rafmacalaba/datause-encoder-data huggingface dataset:
is the sum of an aggregate public revenue from the fuel taxes paid, the profit of the taxi operators, and the surplus or deficit of the bus operators.

# **5. Optimal policies**

The results that we report here optimize over the six possible policy instruments as explained in the Introduction. To repeat, our demand side instruments are the fuel tax rate, the parking tax and the bus fare; and our supply side infrastructure policies are expanding the number of buses that are operating, the road supply and the tram network.<sup>14</sup> The full results of the simulations are reported in Tables 3-5. We will discuss the results by focusing on how each policy affects congestion, in-bus crowding, fuel consumption (and CO2 emissions which are strictly proportional to fuel consumption) and the components of social welfare. All simulations are done while keeping the population of worker-commuters of Grand Casablanca fixed at the baseline level.

Table 3 reports what would happen if each demand-side instrument were to achieve its optimal level while all other instruments remained at their baseline values. The optimal level of the fuel tax rate is 554% of the supplier’s price of fuel which is about ten times the baseline fuel tax rate of 53%. This reduces the car-equivalent traffic load and the level of congestion by 14% and fuel consumption by 21%. Fuel tax revenue increases by 715% and parking tax revenue decreases by 23%. Bus occupancy meanwhile increases by 20% while bus fares remain at their baseline values not reflecting the higher fuel costs. Social welfare gains are 0.76% of income. The bus operating deficit increases by about a third its baseline value and the number of taxis increase by 19%. The tram operating deficit improves by 5% since trams run on electricity and are immune to the fuel tax. The changes under the optimal fuel tax occur because riders of the low occupancy vehicles (private car and motorcycle) switch to high occupancy vehicles (taxi, bus and tram) to avoid in part the impact of the higher tax on them. Because these high occupancy vehicles are shared by many, traffic congestion is reduced. At the same time, the disutility of bus and tram passengers increases as the standing passenger density in these modes increases.

> 14 We do a grid search. The fuel tax rate is varied from its base value of 0.538462 in steps of 0.1 (that is 10 percentage points increase), bus fare is varied from 0 to 10 MAD in steps of 1, parking tax is varied from 0 to 100 MAD in steps of 1, the number of buses are increased from 866 to 5966 in steps of 50 buses, road supply (square kilometers) is varied from its base value in steps of 1, and tram expansion is varied from 0.0065 square kilometer (equivalent to 1 km of tram line) to 0.65 square kilometer (equivalent to 100 km of tram line) in steps of 0.0065 . Our code is in MATLAB and solves the model for each grid point in the six dimensional policy space. The search could be refined near the optimum for more accurate pinpointing of the optimum but the benefits of doing so are negligible and the qualitative conclusions do not change.

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level a<sup>1</sup> , as they all fall within the bottom 42 percent of the distribution. Similarly, all women with middle school completed in 1987 are of ability level a<sup>4</sup> , but their 2011 counterparts are of ability levels a<sup>1</sup> , a<sup>2</sup> and a<sup>3</sup> ; and so on for higher education levels. Thus average ability declines at all education levels above illiteracy.

# **Figure 5: Women’s educational attainment in urban India**

<!-- Start of picture text --> Illiterate Lit. < primary Primary Middle Secondary Graduate<br>2011<br>1987<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br><!-- End of picture text --> Note: distribution of married women age 25-54 across levels of educational attainment in 1987 and 2011. Source: NSS Employment and Unemployment Survey

# **Figure** **<u>6: Estimated and reweighted marginal effect of education, 1987 and 2011</u>**

<!-- Start of picture text --> 0.350<br>0.300<br>0.250<br>0.200<br>0.150 Estimated, 1987<br>Estimated, 2011<br>0.100<br>Reweighted, 2011<br>0.050<br>0.000<br>-0.050<br>-0.100<br>Secondary Graduate<br><!-- End of picture text --> Note: see Table A1.5 in the appendix.

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(1988) On the Mechanics of Economic Development. _Journal of Monetary_ _Economics, July._ Lubell, H.

(1991) _The Infonnal Sector in the 1980s and 1990s._ Development Centre Studies, OECD, Paris.

# Mazumdar, D.

(1976) The Urban Informal Sector. _World Development,_ Vol. 4, No. 8.

# Nipon, P.

(1991) The Informal Sector in Thailand. _The Silent Revolution._ Eds. A. Chickering and M. Salahdine. International Center for Economic Growth, San Francisco.

# North, D.

(1981) _Structure and Change in Economic History._ Norton, New York.

# Olson, M.

(1982) _The Rise and Decline of Nations: Economic Growth, Stagflation, and_ _Social Rigidities._ Yale University Press, New Haven.

