rafmacalaba/datause-encoder-data huggingface dataset:
importables more expensive, and non‐tradables less expensive. Energy is an important input in the production of many final goods, and imports can be, in part, substituted by domestic varieties. The model results take into account these general equilibrium effects and, even if production of food tends to be energy intensive, the reduction of prices of other goods is expected to be larger (see last two rows of Table 1).

# _The distributive effects of the oil price shock_

The oil price shock will affect all households, but the final impact will likely be larger for some groups than others. In terms of per capita consumption, the poorest bottom 40 percent loses 6.2 percent of its welfare and the top 60 percent suffers a loss of 7.0 percent (see Table 3). This minor progressive impact, i.e. the fact that richer people experience welfare losses slightly larger than those for poorer people, amounts to a marginal change of the Gini coefficient, a standard indicator of inequality, which decreases from 41.06 to 40.86. Worryingly, poverty rates rise for all the usual poverty lines. For example, when measured at the low 2.5 $ a day line, the oil shock increases poverty by dragging about 100,000 additional people below that line. At higher poverty lines, such as 5 and 10 $ a day, 900,000 and 4.1 million additional people fall behind and poverty rates increase to almost 4 and 22 percent, respectively.

_Table 3: Welfare, poverty and distributional impact of the oil shock in the Russian Federation_

||Without Oil<br>Shock|With Oil<br>Shock|Change|
|---|---|---|---|
|Per capita Consumption|USD in consta|nt 2011 PPP|%|
|All Population|8,971|8,354|‐6.88|
|Bottom 40|3,619|3,395|‐6.20|
|Top 60|12,539|11,660|‐7.01|
|Poverty headcount|Headcoun|t ratios (%)|% Points|
|2.5 $ a day line|0.25|0.33|0.07|
|5 $ a day line|3.14|3.77|0.64|
|10 $ a day line|19.06|21.98|2.92|
|Gini coefficient|41.06|40.86|‐0.20|

Source: HBS data and micro‐simulation results. Note: per capita consumption is expressed in 2011 US PPP $; poverty headcounts and Gini in percentages The impact of the oil shock along the full welfare distribution of the Russian population can be seen in the growth incidence curve (GIC) of Figure 1. For all percentiles, per capita consumption is measured with and without the oil price shock and the difference between these two situations is plotted in the graph. The welfare distribution obtained from the household survey of 2011 is assumed to represent the situation without the oil shock. The microsimulation model is used to generate the counterfactual distribution with 14
practitioners. This conclusion becomes even more forceful when taken together with the comparison of the time required for GPS compared to CR measurement, with our data showing GPS to lead CR by several orders of magnitude.

This strong message in support of the adoption of GPS in survey fieldwork, is, however, mediated by a number of considerations regarding outstanding challenges with GPS measurement. One that emerges from the analysis is that while the GPS measurement error is almost universally small in magnitude (only 5 percent of observations recording a discrepancy with CR of more than 0.09 acre), in relative terms a discrepancy of plus or minus 10 percent is not uncommon. However, as illustrated, the CR method is not immune to measurement error thus some of the problematic cases could be attributable to noise in the CR data. Considering that GPS measures in large scale surveys are often plagued by a missingness rate in the range of 15-30 percent, which suggests that a large scale dataset of GPS plot measurements could be plagued by as much as 50 percent problem cases. Complementing data collection with information that can aid in identifying those problem cases, as well as enforcing sound field implementation protocols, can significantly reduce both bias and missingness in GPS measures.

Despite the evidence that subjective measurements can be riddled with problems, farmer self-reported estimates of area should still be included in household surveys, but not as the primary measurement method. Objective measurements come with their own challenges, including time and equipment requirements, questions of accuracy at small-plot levels, and feasibility of full plot sample measurement. Subjective measurements have negligible fieldwork costs, and, more importantly, they can serve as a baseline for imputation where objective measurements may be missing (Kilic et al. 2013). Therefore, we recommend GPS measurement (where feasible) complemented by farmer self-reported estimated area (for all plots).

