rafmacalaba/data-use-mentions-extended huggingface dataset:
slightly more frequent power outages, more interruptions in phone and internet services, and higher costs for getting access to phone and internet services (see Panel D in Table 1).

With respect to _government regulation and expropriation_ , we focus on regulations on firm entry as well as expropriation activities of the government. Since comprehensive measures of entry reforms are infeasible, and the enforcement of entry regulations varies across regions, we focus on the measurable effects of entry regulation, the _degree of competition_ , which is captured by the Herfindahl–Hirschman Index in a specific sector (referred as HH-index hereafter). Relying on the annual surveys of industrial firms—the most comprehensive panel data of Chinese firms--we explore how competition has evolved and differed across regions. The degree of competition has risen substantially over time (Figure 2a), especially after the 2001 entry to the WTO. The increase in competition persisted for three years afterwards, stabilizing during 2004 to 2007. Firms in inland regions face much less competition than those in coastal regions (see Figure 2b), which feature more developed markets and denser concentration of firms.

Government expropriation of firms is captured by three variables. The first is the tax burden, perhaps the most common source of complaints from entrepreneurs worldwide. In developing economies, the informality of tax collection process, coupled with poor administration and corruption, places a disproportionate burden on those who comply, and thus distorts competition. The second variable measures extralegal fees and special assessment fees collected by local governments, which are more arbitrary and likely more harmful than taxes (Fisman and Svensson, 2007). The third variable is firms’ expenses on entertaining local cadres and cultivating public relationship (referred to as ETC),<sup>12</sup> which are shown to be mostly related to bribery and corruption and a good proxy of corruption (Cai et al., 2011).

The extent of government expropriation faced by private firms has experienced little improvement despite the rapid growth of the private sector in China (
description. If a segment is associated with multiple SIC codes, it is broken down equally so that each segment is associated with one SIC code. In the second step, we redefine segments at the two-digit SIC level and aggregate segment sales to that level.

We classify firms as single-segment if at least 90 percent of their total sales are derived from one two-digit SIC segment. Firms are classified as multi-segment if they

> **6** Although China is also an interesting case, we can locate only a handful public firms from Worldscope. The dominant corporate units in China are state-owned enterprises. The few publicly traded firms are far from representative of the census of firms in China.

6
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- Liu, Tianjia, Loretta J Mickley, Sukhwinder Singh, Meha Jain, Ruth S DeFries, and Miriam E Marlier (2020), “Crop residue burning practices across north india inferred from household survey data: bridging gaps in satellite observations.” _Atmospheric Environment: X_ , 8, 100091.

- Lohan, Shiv Kumar, HS Jat, Arvind Kumar Yadav, HS Sidhu, ML Jat, Madhu Choudhary, Jyotsna Kiran Peter, and PC Sharma (2018), “Burning issues of paddy residue management in north-west states of india.” _Renewable and Sustainable Energy Reviews_ , 81, 693–706.

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generating a simple and transparent index that focuses on the variation in the quantity and quality of education across countries, the HCI uses a single benchmark value of φ�0.08 , or 8 percent per year of school, for all countries and all levels of school. To put this value in perspective, consider the following sets of estimates from the existing literature:

- Montenegro and Patrinos (2014) estimate returns to education using household survey data from 139 countries.  They find an overall average return to an additional year of school of 10.1 percent, and disaggregated returns of 10.6 (7.2) (15.2) for primary (secondary) (tertiary). Averaging the primary and secondary returns which are most relevant for the HCI gives a value of 8.9 percent.  Jedwab and Islam (2018) update these estimates in background analysis for the 2019 World Development Report, and find an average return to school of around 8 percent as well.

- In a highly‐influential review of the development accounting literature, Caselli (2005) summarizes the empirical consensus on returns to school as 13 percent (for less than four years), 10 percent (for four to eight years) and 7 percent (for more than eight years).  This parameterization was also adopted in the Penn World Tables 9.0 estimates of human capital (Feenstra, Inklaar and Timmer (2015)).  Assuming that primary and secondary school each last �

- six years, the baseline parameterization implies a return to primary school of �0.13 � �

- � � � �0.10 �0.12 and a return to secondary school of �0.10 � �0.07 �0.08 .

- � � �

The value of φ�0.08 is deliberately chosen to be at the low end of this range. This is because the vast majority of estimates of the return to school do not also control for health, while the human capital measure
# **2 Estimates of Empty Driving**

In this section, I survey estimates of empty driving from industry reports, local and national governments, and academic studies. The settings span many countries and range from the late 1970’s to the present day. In terms of coverage, the mix of studies significantly over-weights North America and the European Union and under-weights Europe & Central Asia and the Middle East & North Africa. On the time dimension, the median study was conducted in 2009, with an overall large span of 40 years between the earliest and latest studies. As a result, controlling for the role of time will be relevant. For more details on geographic and temporal coverage, see Appendix A.

