rafmacalaba/datause-encoder-data huggingface dataset:
Blackman et al. (2015) found that the median annual WTP for an exemption from HNC was equivalent to US$103 per vehicle. This value reflects the households’ costs associated with changes in travel behavior and vehicle ownership that result from limiting the transportation choice set due to HNC.<sup>33</sup> It is an unconditional measure of the cost of being exempt from the policy, that is, it takes respondents with positive and zero WTP into account. To estimate the PYP costs, I divide this value by the annual number of restricted days in Mexico (64) and then multiply by the ratio of GDP per capita based on purchasing power parity between Colombia and Mexico (0.78).<sup>34</sup> This yields a daily PYP cost of US$1.26 (COP$2,432) at 2013 prices. The annual figure, that is, the daily value multiplied by the number of days that the restriction applied in Bogotá (104), is US$131.25 (COP$252,905) per vehicle per year, or roughly 2 percent of the income per capita. Considering that the vehicle fleet after the implementation of the drastic phase was around 1 million cars, the total annual cost in Bogotá reaches US$131.25 million (COP$0.253 billion). This cost is about 15 percent of the total governmental budget of the Transport Agency in Bogotá in 2013.<sup>35</sup> Although these costs are only an approximate measure, the exercise suggests that PYP imposed considerable costs on drivers.

It is worth pointing out that these welfare losses are lower bound costs since other effects, for example, enforcement costs and impacts on commerce and labor, are not considered in the present analysis. These issues are outside the scope of this study but may increase the overall costs of the program.

> 33. Blackman et al. (2015) provided examples of those changes: reducing or rescheduling driving, increasing travel by other modes, and buying a second car, among other household decisions.

> 34. Data accessed from  http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD.

35. Income per capita obtained from www.dane.gov.co, budget values from www.alcaldiabogota.gov.co, and exchage rate from www.banrep.gov.co.

25
**_Table 2. Contracts with enterprises per total projects and funding_**

||Share in number of<br>research projects|Share in value of<br>research funding|
|---|---|---|
||(%)|(%)|
|Research|17|35|
|Institutes|||
|Universities|6.1|28|

<!-- Start of picture text --> Source: NISTPASS, 2000<br><!-- End of picture text --> FDI firms are strongly concentrated in the manufacturing sector, chiefly in light industries. Few investors are in service industries and at this stage there is little FDI in high-tech sectors. Regardless of the extent to which FDI has accounted for technological advancement of local industries, it is the general consensus that S&T institutions have not done much to support these gains. NISTPASS (1997) observed that the production sector considers that S&T institutions cannot provide necessary support for their production problems. NISTPASS (2000) also found that S&T institutions had few contracts from the production sector in the period from 1995 to 1998, and that the type of contracts that did exist were small scale, simple and low-risk in nature.

As we can see from this section, different types of firms exhibit different behavior and extent of engagement with regard to innovation. The SME sector in general is weak and sources its R&D externally, relying little on R&D institutions and universities. Large corporations are in somewhat better position to do the same. FDI firms tend to rely on 10
Table 6. Impacts of the Amnesty on Crimes Committed by Venezuelan Migrants

||Homicides|Threats|Domestic Vio.|Thefts|Sex Crimes|Total Crimes|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Panel A. All Venezuelans (ratesp_|_er 100,000)_||||||
|_PEPc × I_[_PostAugust_2018]_t_|-0.146|-0.098|-0.217|0.274|0.219*|-0.151|
||(0.123)|(0.071)|(0.183)|(0.189)|(0.128)|(0.244)|
|_CrimeReports_<sup>_T OT_</sup><br>_ct_|0.033|0.102***|0.334***|0.257**|0.340***|0.886***|
||(0.066)|(0.022)|(0.062)|(0.106)|(0.111)|(0.218)|
|R-squared|0.835|0.801|0.844|0.923|0.902|0.936|
|KP F Stat|7.730|8.544|7.754|8.359|9.255|7.532|
|_Panel B: Male Venezuelans (rates_|_per 100,000)_||||||
|_PEPc × I_[_PostAugust_2018]_t_|-0.194**|-0.053|-0.187|0.238|0.229*|-0.230|
||(0.092)|(0.052)|(0.176)|(0.173)|(0.123)|(0.237)|
|_CrimeReports_<sup>_T OT_</sup><br>_ct_|0.079|0.095***|0.342***|0.207**|0.364***|0.919***|
||(0.072)|(0.020)|(0.061)|(0.098)|(0.106)|(0.180)|
|R-squared|0.837|0.822|0.854|0.916|0.905|0.935|
|KP F Stat|7.730|8.544|7.754|8.359|9.255|7.532|
|_Panel C: Female Venezuelans (ra_|_tesper 100,000_|_)_|||||
|_PEPc × I_[_PostAugust_2018]_t_|0.023|-0.081|-0.015|-0.011|-0.001|-0.125|
||(0.050)|(0.057)|(0.057)|-0.09|(0.014)|(0.126)|
|_CrimeReports_<sup>_T OT_</sup><br>_ct_|-0.044|0.014|0.038|0.063|-0.047*|0.263*|
||(0.046)|(0.011)|(0.034)|(0.047)|(0.025)|(0.148)|
|R-squared|0.716|0.737|0.695|0.755|0.730|0.808|
|KP F Stat|7.730|8.544|7.754|8.359|9.255|7.532|
|Observations (All Panels)|150|150|150|150|150|150|

