rafmacalaba/datause-encoder-data huggingface dataset:
(or status as a post-Soviet state) from various sources.

**Income and pollution in the United Stats–** To assess the correlation between income and ambient pollution in the United States we utilize census tract level median income from the American Community Survey 5-year estimate 2016-2020. Ambient pollution comes from the same PM2 _._ 5 data used in the global analysis but averaged to the census tract level.

# **4.2 Methods**

To measure correlation between wealth and ambient concentrations of pollution we estimate fixed effects regressions of the form: where _PMg_ is the measure of pollution at RWI grid-cell _g_ aggregated over our time period in one of the three ways described in the Data section. _RWIg_ is the RWI value at that grid-cell. _ψc_ is a country fixed effect that we include in the pooled regressions (these are omitted in the country specific regressions). We employ various weighting schemes, weighting by: grid-cell population, the inverse of the reported RWI estimation error, the grid-cell population multiplied by the inverse of the error, the population of the country containing the grid-cell, and the inverse of the error times the population of the country containing the error.

In a separate analysis we compare the share of pollution in urban and rural areas coming from dust and sea salt to the share coming from other sources. To do this we first measure the average levels of exposure in rural and urban areas using the total PM2 _._ 5 pollution data. We then repeat this calculation using the dust and sea salt removed measures of PM2 _._ 5. We attribute the difference in these averages to dust and sea salt.

To assess heterogeneity we use several different approaches. To examine heterogeneity based on primary emissions sources, level of per capita income, or colonization history we simply stratify the sample by the relevant measure of heterogeneity and re-estimate equation 1 (omitting the fixed effects) within each country (or city) within each strata.

To assess the role that elevation plays in driving heterogeneity across cities we calculate the correlation between wealth and elevation separately within each city and the correlation between wealth and pollution within each city. We then run a simple OLS regression at the city level with the correlation between wealth and pollution as the dependent variable and the correlation between wealth and elevation as the independent variable. Our estimation, where _i_ denotes cities, takes the form: Corr(Wealth/Pollution) _i_ = _β_ Corr(Wealth/Elevation) _i_ + _ε_ (2) 15
For example, we do not discuss behavioral responses, social stigmas, or implications for social cohesion and political support for poverty programs.<sup>20</sup> A few caveats are also in order. The paper uses existing household consumption surveys to simulate the targeting performance of various methods and the likely impacts on poverty of using the methods to allocate a fixed budget. In a sense, we are creating a “laboratory.” While this is inevitably somewhat artificial, we believe it to be essential for properly testing the performance of the various targeting methods found in practice. It must be acknowledged, however, that things happen in the real world that cannot be predicted in our laboratory setting. None of the methods considered here could be implemented flawlessly. While we do allow for lags in implementation, idiosyncratic mistakes will no doubt also be made. Most likely the methods will perform less well than our calculations suggest. We present and compare the best case results for the various methods reviewed, unaffected by potentially differential costs, ease of implementation, and susceptibility to manipulation and corruption.

In addition, there are potential benefits of each method in practice that we cannot identify in our laboratory setting. Our purpose here is to judge the methods solely on how accurately they reach the poor and by how much transfers targeted in these ways would reduce poverty. These are undeniably important as goals for targeted programs and are usually explicit in the justifications provided by donors and governments. Nonetheless, there may be other objectives in practice such as capacity building or building systems. Donors, governments and NGOs implementing these methods do sometimes make considerable effort to include communities in the selection of beneficiaries, build robust grievance and redress procedures, and continuously monitor and evaluate the performance of the targeting systems. Such efforts could well be important in building administrative capacity for social protection over the longer term and also improve targeting. It is not obvious how an accounting for such potential benefits would alter the relative assessments of alternative methods based on our simulations.