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include initial per capita income and a proxy for human capital. The human capital variable is an index of the average schooling of the population over 25 years old at the beginning of each sample period (1960 and 1975). It is taken from Barro and Lee (1992) and is superior to the usual primary and secondary enrollment ratios, which do not proxy for the stock of human capital.

It is expected that poor countiies will grow faster because of a higher marginal product of capital in poor countries, so the expected sign of initial income is expected to be negative. On the other hand, it is expected that the proxy for human capital will enter with a positive sign.' The results are reported in tables 6 and 7. We test for the effects of RI by including dummy variables for the following arrangements (years of implementation in parenthesis): (i) arnnng developed countries, EC (1960) and EFTA (1960); (ii) among developing countries CACM (1960), LAFTA (1960), SACU (1969), Andean (1969), UDEAC (1973) and CEAO (1974). Among the latter group, SACU (Botswana, Lesotho, South Africa and Swaziland) is an example of a North-South RI scheme and CEAO which includes former French colonies are also part of a monetary union since they are all members of the CFA zone.' The overall fit of the model is satisfactory and similar to those obtained by other crosscountry growth exercises [e.g. Barro (1991)]. Note, however, that even though all the explanatory variables have the expected signs, the overall fit is much poorer for the developed-country sample during the second period. Undoubtedly, this reflects a combination of omitted-variable bias, misspecification and endogeneity of regressors (see below) as well as the lack of variables capturing the effects of the supply-side shocks starting in 1973.

2 We also tested for the importance of economies of scale by including as a proxy for market size the initial level on industrial output. As expected this variable is highly correlated with income so that, when entered together, they lose their significance.

I' Other RI schemes that were not included either did not have a long enough existence (e.g. SADC, Canada-U.S. FTA, etc.) or did not achieve much by way of integration.

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# **2.2 Data: Test scores**

While most KLPS variables are quite stable over survey rounds, self-reported KCPE scores are not. Grade in school in 1999, for example, has a correlation of 0.95 between responses given in KLPS1 and four years later in KLPS2, while self-reported test score has a correlation closer to 0.7. The noise in test scores could pose several problems, since I use KCPE score as the regression discontinuity running variable.<sup>7</sup> Noise in the form of classical measurement error for only a random subset of the data would simply reduce the power of the regression discontinuity design. Classical measurement error in all of the data could eliminate the discontinuity entirely.<sup>8</sup> On the other hand, nonclassical error could invalidate the regression discontinuity design, if either mis-reporting or test repetition were driven by unobservables correlated with outcomes.<sup>9</sup> A histogram of the self-reported scores, in the upper left panel of Figure 1, shows that the distribution of scores shows signs of non-classical error, in the form of manipulation of the reported scores around the “passing” point; a test for density smoothness proposed by McCrary (2008), shown in the lower left panel, rejects at this point.

This feature of the distribution could arise simply from repeated testtaking: if many of those who fail the test try again until they pass, the distribution of most recent test scores could include more mass just to the right of the cutoff than to the left.<sup>10</sup> To see whether this phenomenon is solely responsible for the shape of the distribution, I consider a slice of the

> 7Some authors, such as Imbens and Lemieux (2008), refer to this as a “forcing variable.”

> 8More detailed discussion of these points is provided in Appendix A.2.

> 9See Martorell (2004) for discussion of multiple potential effects of test repetition.

> 10See Appendix A.2 for a concrete example.

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# **IV. DB and “favored” and “disfavored” firms in the ES**

The ES data reveal that, for any given country or question, there are enormous reported differences _across_ firms. When firms are asked about time to obtain an operating license, construction permit, or clear goods through customs the responses not only do not cluster tightly around the DB average but do not cluster around the ES average. A significant fraction of firms report that (their version of) regulatory compliance takes essentially not time at all, while others report significant delays. So there is an important question of how the _levels_ of the DB indicators are associated with the _dispersion_ and entire _distribution_ of firm level responses. One notion might be that an increase in the DB time from say 20 days to 50 days would just shift the entire distribution up, so that all firms would have roughly X additional days. However that is not at all what the data suggest. Rather the data suggest that the ―favored‖ firms are affected roughly not at all by increases in the DB figure while, at low levels the dispersion increases as the firms who report relatively long delays report longer delays as the DB value goes up, but only to a point, after which there is roughly no effect on the distribution of responses at all.

The figures 2a, 3a and 4a show the scatter plots for each country of the 10<sup>th</sup> and the 90<sup>th</sup> percentile of the firm responses plotted against the DB for imports, business licenses, and construction permits respectively to show how the dispersion of ES responses evolves with DB.

