rafmacalaba/datause-encoder-data 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_ .

14
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Figure 4: Farm Size-Efficiency, Northwestern Free State, 1979/80 <!-- Start of picture text --> 100<br>x TFP Index<br>X 80 - * * - Poly. (TFP Index)<br>60<br>40~~~~~~~~~~~~~~~~~<br>U-<br>o 20<br>0<br>0 500 1000 1500 2000 2500<br>Quality Adjusted Farm Size (ha)<br><!-- End of picture text --> Using market prices to measure productivity assesses differences in private efficiency, while the use of social opportunity costs as a measure eliminates the impact of distortion and measures differences in social efficiency. Few studies, none of them in South Africa, has made this distinction in the analysis of the farm size-efficiency relationship. During the period under consideration, the price of capital was distorted by several factors, including tax benefits and interest rate subsidies. This contributed, amongst other things, to over-capitalization of specifically larger farms (see table 7). On the other hand, output prices were also distorted due to protection and market price support.<sup>20</sup> Most of these privileges went to relatively large farms. Accounting for these distortions is thus important when looking at farm structure and production relations from a social point of view.

Social efficiency estimates were calculated for four of the data sets analyzed above, namely Eastern Free State (1988/89) and Ruens (1987/88), as well as Western Transvaal (1981/82) and Eastern Free State (1979/80). These four surveys respectively represent those with the two largest differences and two smallest differences in average TFP between small and large farms in table 7. Alternatively, they can also be regarded as representative of the beginning and the end of the decade of the 1980s -- thus prereform and just after the first reforms started taking effect. They are also representative of all the areas in the analysis. Social opportunity costs for capital, labor, variable farm inputs and farm outputs (both crops 20 See Van Zyl and Groenewald (1988) and World Bank (1994) for a synopsis of these policies and their effects.

29
To see this, notice that, for given intermediation costs, a sufficiently low international price can make the Profit Maximization and the Zero Profit loci not to cross in the positive orthant. Similarly, even if the international price is high, prohibitive marketing costs can lead to an autarky equilibrium with farmers specialized in low-return food crops. In this situation, high marketing costs prevent farmers from realizing the gains from exports.

# **3 The Uganda National Household Survey**

To test the relationship between marketing costs, export cropping, and poverty, we work with the 1999/2000 Uganda National Household Survey (UNHS). The data are a large-scale household survey conducted by the Uganda Bureau of Statistics that covers the entire country, rural and urban areas. The sampling design is a stratified two-stage sampling. The first stage units are the Enumerator Areas of the 1991 Population Census and the second stage units are the households.

There are two sets of data in the UNHS: household data and community data. From the household data we construct measures of poverty and export participation at the household level. With the community data, we construct a measure of marketing costs at the district level.

The household-level data include socio-economic and enterprise modules. There are modules related to household characteristics (household composition, demographics), activity status, health, education, and housing amenities. There are also questions on consumption expenditure and sources of income (farm and non-farm activities, employment, remittances), and a crop module with questions on land allocation, production, sales, home-consumption, etc. Samples statistics are reported in Table 1.

To determine the poverty status of each household, we use the expenditure module and compare household expenditures with poverty lines. We use the head count as a measure of poverty whereby a household with a per capita expenditure below the poverty line is considered poor. Average per capita expenditure in rural Uganda was 16.48 U.S. dollars per month (23,022 Uganda Shillings). Rural poverty lines for each province were constructed by 9
**Figure 3: Evolution of BREGS wages and days worked over the entire survey period**

<!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>01jul2008 01jan2009 01jul2009 01jan2010 01jul2010<br>survey<br>number of BREGS days reported<br>BREGS stipulated wage rate<br>reported BREGS wage rate (female)<br>reported BREGS wage rate (male)<br>30<br>15<br>days(millions) 10 rupees per day<br>5<br>0<br><!-- End of picture text --> _Note_ : Based on individual questionnaire with recall over last year. Wage data reported are household-weighted wages owed to the worker. _Source:_ Authors’ estimates based on BREGS Survey.

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3, 4, 5, 6 and 27.