The paper finds that when the counterfactual is a uniform allocation of the same budget, even our Basic PMT allows a substantial reduction in the rate of inclusion errors; in this setting it should be possible to roughly halve the rate of inclusion errors using econometric targeting. When judged against a fixed poverty line, this success at avoiding leakage to the nonpoor comes with seemingly weak coverage of poor people—a high rate of exclusion errors. In other words, the method helps exclude the poor as well as the non-poor. The paper finds that econometric targeting typically provides at most

> 20 Smolensky et al. (1995) conclude that none of these issues is likely to be decisive for or against targeting. Atkinson (1995) argues that broader objectives of social policy (including social solidarity) warn against targeting.

7
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Challenges**

Port Sudan remains one of the most inefficient ports in Africa. Compared to regional benchmarks, container dwell time at Port Sudan is over four times that of global best practices and among the worst in Sub-Saharan Africa. Truck cycle times for receipt and delivery of cargo at Port Sudan are 24 times higher than global benchmarks, and crane productivity is less than a third of what is observed in several parts of Africa. The handling charges at Port Sudan are at the lower end of what is charged in other African ports, but the inefficiencies present a huge deterrent to increased usage of the port (table 7).

**Table 7. Benchmarking Port Sudan’s performance**

||**Port Sudan**|**Mombasa**|**Dar es Salaam**|Southern<br>Africa|West Africa|Global best<br>practice|
|---|---|---|---|---|---|---|
|**Performance**|||||||
|Container dwell time (days)|28|5|7|4–8|11–30|<7|
|Truck processing time (hours)|24|4.5|5|2–12|6–24|1|
|Crane productivity (containers per<br>hour)|8|10|20|8–22|7–20|20–30|
|Crane productivity (tonnes per hour)|8|21|23|10–25|7–15|>30|
|**Charges**|||||||
|Container handling ($ per TEU)|150|67.5|275|110–243|100–320|80–150|
|General-cargo handling ($ per tonne)|10|6.5|13.5|11–15|8–15|7–9|
|Container handling ($ per TEU)|-130|-54.5|-248|110–244|100–321|80–151|

_Source:_ AICD ports database. _Note:_ TEU = 20-foot equivalent unit.

A steady increase in containers handled at Port Sudan has created serious port congestion problems, adding significant delays to the movement of freight. Several of the region’s ports are beginning to experience serious capacity constraints (figure 10) due to burgeoning demand associated with increases in container traffic and dry-bulk cargo. Port Sudan in particular is already operating at 80 percent capacity at least, a level of intensity that creates problems in terms of congestion. There is some scope for easing capacity constraints by improving the efficiency of port performance, although ultimately new investments will be needed (see figure 10).

Labor requirements at the port lead to overstaffing, poor productivity, and higher costs. In the North Port, the equivalent of a dock labor board exists and there is a legal requirement to use labor from a pool of 25,000 workers. In the container terminal, the SPC provides the labor and acknowledges that it is overstaffed by 7,300 workers (World Bank 2008).

> <sup>18</sup>
simple methodological innovation, we build a globally comparable database of 163 countries/areas from 1965-2015, approximately two-thirds of which are in developing economies, and 30 in Sub-Saharan Africa, representing more than 90 percent of the global population. We build both a cross-sectional and panel database.

While our methodology has limitations, our robustness tests indicate that this dataset produces similar results to each underlying assessment used, as well as Item Response Theory (IRT)linking methodologies where there is overlap. We include double the number of countries of any individual assessment or IRT-linking methodology, while demonstrating relatively consistent estimates where possible.

We contribute to the literature in several ways. This is the largest and most current globally comparable dataset, including the most developing countries. In addition to mean scores, this dataset also contains measures of globally comparable achievement distributions, namely, threshold attainment of low, intermediate and advanced proficiency thresholds. Moreover, this dataset uses multiple methods to link assessments, including pseudo-linear linking and presmoothed equipercentile linking methods. This enhances the robustness of each estimate. To enhance the robustness and reliability of the dataset further, we include standard errors of our estimates, enabling explicit quantification of the degree of certainty around each estimate. We also include estimates that are disaggregated across multiple parameters: gender, socioeconomic status, rural/urban, language, and immigration status, thus enabling greater precision and equity analysis.

