rafmacalaba/datause-encoder-data huggingface dataset:
together gives us its length. We use the logarithm of this (in years) as a measure of project duration ( _LNLENGTH_ ). We also have information on the distribution of projects across economic sectors (summarized in Table 1). We use a set of sector dummies to capture any sectoral variation in average project outcomes. Finally, we have data on preparation and supervision costs for each project. We express this as a fraction of the total size of the project, and use the logarithm of this in our regressions ( _LNPREPCOST_ and _LNSUPERCOST_ ).

A second set of project-level variables is obtained from the ISR process of monitoring and eventual evaluation of projects. We have retrieved data from the end-of-fiscal-year ISR for every project, and for each year during the life of the project. This provides us with information reported by the task manager on a variety of interim measures of project performance, on an annual basis over the life of the project. Each year, the implementation status of the project is rated relative to the ultimate development objective, and if this rating is unsatisfactory, the project is flagged as a “problem project”. In addition, task managers indicate with a series of 12 flags whether there are concerns about specific dimensions of project performance, including problems with financial management, compliance with safeguard, quality of monitoring and evaluation, legal issues, etc. If three or more of these flags are raised at any one point in time, the project is identified as a “potential problem project”. Even if a project is flagged as a potential problem project, country management units have the authority to override this classification using a “golden flag”, on which we also have data.

While in principle the flag data are a rich source of information on leading indicators of project performance, they need to be treated with some caution. While some of the flags are automatically triggered (for example, by objectively-measured disbursement delays, or by lags between project approval and the start of work on the project), the decision to raise others is at the discretion of task managers, who for natural reasons may be reluctant to do so. This could be due to optimism about the ultimate outcome of the project, or reputational concerns on the part of the task manager and/or counterparts. Indeed, a perennial concern for Bank management has been the frequency of projects exhibiting “disconnect” – projects that were rated as satisfactory throughout the implementation process but were then ultimately rated as unsatisfactory upon completion. Despite these caveats, these flags are an extremely important set of candidate predictors of project success since they are routinely generated and are readily available to World Bank decision makers who can act on them to improve the ultimate outcome of the project.

11
Table 3: Regression-Based Decomposition Results

||1989|1992|1994|1996|1998|2000|
|---|---|---|---|---|---|---|
|Schooling|158_._0_∗∗_|263_._0_∗∗_|305_._0_∗∗_|168_._0_∗∗_|187_._0_∗∗_|237_._0_∗∗_|
|Schooling*(H)|100_._0_∗∗_|153_._0_∗∗_|149_._0_∗∗_|114_._0_∗∗_|130_._0_∗∗_|154_._0_∗∗_|
|Experience|73_._0_∗∗_|174_._0_∗∗_|131_._0_∗∗_|49_._0_∗∗_|78_._0_∗∗_|128_._0_∗∗_|
|Experience Sq.|_−_0_._7_∗∗_|_−_2_._0_∗∗_|_−_1_._2_∗∗_|_−_0_._3|_−_0_._7_∗_|_−_1_._2_∗_|
|Gender|1_._6|462_._5|116_._9|206_._4|98_._3|_−_86_._1|
|HH size|_−_157_._0_∗∗_|_−_255_._0_∗∗_|_−_245_._0_∗∗_|_−_214_._0_∗∗_|_−_247_._0_∗∗_|_−_324_._0_∗∗_|
|Dep. Ratio<br>|_−_2289_._0_∗∗_|_−_2922_._0_∗∗_|_−_3179_._0_∗∗_|_−_1893_._0_∗∗_|_−_2096_._0_∗∗_|_−_2548_._0_∗∗_|
|Region|256_._0_∗_|77_._0|185_._0|170_._0|301_._0_∗∗_|598_._0_∗∗_|
|Intercept|1630_._0_∗∗_|103_._0|850_._0_∗_|1396_._0_∗∗_|1159_._0_∗∗_|830_._0|
|R-squared|0.102|0.231|0.306|0.280|0.295|0.291|
|N|10,005|9,181|11,123|12,220|9,430|8,593|
|**% Contribution **|**to total **|**inequality**|||||

