rafmacalaba/data-use-mentions-extended huggingface dataset:
interval of 27 percent to 31 percent empty miles. Figure 3 presents the individual study estimates, their weights, and the mean estimate in a forest plot. The _I_<sup>2</sup> index is 99.27 percent, suggesting a very high degree of true heterogeneity across the studies.

Given the substantial heterogeneity across the studies, I conduct a meta regression of effect sizes on the statistic estimated, the data source, the year of the study, and an indicator for whether the country is High Income. I present the regression results in Column 1 of Table 1. Studies of High Income countries find significantly lower rates of empty miles compared to studies of Non-High Income countries. In addition, vehicle-level studies find lower levels of empty miles, suggesting that trip- or site-level studies may be more likely to sample empty trips relative to vehicle-level studies.

In Column 2, I extend the meta regression to include countries as moderators. Given the country moderators, High Income status has a stronger negative effect on empty trips. In addition, both site-based and vehicle-based studies find lower levels of empty miles than trip-based studies. Finally, backhaul studies tend to find lower levels of empty miles than empty-mile or empty-trip studies, which is consistent with a non-zero number of fronthauls biasing backhaul study estimates downward.

**Regional Patterns** Looking at the regional level, Eurostat has collected surveys of truck trips since 1999, for both European Union and European Free Trade Association members. In Figure 4, I report average empty trip shares for countries in the Eurostat dataset. In Panel A, which aggregates over all types of trips, there is significant variation in empty mile share within region, with island states such as Cyprus and Ireland having empty trip shares which are 15 to 20 percentage points higher than states like Belgium and Denmark. Panel B compares the empty share of domestic versus international trips for each country. The vast majority of countries have more empty trips when their truckers are traveling domestically compared to international trips.

*
_NRA_ , all key variables have signs consistent with our hypotheses. Although the regression results with _emps_ are less stable than those with _gdppc_ (likely as a result of severe measurement errors in the _emps_ variable), the key conclusions are robust to a change in the structural variable.<sup>15</sup> Finally, as discussed in the conceptual framework section, there may be additional nonlinearity in the relationship between media coverage and policies resulting from a nonlinear relationship between economic growth and the rural-urban income gap and the relative urban-rural gap in TV distribution, which would affect the value of both groups to advertisers and thus the channel through which the mass media affect agricultural protection. To test for this, we added an additional interaction effect between TV and the square of _gdppc_ to the model specifications. In some specifications, this additional media 15 There are well-known problems with the agricultural employment data, which are generally linear interpolations between a few observations (often one per decade) and suffer from serious measurement errors (see Timmer and de Vries, 2007). The model specification tests reported at the bottom of the table indicate a well-specified SYS-GMM model for the _RRA_ regression but not for the _NRA_ regression, where the Hansen test rejects the null of the validity of the additional overidentifying restrictions. However, the autocorrelation tests indicate that the model is well specified. Considering the strong measurement errors in the employment data and that Hansen tests have weak power, such results are unsurprising.

28
available through the World Integrated Trade Solutions (WITS) platform<sup>2</sup> . For the entry and survival analysis (sustainability margin) we use firm-level data, based on a customs transactions database for the period 2001-2010. Throughout this note, the performance of Pakistan’s exports is compared to a number of “peer” countries, selected based on factors including: location, similarity in country characteristics (level of income, size, geography, sectoral structure), and their presence as competitors to Pakistan’s exporters in key markets. Due to lack of data availability, not all countries are included across all indicators. Peer countries for comparison using aggregate flows are China, India, Vietnam and Indonesia. In the case of firm level analysis, data availability is more limited, but we were still able to select middle income countries like Bulgaria, Colombia, Peru and South Africa.

> 2 Unless stated otherwise, COMTRADE is the data source for the graphs and figures, with authors’ calculation. Export information is obtained from mirror data.

2
the dynamic approach. As found in other studies (Jalan and Ravallion, 1998; Khandker et. al., 2009), initial state dependence has a significant role in the estimation, reflected by the differences in the fixed-effects estimates with and without these variables.<sup>10</sup> Using the smaller available sample of project and control households between 1997 and 2001, Khandker, Bakht, and Koolwal (2009) also find substantial reductions in transport costs, improvements in per capita expenditure and schooling, as well as a shift to non-agricultural wage work in the RRMIMP area. These can be considered short-term effects, similar to the Project 2 effects shown in Table 3. The long-term results presented here, however, indicate that, except for employment, these effects diminish over time.

