rafmacalaba/datause-encoder-data huggingface dataset:
In recent years, social media, and especially Twitter, has emerged as a source of real-time information. Therefore, it is natural that in a context where there is a dearth of official data on a topic such as road safety, we turn to crowdsourcing through Twitter to produce more comprehensive information on road traffic crashes. As people move around cities that have been plagued by congestion, they have started to rely on social media and citizen reporting to help them avoid major traffic jams and decrease their commutes. Given the relationship between RTCs and congestion, platforms that crowdsource and broadcast traffic updates have the additional benefit of often reporting on RTCs. This makes it possible to use crowdsourced data to identify when and where crashes are occurring, which can be used to supplement and improve on existing official statistics.

While only around 1% of tweets contain geo-metadata, a growing literature has developed geoparses—or algorithms that extract location names from text. Tweets present a unique challenge to geoparses. State-of-the art geoparses, such as OpenCascais and Stanford Named Entity Recognition, rely on grammar rules to identify location mentions; however, tweets often do not follow grammatical capitalization rules and use clipped, ungrammatical sentences [1, 2]. New algorithms have been developed to geoparse tweets. This includes developing an algorithm that accounts for tweets that contain place references that are abbreviated, misspelled or highly localized [2]. Others develop gazetteers (location dictionaries) from sources such as Open Street Map and Geonames and search for names within the gazetteer in tweets, employing different approaches to account for misspellings or tweets using shortened names than are in gazetteers [3, 4, 5]. [5] provides a review of existing approaches.

Here we provide more information on the specific data that we used, how they were processed and the algorithms that were developed. These processes can then be implemented in different contexts where crowdsourced data on RTCs are available.

# **Twitter Data**

We generate crash data from social media. The main source of crowdsourced data comes from Ma3Route, a mobile/web/SMS platform that crowdsources transport data and provides users with information on traffic, matatu (informal bus) directions, driving reports and crashes for Kenya. As of early 2019, Ma3Route had 1.1 million followers on Twitter and around 400,000 subscribed users on their app. When users post a traffic report on the app, Ma3Route displays the report on their S1
7 Table 2: Sensitivity of Individual Capital Account Net InflowC to Current Account Financing Requirements in OE;CD Countries, 1975-1989.

|Country|Direct<br>investment|Portfolio<br>investment|Long-term<br>flows|Short-term<br>flows|Public<br>sector|
|---|---|---|---|---|---|
|United States||||0.55<br>(2.8)||
|Japan||0.51|||u.20|
|||(2.4)|||(2.4)|
|Germany||||0.30<br>(2.0)||
|Canada||0.11||0.88||
|||(1.6)||(3.1)||
|France|0.13|||0.32||
||(2.7)|||(1.6)||
|Italy||||U.38|0.23|
|||||(2.7)|(2.2)|
|United Kingdom|-0.10|||0.27|0.25|
||(-1.8)|||(2.3)|(3.9)|
|Australia|||||0.36<br>(3.1)|
|Belgium-||||||
|Luxembourg|||||0.22<br>(1.6)|
|Netherlands||||0.47<br>(2.1)||

Note: **_t_** statistics in parentheses.

Source: Turner, _Capital Flows_ **_in_** _the 1980s: A Survey of Major Trends_ (Basel: Bank for International Settlements, Monetary and Economic Department, BIS Economic Papers No. 30, 1991), Table 33, p. 92.

investment as the most autonomous, **FDI** or capital inflows to the public sector next, and both long- and short-term bank loans as the most accommodative type of capital flow. All but one of the reported coefficients for **FDI** are less than 0.1; in Malaysia's case the coefficient is 0.14. On the basis of this table, therefore, **FDI** appears to be autonomous.<sup>5</sup> The negative coefficients of **FDI** in India, Korea, and the United Kingdom may reflect the deterrent effect of increasing current account deficits on **FDI** inflows, since large current account deficits can 5For the five Pacific Basin developing market economies, short-term bank loans seem slightly more accommodativ,; than long-term loans, but the general pattern is similar to the pattern in the top part of the table.
LLSV prediction is incorrect as the prediction error is high (13.15 as compared to 11.84). LLSV’s prediction on valuation is also incorrect as the prediction error of Tobin’s Q of India’s synthetic firm is high (0.75 vs. 0.32).