|Schooling|7_._2|14_._8|24_._5|21_._1|18_._2|21_._7|
|---|---|---|---|---|---|---|
|Experience|_−_1_._2|_−_2|_−_2_._9|_−_2_._2|_−_2|_−_1_._9|
|Gender|0|0_._2|0|0_._1|0|0|
|HH charact.|2_._9|7_._2|7_._8|8_._3|6_._4|7_._4|
|Region|0_._1|0|0|0_._1|0_._2|0_._5|
|Residual|91|79_._8|70_._7|72_._6|77|72_._3|

Dependent variable: per capita household incomes *,** significant at the 5% and 1% level respectively In the bottom part of Table 3 we show the percentage contribution to total income inequality (using equation 5 as the decomposition rule) of each element included in the income equation 11. A story similar to the one told by the Shorrocks (1982) orthodox decomposition, or the more general Shapley procedure with inequality index _CV_<sup>2</sup> , is shown by the regression-based method. The amount of inequality explained by our regression model increases during the stable period 1989-1994, mainly driven by the proportion of inequality accounted for by education and household characteristics, and then it marginally reduces during the crisis years.<sup>21</sup> The parallelism between the non-parametric and semi-parametric results suggest that at least part of the covariance

> 21The contribution of elements that enter more than one time in the regression equation is simply the sum of their individual contribution as computed by equation 5.

19
internet access. However, the voluntary nature of participation in an online job-search portal creates an inevitable degree of self-selection bias.

There have been various attempts to correct representation issues, including through use of statistical weights and techniques for predicting missing data (Kureková, Beblavý, and Thum-Thysen 2015). In the case of Babajob, external data can be used as a statistical benchmark to normalize biases, but the lack of sufficiently frequent external data to describe the contemporary Indian labor market remains a constraint. Unstandardized data quality is also a potential source of statistical error, as the data are usually not collected under a specific survey framework and may therefore suffer from an unusually large amount of missing information or internal inconsistencies. Job seeker information is also self-reported and may be inaccurate. However, measurement errors and sampling biases are a risk in most forms of statistical research, and if properly addressed they should not invalidate its results.

# **7. Conclusions**

Research using online job-portal data has enormous potential to inform public policy and advance the academic discourse on labor economics and workforce skills development. These data are not only more frequent and larger in volume than those obtained from traditional employer and workforce surveys, but they also present a greater variety of information and a more intense degree of granularity. Online job portals collect information on employers’ actual demand for skills as expressed through their real business activities, as well as data on job seeker characteristics reported in a context with real economic consequences. Big data analytics has opened new avenues for objectively monitoring workforce skills demand with a wide array of applications for business practices and public policy. The analysis presented above yields five key conclusions.

First, governments should leverage online job-portal data to enhance their labor market policies. Many governments continue to rely on periodic sample-based employer and workforce surveys. However, traditional surveys are conducted too infrequently and their analytical lag time is too long to capture emerging trends in labor market dynamics and demand for workforce skills. While surveys will remain useful for analytical and policy purposes, big data analytics can support more informed decision making by providing real-time data that encompass variables which cannot be observed through any other statistical technique. Moreover, online job-portal data reflect real business activities, not abstract survey questions. These data can provide a richer understanding of employers’ needs, constraints and transaction costs and uncover potential opportunities to address frictional unemployment and other labor market inefficiencies. Job-portal data can also help determine whether apparent skills gaps reflect a real deficit in the necessary 27
that the projected employment level for this sector is lower than the observed employment level in 2020. For the industry and service sectors, the estimation bias is positive in these four countries.

All in all, the sectoral employment projections based on elasticity estimations tend to be more biased for the agriculture sector, and when the direction of the bias differs between sectors, agriculture is the one behaving differently in most cases. This pattern could be related to the differential impact of non-pharmaceutical measures, such as mobility restrictions, across sectors. For instance, agriculture was considered an essential sector in some countries because of its role in food production. As such, the sector continued performing some of its production activities despite the confinement measures. In the next subsection we explore the association between the magnitude of the estimation biases and government non-pharmaceutical measures for the aggregate.