An ancillary story emerging from the data concerns self-reported data. While confirming all the known issues with measurement error in self-reported land data, the analysis presented here provides at least one suggestion for limiting the scope of this error in the future. When land area is collected using nonstandard units, as opposed to forcing respondents or enumerators to perform a conversion to standard units at the time of the interview, data from self-report appears to be a better approximation of the benchmark preferred measure.

Furthermore, our analysis casts some shadows on the benchmark compass and rope measurement. It appears that a good deal of what we labeled for simplicity as GPS measurement error, may in fact be 27
sure of innovation inputs. For example, OECD (2018) uses the number of scientists and engineers as an indicator for measuring a country’s technological human resources. Maloney and Caicedo (2017) present a historical analysis of the impact of engineers density on technological intensification and structural change. de Rassenfosse and van Pottelsberghe (2009) use the number of full-time scientists and engineers as a measure of innovation effort in a patent knowledge function and show evidence of a positive relationship between research effort and patents fillings. Huang and Yu (2011) also use the number of R&D personnel as a measure of in-house R&D, and analyze the effect of competitive and non-competitive R&D collaboration. Finally, Cirillo et al. (2017) use the number of technical-scientific workers as a proxy of innovation when looking at the impact of innovation on within-firm wage inequality in Europe.

We follow this literature and use the measure proposed for Brazil by Ara ́ujo et al. (2009) and Taveira et al. (2019) based on the firm’s share of technical-scientific employees (PoTec) as a proxy for R&D investments (see Table 2 for definition). Ara ́ujo et al. (2009), using RAIS and PINTEC databases, show that PoTec has a high correlation - between 0.8 to 0.9 - to intramural and extramural R&D expenditure. We also compare the R&D expenditures by states, according to the latest PINTEC survey in 2017, with our PoTec firms by state in 2017 and find a correlation of 0.81. Thus, in the absence of direct data on innovation expenditure for the firms in the census, the PoTec approach appears as a good proxy of R&D expenditure intensity. More importantly, it allows using the employer-employee census to follow firms and workers over time and to match worker’s characteristics with those of the innovative and non-innovative firms. Exploiting these dynamics allows exploring transitions from noninnovative to innovative status, which is critical in the causality analysis of innovation and wages.

# **3.4 Methodology**

## **3.4.1 Measuring the innovation premium**

To identify the wage premium associated with innovation a canonical Mincer-type wage equation is estimated: where _i_ indexes individual, _j_ region, _f_ firm and _t_ years; _wijft_ is the hourly wage, which is regressed against our proxy of innovation (innovative) - the share of PoTec workers -and a set of personal characteristics: gender (male = 0), age, tenure, measures of education 9
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 5: Regional log Formal Earnings Premia - Pre-Trends - 1987-1990 <!-- Start of picture text -->
1.7<br>1.4<br>1.1<br>0.8<br>0.5<br>0.2<br>-­‐0.1    1987    1988    1989    1990<br>-­‐0.4<br>-­‐0.7<br>-­‐1<br><!-- End of picture text -->

Each point reflects an individual regression coefficient where the dependent variable is the change in regional log formal earnings premium from 1986 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects. Positive estimates imply larger earnings declines in regions facing larger tariff declines. Standard errors adjusted for 112 mesoregion clusters.

44
country bank surveys conducted by the IFC, and the SME surveys undertaken by FRS (Inmark Group).<sup>23</sup> The data are briefly summarized in Table 1.