_Notes:_ The table presents estimates for the identification strategy described in section IV, using crimes rates per 100,000 inhabitants committed by Venezuelan migrants as the dependent variable. Panel A presents results for the full samples. Panel B presents results for men and panel C for women. The variable _Crime_<sup>_T ot_</sup> _ct_ is the inverse hyperbolic sine of the total crimes for the same type of crime per 100,000 inhabitants. All columns include city and year fixed effects as well as the set of controls described in section IV. Standard errors are clustered at the city level.

# **VII CONCLUDING REMARKS**

This study examines the crime effects of a migration reform that granted regular migratory status to approximately 281,000 Venezuelan migrants in Colombia. We document two key findings. First, we find that cities where more Venezuelans received regular migratory status experienced an increase in crime reports by these migrants. These reports are primarily related to domestic violence and sexual crimes and are predominantly filed by female migrants. Consistent with these findings, we observe a corresponding increase in convictions and trials of male Venezuelan per32
<!-- Start of picture text -->
[8] Caldara, Dario (2011). “The Analytics of SVARs: A Unified Framework<br>to Measure Fiscal Multipliers,” mimeo, Stockholm University.<br>[9] Cloyne, James (2011). “What are the effects of tax shocks in the United<br>Kingdom? New evidence from a narrative evaluation.” mimeo, Univer-<br>sity College London.<br>[10] Favero, Carlo and Francesco Giavazzi (2007) “Debt and the Effects of<br>Fiscal Policy,” NBER Working Papers 12822.<br>[11] Favero, Carlo and Francesco Giavazzi (2010). “Reconciling VAR-based<br>and Narrative Measures of the Tax-Multiplier,” CEPR Discussion Pa-<br>pers 7769.<br>[12] Giorno, Claude, Pete Richardson, Deborah Roseveare and Paul van den<br>Noord (1995), “Potential Output, Output Gaps and Structural Budget<br>Balances”, OECD Economic Studies, No. 24, pp. 167-209.<br>[13] Uzetzki, Ethan. “Rent-Seeking Distortions and Fiscal Procyclicality.”<br>Forthcoming in the Journal of Development Economics.<br>[14] Uzetzki, Ethan, Enrique G. Mendoza and Carlos A. Végh (2010). “How<br>Big (Small?) are Fiscal Multipliers?,” CEP Discussion Papers DP1016,<br>Centre for Economic Performance, LSE.<br>[15] Uzetzki, Ethan, Carmen Reinhart and Kenneth Rogoff (2011), “Ex-<br>change rate arrangements entering the 21st century: Which anchor will<br>hold?” (mimeo, LSE, University of Maryland and Harvard University).<br>[16] International Monetary Fund (2010) World Economic Outlook, October.<br>32<br><!-- End of picture text -->
# **Table S3: Model Performance (alternate metrics, out of sample)**