# **2.2 Explaining the bias in employment projections**

The large biases found for some of the countries and methods are related to the distinctive features of the COVID-19 crisis. The implementation of lockdown measures meant dramatic reductions in economic activity. Most governments implemented social protection measures which can protect employment in some cases and distort incentives to work in some others.

<mark>In order to explore the association between bias in the employment projections and the distinctive characteristics of the pandemic crisis, we use information on government non-pharmaceutical measures from the Oxford COVID Tracker, i.e., the severity of stay-at-home orders, changes in workplace mobility from Google mobility data, and a measure of government social protection</mark> policies from Gentilini et al. (2021).

<mark>The stay-at-home order from the Oxford COVID Tracker ranges from 0 to 4 with a higher value indicating stricter requirements. We calculate the average of the stay-at-home order for each country using daily information from March 15 to December 31 of 2020. The correlation between the stay-at-home order and the bias of our employment projections appears in Figure 2. For all the elasticity methods we find a positive association between the stringency of the stay-at-home requirements and the percentage differences between our employment projections for 2020 and actual 2020 employment levels. In other words, pre-crisis employment elasticities estimates are more likely to underestimate employment losses in countries with more stringent stay-at-home orders. This may be because more stringent stay-at-home requirements led to additional job losses that were not reflected in GDP growth estimates, making the elasticity of employment with respect to growth greater than it usually would be. When excluding Peru from the sample –the country with the largest difference between our 2020 employment projection and the observed value, we still find positive associations between the bias and the stay-at-home orders, although smaller in</mark> magnitude.

9
it was initially high, peaking at 81 percent in 1981. This observation reflects our empirical finding that trade in high-gap goods stagnated relative to trade in other goods for several years after the 1980 reform. Starting in 1986, when China applied to join the international trade arrangement that would become the WTO, this probability began to fall rapidly as trade growth in high-gap industries began to accelerate. By the mid to late 1990s, this probability was only about 5–10 percent, on par with the estimates of Alessandria et al. (2019) but lower than the estimates by Handley and Lim ̃ao (2017). Joining the WTO had a minor effect on the probability of losing access to MFN tariff rates, which is reflected in the trade data: only 10–15 percent of the overall growth in imports of products with a high tariff gap occurred in the 10-year window around WTO accession (1997–2007).

Our model allows us to ask the counterfactual question: What would the transition dynamics of U.S. imports from China have looked like if the path of trade policy had been certain, rather than uncertain? In this scenario, with perfect foresight over trade policy, aggregate trade grows faster and the elasticity of trade to the NNTR gap shrinks more quickly than in the benchmark model with uncertainty (and in the data). By 1985, in the model without uncertainty, the transition is about 90 percent complete, whereas in the benchmark model, only about one third of the trade growth has been achieved. The faster trade growth is particularly strong in high-gap industries and leads to a counterfactual elasticity of trade to the NNTR gap that is less than one third of the estimated value at the start of the 1980 liberalization. A key feature of the model without uncertainty is the counterfactually high trade growth in high-gap industries in anticipation of the 1980 reform. By contrast, in the data, the coefficient on the gap grows larger during the late 1970s, consistent with a low probability of moving from NNTR to NTR during this period and a high initial probability of returning to NNTR from MFN in the early 1980s.