# **7. Results: Distributional program impacts**

# **7.1** **<u>Access and quality</u>**

We examine the distributional impacts through various approaches. First, following equation (4), we relax the assumption in the previous section that RRMIMP has a homogeneous effect on households across all project areas. Variation in such project characteristics as quality of paving and timing of completion will likely affect the benefits households receive across project villages. The new round of the BIDS data has additional variables on road access (distance to the project road) and quality (share of road paved to a bitumen surface) over time, which we include in separate regressions to examine heterogeneity in program impacts. Tables 4 and 5 use the BIDS data on road access and quality to illuminate some of the distributional impacts of road development, as well as the mechanisms by which household outcomes are affected. Table 4 presents results that interact project status with household distance to the project road, while Table

> 10 The full results, including those for initial conditions, are available in appendix form upon request.

18
63 business functions: seven general business functions (GBF) relevant to all sectors, and 56 sector-specific business functions (SSBF) across 12 sectors (agriculture, livestock, food processing, apparel, leather goods, automotive, pharmaceutical, other manufacturing, wholesale and retail, financial services, land transport services, and health services). In total, the grid spans 305 technologies.

The grid has three properties. First, it is comprehensive both in terms of the business functions and of the technologies considered in each business function. Second, it is relevant for any establishment and country, regardless of its level of development. Third, the technologies in each business function are ranked according to their sophistication, from the simplest to the most complex which represents the world technology frontier.

We implement the grid in the Firm Adoption of Technology (FAT) survey, administered to over 21,000 establishments that constitute representative samples in 15 countries: South Korea, Poland, Croatia, Chile, the Brazilian state of Ceará, Georgia, Vietnam, the Indian states of Uttar Pradesh, Tamil Nadu, Gujarat and Maharashtra, Ghana, Bangladesh, Kenya, Cambodia, Senegal, Ethiopia, and Burkina Faso. FAT collects three types of information. First, it gathers establishment-level data on sales, inputs, education of the workers and the managers, management practices, etc. Second, it records whether each sector-specific business function is conducted in-house. Third, and most relevant, FAT documents the technologies from the grid used by each establishment in each business function and, of these, which one is the most widely used technology.

Using the information from FAT, we develop two measures of technology sophistication at the business function-establishment level: ‘MOST’ for the most widely used technology, and ‘MAX’ for the most advanced technology available. We use these measures to study three topics: the use of technology at the business function level, the cross-establishment variation in technology sophistication, and the relationship between technology sophistication and productivity across establishments.

At the
# **1. Introduction**

Influenced by the work of Hernando de Soto (1989), and the World Bank/IFC’s _Doing Business_ project, efforts to make it easier for individuals to register a business have been the most common form of regulatory reform over the past decade. Since 2004, 75 percent of the countries included in the Doing Business survey have adopted at least one reform making it easier to register a business (IFC, 2009). These is some evidence that, starting from a point of burdensome entry, big reductions in barriers to entry are associated with increases in firm registrations. Djankov et al. (2002) show that countries with more burdensome entry regulations have larger informal sectors, while Klapper et al. (2006) find that costly entry regulations are associated with the creation of fewer limited liability companies. Reforms which dramatically reduced the time taken to start a business led to increases in formalization in Mexico (Bruhn, 2011; Bruhn 2012; Kaplan et al, 2011), Peru (Mullainathan and Schnabl, 2010), and Portugal (Branstetter et al, 2010).

However, recent research has suggested there is little benefit to reform if the reduction in the barriers to entry are not large (Klapper and Love, 2011), and despite these reforms, the majority of firms in many developing countries remain informal. That is, although some informal firms register when a reform makes it easier to do so, this number is small compared to the overall stock of informal firms. While there is evidence that distance and information serve as barriers to some firms registering who would benefit from doing so (McKenzie and Sakho, 2010), several recent experiments which have provided firms with information on how to register, and which have lowered the cost of registering, have found very few informal firms formalize as a result (de Mel et al, 2012 in Sri Lanka; Jaramillo, 2009 in Peru; Andrade et al, 2012 in Brazil).<sup>1
entrepreneurship. This choice is often made by default given that self-employment questions have typically been included in censuses of families or households. Thus, data on selfemployment rates became available much earlier than any other potential measure.

Recent studies note, however, the challenges that come with using self-employment metrics to describe the entrepreneurship necessary for economic growth and job creation. Due to the much larger raw count of self-employed workers, self-employment indices accord much more weight to small-scale, independent businesses and hobby entrepreneurship than entrepreneurship that can lead to substantial job creation for others. The vast majority of selfemployment businesses as captured on labor and census surveys will not generate employment opportunities for other workers, and they may even be the product of a lack of employment opportunities for the business owner (e.g., Astebro et al. 2010, Schoar 2009). This latter factor is particularly acute in South Asia given its earlier development and large, persistent informal sector.