In conclusion, our empirical analysis on the listed firms in India demonstrates that India does not conform precisely to LLSV’s predictions and findings on legal protection and firm characteristics. However, considering that investor protection in India is poor in practice, our analysis is not inconsistent with the spirit of LLSV predictions. With English common-law origin and strong investor protection by law but not in practice, India’s listed firms actually behave more like firms from countries with poor investor protection: The equity ownership of Indian firms is highly concentrated within the founder’s family or the controlling (individual) shareholder; and they tend to pay lower dividend and have low valuations compared to companies from countries with strong legal protection.

# **V. Survey Evidence on India’s Small- and Medium-sized Firms**

In order to go beyond reported statistics and secondary financial information, and understand better the business realities and financing decisions of small and medium businesses in India at the ground level, we conducted a survey of SME enterprises in India. Our surveys led to a number of interesting findings about the SME sector in India. They reinforced the difficulty in obtaining external funds from formal channels, a point mentioned in Section III above. What is perhaps an even more interesting finding is the role and importance of informal and alternative financing channels, as well as informal mechanisms in contract enforcement that characterize the environment in which these firms operate in India.

28
<!-- Start of picture text --> Figure 14: Reform Indicators on Price Formation and Full-Cost Recovery (C3)<br>a) Cost Recovery and Regulation Index b) Change in Cost Recovery and Regulation Index<br>90% 90%<br>R2 = 0.042 R2 = 0.0016<br>80% Philippines 80% Philippines*<br>70% 70%<br>Peru<br>60% Pakistan Peru 60%Dominican Pakistan India -Odisha<br>Dominican Republic India -Odisha Ukraine Republic India - Ukraine<br>50% 50% India -Andhra Rajasthan<br>Pradesh<br>40% Vietnam 40% Vietnam<br>Egypt, Arab Kenya Colombia Kenya Colombia<br>30% Rep. Uganda 30% Uganda<br>Egypt, Arab<br>20% 20% Rep.<br>10%0% Tanzania Tajikistan Senegal Morocco 10%0% Morocco Tanzania Senegal Tajikistan<br>50% 70% 90% 110% -50% 0% 50% 100%<br>Full-Cost Recovery Change in Full-Cost Recovery<br>c) Cost Recovery and d) Change in Cost Recovery and<br>De Facto Regulatory Autonomy Index De Facto Regulatory Autonomy Index<br>120% 120%<br>R2 = 0.0078 R2 = 0.0004<br>100% Peru 100% Peru<br>80% Egypt, Arab Senegal 80% Dominican Senegal India -Odisha<br>Rep. Uganda India -AP Uganda<br>Dominican Republic Pakistan India -OdishaUkraine Republic Pakistan India - Egypt, Arab Rep. Ukraine<br>60% Tanzania Colombia 60% Tanzania Rajasthan Colombia<br>Kenya Philippines Kenya<br>Philippines*<br>40% 40%<br>Tajikistan Tajikistan<br>20% Vietnam 20% Vietnam<br>0%<br>0%<br>50% 70% 90% 110%<br>-50% 0% 50% 100%<br>Full-Cost Recovery Change in Full-Cost Recovery<br>Regulation Index Regulation Index<br>Regulatory Autonomy Regulatory Autonomy<br><!-- End of picture text --> _Notes:_ *The Philippines saw a decline in cost recovery, but post-reform cost recovery was still close to 100 percent on average. _Source:_ World Bank staff.

58. The relationship between the de facto tariff regulation index (capturing what tariff regulations are enforced in practice) and absolute levels of cost recovery or improvement in cost recovery is stronger (Figure 14a and b) This suggests that the structure of the sector—i.e., if an independent regulator exists and whether or not it is autonomous—is less important than the tariff regulation implemented in practice. For example, Colombia, which has reached full cost recovery, scores lower than the sample average on the regulation index (35 percent) and regulatory autonomy index (60 percent), but scores high on the de facto tariff regulation index (92 percent). The country has been able to achieve cost reflective tariffs with regular adjustments that are promptly implemented, despite having a regulator that is exposed to 30
13 in the interaction term) there may be threshold effects. The negative impact of population growth may be very small up to a point **and** then increase dramatically. Below we generally use the average of possible Z values to divide the sample as we have no particular prior knowledge, but there is no reason to expect the critical value above which population consequences are severe corresponds to the average. The combination of average slope and cutoff point does put some limits on the values of the paTameters however, as if the slope of the "worsew half of the sample is low, then either the slope even for the "worse" cases really is low, or only a small fraction of the countries are in the "worse" case.

