rafmacalaba/data-use-mentions-extended huggingface dataset:
where **Xi** is a set of relevant exogenous variables whose effects are described by parameter _γ_ , _β_ 0 is the intercept, and _εi_ is the idiosyncratic error term. The parameter of interest is _β_ 1, which captures the impact of a change in congestion _Ci_ on the outcome of interest _Yi_ .

The principal challenge in estimating equation 1 is the potential endogeneity of traffic conditions. Examples of such endogeneity issues may arise from the non-random supply of transport services or the spatial sorting of users of the transport system. For instance, although congestion may affect economic growth there can be a reverse causality whereby growth induces greater demand for transportation. Traditionally, the emeconomics literature has relied on instrumental variables or natural pirical approaches or quasi-natural experiments to address these potential endogeneity issues (see, e.g., Hymel, 2009; Sweet, 2014). By facilitating access to new data sources, improving the measurement of relevant variables (including those that were previously unobservable), big data has the potential to attenuate some of the endogeneity biases through reduced measurement errors and better controls (Einav and Levin, 2014b; Glaeser et al., 2018), but does not solve all endogeneity issues.

For economists, the valued-added of big data also lies in the ability to explore a wider range of outcomes usually absent in traditional data sets. The often near-universal of data also new avenues to examine the and coverage big opens up heterogeneous distributional effects of transport conditions and policies.

# - B. Application: The Congestion Impact of the Covid 19 Lockdown

As an illustration, we examine traffic data constructed from publicly available information on Google Maps to estimate the congestion effect of mobility restriction policies during the Sars-Cov-2 pandemic.<sup>4</sup> We rely on the Bayesian Structural Time Series (BSTS)

> 4 Mobility restriction policies are part of so-called non-pharmaceutical interventions (NPI) in response to the Covid-19 pandemic. They have been implemented at various geographical levels globally and are commonly referred to as _stay-at-home orders_ or _lockdowns_ .
*|0.410***|
|||[0.031]|[0.031]|[0.031]||[0.032]|[0.032]|[0.032]|
|Married|||0.154***|0.155***|||-0.047***|-0.048***|
||||[0.013]|[0.013]|||[0.015]|[0.015]|
|Rural||||0.189||||0.224|
|||||[0.222]||||[0.196]|
|Observations|23,107|21,900|21,886|21,886|23,383|22,526|22,514|22,514|
|R-squared|0.787|0.823|0.828|0.828|0.720|0.742|0.743|0.743|
|Individual fixed effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Year fixed effect|YES|YES|YES|YES|YES|YES|YES|YES|
|Region dummies|NO|NO|NO|YES|NO|NO|NO|YES|
|Dependent variable mean|0.799|0.797|0.797|0.797|0.223|0.225|0.224|0.224|

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression relying on panel data estimation. The analysis is restricted to working age individuals in the years 2012 and 2018.
through regular risk based audits and use of third party information (e.g. from banks, customs, land registration records) to prevent tax evasion.

Broadening the tax base and strengthening public administration will improve revenue collection. This will create space for reducing corporate income tax rates (that are currently higher than in other developing regions) without sacrificing essential public spending. Lower rates and more predictable tax policy and administration will increase investment attractiveness of South Asian countries. Tax reforms will have a stronger impact on business and investment growth if implemented as part of a wider reform package to improve the regulatory environment.

26
**_<u>________________________________________________________</u>_**

# _<u>World Economic Forum: Global Competitiveness Report (GCR)</u>_

The World Economic Forum is an independent organization which identifies strategic issues and provides a platform for decision-makers to effect constructive change. The Forum facilitates dialogue between corporate, political, intellectual and other leaders on matters of global, regional, corporate and industry importance.

The fundamental objective of the _Global Competitiveness Report_ (GCR) is to evaluate the economic competitiveness of a large sample of countries. Traditionally, the GCR has focused on two complementary approaches to analyzing competitiveness. The first, called the _Growth Competitiveness Index (GCI)_ , was developed by Jeffrey D. Sachs of Columbia University and John W. McArthur of The Earth Institute and was presented in _The Global Competitiveness Report 2001–2002_ .The second index, now labeled the _Business Competitiveness Index (BCI)_ , was developed by Michael Porter of Harvard University and was first introduced in _The Global Competitiveness Report 2000_ .

The two indexes combine available hard data and data from the Executive Opinion Survey (Survey) conducted annually by the World Economic Forum. The Survey is conducted in the first half of every year. Input is contributed exclusively by leading business executives and entrepreneurs whose current perceptions of the business environment in which they work are captured in their responses to a comprehensive and scientifically constructed questionnaire.