These estimates span different sampling methodologies, measure different objects, and cover different segments of the trucking market. Empirical estimates of empty trips have been generated by a variety of data sources. The dominant source is survey data, such as Colombia’s Ministry of Transportation Freight Origin-Destination Survey program as used in Gonzalez-Calderon et al. (2012a). Firm surveys begin with a census of firms in the sector, and ask a sample of firms what their overall fleet-level empty trip share is. Vehicle surveys instead use a sample of registered vehicles and ask the vehicle’s operator how often that vehicle was empty. Finally, site surveys stop a random sample of trucks as they pass a fixed site (such as a rest stop or administrative checkpoint), and interview the drivers for their empty or loaded status. These variations may result in different weights or accuracy for different segments of the market. Trip audits or diaries, such as the National Survey of Transport of Goods by Road conducted in Ireland and studied in Council (2017), offer deeper information on truck driving patterns. A sample of trucks is tracked over the course of several days, and the loaded or empty status tracked over that period. Finally, digitization of the trucking economy has generated new datasets of mobile app transactions, as studied in
where data is available, the law does not include preferential treatment clauses against foreign bidders. A domestic sourcing provision is present in 58 percent of the sample. We find that the share of countries with protectionist laws in government procurement is lower among countries that participate in major international agreements that liberalize procurement markets, the WTO GPA and the EU, and in high-income countries. We also find that this share tends to be lower in countries with institutions of better quality, as measured by the World Governance Indicators.

To further validate the national law data and investigate the variation in trade openness across countries, we estimate the relationship between import share in total spending and the protectionism index. Because the national law data were collected during 2021-22 while the trade data cover the 2000-2018 period, and in absence of an exogenous shifter for the protectionism variables, we interpret the empirical estimates as suggestive of the correlation between trade and the protectionist character of national laws. The results indicate a systematically negative correlation between import shares and the protectionism in government procurement, which is robust to controlling for the influence of other country characteristics and different definition of government procurement in the trade data. Lower import shares in government procurement are especially observed in countries where the law provides for preferential treatment of domestic bidders. These results from cross-country regressions are confirmed in estimations of gravity models for bilateral imports. In particular, we find that the “border effect” (how much the country’s spending market is local relative to international, after controlling for standard determinants of bilateral trade) increases with protectionism in government procurement, with the effect being again stronger for preferential treatment provisions.

The empirical exercise that we propose in this paper is subject to a number of limitations. In addition to the lack of a credible identification strategy that could support a causal interpretation of the estimates, there are measurement issues in protectionism in government procurement. These can explain why our estimates reveal correlations between trade openness and protectionism as observed in the law that are imprecisely estimated, especially for some components of
Corruption incorporates a broad range of actions and behaviors, where the main common factor is a negative externality. Corruption can be seen in both the public and private sectors, occurs at any institutional level, and can constitute a number of behaviors in a variety of organizational settings.<sup>4</sup> Identical policies fail in one institutional context but succeed in another (Dininio 2005). Cross-cultural studies are necessary to determine causal factors that contribute to policy success or failure, and lab experiments play an important role in identifying causal relationships.

The major benefits of experimental studies (and lab experiments in particular) are as follows. First, the experimental setting ensures that corrupt behavior can be observed. Because corruption is a clandestine activity, it is often hidden from view, making empirical data in this area particularly difficult to observe. Second, the control afforded by the lab allows institutions and context to be isolated, as in cross-cultural studies, or manipulated directly by the experimenters, so as to identify causal effects. This enables a better understanding of motivating factors for corrupt behavior, and allows the researcher to begin formulating policies that are specifically designed to reduce the incentives for engaging in such behavior. Third, lab studies facilitate testing of various anti-corruption policies and solutions in environments free of other contaminating factors (Roth 2002). The lab provides a low-cost environment where various policies can be pre-tested in order to identify those with the greatest probability of success, and the behavioral mechanisms that they employ to achieve success. Fourth, the lab facilitates replication, allowing the robustness of results to be explored. Experimental research constitutes an exciting avenue for policy making in general, and for scientific study of corruption in particular.

