rafmacalaba/data-use-mentions-extended huggingface dataset:
|Sequence vs. Control|0.574***<br>(0.131)|||
|---|---|---|---|
|Low experience|-0.301|-0.113|-0.752***|
||(0.208)|(0.256)|(0.199)|
|(Sequence vs. Control)*Low experience|-0.333<br>(0.282)|||
|Coaches vs. Control||0.570***<br>(0.147)||
|(Coaches vs. Control)*Low experience||0.288<br>(0.335)||
|Coaches vs. Sequence|||-0.175<br>(0.141)|
|(Coaches vs. Sequence)*Low experience|||0.824**<br>(0.333)|
|Observations|1,042|1,072|1,102|

_Source_ : Author's analysis based on experimental data generated for this study.

_Note_ : ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. Controls: (a) students’ characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his/her home), (b) teacher characteristics (gender, age, general teaching experience [in years], if she/he has postgraduate certificates), and (c) school characteristics (school size, seventh size, seventh repetition rate, FEPBA score in Language, school district, or location). Low experience represents a dummy variable equal to 1 if the teacher has less than two years of experience in teaching Science
education ratio|-0.639***|-|-0.203|-|
|High-low education ratio square|0.021***|-|0.010**|-|
|Consumer price index (2005)|-0.001|-|0.002|-|
|Services value added share|0.078*|-|0.071|-|
|Manufacturing value added share|-0.047|-|0.069|-|
|Agriculture value added share|-0.091*|-|-0.112**|-|
|Log labor force|-|-7.916***|-|-17.991***|
|Log gross fixed capital formation|-|3.802***|-|7.472***|
|Log exports of goods and services|-|2.422**|-|3.422***|
|Log REER|-|5.082***|-|1.353|
|World oil price index|-|0.067***|-|0.067***|
|WorldMetalpriceindex|-|0.013|-|0.006|
|Observations|1015|1034|873|873|
|R2|0.058|0.383|0.910|0.858|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Table 7 shows the results of the non-recursive baseline model specification estimation— that is, the results obtained once the endogeneity related to the feedback effect of including the Gini in the GDP equation, and vice versa, has been accounted for. The estimation results confirm the previous findings from the recursive model, though the

> 15 Based on an IMF definition of ‘resource‐rich’
# **Notes**

> 1See Restuccia and Rogerson (2013) for a review on misallocation.

> 2The price-cost margins a l`a Hall (1986, 1988), requires an estimation of a production function to measure markups. Standard approaches to estimate production function exhibit biases when factors such as demand shocks and quality of inputs are confounded in productivity estimates (Foster et al., 2008; De Loecker, 2011). Following De Loecker et al. (2016), the article amends this shortcoming by including input price bias in the production function estimation.

> 3Hirschman (1968) identified balance of payments difficulties, deliberate development policy, gradual growth of income, and wars as the four main reasons that lead to the spread of import substitution policies in developing countries.

- 4The quest for rapid industrialization was the theme of the Second Development Plan adopted by the

- then government.

- 5For a detailed description of policy actions by each government, see Killick (2010). Notice that these

- trade restriction measures were implemented in spite of Ghana being a member of GATT.

- 6Ghana had 5 Heads of State during the crisis period, each with an average of 1.42 years in office.

- 7See Goldberg and Pavcnik (2016) on the discussion of a similar situation in India, where IMF dictated

- trade liberalization.

- 8The sub-section 2.2 provides detailed information on the sources of tariffs data.

> 9In this article simple average tariffs aggregated at the three-digit sector level were used for the estimation of the main specification. Results of replication using trade-weighted average tariffs are available from the author upon request.

- 10From the labor market setting outlined above, the efficient bargaining and monopsony settings require

- further comment, with particular emphasis on the monopsony case.

- 11All other notation is as earlier.

> 12Steel (1972) recounts actions by various governments to retort to tariffs to raise revenue. A detailed explanation of such behavior is presented under historical political economy theory as “Gatekeeper States” in Cooper
of approximately US$7, distributed in monthly installments. These decisions were in line with the recommendations of Dabalen et al. (2016) and were taken to ensure that the phones remained active and that the respondents stayed motivated.

Finally, each D1 and D2 household was provided with sacks and a hanging weighing scale to record the fresh cassava harvest in their diaries in kilograms. During the first interview, the diary respondents were trained in the use of the diaryP20F<sup>21</sup> and the weighing scale. Each scale was valued at approximately US$10 and was left with the household at the end of the fieldwork period. The sacks provided to the diary households were valued at approximately US$4. Overall, the total value of the incentive package was approximately US$20 for the D1 households, and US$53 for the D2 households, as shown in Table 1.