|**Scope Name**|**Method Name**|**AUC**|**H**|**R**<sup>**2**</sup>**mean**|**R**<sup>**2**</sup>**min**|**R**<sup>**2**</sup>**max**|**MSPE**|
|---|---|---|---|---|---|---|---|
|Credit Bureau|Logistic Stepwise|0.565|0.013|0.005|0.002|0.013|0.100|
|Credit Bureau|Random Forest|0.516|0.007|0.001|0.000|0.002|0.111|
|Phone Indicators|Logistic Stepwise|0.760|0.129|0.077|0.058|0.110|0.094|
|Phone Indicators|OLS FE|0.633|0.049|0.021|0.000|0.044|0.100|
|Phone Indicators|Random Forest|0.710|0.093|0.059|0.035|0.077|0.095|
|Phone Indicators|Random Forest Weekly Ensemble|0.616|0.038|0.018|0.008|0.028|0.099|
|Credit Bureau & Phone<br>Indicators|Logistic Stepwise|0.772|0.145|0.090|0.059|0.114|0.093|
|Credit Bureau & Phone<br>Indicators|OLS FE|0.645|0.054|0.025|0.010|0.046|0.099|
|Credit Bureau & Phone<br>Indicators|Random Forest|0.711|0.095|0.060|0.038|0.083|0.095|
|Credit Bureau & Phone<br>Indicators|Random Forest Weekly Ensemble|0.618|0.039|0.018|0.007|0.030|0.099|

_Note:_ All measures are out of sample. AUC represents the area under the receiver operating characteristic curve; H represents the H- measure (Hand, 2009). R2 measures aggregated over multiple fold draws. Mean Squared Prediction Error (MSPE) can be swayed by absolute differences, which are not relevant for the decision problem we consider: when a lender is setting an approval threshold only relative differences matter. An outlier observation was removed for computing the MSPE of the OLS Fixed Effect (FE) models. Source: authors’ analysis from telecom data.

10
28 evidence suggests that follow-up visits to reinforce skills learned in training are important to effective training. Further documentation of detailed program characteristics, coupled with rigorous evaluation, will continue to inform effective evaluations.

Third, by comparing the means of at-scale PD programs with top-performing evaluated programs, our findings highlight gaps between what evidence suggests are effective characteristics of teacher PD programs and the contextual realities of most teacher PD programs in their design, content, and delivery. In particular, our findings taken together suggest that at-scale programs often lack key characteristics of top-performing training programs. At-scale programs are much less likely to be linked to career incentives, to provide storybooks or other reading materials, to have a subject content focus, to include time for practicing with other teachers, or to include follow-up visits.

The approach taken by this paper centers on using the ITTSI to collect and compare data on rigorously evaluated and at-scale, government-funded teacher PD programs. This approach has limitations. First, the evidence of what works within rigorously evaluated programs is limited by those programs that have been evaluated. There may be innovative professional development programs that are not among the “top performers” simply because they have yet to be evaluated. While this evidence base can push policy makers away from approaches that do not work, it should not deter policy makers from innovating and evaluating those innovations.

A second, related limitation concerns the relatively small sample of evaluated teacher PD programs in low and middle-income countries, on which our findings about effective PD characteristics are based. Some of the larger coefficients in the regressions are driven by a small number of teacher training programs. These instances have been noted in the text. As more evaluations of PD programs are conducted, the ITTSI can be applied to these and our analyses re-
<!-- Start of picture text --> China's outward investment in BRI countries by sector China's outward construction in BRI countries by sector<br>2 3<br>vt<br>re} [-}<br>3 Qa 3<br>2 2<br>ag = ae<br>E a . |<br>=oS || t=sk<br>ejaia = tl o> —<br>° °<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>HE Agriculture ME Chemicals MMMM Energy HEE Agriculture MEE Chemicals MMMM Energy<br>ME Entertainment MEE Finance EEE Metals ME Entertainment MEM Metals MEE Real estate<br>(NN Real estate Technology MEME Tourism (NN Technology Tourism MEE Transport<br>MO Transport MEE Utilities MA Utilities<br><!-- End of picture text -->
MENA countries that do not have state banks may not find it necessary to create new ones, because they have been addressing their policy objectives through alternative and probably more effective policy interventions. For example, Lebanon does not have state banks but has achieved a relatively high share of SME lending by MENA standards through the use of partial credit guarantee schemes.<sup>16</sup> Note that these schemes have also played a counter-cyclical role in many countries within and outside MENA, and this is one of the arguments that have been put forward to justify the presence of state banks.<sup>17</sup> Lastly, we note that credit guarantee schemes may also be a preferable form of policy intervention because they provide an easier exit mechanism.