One common pattern is clearest for construction permits (Figure 3a). When the DB value moves from its lowest value of 77 days (for Lesotho) to the (third) highest value of 471 days in Ukraine the predicted 10<sup>th</sup> percentile (using the two spline specification) increases from 4.5 days to 8.2—hence a 400 day increase in the DB value is associated with a less than 4 days increase in the predicted value of the 10<sup>th</sup> percentile (and only 5 and a half days in the 25<sup>th</sup> percentile). In sharp contrast, the increase from 77 days to the tercile of 172 (Laos) increases the predicted ES response in the two spline specification from 29 days to 189 days while the change from 172 days to 471 days (Ukraine) leads to a small _decrease_ in predicted ES responses at the 90<sup>th</sup> percentile from 189 days to 167 days.

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capital investment, as is predicted by our model. Thus the empirical data from 53 countries shows that the level of foreign capital imports has a positive impact on the growth rate of income. The estimated coefficient on the population growth rate is positive but not statistically significant in the fixed-effects model (th,e second column of Table 2), which is the favored model.

Although the results from Table 2 produce the right signs for the coefficients on the investment of foreign capital equipment and that of home capital, there are several problems. Fir st, as mentioned above, the estimated coefficient on population growth turns out to be insignificant. Second, the magnitudes of the estimated coefficients on the three variables _(Sf 1Sf, S_ **_h_** _Sh, and kl/N)_ are too small. The implied a and (3, which are the reladve share of home capital and imported capital in production, are smaller than 0.02 and 0.06 respectively. And the estimated coefficient on population growth is also much smaller than 1, as the model predicted. The third problem is that the independent variables in all three regressions explain very little of the variation of the dependent variable, as indicated by the extremely low _i2s. 6_

# 3.3.2 Omitted variable problem

We suspect that the above problems may arise because of the many omitted variables. As mentioned in the last section, our model specification are based on strong neoclassical assumptions that are not true in the real world. In reality, the economic development process in developing countries is affected not only by factors of production, but also by many social and institutional factors. These omitted variables may cause biased estimates in our m6del.

> 6Please note that the smaller _R2s_ in the variable-intercept models are due to the fact that a large number of constants are used in these models.
the start of the conflict are dropped, since they are only 14 at the time of the 2006 survey and so not eligible to be interviewed individually.

I then estimate the effect of exposure to conflict during marriageable age in a similar specification, but modifying the definitions of the dependent variable, and of the treated and control cohorts accordingly. The exposed cohort comprises individuals aged 13 to 17 years old at the start of the conflict in 1996 and the control cohort comprises those aged 23 to 27 in 1996. This is based on the observation that marriages tend to start taking place in the mid-teens and that, at the time of the 2006 DHS, all women in the treated cohort were 21 or older. Therefore, the outcome variable is a dummy equal to one if the index woman was married by the age of 21 to account for the right-truncation of the marriage variable. The youngest woman included in the control cohort was aged 23 at the start of the conflict, so that the outcome of interest (whether married by 21) was realised before the start of the conflict for this cohort. When using NLSS data, I keep the same definitions for the treated and control cohorts, but given the earlier date of the survey, the treated cohort is observed at a younger age. Therefore, I consider the probability of marriage by 18 years old instead.

All specifications are estimated using district fixed-effects panel data models, and all models allow for residuals to be correlated in an arbitrary fashion within district to avoid over-rejection of the null hypothesis of zero treatment effect due to serial correlation, following Bertrand et al. (2004). All outcome variables used in the analysis are listed in Table 2.

# Table 2 goes about here

# **Test of pre-conflict differences in trends**

The main threat to identification in a difference-in-difference setting is the potential for unobserved time-varying omitted factors correlated with the treatment. It is impossible to test whether there are such omitted variables coinciding precisely with the treatment at hand. But one can test whether there were differences in time-varying unobservables correlated with future conflict variables _before_ the conflict began. I test for differences in pre-conflict trends correlated with future conflict intensity by running regressions of the form: where _yeart_ are year of birth dummies, _pre_conflict_yeart_ is the set of year dummies corresponding to the five youngest cohorts who could not be affected by the conflict, and 18
# **A NO** 2 **: Context and Characteristics**

## **A.1 OMI Details and Alternative Sources**

This paper uses OMI NO2 VCDs from NASA. Like night lights, NO2 is not measurable in certain circumstances, such as if there is too much cloud cover. In all results, we use data that are not flagged for misreadings from other aerosols, misleading snow-covered backgrounds, or if clouds exceed 30% of the field of view. These choices are consistent with standards in scientific studies to avoid spurious conclusions. Averaging over days and using data quality flags correctly are critical to drawing proper conclusions from VCDs.

