rafmacalaba/data-use-mentions-extended huggingface dataset:
trade data of the products that were targeted and not-targeted by US TTBs in addition to the exporters that were targeted and not-targeted by US TTBs within categories of targeted products.

# **3.4 Data**

# **3.4.1  Trade data**

To estimate the model, we use product-level data on US imports of steel at the 10-digit Harmonized Tariff Schedule (HTS) level, where most steel products are allocated to chapter 72 or 73. Annual import data for

> 22 Because of the dynamic panel structure of our data, two potential problems with the IV estimator used in equation (2) are bias that is associated with the use of a weak instrument and bias that is associated with correlation in measurement error.

> 23 Konings, Vandenbussche, and Springael (2001) apply Prusa's approach to AD cases in the European Union.

14
small‐scale food producers (SDG indicator 2.3.2). DHS, instead, only collect data on employment and occupation and can be used to monitor a few SDG indicators. All surveys allow disaggregation by age and sex.

A starting point of monitoring the SDGs and, more generally, studying labor markets is the classification of persons as either employed or unemployment/out of the labor force. Most of the reviewed surveys allow such a classification. There are a few exceptions such as the 2015/16 Ethiopia LSMS‐ISA survey that lacks a question on formal attachment to a job, which is required to correctly classify persons temporarily absent from their job. By contrast, fewer surveys allow distinguishing the unemployed from persons out of the labor force. This requires questions on job search and the availability to start working within the next (two) weeks. Whereas all 11 LFS include all required questions, 2 of the 8 LSMS‐ISA surveys and 3 of 6 LSMS‐ type surveys lack at least one of these two questions.

7
patterns are discussed to demonstrate the richness of these data. For this analysis, only the 1998-1999 Time-Use Survey and REE imputation procedure are used.

Figure 1 plots household TEE (averaged across individuals within the household) against mean years of schooling for members aged 18 and up, monthly per capita expenditures, and household size. Panel A shows that TEE declines in years of schooling, a net effect that combines a sharper decline in AL and a partly offsetting rise in REE. Panel B shows a similar pattern for per capita expenditures, except that in this AL rises with per capita expenditure for the poorest households, likely reflecting a combination of unemployment/underemployment for the poorest households and a higher proportion of non-earning children. TEE is falling in household size, particularly in the range of 2 to 6 members, which is mostly driven by lower 8
mining industry, exploiting the country’s rich resource endowment. This led to the underdevelopment of downstream industries – as cheap energy provided by the state electricity utility allowed for the exploitation of South Africa’s mineral wealth and the development of capital-intensive upstream industries (Roberts and Rustomjee, 2009). The apartheid government’s interventions in telecommunications, mining, agriculture, and energy led to the creation of large firms and high levels of market

> 2 Two key findings were that: (i) productivity varies strongly by firm size, with larger firms being more productive; and (ii) firms with linkages to international markets, through importing or exporting goods, are more productive than other firms.

> 3 A special issue focusing on a collaborative effort between the National Treasury and the South African Revenue Service (SARS) to employ administrative record data from SARS for economic policy analysis was published by the South African Journal of Economics (https://onlinelibrary.wiley.com/doi/abs/10.1111/saje.12183).

2
SMEs Micro Small Medium Total<br>Jan 2010 Mar 2010 May 2010 Jul 2010 Sep 2010 Nov 2010 Jan 2011 Mar 2011 May 2011 Jul 2011 Sep 2011 Nov 2011 Jan 2012 Mar 2012<br><!-- End of picture text --> Source: Panel a, ECB Statistics–MFIs Loans to Non-financial Corporations; Panels b and c, ECB Survey (SAFE).
their abilities _before_ they formulate productive projects that form the basis for "shopping" for land, it is very difficult to break this deadlock. To this end, agricultural professionals that are contracted through beneficiary representatives help establish crop budgets for a range of options actually practiced in the _municipio_ and conduct training courses and meetings to disseminate them. Aggregation of these into farm plans involving more intensive land use and sustainable income to beneficiaries provides the basis for the formulation of "productive projects" by individual beneficiaries. Only after

> effective form of satisfying demands for land. Due to the Imiited extent of the pilot, no such cases have, as of yet, been encountered. R7 This perception is illustrated by the fact that official records for the land transferred in 1996 indicate that in all cases where no credit from Caja Agraria was needed (because the seller advanced the 30% complementary credit to the buyer), a farm plan or productive project was "not required".

16
elements discussed above, and has limitations relating to interpretation, measurement and endogeneity.