Foreign banks in MENA remain generally small but would probably expand faster and contribute to more competitive and efficient financial systems if they had access to more and better credit information. As noted above and in other studies, addressing the weakness on credit reporting systems should remain one of the key items in the financial development agenda of MENA countries. This would entail both upgrading public credit registries and, especially, introducing private credit bureaus able to expand coverage and improve the depth of credit information.<sup>18</sup> Listed banks have performed better than non-listed banks, and this may be due to the stricter governance standards and disclosure requirements imposed on these banks. Introducing listing obligations for all licensed banks may be one option to improve the performance of familyowned and non-listed banks, but the same outcome may be achieved by the bank regulator by simply imposing and enforcing the higher governance standards and disclosure requirements on all banks, listed and non-listed.<sup>19</sup> In this regard, an interesting question is whether the listing of public banks could contribute to improvements in their performance. Unfortunately, the listing of MENA public banks is very recent and the sample too small to enable statistical testing, but

> 16 Rocha, Farazi, Khouri, and Pearce (2010).

> 17 The IFC (2010) provides some evidence of the use of credit guarantee schemes for counter-cyclical purposes.

> 18 Maddedu (2010) and Anzoategui, Martinez Peria and Rocha (2010).

> 19 The OECD (2009) provides an agenda for stronger bank governance standards in the MENA region.

27
fraction of freight hauled by trucks annually in India, the geographical coverage of the data set is quite extensive.

# 3 **Transport costs and their determinants: Descriptive Analysis**

We start with summary statistics from the pre-pandemic period trucking data set which are reported in Table 1. On average, a truck in our data set travels about 924 km and spends a little over 17 hours on the trip. The truck capacity varies from light (1 to 10 tons) to heavy-duty trucks with carrying capacity up to 35 tons. The average truck size is around 18 tons. The share of trips taken by light trucks is about 22 percent which is consistent with the national average (21 percent). The average number of trips between origin-destination is about 286 though about 8 percent of origin-destination pairs have 5 or fewer trips. We utilize the trucking data to establish key features of trucking costs in India. Table 2 reports several regression results for freight rate per ton-km as a dependent variable. The regressions are used to detect correlations between freight rate and economic density and should not be interpreted as causal relationships. We take total night light luminosity as a measure of economic density in a district. The basic regression specification controls for the log of the distance between origin and destination districts, types of vehicles used (container, trailer or open bedded) and commodity transported, size of the truck, and appropriate location fixed effects. The standard errors are clustered at the route level. In the following, we report the main stylized facts.

## **_Stylized Fact 1: The freight rate per ton-km is relatively low in India._**

The average freight rate is about Rs.3.92 per ton-kilometer (mt-km, for short) (Table 1) which translates into about 5 cents in US $. This is much lower than about 13 cents/mt-km in the United States, and 20.5 cents/mt-km in Colombia (Allen et al, 2020) before the pandemic. Gasoline price in India was much higher ($4.15/gallon) compared with the United States ($3/gallon) during the pre-pandemic period. The lower labor and capital costs more than offset the higher gasoline costs, making transporting goods by trucks quite cheap in India.

8
Figure M3 Inflation and current account positions <!-- Start of picture text --> Korea<br><!-- End of picture text --> <!-- Start of picture text --> Irdormia Korea<br>O- 0-<br>6 -~ ~~~~~~~~~~~6<br>6- 6- -<br>4 -<br>2 -<br>2 -<br>2-| | | " o 0 _ | | __ |<br>2-EfUll 4-<br>4. 6.<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1989 1990 1991 1992 1993 1994 1995 1996 1997<br>- _ carwnt inflation (percent scoount (percnt per annum) of GDP) - - currnt acount (percent innstion (percont per annur)ofGDP)<br>Malaysia Philippines<br>e- t percent<br>6 - 0 -<br>4- 5<br>2 -<br>0 -<br>a -<br>2 - o<br>4 - _ - - 0<br>4 -<br>-<br>6 - 5<br>0.0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1989 1990 1991 1992 1993 1994 1995 1996 1997<br>c currnt account (percent ofGDP) l curent account (prcent of GDP)<br>inflation (percont per amurn) - infltion (percOnt per 8nnum)<br>Thailand<br>percent<br>6-<br>4-<br>2 -<br>0<br>2-<br>4-<br>0.<br>1989 1990 1991 1992 1993 1994 1995 1996 1997<br>- currnt account infbaon (peent per (prcent of GOP) annurn)<br><!-- End of picture text -->

19
8 (ii) θ _r_ itself satisfies the axioms of population replication, scale invariance, normalization, and within-type symmetry, where the latter two are defined in Ferreira and Gignoux (2008).

