rafmacalaba/datause-encoder-data huggingface dataset:
coecient using the step-down approach of Romano and Wolf (2005). In this way we control for the family-wise error rate (FWE). The FWE is dened as the probability of incorrectly identifying at least one coecient as signicant, which becomes more likely as the number of hypothesis tests increases. The Romano-Wolf approach improves upon more conservative classical methods such as the Bonferroni correction by applying a step-down" algorithm that takes advantage of the dependence structure of individual tests. Our approach is to control for a FWE of 5 and 10 percent and mark each coecient that is signicant at each of these rates with _††_ and _†_ respectively. However, testing too many hypotheses at once may reduce power to detect anything signicant. We thus test multiple hypotheses in related groups rather than for all eects reported in the paper.

# 5 Results

## 5.1 Overall Treatment Eects

We begin by showing the overall eect of the program for grades 2 and 4 at baseline (they were in grade 3 and 5 at follow-up). As explained above, at rst follow-up we have measurements of student performance in written tests in French and mathematics, as well as an oral test that covers sound, letter and word recognition, and reading comprehension, but (for cost reasons) was only administered to a third of the students who take written tests. For each of these three tests we compute the proportion of correct answers given by each student. In addition, we use the rst principal component as a summary index of these three tests, which is standardized to have mean zero and standard deviation 1. For the second follow-up, we also have scores for the Peabody Picture Vocabulary Test, which is standardized to have mean zero and standard deviation 1 (within grade).

The results are in Table 2. Panel A concerns the rst follow up, which was administered at the start of grades 3 and 5 respectively about a year after the disbursement of the project funds, while Panel B relates to the the second follow up at the end of grade 3 and 5 for the same children, which we are following throughout. The rst three columns report eects on French, mathematics, and oral test scores. The fourth column provides a summary measure by reporting the rst principal component of these three tests.<sup>12</sup> Column 5 reports PPVT scores, which were obtained only in

> 12One interpretation of the individual tests is that they are noisy measurements of one underlying human capital factor. By using the rst principal component of the three tests, we may improve precision.

12
centralized units, and are as efficient in construction as centralized units. The impact of decentralization on preference costs is not measurable in such a case.

Model 4: The Impact of Decentralization when Resource and Preference Costs are Recursively Determined In Table 8 we show the estimated results for the specification where preference costs determine resource costs. We see that for this specification, decentralized maintenance results in higher resource costs while decentralized administration results in lower resource costs (second order effect) which increase with increasing degrees of decentralization but at a lower rate than the initial decrease (third order effect).

The shape of the estimated functions changes with this model specification for the case of administration (see figure 7). We see that the justification for the role of the central government is no longer there, when the preferences of users are explicitly used to make decisions on spending in the road sector (i.e., when quality standards are set first then resource costs are determined in order to meet these standards). Decentralized units spend more than centralized ones for the same level of quality, but they spend less in administration of road activities. This is an important finding which may also be indicating that when uniform and/or high standards are regulated, the efficiency gains of decentralized provision may be offset by the regulated quality levels. In these cases, it may be more efficient to manage the provision of quality services centrally, as predicted by models 1 and 3. Germany is a good case which demonstrates this effect as shown in the following sections.

**Table 8: Recursive Determination of Resource Costs from Preference Costs**

|Variable|Construction|Maintenance|Administration|
|---|---|---|---|
|fiscal|4.31|-<br>70.44||
|decentralization'|(0.74)|(|(1.37)|
|fiscal<br>dtliti<sup>2</sup><br>fiscal|-0.03......<br>_05_<br>-0.0003|1 6X<br>0 0001~.....|1<br>Z<br>...... 0fO..............|
|ecenrazaon|_(-._|(o|<br>(-|
|decentralization<sup>3</sup>|(-0.47)|(.1 .16)...||
|R-squared|0.72|0.85|0.81|
|Observations|261|261|261|

Fiscal decentralization measured separately as the percentage local government contribution to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at 90% level of confidence.

* significant at 85% level of confidence.

** significant at the 80% level of confidence.

24
Indicators, World Bank). If Government Effectiveness or Control of Corruption provides greater certainty, one would expect a positive association with the average level of consistency reported and a negative association with the variation. Indeed, this is what we find. So building institutional capacity may well be one way to address policy uncertainty.

While policy implementation uncertainty is related institutional performance and government efficacy, these evolve slowly. Are there ways to shift expectations about policy uncertainty in the short run? We wish to address two, definitive policy shifts (often associated with clear political shifts) or the use of a selective reduction of policy obstacles for favored firms.

54
sexual behavior outcomes were also similar across assignment groups.