# **III. Measurement Challenges and Estimation Methodology**

# Data

Table 1 details the sources of data for the four countries used. Each is an annual manufacturing survey containing the standard input and performance variables for plants of over 10 workers or, in the case of Indonesia, 20. In addition, the included countries meet four criteria which sharply reduce the universe of possible candidates. _First_ , they need to have data on geographical location of plants, and to be linkable to other data sets that tabulate population density, as well as variables such as precipitation, temperature, elevation, terrain ruggedness and slope needed for instruments _Second_ , though we treat the data in a panel context, identification is largely driven by cross sectional variation in population density and hence we need large countries with many heterogeneous subnational units. Three of the four have between 200 and 300

> 5 For example, the cost elasticities with respect to city size in France and Colombia are comparable in magnitude to that of agglomeration (Combes, Duranton, and Gobillon 2019; Duranton 2016). Burger, Ianchovichina and Akbar (2022) also estimate the “pure” firm productivity gains of density, net of negative externalities associated with limited mobility, crime, and pollution and suggest that the benefits of agglomeration are lost through both limited uncongested mobility and congestion.

5
changing distortions at the 90<sup>th</sup> percentile also diminished and ranges over 3 – 17% of actual growth compared to 7 – 22% in the benchmark.

# **5.2 Disproportionate Changes in Factor Shares across Sectors**

As discussed earlier, the distortions are unchanged if the changes in factor shares are proportionate across sectors. As such, the differences in productivity estimates from the benchmark case are to an extent insulated against such changes in factor shares. In this exercise, I carry out the first counterfactual exercise with disproportionate factor shares changes across sectors that lead to a divergence in estimated distortions from the benchmark case.

Once again I need reasonable estimates of sector-specific changes in labor shares and in this case also I use the findings in Karabarbounis & Neiman (2014). Using the EU KLEMS data starting from 1975 to 2007, Karabarbounis & Neiman (2014) estimate the trends in labor share for 10 non20
fuel tax could be a proxy of the carbon tax effect. A few recent studies have used energy price change as a proxy to study the impact of carbon tax (Coste, Cali, Cantore, and Heine 2019; Mundaca, Strand and Young 2021). Most studies on the effectiveness of fuel prices or carbon taxes focus on their environmental or economic impact only or separately. Few studies are available that target a combined economic and environmental performance matrix, such as emissions intensity, _i.e._ , the ratio of emissions to a non-emission measure, usually GDP. This is surprising as emissions intensities are measures of both theoretical and practical importance, and has been adopted as targets at the firm, sector, and country levels (Herzog, Pershing, and Baumert 2006) including as Nationally Determined Contributions (NDCs) target. In this stream, Ryan et al. (2009) examines the impact of fiscal measures on fleet emission intensity for EU15 countries and found that fuel and vehicle taxes, rather than voluntary agreements of automobile manufacturers, have an impact on emission intensity. Brännlund et al. (2014) analyzed carbon intensity of Swedish industry using firm-level data and found that it responds to both carbon tax and fuel price. A few more studies examine the relationship between energy price and energy intensity (Filipović et al. 2015; Grubb et al. 2018).

To the best of our knowledge, this might be the first study on the impact of fuel prices on emission intensity of the transportation sector for Brazil utilizing insights from panel analysis. We benefit from the World Bank’s recent Economic Memorandum for Brazil (Hanusch Ed. 2023) in understanding the context of Brazil’s economic development and environmental challenge nexus. While most studies on Brazil’s fuel price elasticities of demand adopted time series analysis at national aggregate level, we are informed by a few studies that employed panel analysis at the state or municipality level (da Silva et al. 2009; Santos 2012; Uchôa, et al. 2019
to export. A second challenge is the lack of data on technology use. Most of the evidence focuses on indirect technology measures such as patents or R&D; only Bustos (2011) and Lileeva and Trefler (2010) use direct technology measures. Third, the use of technology is multidimensional in its application to different business functions. Establishments use different technologies for different tasks, and even within the same business function. Thus, the export effects on technology adoption may differ for different tasks and technologies.

In this paper, we aim to narrow the existing gap in the literature in understanding the relationship between exporting and the technology gap. We use a unique and novel database, the Firm-level Adoption of Technology (FAT) survey and explore the impact of exporting on technology sophistication and the adoption of selected individual technologies. The survey includes more than 1,500 firms in Brazil and provides granular information on the adoption of more than 300 technologies for different business functions as well as participation in international trading activities.

2
completing the picture of the environment in which microfinance is operating. The experts need not be providers, indeed conflict of interest considerations suggests that it is probably better when they are not. (The _Doing Business_ survey has used central bankers among those responding to questions about credit availability, and registrars for questions relating to registries.)