A first analysis of this dataset reveals a few important trends:

1. Learning outcomes in developing countries often cluster at the bottom of a global scale 2. Although variation in performance is high in developing countries, the top performers still often perform worse than the bottom performers in developed countries

3. Gender gaps are relatively small, with high variation in the direction of the gap by region 4. Distributions reveal meaningfully different trends than mean scores, with less than 50 percent of students reaching the minimum global threshold of proficiency in developing countries relative to 86 percent in developed countries.

Our goal in this paper is not to provide a perfect measure of education quality. Rather, we provide a practical yet rigorous and globally comparable set of estimates with large and inclusive country coverage over time. We hope this dataset can be used to reveal important 44
O.A.M.D.G.

|**Asset**|**Productive**<br>**lifetime (years)**|**Estimated yearly**<br>**payoff (USD)**|Data Table 2: Asset<br>lifetimes and yearly<br>aoffs|
|---|---|---|---|
|bicycle|6|9.80|py|
|boat/canoe|10|37.50|_Note:_<br>_1 di lifi_|
|chicken|2|2.90|_) prouctve etmes are_<br>_average lifetimes for assets in_<br>_a SISBEN 1 environment, i.e._|
|cow|5|90|_below average quality and_<br>_frequent use._|
|portable diesel<br>generator|10|250|_2) Values (and therefore_<br>_payoffs) of real estate differ for_<br>_each household and are_<br>|
|donkey|12|5.20|_therefore not reported._<br>_Sources:_|
|duck|10|4.20|_Productive lifetimes: personal_<br>_survey of people in SISBEN 1;_<br>|
|fan|8|5.60|_Honda, Ontario Ministry of_<br>_Agriculture and Food,_<br>_Goodman (1997);_|
|fridge|10|21|_Yearly payoffs: own_<br>_lli i_|
|goat|9|7|_cacuatons, assumng_<br>_constant real cost of capital of_<br>_14% (average Colombian_<br>|
|horse|12|8.20|_banks’ real credit interest rate,_<br>_Banco de la República 2001)_|
|kerosene lamp|5|6.30||
|manual sewing<br>machine|8|12||
|mixer|8|4||
|motorcycle|5|820||
|pig|9|6.60||
|rabbit|5|2.50||
|stereo equipment|5|25||
|television b/w|8|5||
|television color|8|57||

The Data Table below reports the basic statistics for savings calculated from both income measures, salary + estimated asset returns (1), and salary only (2). While considering salary and the waged households only generates negative mean savings, the fuller accounting for household income produces positive mean savings, of around 130
A related concern, not explicitly raised by the critics we address here, but commonly heard nevertheless, is that expert assessments are not just biased (possibly to the interests of business elites), but rather that the experts simply get things wrong. In a recent paper Razafindrakoto and Roubaud (2006) use specially-designed surveys in eight African countries to contrast corruption perceptions based on household surveys with those based on expert assessments. The unique feature of this exercise is that the experts were asked to predict the country-level average responses from the household survey. In this sample of eight countries it turns out that the experts' ratings were essentially uncorrelated with the household survey responses. Razafindrakoto and Roubaud (2006) conclude that the household surveys capture the "objective reality" of petty corruption and that the experts are just plain wrong. While this is a very creative and interesting effort, we disagree with their conclusion for two reasons.

- First, it is not at all clear why there should be measurement error only in the expert assessment and not in the household survey.  Households were asked whether they had been a "victim of corruption".  There are a variety of reasons why households might think they were victimized by corruption when in fact it was not present.  For example, a patient waiting in the queue to see a stateprovided doctor might think (incorrectly) that people at the head of the queue had bribed someone to get there.  Conversely households might well have paid a bribe, received the associated benefit, and found themselves quite satisfied and not at all "victimized" by the transaction.  Our rather more modest interpretation of their finding is that there is measurement error in any estimate of corruption, regardless of the identity of the respondent.