The attrition rate was low (table 3), with 95.4% (2888 out if 3029) of the participants surveyed and tested in the last round and 94.6% (2,865 out of 3,029) of the participants surveyed and tested in the one-year follow-up round. The attrition rate (5-6%) was similar across the three assignment groups and not predicted by any of the baseline sociodemographic characteristics listed in table 2, panel B, or STI status, except that HIV positive individuals at baseline were more likely to be lost to follow-up (41 of 507 or 8% of HIV positive individuals at baseline were lost to follow up).

# **STIs as markers for risky sexual behavior**

The main objective of the intervention was to incentivize safer sexual behavior as a route to reducing the spread of HIV. The lottery incentives, however, were tied to STI status. Table A1 in appendix reports correlations between STI status and HIV status and selfsexual behavior baseline data. individuals were reported using STI-positive approximately 2.5 times more likely than STI-negative individuals to be HIV positive at baseline (column 1); i.e., there is a strong positive correlation between STI and HIV status. Columns (2)-(6) show that STI-positive individuals were also more likely to be involved in (self-reported) risky sexual behavior. The average standardized effect (Practice safe sex) is significantly negative and precisely estimated, providing evidence in favor of the assumption that prevalence of the two STIs can be viewed as a marker for risky sexual behavior.

# **Average treatment effects: HIV and STI**

To assess the impact of the lottery intervention, we compare mean outcomes after accounting for stratification. That is, we estimate where _yij_ is a binary variable that for HIV incidence takes the value 1 if the individual _i_ living in village _j_ became HIV infected over the trial period and 0 otherwise and for HIV [STI] prevalence takes the value 1 if the individual was HIV [STI] positive at the end of the trial, and 0 otherwise. _T_<sup>_L_</sup> and _T_<sup>_H_</sup> are indicator variables for assignment to the two intervention groups (low and high lottery group, respectively), _η j_ are village fixed effects, and _εij_ is an error term. Standard errors are clustered at the village level. We estimate the effects for the high- and low-value lottery arm and the pooled treatment effect using both OLS and a Probit model. In appendix, we also report odds-ratios and relative risk ratios.

Table 4 describes the impact of the lottery program on HIV incidence - the primary study outcome. Over the two-year trial period, the HIV incidence rate was reduced by 10
- iii. -

|Table 4.5|Plndyck'c Total Own Prlce ElastLcLtLes for<br>the Industrlal<br>80ctor...................................58|
|---|---|
|Table 4.6|Plndyck's Total Cros. Prlce Fuel BlastLcLtLes<br>for Industry.,-*...,...<br>........ . ............<br>58|
|Table 4.7|ConuLdLne's Partlal Prlce ElastLcltLes for<br>U.S. Statlonary Fuel CombustLon ln 1970 ........ ,<br>........ 61|
|Table 4.8|ConsLdLne's Partlal Prlce ElastLiLtLes for<br>U.S. Statlonary Fuel CombustLon ln 1985 ..................62|
|Table 4.9|Plndyck's Partlal Prlce ElastLcLtLes for<br>U.S. Industry ...................<br>..<br>.**....<br>63|
|Table 4.10|Mountaln and HlaLo's Partial Cross Price ElasticitLes<br>for IndustrLes in Quebec or 1974 .............<br>*65|
|Table<br>4.11|Costs of Electricity Generatlon by Type of Fuel .......... 74|
use to determine whether to pay for WSS, program designers can create policies to increase demand for WSS.

The use of conjoint methods is not new to infrastructure service industries.<sup>2</sup> Service attribute quantification has been common among the electricity industry for some time, with information on the value of reliability to customers used to design appropriate services and tariffs (Eto et al., 2001). In service delivery programs, we can think of conjoint analysis as a tool to determine what combination of attributes consumers will most likely purchase. Hensher et al. (2004) assess the value that households in Canberra, Australia, place on drinking and wastewater attributes. Respondents to their surveys valued attributes of WSS such as reliability of water services in addition to cost. They find evidence that customers place value on operational procedures within the water industry.

Estimation of demand for WSS and evaluation of differences in household preferences across sociodemographic groups typically requires at least three types of data that vary across the sample: (a) access and quantity of WSS, (b) price, and (c) socio-demographics. Given the variety of situations and scenarios that are considered for policy design and evaluation, researchers have used both stated preference (SP) and revealed preference (RP) methods to assess demand and understand preferences. SP approaches use data from surveys that describe potential scenarios to estimate the _ex-ante_ demand for goods and services such as WSS. The contingent valuation and conjoint analysis methods are examples of SP approaches. RP approaches use behavioral data, often from surveys, to estimate _ex-post_ demand for WSS. The hedonic price, averting and coping models are examples of RP approaches. In the case of WSS services, consumer data (RP data) typically does not contain enough variation across households to estimate the value of individual service attributes. Instead, water policy analysts can use stated preference methodologies such as conjoint surveys to understand the importance of service delivery attributes and cost to consumers of WSS and the extent to which consumers are willing to make trade-offs between service attributes and cost.