NASA currently expects OMI to operate at least until 2022, and likely beyond. However, it is not the only available satellite data resource for NO2. For example, the earlier European Space Agency (ESA) instrument GOME, with lower spatial resolution than OMI, was in service from 1995 to 2011 (Geddes et al., 2016). More recent is the ESA’s TROPOMI instrument, which was launched in 2017, and has a higher resolution than OMI (7 km _×_ 7 km) (Veefkind et al., 2012). We opt for OMI data in this study not only because the time sample is longer, but because the NO2 product uses the same algorithm for the entire, uninterrupted time period, which is not the case for all satellites. Other data products are available for inferring higher-resolution data from OMI in subsamples (Laughner et al., 2018) and exciting near-future technologies including NASA’s TEMPO instrument promise to have a sharper spatial resolution, about 2 km _×_ 4.5 km.

## **A.2 US Downward Trend and Cycles**

Figure S1 presents both the 365 day rolling average of NO2 VCDs for the US, along with the cyclical component. There are two points worth emphasizing. First, the US is one of the developed nations in the sample with a distinctly downward trend in NO2 (See Table 1, Panel B). In all the analyses, we include country-specific time trends. Thus, we are considering just the cyclical component of the data, which has no trend, and is an indicator of cyclical economic activity rather than trend growth. Second, in US cyclical NO2 data, the mild 2019 “manufacturing recession” is evident. This is part of the reason why world NO2 concentrations were already below trend before 2020 (Figure 2).

## **A.3 World Geographic Heterogeneity**

Figure S2 shows the clear correspondence between global economic activity and NO2 as of yearend 2019. The figure is also suggestive that it may be the case that some developing countries are subject to more pollution for the same amount of output (e.g. China and India). This is another reason why controlling for country-specific trends is important.

S1
be exact or accurate (ACCURATE), and to handle structured vs. unstructured work (STRUCTURED).

Fourth, _non-routine cognitive/analytical_ consisting of thought processes requiring the absorption, processing and decision-making of abstract information. Computer programmers, teachers, lawyers, doctors and nurses, and many other professional occupations will need such abilities intensively. The underlying O*NET skills/tasks included in this category are the ability to analyze data/information (ANALYZE), to think creatively (THINK), to interpret information form others (INTERPRET).

Lastly, _non-routine cognitive/interpersonal_<sup>5</sup> characterize personality traits that underlie behaviors such as teamwork, reliability, discipline, and work effort. These are important for professional occupations as well as for all team-based work environments as well as services which establish direct client contact. The underlying O*NET skills/tasks included in this category are the capacity to establish and maintain personal relationships (RELATIONSHIPS), to guide, direct and motivate subordinates (GUIDE), and to coach/develop others (COACH).

NON−ROUTINE COGNITIVE/INTERPERSONAL RELATIONSHIPS GUIDE COACH Xi = f�xi , xi , xi � Figure 1 summarizes the different characteristics of the five skill measures and provides occupational examples.

A vector X with skill information for all the 974 occupations that have information on the O*NET database is constructed: After the vector X is constructed, it needs to be linked to the occupational structure so that weighted skills measures for economies can be computed. Employing Census data for

> 5 These skills have obtained much attention in the more recent literature E.g., Blom and Saeki (2011), World Bank (2011, 2012), Heckman, J. J., Stixrud, J., & Urzua, J. (2005), Cunha, F., & Heckman, J. J.  (2008), Borghans, L, Duckworth, A. L., Heckman, J. J., & ter Weel, B. (2008).

**6**
The mean values of the outcome indicators for the three and five nearest neighbors were computed using labor market status, hourly earnings, earnings, economic sector, and training. The difference between the mean and the actual value for the treated observation is the estimate of the gain due to the program for that observation. The mean of these individual gains was computed to obtain the average overall gain.

# **VI.2 Results**

In order to assess CONALEP's effectiveness, we examine CONALEP graduates versus the control group in terms of labor force participation, status in the labor market, sector, further training at work, wages, and hours worked. Interpretation and tabular data of each area are presented in the following subsections.

# _Labor Force Participation'_

**Figure 2.**

Irrespective of distance criteria or nearest neighbors, the proportion of individuals seeking employment in the CONALEP group is higher than in the control group. It is unclear whether labor force participation of the CONALEP group has declined with respect to the control group over time. Additionally, the percent of individuals who are searching for a job is higher in the CONALEP group than in the control group.

<!-- Start of picture text --> Percent of Individuals Seeking<br>Jobs<br>94 - 97<br>9<br>0<br>91 - 94<br>90-93<br>0% 5% 10% 15%<br>Percent<br>*CtrI Group mConalep<br><!-- End of picture text --> 8 Data for this section are presented as follows. Table 6 shows the labor force participation of the CONALEP graduates compared to the ENE98 control group. Table 7 shows the labor force participation of the CONALEP graduates compared to the ENECE99 control group. Both tables were calculated using the three nearest neighbors' distance. Tables A2. 1 and A2.2 show the results using the five nearest neighbors criteria.

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