- Second we note that the low correlation between expert and household survey responses does not seem to be more broadly true in larger samples of countries. As pointed out elsewhere in this note we have found many cases where household of firm survey responses about corruption and other dimensions of governance are highly correlated with those of expert assessors.  And we note also that correlations among different household survey measures of corruption are not particularly high, as would be the case if household surveys are the most reliable way to measure corruption.

14
resist even major earthquakes with the highest seismic intensities.<sup>3</sup> Consequently, an accurate assessment of the LoF requires information on (a) the kinds of disruptions that infrastructure projects are likely to face and (b) the expected functionality levels resulting from these disruptions at various levels of intensity.

# 1.2. Recoverability

_Recoverability_ refers to the ability of an asset to recover its functionality quickly. Recoverability is associated not only with physical factors, but also with social, organizational, resourcerelated, and managerial factors. Recoverability is dependent on the readiness of resources and preparedness of organizations to respond to disaster events. Recovery of functionality requires the availability of human and capital resources (e.g., machinery) as well as organizational plans and processes required to take necessary actions rapidly and effectively.

For example, the recovery of destroyed road facilities requires construction manpower and equipment in addition to established processes and institutional measures, such as agreements between the public and the private sectors to collaborate in an emergency or the establishment of a clear incident management system. A proxy measurement of recoverability is the duration required to reach a target recovery level.

# 1.3. Project-Level versus System-Level Resilience

When evaluating and measuring resilience, it becomes important to clarify the level of analysis. In other words, the 'resilience of what' matters. To apply the notion of resilience in infrastructure prioritization, it is helpful to distinguish between project-level and system-level resilience. Assessment of project-level resilience considers the functionality loss in the event of a shock and the ability of an asset to quickly recover from functionality loss. Asset functionality loss is a function of both the structural robustness of the facility and the level of exposure to a disruptive event. Recoverability depends on the availability of resources for reconstruction and the governance of emergency response management.

System-level resilience, on the other hand, refers to the region's or infrastructure network's capacity to absorb and recover from disasters. Infrastructure projects are generally elements

> 3 The Japan Meteorological Agency uses a seismic scale of zero to seven to describe the degree of shaking at any given time during an earthquake.

6
tral) employment effect at the plant level corroborate the hypothesis that non adopting plants may benefit from adopting plants in the same industry. This is consistent with the positive inter-plant spillovers documented above. In light of these results, the spillovers are likely to be intra-industry.

Columns 5-8 of Table 12 split manufacturing employment and wages for production and non-production workers. Columns (5) and (7) show that the positive impact of robots on manufacturing employment is stronger for non-production workers. This is consistent with the plant level analysis, which reveals a positive direct impact of automation on non-production workers. For production employment, the coefficient reflects the negative direct impact vs the positive indirect impact due to vertical spillovers. The coefficient of non-production wages is also positive and significant.

Table 13 presents the results of estimating equation (8) through 2SLS for non-manufacturing sectors. The evidence supports the hypothesis that automation in manufacturing generates positive employment and wage spillovers in services. The coefficients in columns (1) and (2) imply that on average over the period, robots have increased employment by 3 percent and services wages by 5 percent in the most exposed regencies. The impact on services is consistent with the plant level results as well as with extensive evidence showing the presence of backward linkages of manufacturing and services, including in Indonesia (Hallward-Driemeier and Nayyar (2017); Duggan et al. (2013)). For Construction and Utilities, the coefficients in columns 3 and 4 are not statistically significant.

The coefficient for employment in agriculture and mining, on the other hand, is large, negative and statistically significant. The estimated impact of robots on agriculture employment is almost 10 percent in the most exposed regencies. This is consistent with reallocation of labor towards manufacturing and services facilitated by the robots’ adoption.