> 2 Mercer and Snook (2004) farmers’ preferences for land use systems through conjoint analysis. In their study, conjoint analysis is used to determine how farmers value different attributes of land use systems and to estimate how these values affect management decisions. The researchers use this information to design land use systems that are most likely to be adopted by farmers.

5
Paris and London—to uncover the elements external to firms that contributed significantly to their innovative activity. [<sup>5</sup> ] No one set of factors underpinned innovation in all five cities, the researchers found, but taken together, the cities revealed the following "essential elements of urban innovativeness" (Simmie 2001: ch. 7).

(1) A history of successful adaptation and reinvention in response to outside events.

(2) Strategic resources provided by large firms and inter-firm relations; (3) "Urbanization economies" based upon city size, density and diversity, including scale economies, accessibility to transport and service infrastructure, and a wide variety of knowledge sources.

(4) "Localization economies" consisting of networks among small firms clustered into industrial districts, sustained by continuity and synergy in the exchange of specialized knowledge and skills, and benefiting from liaisons with public agencies.

(5) Location in a strong national system of innovation and a high ranking position in the national urban system. [<sup>6</sup> ] (6) Global linkages forged by city workforce's expertise in acquiring and adapting the world's best practices in their specialized fields, and a transport and communications infrastructure facilitating international exchange.

The ERSC study concludes that the innovative roles of small firms, industrial clusters, and regional milieu have been overemphasized in recent studies. Industrial districts are more important in some urban contexts than in others: among the five study cities, in Stuttgart and Milan more than in internationally oriented metropolitan cities that dominate urban systems, like London, Paris and Amsterdam. The relationship between local development and the globalizing economy is more strongly influenced by a national innovation system, national urban systems, and the strategies of large corporations than by local milieu and small business networks. Innovation, the authors conclude, is not primarily producer driven, as cluster theories claim, but market driven by international, service based demands for complex goods and services. In short, Simmie and his colleagues favor the international hub theory (2001: 240-241).

5
(e.g., Pigouvian pricing of externalities) and characterize their impacts. For example, call detail records (CDR) from mobile phones have become a compelling and cost-effective way to measure traffic flows and, combined with other data, can be used to assess the links between congestion and other social, economic, and environmental outcomes (e.g., emissions measured from remote sensing or in situ devices).

These improvements in outcome measurements have generated new venues for research, promising policy opportunities (e.g., the implementation of real-time traffic management systems) and, more generally, stimulated evidence-based policy designs. There are, however, multifaceted challenges that are inherent to the collection and use of big data, including technical barriers, privacy concerns, and political risks. It is also worth mentioning that although big data makes it possible to have better measurements of phenomena, track wider social and economic outcomes, and improve predictive modeling, it does not establish causal There is thus a for eco- automatically relationships. space nomic analysis to make use of big data in order to infer causality, in particular through structural modeling or quasi-experimental designs.

The paper is structured as follows. In Section II, we present a taxonomy of new traffic data sources and their potential for improving transportation studies. In Section III, we examine the economics of traffic externalities in the age of big data, reviewing recent empirical efforts to quantify welfare, productivity, and environmental costs. Section IV acknowledges the potential of big data analytics to improve our understanding of these externalities—through better measurement and accurate predictions—and, as a proof of concept, proposes a simple application that measures the causal link of stay-at-home orders on urban Section V concludes with a discussion on traffic congestion. policy opportunities and challenges.