# **4. Empirical Strategy and Findings**

To provide the reader with a better understanding of the empirical strategy we adopt to evaluate the performance of the RDD, Table 2 provides a schematic decomposition of the PROGRESA evaluation sample and the various alternatives available for selecting comparison groups for an evaluation of the impact of the program. Within any survey round before ( _t_ = 0) or after the start of the program ( _t_ = 1,2,3...), the total population surveyed can be divided into 4 different groups depending on whether an individual child or adult belongs in a household classified as eligible to receive PROGRESA benefits ( _B_ =1 for eligible households and _B_ =0 for non-eligible households) and according to whether the household that the individual belongs to resides in a locality where PROGRESA is in operation (treatment locality or _T_ =1) or not (control locality or _T_ =0).

A social experiment, randomly assigning individuals or communities into treatment and control groups, solves the evaluation problem by using information from individuals or households in the control group to construct an estimate of what participants would have experienced had they not participated in the program (i.e. individuals from group B or D if needed).<sup>14</sup> Non-experimental and quasi-experimental estimators on the other hand, assuming that the program has already covered all of the targeted localities, are constrained to estimating program impact based on comparisons of households or individuals between groups and A and C. Specifically, the RDD estimator evaluates impact using children from households who are eligible for PROGRESA benefits (i.e. with household discriminant scores just below the 14 In fact all of the evaluation of the PROGRESA program has relied exclusively on comparison 12
# WPS4051

## THE FISCAL FRAMEWORK AND URBAN INFRASTRUCTURE FINANCE IN CHINA

By Ming Su and Quanhou Zhao<sup>1</sup> World Bank Policy Research Working Paper 4051, November 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 The Research Institute for Fiscal Science, MOF. P.R.China

1
Table 3 shows the estimates from OLS, Poisson, and Negative Binomial regressions. Again, the tests of over-dispersion (not reported) significantly rejected the null of no over-dispersion with a p-value of zero. The results suggest that there is no robust evidence of substitution effects in third markets. In fact, of the 48 estimates, only 3 are negative and significant, and none maintain their signs across the three estimators. Interestingly, the estimated elasticities of substitution between Chinese exports and LAC exports to third markets (first row in italics and bold under each LAC-country group heading) are all positive except the OLS estimate for the Caribbean. The latter changes sign with the Poisson estimator.

Table 4 contains the estimated elasticities for India. There are 7 statistically significant (at the 10% level) and negative estimates, but none of these are robust across the three estimators. If we focus on the signs of the estimates only, there are two sets of elasticities that are consistently negative. These are associated with Central America and Mexico. One concerns Indian imports from third countries, the other concerns Indian exports to Central America and Mexico. In contrast, all estimates of the effects of Indian exports to third markets on LAC exports are positive, but with one exception, namely the OLS estimate for the Caribbean. The latter becomes positive with the Poisson and Negative Binomial estimators.

Overall, the estimates of the effects of China and India’s trade on LAC exports to third markets show little evidence of strong substitution effects between the Asian economies’ growing presence in world markets and LAC exports to third markets. Nonetheless, we must be careful as not to interpret the estimated elasticities as evidence of causal effects, because omitted variables may be affecting these correlations. For example, our estimations do not control for bilateral terms of trade. Also, although we do include exporter and importer dummies, we do not control for any trade-policy changes that might have affected bilateral and global trade flows during any year in the period 20002004. Furthermore, exports to third markets by LAC countries could be causing increases in exports from LAC to China or India, rather than the reverse. Still, at first sight, there is 13
information campaigns and intensity of advertisement about the program<sup>6</sup> , as well as regional variations in youth unemployment and perceived employment opportunities.<sup>7</sup> Baseline data for the impact evaluation are obtained from two sources. An application form was collected in November and December 2009. Additional information (particularly on individual entrepreneurship traits) was collected through a phone survey in January and February 2010.

The baseline survey suggests that the intervention responded to a strong demand from students and that applicants had high expectations for their participation in the program. 85 percent of applicants already had a project idea at the time of application. Students had various reasons for applying: 72 percent of applicants listed their willingness to realize one of their project ideas as the main reason; 56 percent of students indicated that they applied to have more flexibility; 30 percent to gain experience to find a job; 29 percent due to the lack of salaried jobs; and 12 percent because of family tradition. Applicants’ expectations were high: 88 percent of applicants expected that the intervention would facilitate their insertion in the labor market, and 89 percent expected to have higher earnings thanks to the intervention.