# **6. The way forward: much known, much missing**

# _State of knowledge_

We have proposed a basic framework within which the analysis of the interesting questions: (i) how does access affect poverty and productivity and (ii) what hinders access, can be considered. These questions need information from user surveys, both household and business, and from surveys of providers and their regulators. Expert surveys will be a necessary complement.<sup>32</sup> It will be evident from the discussion so far that quite a lot of relevant information has been collected, that its reliability, timeliness, comprehensiveness and comparability all remain somewhat deficient. On the other hand, less efforts have so far been made to bring together what is available – the _Doing Business_ study and The Mix’s effort and Christen et al. (2004) being notable exceptions. Doing so will help raise awareness of the access issue in general, as well as allowing countries to begin to benchmark their financial systems even on simple criteria such as percentage of households with deposits or loans from the financial sector.

The program of business user surveys is moving ahead well. While the questionnaire would benefit from an overhaul, the extent of country coverage and the usefulness of the information collected in the PICS is indisputable.

The position with regard to household surveys is somewhat less satisfactory, despite recent advances for a few countries. Even if the right questions are being asked, they are not being asked in a sufficiently large number of countries. Perhaps now is the time to launch a new wave of _specialized_ household surveys of financial service usage, building on what has been learnt from the specialized cases
<!-- Start of picture text --> Figure 2: Ownership and Business Figure 3: Domestic vs. Foreign<br>Constraints Ownership and Business Constraints<br>60% 60.0<br>50% 50.0<br>40% 40.0<br>30% 30.0<br>20% 20.0<br>10% 10.0<br>0% 0.0<br>Cost of Financing Business licensing and S kills and educat ion of Tax admin. Cost of Electricity Transport Access t o<br>operat ing permits available workers Finance Land<br>DE NOVO P RIVATIZED S TATE/ S OCIAL Domestic Foreign<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4: Exporters vs. Non-Exporters and<br>Business Constraints<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Customs and Transport ation Access t o Land S kills and<br>trade regulations educat ion of<br>available workers<br>Exporter Non-Exporter<br><!-- End of picture text -->

# **Business community perceptions**

While the analysis described above provides guidelines about the relative importance of ownership and perceptions of the investment climate based on their effects on productivity and other performance measures, this section provides an alternative approach based on the ranking of the same perceptions by the survey respondents.

In the 2003 PICS survey, Serbian managers were invited to rate 18 dimensions of the investment climate in terms of whether they were important obstacles to doing business. These dimensions were rated from 0 (factor is no problem at all) to 4 (factor is a major obstacle
different occupations. This is exceptional, as such performance differences exist in all comparison countries. Household income is significantly and strongly related to student performance in Colombia.<sup>14</sup> The same is true in all other countries with income data. However, due to differing national currencies and thus income categories, the estimates are not directly comparable across countries.

In contrast to all comparison countries, there is no statistically significant difference in student performance between any category of school location in Argentina. In the other LPCs, students in rural areas perform systematically worse than students in urban areas, and even than students in large cities of more than half a million inhabitants.

Not considering the imputation controls, the student background variables considered in the model together account for as much as 27% of the total variation in student performance both in Argentina and in Colombia. This is similar to Greece and England, slightly smaller than in Germany and Macedonia, but substantially larger than in Turkey and Italy.

# **5. School Characteristics and Educational Achievement**

Tables 4a and 4b report the relationship between school characteristics and student achievement, estimated using equation (2) above, which controls for the effects of student background characteristics. The considered school characteristics fall into two broad categories, namely measures of the resource endowment of classrooms and schools and measures of the institutional features of schools.

## **_5.1 Resource Endowment_**

In all considered countries, there is no consistent evidence of significant relationships between resource endowments and student performance. The few statistically significant estimates of a positive relationship tend to be matched by statistically significant estimates of negative relationships. This is true for Argentina and Colombia as much as for the comparison countries in the LPC and HPC group.

In no country are smaller classes related to better performance in a statistically significant way. However, estimates of class-size effects are particularly prone to endogeneity bias. For example, if class size is allocated in a compensatory way, so that weak students are placed
4 elucidating differences across settings. In these countries, a total of 31,848 households were interviewed, with sample sizes per country varying between 2,716 (Uganda) and 12,271 households (Malawi), of which, on average, 76% were rural. Burkina Faso and Mali have joined the Initiative more recently and their survey findings are not included here.