The cross-bank data for Argentina, Chile, Colombia, and Serbia was gathered via an especially designed questionnaire administered to banks during on-site, detailed interviews conducted with banks’ top management in late 2006 and 2007.<sup>24</sup> For each country study, different types of banks were interviewed by different teams. The samples include both banks that were ex-ante thought to be lending to SMEs and banks that were perceived ex-ante (by the authorities and World Bank experts) to have little or no relation with SMEs. Banks were selected to represent the domestic financial sector and cover a large fraction of it. The banks interviewed per country account for about 70% of banking sector assets at the time of the interviews. A total of 37 banks and 1 leasing company were interviewed across these 4 countries.<sup>25</sup> In all cases, the interviews and data processing were confidential and conducted only by World Bank staff. All banks were informed that the data would be reported only in an aggregate way, without disclosing the strategy or positions of individual banks. The individual bank data are not available even to the country authorities, so banks had in principle no constraints in sharing information.

The questionnaire includes a maximum of 92 questions and was designed to gather information on 3 broad areas.<sup>26</sup> The first area addresses banks’ strategic interest in SME business. The second area assesses the determinants of bank financing to SMEs, in

> 23 The countries and banks interviewed were selected based on the interest of financial experts at the IFC and the World Bank. Within countries the sample of banks was chosen with the agreement of the respective central banks.

> 24 The questionnaire for each bank was filled in the context of interviews that would typically take at least 5 hours. The interviews would normally involve a discussion on strategy with the top bank manager, as well as discussions with the managers of the business units and risk management units.

> 25 Total assets stands for liquid assets, public and private securities, loans, and other bank assets.

> 26 The questionnaire used to interview banks is available upon request to the authors.

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FIGURE 4. HISTOGRAMS OF SUBMISSION PERIOD LENGTH (DAYS) BY TREATMENT STATUS <!-- Start of picture text --> Manual e-GP<br>20% 20%<br>Mean = 19.5<br>Mean = 25.6<br>15% 15%<br>10%<br>10%<br>5%<br>5%<br>0%<br>0% 0 5 10 15 20 25 30 35 40 45 50<br>0 5 10 15 20 25 30 35 40 45 50<br>8% 8%<br>Mean = 17.8 Mean = 16.1<br>6% 6%<br>4% 4%<br>2% 2%<br>0% 0%<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>Submission Period<br>Signing Period<br><!-- End of picture text --> Note 1: Number of tenders: Manual = 30661; electronic = 43137 Note 2: The red regions of the graph correspond to the stipulated submission and signing periods as per the procurement rules

# 7. CONCLUSIONS AND POLICY IMPLICATIONS

86. In this impact evaluation, we estimate the effects of Bangladesh’s e-procurement system on metrics of access, economy and efficiency of public tenders. Bangladesh’s e-procurement system is advanced compared to most of its comparators around the world in that it not only provides an online advertisement portal, but rather requires that all notable administrative actions are administered digitally, from tender preparation to contract signature. To identify the impact of e-procurement on a wide range of outcomes, we construct a novel dataset, comprising all published electronic public procurement tenders and data extracted from a large sample of paper-based procurement records from there major GOB agencies, which are responsible for the bulk of public works projects.

87. We rely on a range of fixed effect regression models combined with coarsened exact matching estimators, to address potential threats to causal identification. We find overwhelming positive impacts of e-procurement on a wide range of outcomes.

33
address this issue because the results will then be determined by the MLI scores of two or three of the largest agreements; if these PTAs include sensitive lists and longer tariff reduction schedules, this approach will bias the results upward. Finally, the variables used in the simulations represent only one way of combining the relevant MLI categories into explanatory variables. Thus, alternative definitions of _dexc_ and _dyrs_ variables may have an impact on the results. For there reasons, it is difficult to conclude that product exclusions and length of tariff reduction schedules alone are responsible for the smaller than expected increase in the PTA dummy coefficient. Although the liberalization of these distortions is likely to yield a large increase in the value of preferential trade, PTAs may still affect trade in lines where the margin of tariff preference is negligible.