These criticisms are evident in some simple examples. Silicon Valley is the poster child of US regional entrepreneurship and the world‘s largest venture capital market. Yet San Jose, CA, ranks near last among America‘s 300+ metropolitan areas in terms of self-employment, with West Palm Beach, FL, instead having the highest self-employment rate. This questionable pattern is also evident in country rankings. Southern European countries like Portugal and Greece rank much higher than Scandinavian countries in terms of self-employment within Europe, but much lower in terms of most growth-entrepreneurship indicators like venture capital markets (e.g., Bozkaya and Kerr 2011). Klapper et al. (2010) document the negative correlation between self-employment levels and economic development across a broad cross-section of countries, and Ghani et al. (2011) show that self-employment metrics for India perform poorly for describing job growth compared to better-specified alternatives.

Hence, most recent studies of entrepreneurship and growth instead focus exclusively on formal firms that employ paid workers. These thresholds—being incorporated, paying payroll taxes—are in some sense arbitrary, but
. _Journal of International Economics_ , 89(2):379–392.

- Jorgenson, D. W., Ho, M. S., and Stiroh, K. J. (2008). A retrospective look at the us productivity growth resurgence. _The Journal of Economic Perspectives_ , 22(1):3.

OECD (2007). Investment in ICT. _OECD Factbook, SourceOECD database_ .

- Oliner, S. D., Sichel, D. E., and Stiroh, K. J. (2008). Explaining a productive decade. _Journal of policy Modeling_ , 30(4):633–673.

- Solow, R. M. (1987). We’d better watch out. _New York Times Book Review, July 12_ , page 36.

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- Syverson, C. (2011). What determines productivity? _Journal of Economic Literature_ , 49(2):326–65.

- Van Ark, B., Inklaar, R., and McGuckin, R. H. (2003). “Changing Gear: Productivity, ICT and service industries in Europe and the United States. _The Industrial Dynamics of the New Digital Economy, Edward Elgar_ , pages 56–99.

- Van Ark, B., O’Mahony, M., and Timmer, M. P. (2008). The productivity gap between europe and the united states: trends and causes. _The Journal of Economic Perspectives_ , pages 25–44.

10
# **Table A5: Impacts of mental health on labor income – Sample of workers with positive incomes**

|Dependent variable:|Weeklylabor in|come($1,000 AUD)|
|---|---|---|
||FE|IV-FE|
||(1)|(2)|
|Standardized mental health<br>scores|-0.004*|-0.233**|
||(0.002)|(0.110)|
|_First stage of 2SLS (dependen_<br>_scores)_|_t variable is stand_|_ardized mental health_|
|Trauma experience*Year<br>after arrival||-0.064**<br>(0.028)|
|Kleibergen-Paap F stat.||55.517|
|AR 95-CIs||[-2.023, -0.112]|
|Dep. Mean|0.716|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|1,772|1,772|
|Number of individuals|843|843|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of FE model in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo
’Alphab ́etisation et de la Promotion des Langues nationales.** 2020. “Annuaire Statistique de l’Education Prescolaire 2019/2020.”

- **Minist`eres en charge de l’ ́education et de la Formation, UNICEF, UNESCO Pˆole de Dakar de IIPE.** 2017. _Burkina Faso: rapport d’ ́etat du syst`eme  ́educatif national du Burkina Faso: pour une politique nouvelle dans le cadre de la r ́eforme du continuum d’ ́education de base._

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- **Radloff, Lenore S.** 1977. “The CES-D Scale: A self-report depression scale for research in the general population.” _Applied psychological measurement_ , 1(3).

- **Rosero, Jose, and Hessel Oosterbeek.** 2011. “Trade-offs between different early childhood interventions: Evidence from Ecuador.” Tinbergen Institute Tinbergen Institute Discussion Papers 11-102/3.

- **Rossi, Pauline.** 2018. “Strategic Choices in Polygamous Households: Theory and Evidence from Senegal.” _The Review of Economic Studies_ , 86(3): 1332–1370.

**World Bank.** 2012. _World Development Report 2012._ Washington, DC: World Bank.

- **World Bank.** 2014. “Burkina Faso Early Childhood Development: SABER Country Report 2014.”

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9.6|9.6||US$1,481,694|US$1,481,694|

# Annex 2. Methodology for Calculating Affected Population

The East Road runs across wards Fauabu (#4) and Nafinua (#15). The population in these two wards was estimated using distribution data from WorldPop open source data (updated in year 2015). The total population was estimated at 14,700. Using Census data for 2015, the estimated population in wards #4 and #5 would be 13,767, based on an average growth rate between 1999 and 2009. Given that the difference between the two estimation was not significant (as seen in the table below), the WorldPop open source data were adopted as the baseline population layer for the ArcGIS models.

|Ward #|Ward Name|Census data<br>(2015)|WorldPop Estimates<br>(2015)|
|---|---|---|---|
|4|Fauabu|9,207|9,858|
|15|Nafinua|4,560|4,842|
||Total Population|13,767|14,700|

With the WorldPop data, the fine-grain level population distribution was established. With a 3-km buffer zone, a total population of 10,648 could potentially be affected potentially with respect to market access, whereas within a 10-km buffer zone, a total population of 31,867 could potentially be affected with respect to access to hospitals.