A mimerical example will illustrate this point that even if the effect is non-linear in some unknown threshold an arbiitary splitting of the sample should detect the relationship. Say the true model for the impact of laborYforce growth I on output per person growth y is non-linear in some variable (liMe resource scarcity, call it z) such tha there is no effect up to some threshold lev,el zA but that after z the slope is incresing in z. The model would be: _YIfSu{L.)_ , **os1** + +e4"Ž _r zIz'_ Now, as a example, asmne 1 and z are normally distributed and uncorrelated and that the threshold point z' is such that f percent of the observations lie above the i. If a linear regression. on the whole sample is estimated, the coefficient will be £ = ***(+'),** where z **_=m7aaz)_** (z>z-* Iff=.10 and = **2** ,for exanple, **£o.10*1_69** =338 If on the other hand the sample is divided in half and the regression is run only on those observations
# **Figure 10 Relative market shares in the EU by industry clusters, 1999**

<!-- Start of picture text --> Mainstream Labor-intensive Capital-intensive Marketing-driven Technology-driven<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Latvia Lithuania Estonia Poland Slovak Rep. Finland Hungary Ireland<br>Note: Market shares of industry clusters relative to country's total market shares in all imports of the EU.<br><!-- End of picture text --> _Source_ : WIIW, 2001

# <u>C. FDI Flows</u>

In principle, FDI can play an important role in improving the competitiveness of the local manufacturing base. For example, a recent UNCTAD study identified a strong relationship between inward FDI and manufacturing export performance in a number of countries. (UN, 1999: p. 244-255).<sup>15</sup> However, technology spillovers from foreign-owned firms to domestic firms do not emerge automatically.<sup>16</sup> Empirical studies conducted in Hungary, Slovenia and Estonia indicate that technology spillovers from foreign firms to local firms occur only occasionally and do not induce significant innovative activities within domestic firms.<sup>17</sup> Several factors account for the relatively low incidence of spillovers including:

- There is little evidence to suggest that local companies have the financial or technical capability to reverse engineer the production processes and technologies

- 15  UNIDO, _World Investment Report_ . 1999 Foreign Direct Investment and the Challenge of Development. UN. New York and Geneva.

- 16  Technology spillovers are defined as the transfer of technology that could be externality (“learning-bywatching”, reverse engineering or labor mobility) or linkage based (conscious and intentional transfer through supplier and customer contacts or networking). The distinction between these five observable mechanisms of technology-spillovers is an analytical approach and in practice they can often overlap. So it is highly probable that e.g. supplier- or customer-contacts are accompanied by “learning-bywatching”.

- 17  Günther, Jutta, _The Significance of FDI for Innovation Activities within Domestic Firms: The Case of Central East European Transition Economies_ . Halle Institute for Economic Research Discussion Paper No. 162, May 2002: www.iwh-halle.de/e/publik/disc/162.pdf

Hunya, Gabor, _Recent Impacts of FDI on Growth and Restructuring in Central European Transition Countries_ . WIIW Research Report No. 284, May 2002.

19
# Electoral Cycles and Public Spending during the Pandemic

_Michael Lokshin, Aylén Rodriguez-Ferrari, and Iván Torre*_

**JEL:** H53, I38, O15

**Keywords:** Social protection, pandemic, governance, electoral cycle

> *The authors are with the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
that the treatments have equivalent effects. To both improve patient survival and contain the epidemic, it is particularly important that infected patients report. We find large increases in the average number of confirmed cases reporting in treated sections: for every confirmed case in control, we count five confirmed cases in treated sections.