The results in Tables 12 and 13 are robust to different alternative specifications. First, Table A5 reports the results in which the share of employment at risk of automation is based on (secondary) educational attainments. The coefficients are in line with the baseline results, but in this specification exposure to robots has a positive and significant 34
restricts the value of the posterior estimates to nonnegative. This is a desirable property of estimating inter-regional trade data.<sup>6</sup> Thirdly, the weights in the objective function reflect the relative reliability of a given set of initial estimates. The interpretation of the reliability weights is straightforward. Other things equal, entries with higher reliability should be changed less than entries with a lower reliability. The choice of those weights is also very flexible. They will use the best available information to insure that reliable data in the initial estimates are not being modified by the optimization model as much as unreliable data. In practice, such reliability weights can be put into a second array that has the same dimension and structure as the initial estimates. The inverted variance-covariance matrix of the initial estimates is statistically interpreted as the best index of the reliability for the initial data.

Finally, solution of this estimation problem exactly provides the data needed to construct a so-called multi-regional input-output (MRIO) model (Miller and Blair, 1985, Isard, et al. 1998). This model was pioneered by professor Polenske and her associates at MIT in the 1970s (Polenske, 1980), and is still widely used in regional economic impact analysis today.

The above model could be easily extended to further allocate **Z** and **D** to distinguish intermediate and final delivery of goods and services within a national system of economic regions. The extended model will be similar in many aspects with the interregional accounting framework proposed by Batten (1982) two decades ago. However, as we will show later in this paper, it becomes more operational and provides better empirical estimation results on interregional shipments because of the explicit incorporation of interregional trade flow information into both the initial estimates and the accounting framework.

> To demonstrate, denote<sup>_z_</sup> _ijsr_ as intermediate inputs delivered from sector i in region s to

sector j in region r within a nation, and _yihsr_ as final goods and services delivered from sector i in _r r_ region s to type h final demand in region r. Further, denote _mij_ and _mih_ as imported (from other 14
# **Table 7.  Results for old and young firms**

||**Lgrowth**<br>||**LPgrowth**<br>||
|---|---|---|---|---|
||**coef**|**se**|**coef**|**se**|
|Corruption obstacle|**-0.019****|0.007|**0.019**|0.014|
|Corruption obstacle * Young|**0.012***|0.006|**-0.002**|0.011|
|Skilled labor obstacle|**-0.003**|0.007|**-0.019**|0.013|
|Skilled labor obstacle * Young|**0.019***|0.009|**0.025**|0.015|
|Web Intensity|**0.012**|0.006|**0.030***|0.013|
|Web Intensity * Young|**0.017***|0.007|**0.017**|0.012|
|Land access obstacle|**0.000**|0.007|**0.003**|0.015|
|Land access obstacle * Young|**-0.020***|0.009|**-0.020**|0.015|
|Overdraft facility|**0.016***|0.007|**0.042****|0.015|
|Overdraft facility * Young|**-0.006**|0.007|**-0.027***|0.012|
|Trade credit|**-0.011**|0.009|**0.035***|0.017|
|Trade credit * Young|**0.009**|0.009|**-0.002**|0.015|
|Labor regulation obstacle|**0.013**|0.008|**0.012**|0.015|
|Labor regulation obstacle * Young|**-0.035****|0.011|**-0.028**|0.020|
|Tax rate obstacle|**-0.014**|0.008|**0.002**|0.016|
|Tax rate obstacle * Young|**0.020***|0.009|**0.022**|0.014|
|In Big City|**0.006**|0.003|**0.004**|0.005|
|In Big City * Young|**-0.000**|0.003|**-0.012***|0.006|
|Inf competition|**-0.010**|0.005|**0.016**|0.012|
|Inf competition * Young|**-0.004**|0.006|**-0.029***|0.012|
|Capacity agglomeration|**0.060****|0.016|**-0.007**|0.026|
|Capacity agglomeration * Young|**-0.038****|0.014|**0.064***|0.026|
|controls in panel B of Table 4|Yes||Yes||
|N|51,685||38,120||
|Country, industry, year FEs|Yes||Yes||
|adjusted R-squared|0.117||0.272||

*, ** represent statistical significance at the 5 and 1 percent levels. Heteroskedasticity-corrected standard errors clustered at the country level in columns indicated by “se”.

Those variables without significant interaction terms with Young have similar coefficients as in Table 5 and are not reported.

41