# **4.3 Alternative specifications of the PTA variable**

One of the main advantages of the PTA sample used in this study is its breadth of coverage—we can distinguish between PTAs that have been notified to the WTO and those that have not, PTAs with two or more members, and North-South vs. South-South vs. North-North agreements. However, up to this point our econometric estimates considered only the aggregate effects of all PTAs, without taking advantage of the richness of the data. Using the preferential model of Table 5 as a baseline, this section will investigate whether meaningful insights can be gained from disaggregating our sample into various types of PTAs. The estimates are summarized in Table 9, which reports regression results from four alternative specifications of the PTA variable. For convenience and ease of comparison with the baseline estimates, column (1) repeats the preferential results from Table 5. In each subsequent model, the number of observations remains the same as the original specification, the _F_ statistic shows that the coefficients are significant as a group, and the _R_<sup>2</sup> remains unchanged at the two decimal level. Therefore, the estimated coefficients can be readily compared across the alternative specifications.

Column (2) of Table 9 shows the results of estimating a model with two PTA dummy variables— one for agreements notified to the WTO, _PTAnt_ , and one for agreements that have not been notified,

> trade gains in the simulation. If we also assume that the members of this PTA are small countries (again consider the COMESA example), their intra-PTA trade is going to be small compared to intra-PTA trade of large agreements like NAFTA and MERCOSUR. Following the simulated removal of exclusions and phase-in periods, this already small intra-PTA trade value will remain unchanged and will considerably lower the overall average, therefore reducing the simulated gains.

43
and a “matched sample”. The restricted sample defines the pre-reform period as the two years preceding the reform. A close inspection of crude palm prices at the world market identifies one matching sample period during the pre-reform period. Consistent with post intervention price trends, prices in 2008 and the first half of 2009 continued to decline from a high in early 2008. The “matched sample” thus takes out the period of palm price rise in the international market from the pre-intervention sample. Since the margins become smaller in upswing phase of the international market reflecting imperfect pass-through, a point discussed earlier, a preintervention sample that includes such a phase can potentially bias the estimated policy effects upward. We thus believe that the estimates from the matched sample are the most credible.

Column 1 reports estimates for our simplest specification with year and quarter fixed effects without any other controls, while column 2 presents the results with a number of control variables including year and quarter fixed effects. In addition to the variables discussed in the context of the DID design in section (5) above, we include a dummy that takes on the value of unity during the Ramadan month in a year and zero otherwise to control for anticipated positive demand shift during the Ramadan.<sup>23</sup> Columns 1 and 2 of Table 1 report estimates for the full sample, and show statistically significant and numerically substantial effect of both policy announcement and implementation. More importantly, the estimates indicate that both the intercept and the absolute slope of the margin equation _increased_ following the reform which contradicts the predictions of both the standard double marginalization model and the financing model where DOTs provide low cost credit, but do not relax credit constraints faced by the wholesale traders. To check if the results depend on the pre-intervention sample period, we present results based on a restricted sample that compares post-intervention with the two immediate preceding years (please see columns 3 and 4 in Table 1).<sup>24</sup> The results indicate that if anything, the magnitudes of the increase in the

> 23Ramadan is the holy month when the Muslims (85 percent of population) fast from sunrise to sunset. It is customary to break the fast by eating deep fried food, causing a demand shift and higher prices for edible oils. Note also that there were no significant difference between aggregate growth or macroeconomic stability in pre and post reform period, thus it is unlikely that positive demand shifts in the post-reform period can explain the results reported in the paper. The GDP growth rate for the period 2006-2010 was 6.2 percent, and 6.5 percent over 2011-2012.

> 24In a standard before-after design, it is usually better to select a comparison period closer to the intervention date to avoid confounding by long-run structural changes in the economy. In this sense, a shorter pre-intervention

26
Equation (1) tells us that to arrive at the relative contribution of the two components, one would need a value for _α_ , which we wish to remain agnostic about. Our approach is to compute for each country (in each scenario) the value of _α_ an observer would have to hold so as to judge that Covid-related mortality and additional poverty make identical contributions to the welfare costs of the pandemic, given the observed outcomes. Using a superscript _A_ to denote actual, or estimated, outcomes, this "break-even" _α_ , which we ˆ call _α_ , is given by: ˆ For any _α < α_ , additional poverty is the dominant source of the current welfare costs of the pandemic.