GCR includes an index based on quality perception for rating railroad services, port facilities and air transport services (Ranking from 1 to 7, with 1=underdeveloped, 7=developed as the world’s best). GCI 2004-2005 covers a total of 104 countries and BCI covers a total of 103 countries.

# **2.1.2 Governance Indicators**

# _The World Bank_ _<u>Governance Indicators : 1996-2004</u>_

This dataset contains composite indicators of six dimensions of governance (Voice and Accountability, Political Stability/Lack of Violence, Government Effectiveness, Regulatory Framework
Policy Research Working Paper 10208

# **Abstract**

This paper introduces and provides a descriptive analysis of data from more than 15,000 detailed interviews of representative samples of informal businesses operating in 24 cities across seven countries, namely, India, Iraq, the Lao People’s Democratic Republic, Mozambique, Somalia, Zambia, and Zimbabwe. The paper is a companion paper to a study that presents the methodological underpinnings of the informal business data collection. It is an innovative application of area-based adaptive cluster sampling, rendering a representative sample of these businesses. The paper presents salient descriptive results of the data to motivate further research. The World Bank’s Enterprise Analysis unit started collecting data from the informal sector using the adaptive cluster sampling method in 2017. The combined and standardized data show that informal businesses are small, young, mostly started out of necessity rather than as an opportunity for growth, largely detached from the rest of the economy, and with meager earnings. Few of the informal businesses have ever considered registering formally, with the majority perceiving no benefits from doing so.

This paper is a product of the Global Indicators Group, Development Economics. 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 aaberra@worldbank.org, gayanaaga@worldbank.org, fjolevski@worldbank.org, and nkaralashvili@worldbank.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
|2012-2016|TANESCO (Public)|
|Uganda|1987|2012-2016|UMEME (Private)|
|Ukraine<sup>d</sup>|1994|2012-2017|Dniprooblenergo (Private),<br>Khmelnytskoblenergo (Public)|
|Vietnam|1993|2010-2016|NPC (Public)|

_Notes:_<sup>a</sup> Approximated by data for Edesur as full-country data was not available.<sup>b</sup> Indicates majority ownership. cApproximated by sector-wide data. dC1-C3 cost recovery is approximated by A1-A3 as systematic information on government support to the utilities was not available. _Source:_ World Bank staff.

10
*|0.294||1.170|**|
|Capital controls||-0.633|**|0.008||-0.481||-0.462||
|Initial level of income, share of G7||-9.681|**|-11.277|***|-13.663|**|-20.481|***|
|Vulnerability index, VIX||0.161|***|-0.204|***|-0.117|**|-0.305|***|
|Oil trade balance as share of exports and imports||0.474|***|0.349|***|0.119|**|0.090||
|Constant||-0.536||0.379||5.328|*|0.497||
|Pseudo R-squared||0.396||||0.346||||
|Sources: IMF’s World Economic Outlook database and auth<br> <br>Number of observations|ors’ calculatio<br>|ns.<br> <br>178||||<br>97||||

1/ Robust (and clustered) standard errors. *, **, and *** denotes significance at 10, 5, and 1 percent. All regressors, except the VIX, are entered lagged. 2/ Difference between fiscal balance and cyclically-adjusted fiscal balance; in percent of GDP.

The estimation results are best discussed with a few examples. In the estimation that uses the two year average data, trade openness enters with a negative (but not statistically significant) sign in the lower growth and lower vulnerability (L) cluster. Thus, trade openness reduces the likelihood of being in this cluster relative to the
of this 85% lived in cities with more than 100,000 residents (UN 2011). Furthermore, the large, urban municipalities we study in Brazil are increasingly representative of cities in other developing countries due to continued increases in global urbanization rates (UN 2011). We acknowledge the distinct possibility the 253 most populous, urbanized municipalities are not representative of the remaining 5,317 Brazilian municipalities. However, these omitted municipalities are physically large and sparsely populated in most cases. They do not represent the average Brazilian’s experience and are therefore of less interest for our purposes.