2
Policy Research Working Paper 10284

# **Abstract**

The COVID-19 pandemic accelerated the digital transformation of businesses. Using a unique global panel dataset, this paper documents the patterns of digital adoption during the pandemic across firms in 57 (mostly developing) countries. The data show the tale of two stories. On one hand, the pandemic drove firms to increase the use of digital platforms and invest in digital solutions. On the other hand, there is evidence that the digital divide increased. There remain substantial gaps between small and large firms as well as across sectors, particularly for new investments in digital solutions. Firms that did not use any digital platform or channel before the pandemic, also lagged in their response to the pandemic, increasing the gap with those that were more digitally ready. Moreover, although the share of online sales across firms for all size groups increased, there is a growing concentration of online sales among top firms. The paper discusses some of the factors associated with this increase in the digital divide and find that changes in digitalization remain even after mobility restrictions have eased. The analysis suggests that the pandemic has accelerated digitalization, but some firms disproportionately benefited from the digital transformation, potentially increasing the digital divide.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at xcirera@worldbank.org or marciocruz@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|lnM<sup>2</sup>|0.032***<br>(0.001)|0.033***<br>(0.001)|
|---|---|---|
|lnK<sup>2</sup>|0.005***|0.005***|
||(0.001)|(0.001)|
|Firm owns or shares a generator Y/N|0.100***|0.079**|
||(0.034)|(0.036)|
|Losses due to breakage/spoilage shipped domestically (% of value of the<br>products|0.000|-0.000|
||(0.003)|(0.004)|
|Log of age of firm|0.015|0.009|
||(0.018)|(0.018)|
|Foreign ownership Y/N|0.109**|0.106*|
||(0.052)|(0.056)|
|Exports 10% or more of sales Y/N|0.103***|0.122***|
||(0.038)|(0.041)|
|Security costs (% of sales)|-0.011***|-0.011**|
||(0.004)|(0.004)|
|Establishment competes against unregistered or informal firms Y/N|-0.052|-0.051|
||(0.032)|(0.034)|
|No. of years that firm was unregistered at start|-0.006**|-0.007**|
||(0.003)|(0.003)|
|Formal training Y/N|0.049|0.046|
||(0.031)|(0.033)|
|Constant|7.678***|4.015***|
||(0.398)|(0.150)|
|Region (within country) Fixed Effects|Yes|Yes|
|Sector (2 digit) Fixed Effects|Yes|Yes|
|Number of observations|16,847|15,469|

Note: Survey weights have been applied to all regressions, using Stata’s svy prefix. Linearized Taylor standard errors that account for survey stratification are indicated in parentheses. *** p<0.01, ** p<0.05, * p<0.1

# **Table A4: Robustness Checks – Stochastic Frontier Analysis**

|**Log of Sales**|||
|---|---|---|
||**Frontier**<br>**(1)**|**Usigma**<br>**(2)**|
|Log of labor (lnL)|0.874***||
||(0.035)||
|Log of capital (replacement value) (lnK)|0.037***||
||(0.008)||

35
**Table 2. Summary statistics for different fossil fuel prices in US $ cents per liter**<sup>**9**</sup>

||Diesel||Premium|gasoline|Regular|gasoline|Kerosen|e|
|---|---|---|---|---|---|---|---|---|
||n|Mean<br>(sd)|n|Mean<br>(sd)|n|Mean<br>(sd)|n|Mean<br>(sd)|
|All countries|1899|71.25<br>(43.03)|1941|85.55<br>(44.75)|691|65.80<br>(35.87)|460|52.06<br>(31.05)|
|OECD<br>high|260|86.95|262|124.13|171|86.95|0|-|
|income<br>countries||(40.08)||(41.26)||(39.41)|||
|Non<br>OECD|180|74.05|187|89.81|70|87.59|25|14.66|
|high<br>income<br>countries||(55.77)||(57.70)||(32.38)||(6.62)|
|Upper middle|329|68.88|348|80.54|180|57.90|64|45.56|
|income<br>countries||(42.32)||(42.79)||(34.73)||(24.64)|
|Lower middle|558|56.93|567|72.38|239|49.86|148|49.73|
|income<br>countries||(35.03)||(36.12)||(23.97)||(34.69)|
|Low<br>income|572|68.96|577|82.61|31|68.71|223|59.67|
|countries||(37.37)||(40.45)||(23.79)||(28.06)|
|Oil exporters|452|45.25<br>(39.32)|460|56.60<br>(41.45)|170|51.04<br>(35.58)|149|33.31<br>(24.51)|

- _World Bank country classification based on 2010_

- _n = number of (country-year) observations; sd = standard deviation;_

_Source: Deutsche Gesellschaft für technische Zusammenarbeit (GTZ) for diesel-, premium gasoline- and kerosene prices. Regular gasoline prices originate from International Energy Agency._ The data on fuel prices were merged with data on independent socio-economic variables from the World Bank, IMF and United Nations, governance indicators from the World Bank Governance Indicators (WGI)<sup>10</sup> and with institutional and political variables originating from the 2010 release of the _Database on Political Institutions_ (DPI).<sup>11</sup> The resulting panel dataset consists of an unbalanced panel of 201 countries for the period 1991 – 2009.

# **5. Fossil Fuel Energy Subsidies, Public Good Provision and Governance Institutions: Empirics**

## _5.1 Fossil fuel energy subsidies and public good provision_

In order to get a notion of what the data can tell us on the relationship between the provision of public goods by the central government and fossil fuel subsidies we first averaged the panel data over time. For the fossil fuel variables the mean for the period 2006 – 2010 was

> 9 In Table 1, means are arithmetic across countries in each group; not weighted by country-specific consumption levels.

> 10 Kaufman et.al, 2006.

> 11 Keefer, 2010; Beck _et.al_ , 2000. These are objective indicators of political institutions.

14