Using ArcGIS, the East Road was divided into 5,856 segments based on the GIS information provided by the expert (self-collected data using a smartphone were compared with government data and since consistency was found, the former were used as the data input). The Haversine formula was used to determine the length of each segment connecting two points, which was then utilized to calculate the corresponding gradient of the segment. Each segment on average was about 5-10 meters
allocations, individual-level data allow investigation of the potential welfare loss and gains by household members as a result of taxes and transfers (see e.g., Thomas 1990; Browning, Chiappori, and Lewbel 2013). The challenge, however, is allocating expenditures to household members. Some studies that applied the per capita method allocated equal shares of resources to each household member (Glick, Saha, and Younger 2004; Greenspun and Lustig 2013); others used adult equivalence scales <u>(Siddiqui 2009; Browning, Chiappori, and Lewbel 2013; Aziz et al.</u> 2016); and others applied a household structural model (Calvi 2020; Calvi et al. 2020). These approaches do not consider differences in consumption patterns.

This study applies a two-tier framework to assign expenditure items to individuals within the household. The first tier is based on direct identification of the item from the survey data. For these items, either there is gender-disaggregated data or the data can be assigned to either men or women based on consumption patterns, such as alcoholic drinks and stimulants like tobacco and _khat_ . The second tier is allocating non-assignable goods<sup>2</sup> using adult equivalent scale weight. The details of the allocation mechanism are presented in Annex 2.

# **2.2. Income Concepts**

The core of the CEQ approach is the application of different income concepts (Figure 1). This study investigates the redistribution effect by comparing six individual-level income concepts, computed as −∑ + ∑ ,where is post-fiscal income (income after taxes and transfers) for individual _i_ ; depending on the fiscal intervention being analyzed. It denotes gross nn nn ii ii jj=1 jj mm=1 mm ii income, net market income, disposable income, consumable income or: YY = II TT BB YY final income. _Ii_ is the market or pre-fiscal income for individual _I_ ; _j_ =1,2,3...n is the type of tax paid
offer limited scope to analyze the links between agriculture and non-farm income activities, as well as other socioeconomic outcomes. To meet the demand for integrated data, multi-topic/multipurpose household surveys (such as a Living Standards Measurement Study [LSMS] survey) are often extended to cover agricultural issues. Yet, cost and logistical considerations usually dictate that the data in these surveys be collected during a single visit to the household. In this case, the household/farmer is asked to report information relating to farming by recalling the details of past events for the last completed agricultural season, often including two or more separate harvests. Depending on the timing of the survey, the interview itself will not necessarily be the most propitious timing for a single visit (i.e. immediately after the main harvest).

2
# **<mark>3.2. Establishing Causality in Educational Research</mark>**

<mark>Establishing causal relationships between education and later life outcomes such as incomes, employability, and even voting behavior has been of interest for many decades. While there is overwhelming evidence for the positive impact that schooling can have, researchers have been cautious in drawing strong inferences about the causal effect of schooling especially in the absence</mark> of experimental evidence.

<mark>However, the emergence of large-scale microeconomic datasets such as OECD’s Programme for International Student Assessment (PISA), Trends in International Mathematics and Science Study (TIMSS), and Progress in International Reading Literacy Study (PIRLS) has provided researchers with more tools to study these relationships. It is now possible to deploy econometric methods such as instrumental variables, regression-discontinuity, propensity score matching, difference-indifference, and different sorts of fixed-effects specifications to establish causality. Cordero and Cristóbal (2017) provide a comprehensive overview of literature that uses such quasi-experimental techniques. The authors also provide examples of studies that have used such techniques to establish the impact of various school education policies. Particularly, the difference-in-difference approach and instrument variables strategy have been most frequently used for comparison between public and private schools, or to study the effects of class size, tracking, instructional time, teaching methods, school entry age, etc. The authors further suggest creating longitudinal datasets to further causal research in the sector. Schlotter et al. (2011) provide further examples</mark> for the applications of these causal methods and the issues faced in aggregation of relevant data.

<mark>Other techniques that have been suggested include using co-twin control designs on many monozygotic twin pairs to understand the impact of schooling on factors such as political knowledge (Weinschenk and Dawes 2019), wages (Bingley et al. 2009), and health (Fujiwara and Kawachi 2009). Heckman et al. (2016) and Card (1999) further present theoretical models that build