_Administrative Capacity: Permanent administrative employees per capita, decentralized management score (IGD)_ The Bolsa Família program is administered at the municipal level and management quality varies considerably. We use operational data from the Ministry of Social Development (MDS), called the Index of Decentralized Management (IGD), to capture this variation. The MDS rates each municipality on how well it administers program elements, such as updates to the Unified Registry and tracking of beneficiaries’ compliance with conditionality requirements. The MDS offers greater funding to cities that perform better on the IGD. Quality of local management should reflect a combination of local political commitment as well as existing municipal state capacity. Quality of local management is likely to influence local outcomes independently from the broad Bolsa Família coverage, which is often high in municipalities where management is poor, such as settings with dense poverty. This variable is continuous from 0 to 1 and each municipality receives an annual score; better management results in scores closer to 1 and worse management closer to zero. The mean score is .76 and the standard deviation is .15.

_Other Sources of Revenue: Capital Revenue, Federal Budget Transfers, Gross Local Product per Capita_ We collect data on sources of revenue beyond local tax collection as well as federal transfers, the primary source of municipal budgets in Brazil. Revenue collected from non-tax sources and federal transfers could influence local tax collection in different directions. On the one hand, access to
presented in Table A2.

# _4.5 Control variables_

In order to isolate the predictive power of the variables describing urban form and to reduce omittedvariable bias, we include in the model a number of control variables including city size, locational variables, natural and urban amenities, as well as country fixed effects. A number of different data sources are used to compute control variables. Population data in gridded format for 2010 were 3 http://www.openstreetmap.org/copyright
# **6.6 Effectiveness and Cost-Effectiveness**

Our effectiveness and cost-effectiveness the of analyses compare community impact _T_ 2 (aspirational videos and m-Learning add-on) with other interventions aimed at imWe use the treatment effects of _T_ 2 as proving learning outcomes in developing countries. it was the intervention that had a significant impact on learning outcomes. Using the metric proposed by Angrist et al. (2020b), we calculated learning-adjusted years of schooling (LAYS). This metric combines educational access and quality; and it allows the comparison across countries using an absolute benchmark. To calculate the effectiveness and cost-effectiveness for this study, we took the following two steps.

First, we converted our aggregate measures of literacy and numeracy skills in LAYS. To facilitate the comparison of our effectiveness and cost-effectiveness results using the same list of educational interventions, this first effectiveness list already uses the 72 interventions that included cost data in Angrist et al. (2020b).<sup>17</sup> Second, we use the cost of our combined intervention to calculate a cost-effectiveness measure that we adjust to country-specific price levels. For this step, we used nominal cost data which is subsequently transformed into 2020 purchasing power parity (PPP) adjusted prices, following JPAL Conducting Cost-effectiveness Analysis guidelines.<sup>18</sup> We then calculated the implied LAYS per US$100 (nominal) and PPP adjusted LAYS per US$100. Finally, we use the latter to compare the cost-effectiveness of our intervention with 72 other interventions, including edtech interventions.

_σ_ Following the above steps, the treatment effects of this study for the target child (0.46 and 0.63 _σ_ for literacy and numeracy, respectively) correspond to 0.57 and 0.79 LAYS, respectively. These metrics fall short of an artificial high-performance benchmark of 0.80, chosen by Angrist et al. (2020b) due to its stability and non-political properties. In terms of relative effectiveness, _T_ 2 ranks 5 _th_ relative to the 72
The results of the analysis of efficiency with monetary inputs are shown in Table 5. These results reveal the sensitivity of the efficiency measures with respect to sampling variation. The biascorrected efficiency (BC) reveals that differences in measurement efficiency are of a different magnitude than when the original efficiency scores (N) are considered. For all of the manufacturing sectors, the efficiency declines slightly.

Table 5 Descriptive statistics of the output and inputs used by sector and country <u>(year 2005).</u>

|**Variables/industry**|**Beverages**|**Food**|**Garments**|**Metals and**<br>**machinery**|
|---|---|---|---|---|
|**Mean**<br>|||||
|Sales (Y)|2476.644|895.9026|592.492|1030.763|
|Materials Cost (X1)|1282.234|453.563|242.7717|497.1371|
|Energy Cost (X2)|136.3903|50.45932|35.12418|53.18183|
|Personal Cost(X3)|209.1959|80.92757|151.6997|149.9907|
|**Median**|||||
|Sales (Y)|196.418|262.3651|118.1627|82.81187|
|Materials Cost (X1)|85.13541|116.6796|46.78612|37.21292|
|Energy Cost (X2)|9.772637|12.94952|6.90287|5.241257|
|Personal Cost(X3)|22.49096|23.00957|27.17094|14.67552|
|**Standard deviation**|||||
|Sales (Y
of cognitive skills in Georgia and Kenya.