**Table 3:** Reported Ebola Cases

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Total|0.281|0.173|0.204|0.148|0.055|5,440|
||(0.727)|(0.084)<sup>_∗∗_</sup>|(0.117)<sup>_∗_</sup>|(0.099)|(0.133)||
|Confrmed|0.011|0.059|0.086|0.039|0.047|5,440|
||(0.129)|(0.024)<sup>_∗∗_</sup>|(0.038)<sup>_∗∗_</sup>|(0.025)|(0.041)||
|Negative|0.238<br>(0.648)|0.1<br>(0.061)|0.079<br>(0.077)|0.115<br>(0.075)|-0.036<br>(0.093)|5,440|
|IHS(Ebola Cases)|||||||
|Total|1.015|0.057|0.065|0.05|0.014|5,440|
||(0.309)|(0.029)**|(0.038)*|(0.034)|(0.044)||
|Confrmed|0.887|0.02|0.024|0.016|0.007|5,440|
||(0.064)|(0.007)***|(0.01)**|(0.008)**|(0.012)||
|Negative|0.998|0.039|0.034|0.043|-0.008|5,440|
||(0.284)|(0.023)*|(0.03)|(0.029)|(0.035)||

_Notes_ : The sample includes 160 sections over 34 weeks. Standard errors clustered on section shown in parentheses. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Difference column reports the difference between the CM and NFA coefficients; the standard error is computed using the delta method. The bottom panel employs the inverse hyperbolic sine transformation (IHS( _y_ ) = log( _y_ + �(1 + _y_<sup>2</sup> )). Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

We take a number of steps to assess robustness. First, we re-estimate our pooled effect dropping one matched triplet at a time (Figure E.4), dropping each possible pair of matched triplets (Figure E.5), or dropping each week (Figure E.6). Second, we estimate the effects by month to assess whether our results are driven by a particular moment in the crisis. The pooled coefficient is positive across every month, and we find significant effects in October, December, February and April (Table E.3). (We find large and significant effects for CM in October 2014 and April 2015; for NFA, in October and December 2014.) The spread of these effects across various months throughout our sample period verifies that the effects are not driven by any particular period. Third, we conduct a placebo test where we substitute the nearest out-of-sample neighbor for each section. We find no significant effects in Table E.12, alleviating concerns that our treated sections are spatially clustered in areas where reporting is higher for reasons unrelated to treatment (e.g., exposure). Finally, we adopt a number of alternative specifications to handle our count data. In the bottom panel of Table 3, we use the inverse hyperbolic sine transformation of the counts and find effects of similar magnitude; the effect of NFA on confirmed cases becomes significant in this specification. We expand upon this in Table E.8, which presents estimates using a linear probability model, the logged count (adding 28
**Table A9:** Summary Statistics: District-Level Estimates of Educational IGM

||||||upward||||||d|ownwa|rd|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|country|districts|mean|median|stdev|min|max|Nmin|Nmean|mean|median|stdev|min|max|Nmin|Nmean|
|Cuba|137|.496|.493|.052|.373|.632|90|333|.212|.209|.038|.107|.325|66|493|
|Peru|168|.437|.416|.176|.104|.84|156|1138|.144|.127|.075|.034|.444|50|1024|
|Chile|179|.318|.311|.123|.088|.82|140|1091|.273|.27|.087|.045|.533|50|513|
|Costa Rica|55|.316|.317|.071|.155|.488|288|1230|.244|.232|.082|.104|.474|59|412|
|Argentina|312|.287|.294|.08|.059|.562|192|2252|.25|.243|.067|.106|.532|50|868|
|Bolivia|80|.281|.267|.114|.102|.592|219|1459|.258|.25|.08|.124|.479|52|784|
|Dominican Republic|66|.277|.282|.058|.136|.466|111|1040|.26|.241|.073|.158|.518|51|427|
|Panama|35|.248|.233|.124|.019|.444|356|1539|.214|.198|.063|.075|.344|52|689|
|Ecuador|78|.237|.217|.081|.096|.423|331|3057|.238|.228|.076|.127|.465|53|952|
|Uruguay|67|.219|.193|.093|.051|.499|169|572|.471|.485|.121|.173|.71|50|233|
|Venezuela|157|.216|.211|.07|.067|.404|289|2234|.327|.317|.09|.125|.599|50|634|
|Brazil|2,040|.213|.208|.09|-.007|.528|365|2373|.273|.261|.096|.055|.659|50|484|
|El Salvador|103|.212|.188|.106|.03|.516|160|539|.279|.277|.073|.117|.464|51|293|
|Colombia|434|.21|.189|.098|-.09|.493|185|1453|.261|.249|.092|.077|.629|50|348|
|Mexico|2,331|.19|.181|.097|-.046|.663|50|813|.244|.234|.076|.059|.54|50|362|
|Nicaragua|68|.161|.159|.072|.034|.312|228|963|.287|.277|.081|.179|.476|50|323|
|Paraguay|64|.15|.132|.084|-.049|.374|187|1326|.264|.259|.064|.151|.406|53|377|
|Guatemala|191|.069|.059|.051|-.006|.268|199|803|.302|.282|.075|.162|.441|55|324|
|Honduras|96|.059|.052|.042|-.004|.219|195|699|.454|.461|.081|.298|.579|51|376|
|Haiti|23|.042|.035|.031|.001|.137|753|3192|.708|.717|.09|.52|.83|59|451|
|total|6,684|.217|.203|.117|-.09|.84|50|1490|.264|.249|.098|.034|.83|50|506|