(iii) Given that not all relevant circumstances **_C_** are ever observed in the data, any empirical partition _Π_ is an incomplete partition in terms of the theoretical full set of circumstances. There may well exist relevant circumstances that lie beyond an individual’s own control and that affect their lifetime advantage, but which are not observed in the data. If we did observe them, and were able to further partition the population into groups defined by those variables, the between-group share of inequality might rise, but could certainly not fall. θ _r_ is therefore a _lower-bound_ on the actual share of between-type inequality.

In the remainder of this paper, we apply this measure of inequality of opportunity to a situation where information on the advantage variable _y_ and the circumstance vector **_C_** are not directly available in the same household survey, so that either _y_ must be constructed as a composite aggregate of various underlying indicators (our “wealth index” method), or information on _y_ from an ancillary survey must be used to impute it into the main survey containing information on **_C_** (our “imputed consumption” method). We compare the two methods in seeking to quantify inequality of opportunity in Turkey.

# **3. The data and two alternative indicators of economic advantage**

In many countries, the analysis of inequality of opportunity is hampered by the fact that no single dataset contains information on both an adequate set of circumstance variables and on the desired advantage variable. This is the case in Turkey, for example. Whereas Turkey’s Demographic and Health Survey (TDHS) provides detailed information on circumstances such as family background, place of birth and language/ethnicity, it contains no detailed data on earnings, income or consumption expenditures. The Turkish Household Budget Survey (HBS), on the other hand, provides detailed information on economic outcomes, but not on some of the most important candidate circumstance variables, such as the education of the parents of present-day workers.
**_Energy demand models for policy_** **_<u>formulation</u>_**

Brendt, E. R. and D. O. Wood., 1979, Engineering and econometric interpretations of energy-capital complimentarity, American Economic Reivew, 69(3), pp. 342-352.

Brown, M. A, M. D. Levine, W. Short and J. G. Koomey, 2001, Scenarios for a clean energy future, Energy Policy, 29, pp. 1179-96.

Brown, P., 1984, Forecasts and pubic policy, The Statistician, 33(1), pp. 51-63.

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Chakravorty, U., F. Fesharaki, et al.?? (2000). "Domestic demand for petroleum in OPEC countries." OPEC Review 24(1): 23-52.

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Cooper, J. C.B., 2003, Price elasticity of demand for crude oil: estimates for 23 countries, OPEC Review, March, pp. 1-8.

102
**Table 4: Girls' Enrollment - Grade 5**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||OLS|FE|FE + Controls|Excluding mosque and<br>primaryschools|Excluding mosque and<br>primary schools+Multan<br>+Khanewal|
|Number of Basic Facilities = 1|||0.148***|0.641*|0.728*|
||||(0.0244)|(0.266)|(0.285)|
|Number of Basic Facilities = 2|||0.197***|0.702**|0.753**|
||||(0.0271)|(0.263)|(0.281)|
|Number of Basic Facilities = 3|||0.0961**|0.699**|0.752**|
||||(0.0368)|(0.267)|(0.286)|
|Constant|7.081***|5.544***|5.408***|12.42***|12.26***|
||(0.0848)|(0.0189)|(0.0264)|(0.255)|(0.273)|
|39<br>Observations|345,196|345,196|345,196|69,047|64,476|
|<br>Number of Schools||58,154|58,154|12,170|11,381|
|Adjusted R-squared|0.012|0.004|0.004|0.004|0.004|
|R-square within||0.00433|0.00442|0.00463|0.00465|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust to heteroskedasticity and clustered at the level of the school. The dependent variable is girls’ enrollment in grade 5. The average school in a treatment district school at baseline had 4 girls enrolled in grade 5. Column 1 reports estimates from an OLS specification. Column 2 reports estimates from a fixed effects specification at the school level. Column 3 adds to column 2 a series of indicators for how many of the following three facilities a school has: toilets, drinking water and electricity. The excluded group is no facilities. The excluded year is the baseline year 2003. Column 4 excludes mosque schools and primary schools from the sample. Column 5 removes two districts whose treatment status was interchanged once the program began: Khanewal (changed from treatment to control) and Multan (changed from control to treatment).