The most common criticism of lab studies concerns the issue of external validity. Corruption research comes from a tradition of macro-level studies with large country-level data sets, where „internal validity‟ – the causal relationships among variables – can be questioned. However, the contribution of these studies is not to demonstrate causality (though quite
COMTRADE database to obtain iceberg transport cost by taking the ratio of the importing country’s c.i.f unit values to exporting country’s f.o.b unit values for each 4-digit SITC product for many countries between 1984 and 2008. While this methodology allows for the measurement of transport costs to vary across countries, product, and time, Feenstra and Romalis (2014) reported that there is a large amount of measurement error in the unit values, and there are many instances when the c.i.f unit value is less than the f.o.b. unit value.

**Stylized Fact 13.** _Matched partner c.i.f./f.o.b. ratios from IMF and UN data are often inaccurate measures of the ad-valorem freight rates._

the for international trade recommend that countries re- Recently, official reporting guidelines port bilateral trade distinguished by five different modes of transport. Thus, the COMTRADE Plus database reports f.o.b. and c.i.f values by mode of transportation. In December 2020, UNCTAD, in collaboration with the World Bank, launched the Global Transport Cost database that has information on transport costs for 105 importing countries and 200 exporting countries. This dataset has the most extensive coverage of any public transport data so far, but it is available only for 2016. The system that has been put in place will allow for more updates and expansion in the future.

# **3.3 Direct Measures of Transportation Costs**

Several data sets contain direct measures of transport costs. We start our discussion of these data with the product-level customs data sets. The United States, New Zealand, and 11 Latin American countries report data on freight expenditures (including insurance) at 10-digit HS product level that are collected using importers’ customs declarations. These data provide a direct measurement of transport costs by mode of transportation together with trade values, quantities, and port of entry (for the US data
Venables, Hugh Wenban-Smith, three anonymous referees, and the participants at the LSE SERC junior seminar, the Urban Economics Association conference in New York and the ISI Annual Conference on Economic Growth and Development in Delhi, who provided useful comments. We are also grateful to Petia Topalova and Pingali Parthasarathy Rao for sharing some of the data and to the Tata Institute of Social Science in Mumbai for their kind hospitality. Financial support by the Chronic Poverty Research Centre and the India Observatory at LSE is gratefully acknowledged. The views expressed here do not necessarily reflect those of the OECD or the Bank of Italy. The other usual disclaimers also apply. A supplemental appendix to this article is available at http://wber.oxfordjournals.org/.

1 In fact, the actual poverty line used by Ravallion et al. (2007) is $1.08/day; to simplify, we refer to it as the $1/day poverty line.

2 On the basis of calculations from UN (2008) data, developing countries are expected to become more urban than rural in 2018, as are least developed countries in 2045.

3 This estimate is based on the authors’ calculations using UN (2008) data.

4 The migrant’s family often provides economic support (monetary or in-kind) to the migrant during his initial stay in the urban area. This support, intended to cover the fixed costs of migration, can be interpreted as an investment whose main return is the counter urban-to-rural remittance flow that is received afterward (Stark 1980). This urban-to-rural remittance flow can somewhat reduce the net resources transferred to rural areas by urban workers.

5 To the extent that rural-urban migration is concentrated among the most productive rural workers, this may counter the productivity-enhancing effect of migration. However, the incentive for highly productive agricultural workers to move from rural to urban areas is partly softened because the skills that make them productive in agriculture are not easily transferable to the urban sector.

6 In addition, an expanding city can benefit
Yano (2017) confirm that anti-corruption measures in China had a strong positive impact on R&D activity of the firms. Similarly, Srholec (2011) stated that the institutional settings within which firms operate have a significant impact on its likelihood to innovate. Some of the drivers of R&D discussed above such as industry competition, exports, and financial development depend in part depend on regulatory and other business environment elements.

Based on the discussion above we control for firm, industry and country-level variables. Unless specified otherwise, data source for the control variables is Enterprise Surveys. See Table A1 in online Appendix for a formal definition of all the variables used in the regressions.

For our baseline specification, we begin with accounting for all industry wide features such as market structure and demand, technological opportunities, growth potential, and so on. We do so by using dummy variables indicating the industry to which the firm belongs (Industry fixed effects). We use industry grouping at the 3-digit ISIC Rev. 3.1 level. There are 28 industries in our baseline sample.

Market structure and competition may also vary across countries for a given industry, and across firms within a country-industry pair. This variation is not captured up by the industry fixed effects. Thus, our next control is the concentration of output within each country-industry pair as measured by the Herfindahl-Hirschman Index (HHI). This equals the sum of the square of market shares (in terms of output) of all firms in each country-industry pair ( _HHI_ ). Industry here is defined at the 3-digit ISIC Rev. 3.1 level. The variable is based on ES data for formal firms. Hence, it captures competition between formal firms. To account for differences in market power across firms within a country-industry pair, we control for firm-size (defined below).