The impact evaluation relies on randomized assignment among applicants to the entrepreneurship track to estimate the causal impact of the program. Given the oversubscription to the program, half of the applicants were randomly assigned to the entrepreneurship track and the other half were assigned to continue with the standard curriculum. Randomized assignment was conducted at the project level, stratified by gender and by the subject students were reading for (divided into fourteen groups<sup>8</sup> ). 757 projects were assigned to the treatment group (658 individual projects, and 99 projects in pair). 742 projects were assigned to the comparison group (652 individual projects; 97 projects in pairs).

> 6 University professors played an instrumental role in informing students about the program. 84 percent of all applicants heard about the program through professors, 39 percent from posters, and 17 percent from other students, friends, and relatives.

> 7 Application to the entrepreneurship track was particularly high in regions with the highest unemployment. For instance, Gafsa exhibited the highest youth unemployment rate (close to 50 percent) and also had the largest application rate to the entrepreneurship track (28.7 percent of all eligible students in _licence appliquée_ ). Similarly, Jendouba has the second highest unemployment rate (35 percent) and a participation rate to the entrepreneurship track of 13.9 percent.

> 8 The fourteen groups of subjects were: Economics and Finance; Accounting; Business Administration; Marketing; Humanities; Languages; Science; Technical; Telecommunications; Civil Engineering; Informatics; Sports and Tourism; Food/Agriculture, and Other.

10
The Mexican CVD workbook contains the standard data items described above as well as a few additional data items described below.

**Table 5.4.1 : Additional Mexican Data Included in CVD-MEX-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In CVD-**|**MEX-Master :**||
|**U.**|**P_CVD_DUTY**|Preliminary “all others” rate|
|**V.**|**F_CVD_DUTY**|Final “all others” rate|
|**W.**|**NOTES**|Comments|

There are a few notable omissions in the current data set. In particular, the P_CVD_DATE and F_CVD_DATE series are missing, since the publication dates of decisions were not verifiable. Normally duties would come into effect the day after publication. We believe that the dates listed are the publication dates, but this would require further checking against the _Diario_ . Finally, the “SUB” and “INJ” columns are redundant, since Mexico has a one-track injury/subsidy investigation process.

# **5.5 United States (USA)**

Countervailing duty cases in the United States are investigated by the International Trade Administration (ITA) and the International Trade Commission (ITC). The ITC handles the injury determination, while the ITA determines the existence of countervailable subsidies. However, a number of cases prior to 1995 did not require ITC rulings because the target country was not a signatory to the Tokyo Round’s plurilateral Subsidy Agreement. Many of these cases are identified by ITC numbers in the form “COUNTRY-#” (such as COLOMBIA-1), which were created during data collection. The data was compiled from the _Federal Register_ .

The United States’ CVD workbook contains the standard data items described above as well as a few additional data items described below.

43
**Table 2:  Percentage of children two standard deviation below the median (z-scores:  children between 6 and 60 months old)**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|**Baseline**|**Weight-for-age**|**Weight-for-Height**|**Height-for-Age**|
|School meals|52.6|29.5|59.9|
|Take Home Rations|56.2|32.3|60.0|
|Control|55.3|31.6|61.7|
|**Follow up**||||
|School meals|42.8|20.6|58.9|
|Take Home Rations|42.8|19.5|53.3|
|Control|42.7|23.8|55.6|

**Table 3:  Percentage of children two standard deviation below the median in rural Burkina Faso, 2003 (z-scores: children between 0 and 60 months old)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Region**|Height-for-age|Weight-for-height|Weight-for-a|ge N|
|Ouagadougou (area)<br>|16.4<br>|12.4<br>|17.9<br>|486<br>|
|North|41.7<br>|19.4<br>|41.8<br>|1587<br>|
|East|47.2|18.7|38.4|2147|
|West|35.7|19.3|37.6|2328|
|Central/South|35.1|19.2|38.4|1722|
|Total Rural|41.4|19.7|40.3|7166|

**Source:** ORC Macro, 2008.  MEASURE DHS.

STATcompiler. http://www.measuredhs.com, August 1 2008.

26
1 The period since the early 1960s has been one of rapid urbanization in developing countries (World Bank, 2009; United Nations, 2018). This process has been linked to a virtuous circle between economic development and urbanization (Henderson, 2010; Duranton, 2015). However, urbanization can also proceed without growth (Bairoch, 1988; Fay and Opal, 2000; Glaeser, 2014; Jedwab and Vollrath, 2015; Castells-Quintana, 2017).