Our calibrated model matches the heterogeneous paths of integration across Chinese industries into the U.S. market. We use the model to recover the long-run trade elasticity from moving between steady states with different tariffs. We find that this elasticity ranges between 15 and 18 and that it increases in the initial tariff rate. These estimates are larger than those reported in the literature<sup>4</sup> and more than twice the elasticity of 6.5 that we

> 4Head and Ries (2001) estimate elasticities between 7.9 and 11.4 for the Canadian-U.S. FTA. Romalis

5
<u>Table 3: Protectionist responses during economic crisis</u>

||Log MFN<br>(1)|Log MFN<br>(2)|Log MFN<br>(3)|Log MFN<br>(4)|Log MFN<br>(5)|Log MFN<br>(6)|_Log MFN_<br>_(7)_|Log MFN<br>(8)|Log MFN<br>(9)|Log MFN<br>(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|Crisis|.0195<br>(.001)a|.022<br>(.001)a|-.114<br>(.010)a|-.089<br>(.011)a|.020<br>(.001)a|0.013<br>(0.003)a|.016<br>(0.06)|-.111<br>(.011)a|-.111<br>(.011)a|-.082<br>(.011)a|
|LGDP||-.060|-.059|-.059||||-.059|-.059|-.059|
|||(.001)a|(.001)a|(.001)a||||(.001)a|(.001)a|(.001)a|
|LGDP*Crisis|||.006<br>(.001)a|.005<br>(.001)a||||.006<br>(.001)a|.006<br>(.001)a|.005<br>(.001)a|
|Neigh||-.060<br>(.001)a||.058<br>(.001)a||||||-.059<br>(.001)a|
|Neigh*crisis||||.001<br>(.004)||||||.001<br>(.004)|
|Lag Crisis|||||-.0245<br>(.001)a||||||
|Interme*Crisis||||||||-.006<br>(.002)a||-.006<br>(.002)b|
|Water*Crisis|||||||||-.002<br>(.001)|-.002<br>(.001)|
|Observations|3455453|3455453|3455453|3455453|3455500|3455453|770|3455453|3455453|3455453|
|Adjusted_R_<sup>2</sup>|0.873|0.873|0.873|0.873|0.873|0.873|0.858|0.873|0.873|0.873|
|Standard errors are<br>regressions include|in parenthesis<br>countryxprod|and are correc<br>uct fixed effec|ted for cluster<br>ts, as well as y|ing across pro<br>ear dummies.|ducts and cou<br>|ntries. a for_p_|< 0.01, b for_p_|< 0.05 and c|stands for_p_<|0.10. All|
pollution and keep it from rising to more dangerous levels.<sup>22</sup> However, according to the Department of Environment (Air quality Management), air quality has deteriorated since October 2017 due to an increase in production of brick kilns during winter months.<sup>23</sup> Pollution is an ongoing problem and is threaten the city’s development rate. Even though Dhaka is outperforming Bangladesh in terms of employment, GDP per capita growth fell between 2006 and 2015, showing that the city is not growing fast enough to accommodate the growing population.

These examples show different types of cities and their struggles and success with pollution and competitiveness. Clearly, the outcome does not depend on the type of city or its economic structure. We find capital cities with a successful story or dealing with pollution and competitive challenges, like Kigali and Dhaka. We also find good examples of pollution management for Market Towns (Kigali), Production Centers (Coimbatore), or Creative and Financial Services (Vancouver). This shows that every city has the potential for developing an agenda that focuses on sustainable growth. The government’s willingness to engage on these new challenges plays a decisive role in successfully addressing the natural consequences development brings.

# Conclusions

Competitive cities grow fast, create jobs and improve living conditions for their citizens. They are able to attract foreign direct investment and provide a good environment where firms and people can get together, trade and interact. In this paper, we focus on three variables that help us identify these cities, toward understanding what makes a city outperform its nation and the challenges it may face.

Among all the challenges a growing city faces, from managing pollution and congestion, to providing its citizens with better amenities and a good quality of life, we chose to focus on air pollution. After identifying which cities are the most polluted, we analyze the relationship between pollution and competitiveness, to shed some light on how growing cities can overcome increasing pollution levels and avoid this problem that jeopardizes their competitiveness.

Air pollution is a natural consequence of growth and we find that cities experience different levels of air pollution depending on their path of development. We observed that the Environmental Kuznets Curve

> 22 This project (April 2009‐June 2014) was funded by the World Bank and focused on improving traffic conditions in the city of Dhaka (including mass transportation options) and the technology on the brick industry.