18
# 3. Data and Methodology

The data used in this paper are obtained from three rounds of Moroccan household budget surveys ( _Enquête Nationale sur les Niveaux de Vie des Ménages_ ) held in 2000, 2006 and 2013. Household expenditures (per capita) are used as the main welfare indicator throughout the analysis. Since reliable income data are difficult to obtain, we use consumption as a measure of well-being (Deaton and Zaidi, 2002). Indeed, informal sectors are an important part of the workforce in these countries, despite difficulties involved in quantifying such income. To compare different years, consumption is deflated using the local CPI and further converted into 2011 international dollars, using the PPP conversion factor obtained from the World Development Indicators website.<sup>5</sup> The methodology used throughout the paper is the “relative distribution” method, a fully nonparametric statistical framework that enables comparison of the entire consumption distribution at two different points in time (Handcock and Morris, 1998, 1999).<sup>6</sup> Despite its name, the relative distribution method produces an _absolute_ measure of polarization. More formally,<sup>7</sup> let<sup>_Y_</sup> 0<sup>be the welfare variable for the reference year (2000) and</sup><sup>_Y_the welfare</sup> variable for the comparison year (2013). The relative distribution is defined as the ratio of the density of the comparison year to the density of the reference year evaluated at the relative data _r_<sup>8</sup> :  1 where _f_  and<sup>_f_</sup> 0  denote the density functions of _Y_ and _Y_ 0 , respectively, and<sup>_y_</sup> _r_  _F_ 0  _r_ is the quantile function of<sup>_Y_</sup> 0<sup>. When no changes occur between the two distributions,<
education.

To address this gap, this study implemented a field experiment that creates variation in two key dimensions of survey design: the level of detail of the questions ‐ whether including or excluding employment screening questions ‐ and the respondent selected: self or proxy – and investigates whether these differences in survey design yield distinct estimates of the returns to education using a common econometric approach and identification strategy.<sup>7</sup>

> 4 While work on survey measurement error in high income countries often focuses on how survey replies deviate from true values using validation studies – comparing for instance in the case of wages between employee survey replies and employer pay records ‐  this approach is typically less fruitful in low income countries where records are often missing, incomplete or less reliable.  As a result, it is more fruitful to compare replies obtained from different survey methods.  (See de Mel et al (2009) for a comparable approach to measure small business profit.)

> 5 In contrast, in the case of discrete dependent variables, such as labor force participation, both classical measurement error and measurement error in the dependent variable biases the point estimates of the coefficients of right‐hand‐side variables (Hausman, Abrevaya, and Scott‐Morton, 1998; Hausman, 2001.)

> 6 This is especially relevant when husbands and wives have separate sources of income stemming from individual activities; which is often the case in both urban and rural developing country contexts.

> 7 Note that the paper does not aspire to improving upon commonly used estimation strategies, but rather takes this as given, to focus on the impact of survey methods when using existing estimation methods.

3
year of the Helen Keller survey and the IFPRI survey which were both collected in 2011, we observe large differences in the number of children classified as adequate in food. Based on the IFPRI survey 33% of the rural children in Bangladesh had access to adequate food but only 14% of both rural and urban children were found to be adequate in food in the Helen Keller survey. Besides sampling a different population, the two surveys lend themselves to different definitions of adequate food. In Helen Keller it is not possible to determine whether a child has met the minimum acceptable diet based on the composition and frequency of meals, but it possible to determine whether the household has experienced food insecurity. In the IFPRI sample it is possible to determine the minimum acceptable diet, but not whether the household experienced food insecurity.

<!-- Start of picture text --> Figure 5b: The Evolution of Access to Adequate Food Security<br>Prevalence of Adequacy in Food<br>68<br>57<br>51<br>46<br>43<br>34 34<br>31<br>29 29 28<br>22<br>14 15<br>12 12<br>BGD, HK BOL IDN NPL ZWE<br>BGD, IFPRI ETH KHM PER<br>Year 1 Year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003, 2008; Cambodia 2005, 2010;<br>Ethiopia 2000, 2011; Indonesia 2010; Nepal 2000, 2011; Peru 2005, 2012; Zimbabwe 2005, 2010.<br>Note: (1) Bangladesh (HK) includes food security but no information on meal frequencies.<br>(2) Bolivia (2003) does not include information on dietary diversity.<br>(3) Indonesia (RKD) only has information on household level dietery diversity, and no information on meal frequencies