self-reported total profits from the main business for the year 2013 in the Ethiopian calendar (which is 2020 in the Gregorian calendar). Outcome 8 is the self-reported value of business capital, where the respondent was asked to estimate the replacement value of all owned equipment and machinery for the main business. Outcome 9 is the self-reported value of inventory stock, where the respondent was asked to estimate the value of final goods for sale that they currently have in stock for their main business. Outcome 10 is the self-reported value of raw materials, where the respondent was asked to estimate the value of product components and raw materials for their main business. Conditional amounts represent only those whose businesses were open, whereas unconditional amounts include those not operating any business with a value of 0. Only Inverse Hyperbolic Sine (IHS) transformed versions of borrowing amounts, profits and business inputs are presented. Psychometric scores in the IE sample ranged from 370 to 452, and the median score used a cut-off is 383 (note that 370 was the qualifying score to receive an uncollateralized loan). Includes a control for number of children in the households that was imbalanced between treatment and control at baseline, as well as if the respondent was above or below the median psychometric score cut-off. Observations represent number of individuals
without the computational and cost burden that would be associated with scaling up the use of data with higher frequency and resolution (Duncan et al. 2023). Developing an automated classifier for conflict-related damage to rural fields would be a straightforward extension over the manual classification adopted here.

Second, we build on a growing body of work that combines high-quality training data and machine learning with remotely sensed data for analysis of changes in land cover not only at the global level (Potapov et al. 2022) or country level (Lark et al. 2017), but also at a more granular scale (Lobell et al. 2020), including the field level (Deines et al. 2021). This allows us to demonstrate the scope for using spatial data to assess the impact of the conflict (Witmer 2015) in a way that uses customized (Ben Yishay et al. 2024) or readily available information (Chiovelli et al. 2018). Use of remotely sensed data to assess effects of conflict on preservation of environmentally sensitive areas or yields can rely on similar techniques in principle if ground-truth data is available.

Third, we add to the literature arguing that the effects of conflict vary over time and extend beyond areas directly affected by violence (Miguel and Roland 2011). Studies show that long-term effects of conflict may arise from displacement (Chiovelli et al. 2021; Ibanez et al. 2022) as exposed individuals may reduce investment in human capital (Akresh et al. 2023; Büttner et al. 2022) or physical capital (Rockmore 2020; Sinha et al. 2022) and alter their risk perceptions (Federle et al. 2022) or activity choices (Arias et al. 2019; Fergusson et al. 2020). Conflict may also affect the cost of trade (Amodio and Di Maio 2018), the level of 3
# **1 Introduction**

While the argument that trade policy affects competition is an old one (see e.g. de Melo and Urata, 1986), there is to date little systematic evidence on how trade policy affects market power at the level of the firm and, in particular, how the effects of different trade-policy instruments play out. In a heterogeneous-firms setting, non-tariff measures (NTMs)—a widely used class of tradepolicy instruments including e.g. technical or sanitary regulations—affect market structure in ways that are not isomorphic to tariffs. Rather than merely shifting rents from foreign to domestic firms, they also change market structure by affecting firms differently depending on their size. For instance, a regulation may inadvertently create barriers to entry which generate a dominant position. Evidence of such effects of tariffs and NTMs is scarce, however, because assessing the market power of firms and how it relates to trade policy requires estimating indicators such as price-cost margins or market shares in well-identified markets. Both the identification of these measures and the definition of a market (and its structure) are problematic, which is why research has so far limited itself to specific sectors, countries, and trade policy instruments.

This paper follows a different route and identifies market power and how it is affected by trade policy using insights from the pricing-to-market (PTM) literature. We rely on a growing literature that establishes, theoretically and empirically, that the extent of firm-level PTM relates to firm size, efficiency and, ultimately, market power.<sup>2</sup> PTM implies that a firm faced with a cost shock will adjust its price differentially across destination markets, depending on the price elasticity of demand it perceives on each of them, which in turn depends on its market power. However, neither cost shocks nor market shares are observed directly at the firm level.<sup>3</sup> Our identification strategy goes around this by relating firm-level export prices (on which we have data) to
urkiye being amenable to working from home. World Bank (2020) uses data from India to estimate that less than 10 percent of jobs are amenable to working from home at the 70<sup>th</sup> and lower percentiles of the earnings distribution and suggests that the actual estimate is likely much lower because of constraints to accessing digital technology. Gottlieb et al. (2021) find that just 13.3 percent of urban workers in Brazil and 10.6 percent in Costa Rica were actually working from home during the pandemic. In addition to feasibility to work from home, there is also considerable evidence of the gender gap in the care responsibilities precipitated by the pandemic and accompanying school closures which may have led to a reduction in women’s labor force participation (Albanesi and Kim 2021; Bau et al. 2022; Levine et al. 2021; Lee et al. 2021; Miguel and Mobarak 2022).<sup>4</sup> Beyond the initial impact, there is also evidence that those who suffered the larger initial

> 4Evidence is more abundant in high-income countries. Sostero et al. (2020) find that in Europe threequarters of employees in the top wage quintile were able to work remotely, compared to only 3 percent of those in the bottom quintile. Adams-Prassl et al. (2022) similarly find that in the UK workers with gross labor income above GBP 70,000 can accomplish some 60 percent of tasks remotely, compared to 20 percent among workers with gross labor income below GBP 10,000.

4
_<_ 0.01 Note: Marginal effects of a one unit increase in each variable on the probability of an EMDE joining convergence ‘Club 1’ relative to other EMDEs. Derived from a logit model, with standard errors calculated using the delta-method. Average years of schooling for males and females from Barro and Lee (2015). Economic complexity index of Hidalgo and Hausmann (2009). Exports and imports as a percent of GDP. Government effectiveness survey from the World Bank’s Worldwide Governance indicators, defined as: perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. A higher index value indicates greater political stability. Gross fixed capital formation and FDI are measured in percent of GDP.

37
‐29.440***|‐40.587***|‐12.719|
||[5.563]|[7.894]|[11.580]|[5.776]|[9.843]|[9.688]|
|Worker ill X Female ill|12.082*|21.880**|‐5.375|13.971*|32.348***|‐4.767|
||[6.813]|[9.082]|[12.888]|[8.052]|[10.896]|[8.543]|
|Observations|2345|1268|889|1456|353|940|
|R‐squared|0.797|0.751|0.744|0.783|0.719|0.815|
|Mean dependent variable|67.03|95.70|40.00|83.54|112.60|72.24|
|Overall effect of female illness|‐16.490|‐16.870|‐18.880|‐15.470|‐8.24|‐17.490|
|P‐value|0.000|0.000|0.000|0.004|0.301|0.001|

_Notes: Worker ill = 1 if a household member reported missing work for an entire week in which s/he was planning to work that week and the reason for missing work was injury/illness. Expected earnings loss from baseline is calculated by taking the average usual reported income from the baseline survey of individuals who are unexpectedly sick over the course of the survey. All specifications include household fixed effects, week of interview dummies interacted with city, and controls for the number of household members planning to work in that round. Standard errors in brackets, clustered at the