> 23 ‐ ‐ Published in The Dhaka Tribune, https://www.dhakatribune.com/bangladesh/environment/2018/01/30/us <u>aqi dhaka‐worst‐air/, accessed on May 4, 2018.</u>

29
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and present the share of total exports accounted for by each group. It is striking that firms exporting a single product account for over a third of total exports, and firms exporting one or two products for over a half. Firms exporting five or more products are responsible for less than a quarter of total exports.

Table 4: Most exports come from firms exporting few products

||1994|1995|1996|1997|1998|1999|2000|2001|2002|2003|
|---|---|---|---|---|---|---|---|---|---|---|
|No. of export products|||||Export|share|||||
|1|41.62|34.29|35.02|35.06|33.37|33.6|33.36|35.01|33.16|36.64|
|2|20.82|22.41|19.56|18.01|18.31|18.34|19.91|14.83|16.98|15.75|
|3|8.48|12.3|12.88|12.17|15.2|11.53|11.12|11.89|13.27|14.63|
|4|8.15|11.1|9.82|9.82|6.6|10.58|10.01|11.32|10.46|9.51|
|5|7.65|4.59|5.23|5.29|6.97|7.96|8.36|7.5|8.1|6.33|
|6|2.44|4.23|5.27|3.32|4.17|2.92|2.22|4.62|4.07|4.32|
|7|4.62|3.27|2.73|3.39|2.07|3.53|4.19|3.45|3.04|3.02|
|8|1.85|0.61|1.58|2.33|2.43|2.7|1.79|1.25|2.01|1.32|
|9|1.49|1.78|1.78|2.54|5.43|2.64|2.36|4.62|4.08|3.52|
|≥10|2.88|5.42|6.13|8.09|5.47|6.19|6.68|5.51|4.82|4.95|
|Total|100|100|100|100|100|100|100|100|100|100|

Notes: Includes single- and multi-plant firms This result contrasts sharply with the findings of Bernard, Redding, and Schott (2006b) for the US where firms exporting five or more products account for 98% of total exports (see Table 5).

Table 5: Multi-product exporters in the US

|No of export products|Share of frms|Share of exports|
|---|---|---|
|1|42.2|0.4|
|2|16.4|0.5|
|3|9.3|0.5|
|4|6.2|0.6|
|≥5|25.9|98|

Source: Bernard, Redding, and Schott (2006b) 9
Yet microfinance was always a contested idea, and statistics were collected to highlight and promote some strands of thought over others. The notable divides were along social versus commercial lines. The conflicts within microfinance have largely been constructive, but the statistics have both revealed and (implicitly) concealed parts of the debate. In terms of statistics and data, the history of microfinance shows that a full view is only possible when very different kinds of data are brought together. The full picture cannot be seen from just reading tables documenting the numbers reached, nor spreadsheets of financial performance, nor randomized controlled trials of economic and social impacts.

The evidence described in this paper poses challenges for microfinance. The data show modest subsidies and modest impacts. They show growing scale, but a shift away from the poorest. And they show regional differences in the kinds of populations served by microfinance. If microfinance has been a clear success institutionally, how to ensure impacts on customers is far less clear.

Investors and entrepreneurs have difficult choices to make in enabling the next steps for microfinance. One vision, provoked by evidence from financial diaries, opens up the possibility of extending the ideas of microfinance to hundreds of millions of potential customers, many in urban areas and most with jobs, who lack a strong interest in business investment. They instead seek finance to help manage cash flows and seize opportunities requiring lumpy sums. The kinds of impacts to expect will not be those initially envisioned, nor those tested in most quantitative studies. Another, complementary vision entails redesigning products to better fit the needs of existing entrepreneurs. The data from the Microcredit Summit suggest that microfinance can evolve at mass scale, while the (re-examined) data from the MIX Market suggest that the subsidy still matters but, in some cases, it is small enough to generate appealing cost-benefit ratios.

35