# 3.1 Six countries with high-quality data

We rst look at a restricted sample of six countries for which we have data on age-specic mortality from Covid-19.<sup>16</sup> Three of them are high-income countries: Belgium, the United Kingdom (UK) and Sweden. We selected these three countries because they had the highest number of Covid-deaths per capita as of early June.<sup>17</sup> The three remaining countries are developing countries for which age-specic mortality data are available as of early June: Pakistan, Peru and the Philippines. For each of these countries, we estimate _LY_<sup>_A_</sup> and _PY_<sup>_A_</sup> as follows.

To estimate a country's number of lost-years, we start from the age-specic mortality information: the number of Covid-related deaths distributed by age categories. Where available for individual countries, this information is obtained from Oces of National Statistics, Ministries of Health or other government oces. A specic list is included in Table A1 in the Appendix.<sup>18</sup> Then, for each death we assume that the number of lost-years is equal to the residual life-expectancy at the age of death, as computed from the country's pre-pandemic age-specic mortality rates, obtained from the Global Burden of Disease Database (Dicker et al., 2018). As noted earlier, we consider this assumption conservative, since individuals who die from Covid-19 are given the same residual lifeexpectancy as that of other individuals with the same age.

One particularity of Covid-19 is that its mortality is concentrated among the old. This concentration is illustrated in Figure 1, which shows a histogram by age categories of the Covid-19 deaths observed in Sweden. This concentration implies that evaluations of mortality costs based on lost-lives, which disregard the age distribution of deaths, would tend to overestimate the welfare consequences of mortality, compared to our evaluation

> 16It is not possible to nd age-specic mortality information for all countries.

> 17Belgium has the highest number, the United Kingdom has the second highest and Sweden has the fth highest, beyond Italy and Spain, but Sweden has particularly detailed age-specic mortality information.

> 18All of our data sources are described in greater detail in the Appendix.

8
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Zimbabwe’s spending is not sufficient to cover maintenance and rehabilitation needs**

<!-- Start of picture text --> 150<br>100<br>50<br>0<br>-50<br>-100<br>Maintenance Rehabilitation<br>Spending as % of requirements<br><!-- End of picture text --> _Source:_ Gwilliam and others 2008 One reason Zimbabwe does not have adequate road maintenance funds is that its fuel levy is too low. Fuel levies stand at $0.011 per liter of diesel and $0.0124 per liter of gasoline, well below more typical levels of $0.05–$0.10 per liter found elsewhere in Africa. The ZINARA plans to increase the gasoline fuel levy to 4 cents per liter to boost cost recovery, but so far its implementation has not been feasible. In addition, toll gates have been added to allow for the application of additional road-user charges. Nevertheless, the ZINARA has been unable to raise sufficient road-user revenues to cover roadmaintenance requirements, in part because it lacks control over the revenue collection, transfer, and allocation mechanisms. Further efforts are therefore needed to provide a secure and adequate source of funding for road maintenance.

Zimbabwe needs to function more effectively as a regional transit country to allow smoother passage of trade flows from neighboring countries and to benefit from the business opportunities that these represent. Although Zimbabwe is on the crossroads of Africa, occupying a strategic position on the North–South corridor, it nonetheless faces one of the highest export and import costs in the region (figure 14). The administrative costs and delays associated with moving freight across its borders are significant and include lengthy documentation time and high customs clearance fees and terminal-handling charges. A Mozambican company entering Zimbabwe must pay a number of charges including an entry visa fee costing approximately $30 for a month, and a customs guarantee costing $120 per year (USITC 2009). These administrative charges add greatly to the costs of trade. As a result, Zimbabwe was ranked 168 among 183 countries in 2010 and 2011 with regard to facilitation of cross-border trade.<sup>9</sup>

> 9 www.doingbusiness.org/data/exploreeconomies/zimbabwe.

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