Notes: This table shows summary statistics for district-level estimates of upward and downward IGM. Upward reflects the likelihood that children, aged 19-25, whose parents have not completed secondary schooling will manage to complete at least secondary education. Downward reflects the likelihood that children, aged 19-25, whose parents have completed secondary schooling or higher will not manage to complete secondary education. “Total” shows the unweighted summary statistics across all districts. The columns “Nmin” and “Nmean” report respectively the smallest and average sample size across districts. Districts with less than 50 observations are omitted.

55
7

## Section 3: Descripti **o** fthe Modeln

The basic model used here is drawn from Romer (1988) . In many respects, it is quite close to the neoclassical model with technological change. The key difference is that the dependence oftechnological change on econo.-^ decisions is explicitly modeled. To do this, it is necessary to depart from the usual framework of perfect competitioa **n** d adopt instead a framework with differentiated comodities and monopolistic competition. Since differentiated commodities imply an important role for trade, the attempt to model endogenous technological change has the unintended benefit of showing why trade policy may be important for growth. The basic difficulty in introducing endogenous technological change into the neoclassical model is well known. Suppose that net national outpu **t** akes the form

# Y = F(L,H,K,A)

where L is the total stock of physical labor, H is the total stock of trained human capital, K is capital, and A is an indicator of the level of the technology. L grows with increases in the populatioa **n** d the labor force participation rate. Since H here is measured in total, not average terms, it grows when L does. It also grows relative to L when the level of training or education increases. (Many formulations make L and H perfect substitutes sothat they can be added together to get a single measure of effective labor. This is a special case of the general formulation described here.) The rate of growth of the capital stock depends on savings behavior,
No targeting mechanism is ever perfect. And one might question whether we are using the right welfare indicator; possibly some of those we classify as “rich” according to current consumption (as measured in our survey data) would be deemed poorer by other (unobserved) criteria. So one might not want to overstate the significance of our finding that the self-targeting mechanism is far from perfect. However, the results in Table 4 do warn against the robustness of the generalizations made by some advocates of the EGS.

Table 6 gives our estimated aggregate supply of casual labor at each EGS wage rate. The overall wage elasticity (estimated as a regression of log labor supply on log EGS wage rate across the 7 observations) is 0.19 (with a standard error of 0.02). The elasticity is slightly lower for the poor; for the poorest quintile the elasticity is 0.16 (standard error of 0.02).

While past estimates of the labor supply function to casual labor may not be a good guide to the labor supply function in the presence of an EGS, it is still of interest to compare these elasticities to previous estimates. In a study using household level data for West Bengal in 197273, Bardhan (1984) obtained wage elasticities of labor supply to casual agricultural labor in the range 0.2 to 0.3 — similar to those implied by our estimates of labor supply at various EGS wage rates. However, our estimates are considerably lower than those reported in Kanwar (2004) who obtained labor supply elasticities in the range 0.8-1.5. (These estimates used the ICRISAT Village Level Surveys for semi-arid areas over 1975-1984.) What are the characteristics of those likely to benefit from the scheme? Table 7 helps answer this question by comparing a range of descriptive statistics between the adult sample as a whole, those who gain from the EGS and the sub-sample of gainers who are attracted into casual work at an EGS wage rate of Rs 50 per day. We find that most adults likely to benefit from an income gain are those who are already in the labor force, and in particular, are already employed 17