The LSMS‐ISA initiative also presents a number of notable innovations on <mark>the World Bank’s Living Standards Measurement Study (LSMS) surveys, which for some time provided important information on the lives of Africans, their income, their economic activities, and their wellbeing. Most importantly, it strengthens the coverage of household agricultural activities—the Integrated‐Surveys‐on‐Agriculture part of LSMS‐ISA. The surveys are based on household samples and designed from the perspective of the household, not the farm. As a result, medium and large scale farms are only sparsely covered in practice (Jayne et al., 2016), even though technically represented in the sample. Information is gathered at both the household and the plot level, covering every aspect of farming life—from the plots they cultivate, the inputs they use, the crops they grow, the time they allocate per plot, the harvest that is achieved, the way they market it, the amount they lose post‐harvest, and so on.</mark> <mark>Second, in addition to the integrated approach to data collection, data gathering takes place at highly disaggregated levels, at the plot level, but also at the individual level, such as for time allocation and plot management. This enables a more refined, gendered perspective on agriculture and rural livelihoods. Third, the surveys make wide use of ICT‐tools. Tablets are used for data collection, improving the quality of data (Caeyers, Chalmers, and De Weerdt, 2012); households are geo‐referenced using Global Positioning System (GPS) devices, enabling
in harassment across spaces even at similar presence of men. This is not what we observe empirically (Figure 4). Another way sorting of men across spaces could manifest in our data would be a decrease in harassment in the public space as perpetrators are able to enter the reserved space, while harassment in the reserved space would increase as share of men in the public space goes down. Instead, we find no impact of presence of men in the reserved space on harassment in the public space, and vice versa (Figure A11). However, we cannot test whether perpetrators sort between cars when the rule _is_ well followed, because we do not observe situations with few men in the public space (Figure A3).

# **8.4 Congestion**

Congestion could play a role in two ways in our analysis. First, if congestion differed between the reserved and public spaces, this would create another mechanism affecting willingness to pay. However, both administrative and rider audit data show that congestion levels are almost identical across the reserved and public spaces (Figure A2). Our main estimates all control for above-median congestion.

Second, congestion could also create a type of measurement error in our analysis. At times of extreme congestion, participants may be physically unable to select the car they prefer, such that the car they select (which we observe) does not represent the car they prefer (unobserved). This would bias our estimates of mean takeup toward 13 - 17 percent for these observations (because one of each train’s six to eight cars is designated as a reserved space). This is well below the takeup rates for the “high level users” and above the levels for the other two thirds of the sample (Figure 3). This suggests that if anything this kind of measurement error could (a) bias the estimates for the high-level users downwards, and (b) reduce the estimated differences between the three groups. We explore this in Figure A12, which describes the
_rit_<sup>_n_1</sup> _−s_<sup>isavectorofaverageavailabilityofdifferenttypesofroadsinneighborhood</sup><sup>_n_1,and</sup> _rit_<sup>_n_2</sup> _−s_<sup>isthesameforneighborhood</sup><sup>_n_2.Wefocusourattentiononoutcomeyears1990and2000</sup> because for each of them we have comparable road data approximately 14 years earlier, i.e., in 1976 and 1986. Road data for 2008 and 2014 are not comparable to either of these or to each other. Coefficient estimates for _γ_ 1 _, γ_ 2 and _γ_ 3 are presented in Figure 6, for each of the four road categories and each of the two outcome years, 1990 and 2000. Coefficient _γ_ 1 gives the predicted % point increase in buildup if there is a road in the cell 14 years before. Coefficients _γ_ 2 and _γ_ 3 give the predicted % point increase in buildup if neighborhoods N1 and N2, respectively, go from no cell with a road to all cells with a road. We note a lot of similarity between the two regressions in terms of relative magnitude of the coefficients: the presence of a class 1 road in the cell or nearby predicts more buildup 14 years later. For the other three categories, the presence of a road in the cell predicts slightly higher buildup later on, but the presence of a road in the more distant 19
are several calibration methods that can be implemented, depending on the level of detail of information available for calibration (Valliant et al, 2013; <mark>Lundström and Särndal, 1999; Andersson and Särndal, 2016).</mark> With a previous in-person survey as a sampling frame (i.e., recontact surveys), there is often a wealth of information associated with each household (or individual or farm), which readily allows modeling the response probability. This was the approach taken in the LSMS-supported HFPS, for example (Gourlay et al., 2021). With sampling frames based on RDD or lists provided by mobile network operators, however, there is usually no or limited information available in addition to telephone numbers. Auxiliary data sets are required for post-survey adjustments to reduce selection biases. A recent census or a recent nationally representative survey may serve this function. The phone survey data and the auxiliary data need to have in common a set of variables, such as demographic and location variables, for weighting adjustments to be possible (Lepkowski et al. 2007; Himelein et al., 2020).

Post-survey adjustments such as reweighting can never fully correct coverage and non-response biases in phone survey data. Their relative success in reducing biases depends on how much of the population is left out, how different the selected sample is from the general population, and how much information is available for bias reduction methods. Ambel et al. (2021) examine how well response propensity adjustments perform at reducing coverage and non-response biases in LSMS-supported HFPS in four African countries. They find biases to be substantially reduced but not fully eradicated in all four surveys. In contrast to Ambel et al. (2021), Brubaker et al. (2021) investigate respondent selection biases in individual-level data from the same surveys. Selection biases are found to be more pronounced at the individual level than at the household level, such that reweighting is relatively less successful in overcoming these often more substantial biases. Obtaining more representative individual-level