In the macro-development literature, urbanization is often a by-product of structural change. As countries develop, people move out of agriculture and engage in urbanbased manufacturing and service activities (Herrendorf et al., 2014). Structural change occurs due to _Green Revolutions_ , which increase food productivity and push labor out of agriculture (Matsuyama, 1992; Gollin, Parente and Rogerson, 2002; Restuccia, Yang and Zhu, 2008; Yang and Zhu, 2013; Lagakos and Waugh, 2013; Gollin, Hansen and Wingender, 2018). In addition, _Industrial Revolutions_ or _Service Revolutions_ increase manufacturing or service productivity and pull labor out of agriculture (Hansen and Prescott, 2002; Lucas, 2004; Alvarez-Cuadrado and Poschke, 2011).<sup>1</sup> Gollin et al. (2016) (henceforth GJV16) show that only in some developing countries rapid urbanization has been accompanied by industrialization, following the historical patterns observed in richer nations. Manufacturing and tradable services agglomerated in _production cities_ whose growth was driven by the countries’ increased _production_ capacity. In others, the spending of resource rents on urban goods and services led to _consumption cities_ whose growth was driven by increased _consumption_ capacity. Since manufactured and tradable services – _urban tradables_ – are often non- goods imported, tradable services – _urban non-tradables_ – dominate their sectoral composition.

This new evidence on the of cities. Whether paper provides “origin” consumption a country has production cities or consumption cities could then matter for economic development. Productivity in manufacturing and non-traditional services varies little across countries, and productivity catch-up in these sectors may explain some of the large gains in aggregate productivity observed across countries (Duarte and Restuccia, 2010, 2020). Hence, low productivity in urban non-tradables in developing economies, and thus the possible lack of catch-up in this sector, might contribute to explaining the lack of

> 1Other mechanisms include quality-of-life amenities (Jedwab and Vollrath, 2019; Gollin, Kirchberger and Lagakos, 2021), demographic growth (Jedwab et al., 2017; Castells-Quintana and Wenban-Smith, 2020; Jedwab et al., 2021a), urban-biased policies (Lipton, 1977; Ades and Glaeser, 1995; Davis and Henderson, 2003; Castells-Quintana, 2017), trade (Glaeser, 2014; Venables, 2017), technology (Jedwab et al., 2021c), and disasters (Barrios et al., 2006; Henderson et al., 2017; Jedwab et al., 2021b; Castells-Quintana et al., 2021).
The Worldwide Governance Indicators and Tautology: Causally Related or Common Cause?

# **Introduction**

In recent years aggregate indexes of the quality of governance, covering large samples of countries, have become enormously popular in comparative political analysis. However, few studies have examined the validity or reliability of these indexes (Thomas, 2007). This study attempts to partially fill this gap. We find that these indexes, purportedly measuring distinct concepts such as control of corruption and rule of law, all appear to be measuring essentially the same broad concept, rather than successfully distinguishing among various aspects of the quality of governance.

Beginning in 1995, Transparency International has annually produced a corruption index covering a large sample of countries (Lambsdorff, 1998). The index does not represent TI’s own assessments, but is simply aggregated from numerous other sources, including expert opinions and surveys of firms and households.

Several researchers at the World Bank adopted this basic approach of the TI index, but attempted to improve on it in several respects (Kaufmann, Kraay and ZoidoLobaton, 1999) in their “Worldwide Governance Indicators” (WGI) project. Most notably, they have exploited the data sources more fully to produce six measurement indexes: in addition to Control of Corruption (CC), these include Voice and Accountability (VA), Rule of Law (RL), Government Effectiveness (GE), Political Stability (PS) and Regulatory Quality (RQ).

There are various ways of reducing the vast content of the numerous available data on quality of governance into a smaller number of aggregate indexes. The six WGI indexes were selected to measure separate, but related, concepts regarding the quality of governance. The content of each of them was determined purely on conceptual grounds, and not by empirical means such as factor analysis.

Yet the task of establishing measurement reliability and validity is not logically any different from that of testing theories about cause and effect. Theories of cause and effect should not only be logically persuasive; they should also be empirically convincing. It is conventional to evaluate the defensibility of a logically persuasive 2
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