Findings from the quantile decompositions indicate that the wage gap follows a “sticky floor” pattern in the Latin American countries as well as Kenya and Vietnam but follows a “glass ceiling” effect in the ECA countries. Previous research has also found evidence for the glass ceiling effect in transitional economies, and this pattern has been observed in Vietnam (Pham and Reilly, 2007). Further, our results indicate that schooling matters, across countries, in explaining differences in earnings between men and women. In some countries, schooling explains a larger share of the wage gap at the top end of the wage distribution (Bolivia and Vietnam) while in others education and skills are found to matter more at the lower end of the wage distribution (Kenya and Armenia).

30
Policy Research Working Paper 5485

# **Abstract**

Firms in developing countries face numerous and serious constraints on their growth, ranging from corruption to lack of infrastructure to inability to access finance. Countries lack the resources to remove all the constraints at once and so would be better off removing the most binding one first.

This paper uses data from World Bank Enterprise Surveys in 2006 –10 to identify the most binding constraints on firm operations in developing countries. While each country faces a different set of constraints, these constraints also vary by firm characteristics, especially firm size. Across all countries, access to finance is among the most binding constraints; other obstacles appear to matter much less. This result is robust for all regions.

Smaller firms must rely more on their own funds to invest and would grow significantly faster if they had greater access to external funds. As a result, a low level of financial development skews the firm size distribution by increasing the relative share of small firms. The results suggest that financing constraints play a significant part in explaining the “missing middle”—the failure of small firms in developing countries to grow into medium-size or large firms.

This paper—a product of the Operations and Strategy Unit, Development Economics—is part of a larger effort in the department to develop new practical insights as to how African countries can significantly improve their production of light manufacturing products. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at HDinh@worldbank.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
# **References**

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, firm-level data on the business environment, some of these studies show the importance of finance, corruption, and property rights (Batra, Kaufmann, and Stone 2003; Ayyagari, Demirgüç-Kunt, and Maksimovic 2006). Others examine the relationship between the business environment and firm growth in individual countries or a small group (Dollar, HallwardDriemeier, and Mengistae 2005 in Bangladesh, China, India, and Pakistan; Fisman and Svensson 2007 and Reinikka and Svensson 2002 in Uganda; Bigsten and Söderbom 2006 in Africa).

Other studies also assess the effect of different dimensions of the business environment on firm growth. Some focus on the importance of access to finance for firm development and growth using subjective data (Rajan and Zingales 1998; Galindo and Micco 2007). Others investigate the impact of employment regulations on firm creation and growth (Djankov and others 2002; Klapper, Laeven, and Rajan 2004).

Several papers have emphasized the importance of financing obstacles. Using firm-level data, Demirgüç-Kunt and Maksimovic (1998) provide evidence on the importance of the financial system and legal enforcement on firm growth. Rajan and Zingales (1998) present supporting evidence on 3
Lastly, it is straightforward in a Bayesian context to express Θ _i_ as a function of several predictor variables, so-called hierarchical co-variates. We denote the vector of these predictors as **_X_**<sup>_H_</sup> _i_<sup>,whichcan</sup> _′ H ′_ be given parameter weights in a matrix _φ_ , giving Θ _i_ = _f_ ( **_X_** _i_<sup>_φ_</sup> ) (Kubinec, 2019). This allows the measurement model to take advantage of a wide array of additional co-variates that are available in our data for the purposes of alleviating sparsity in the data. Because our survey has only one set of observations per firm, and the number of indicators is necessarily limited, estimating a separate intercept for each firm would result in unstable estimates highly dependent on the prior. By parameterizing Θ _i_ with additional survey data— **_X_**<sup>_H_</sup> _i_<sup>can include, for instance, firm perceptions of state-business relations—</sup> we can obtain stable predicted influence scores for an individual firm _i_ with intercepts that vary by country and as such are robustly identified in the posterior. This underlying set of predictors also has the attractive feature of allowing for out-of-sample prediction from surveys without explicit political connections questions, allowing us to expand the index beyond the 41 economies in the original survey data. In the appendix we include a plot of split- _R_<sup>ˆ</sup> values from a model fit with three independent Markov Chain Monte Carlo (MCMC) chains with the Hamiltonian Monte Carlo sampler Stan (Carpenter et al., 2017). The plot shows excellent convergence of the dozens of parameters in the model. We also include a plot of residuals for the two continuous items, government ownership and employee size. This plot shows that the model’s assumptions appear to be approximated by the distributions we assigned to the items.

The model