# **3.1.2. Recall Arms**

The third group of households (R1) were visited twice over the course of the 12-month fieldwork, with 6 months in between visits. At each visit, the R1 households were asked to report cassava

> 20 According to the authors’ calculations based on the Fourth Integrated Household Survey (IHS4) 2016/17, only 11 percent of households in Malawi have access to electricity.

> 21 Enumerators distributed paper production diaries leading up to the start of each calendar month. Following the collection of the paper diaries from the prior month, the supervisors for each district entered the data into the Survey Solutions CAPI platform. A second data entry and verification of the paper diaries took place in September 2016.

10
2004. “Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development.” Journal of Economic Growth 9: 131-165.

- Summers, L. H. 2014. “U.S. Economic Prospects: Secular Stagnation, Hysteresis, and the Zero Lower Bound _.”_ Business Economics 49(2): 65‒73.

- ———. 2018. “Secular Stagnation and Macroeconomic Policy.” IMF Economic Review 66(2):226‒50.

- Sunde, U., and T. Vischer. 2015. “Human Capital and Growth: Specification Matters.” Economica 82 (326): 368-390.

- Teulings, C., and R. Baldwin. 2014. “Secular Stagnation: Facts, Causes and Cures.” Vox eBook.

- Vandenbussche, J., P. Aghion, and C. Meghir. 2006. “Growth, Distance to Frontier and Composition of Human Capital.” Journal of Economic Growth 11(2): 97‒127.

- WEF (World Economic Forum). The Global Competitiveness Report 2016‒17 database.

World Bank Group. World Development Indicators database. World Bank, Washington, DC.

- ———. 2017a. “Lessons from Poland, Insights for Poland: A Sustainable and Inclusive Transition to High Income Status.” World Bank, Washington, DC.

- ———. 2017b. “The Western Balkans: Revving up the Engines of Growth and Prosperity.” World Bank, Washington, DC.

- ———. 2018. “The Western Balkans: Higher but Fragile Growth.” World Bank, Washington, DC.

- ———. 2019a. “Financial Inclusion. ECA Economic Update.” Spring. World Bank, Washington, DC.

- ———. 2019b. “Western Balkans Regular Economic Report. Rising Uncertainties.” No. 16. Fall 2019.

- ———. Forthcoming. “Global Economic Prospect Report. 2020”

-
267|.77|0.42|
|% ha receiving payment|411|267|0.45|0.32|

Note. Rainfall and agricultural variables in panel A come from the agricultural panel, which has municipal-level observations and consists of policy data merged with agricultural production data, as do the statistics in panels B and C. The economic variables in panel A come from the economic panel, which consists of household level observations from ENIGH merged with policy data.

25
been interpreted and applied to projects in four countries. Corresponding empowerment indicators are presented in annex 2. The section also discusses using the framework to

> 1 The term empowerment is commonly used to indicate both a process (of empowering groups or individuals) and an outcome (a person or group is empowered).

2 This paper is an interim product of PRMPRs work on measuring empowerment. It supports and draws on evidence from a five-country study currently underway and managed by PRMPR. Country cases are managed by Lynn Bennett (Nepal), Arianna Legovini (Ethiopia), Mike Walton (Brazil), Mike Woolcock (Indonesia), and Emanuela di Gropello/Nina Heinsohn (Honduras). These task managers are working in collaboration with the following international and local consultants: Kishor Gajural, Kim Armstrong and Sandra Houser (Nepal), the Ethiopian Economic Association (Ethiopia), Gianpaolo Baiocchi, Shubham Chaudhuri, Patrick Heller and the Centro de Assessoria e Estudios Urbanos (Brazil), Patrick Barron, Leni Dharmawan, Claire Smith, Rachael Diprose, Lutfi Ashari, Adam Satu, and Saifullah Barwani (Indonesia), and ESA Consultores (Honduras). For additional information please visit: www.worldbank.org/empowerment/.

> 3 Following the publication of the _World Development Report 2000-2001_ , the World Bank launched _Empowerment and Poverty Reduction: A Sourcebook_ (Narayan 2002), an initial attempt to define and explain the concept of empowerment. The work presented in this paper compliments the _Sourcebook_ by providing a tool for analyzing and measuring empowerment. It also compliments the Bank’s work on measuring social capital.

5
coordination among economic agents. Buyers and sellers incur these costs in search of information on input and output prices and other market and non-market information. Second, it allows better management of the supply chains and thereby improves producers’ productive efficiencies. Third, it is associated with job creation potential – not just for telecom related services but much more widely. Fourth, improved information exchanges that come with mobile phone communication enable better assessment of shocks and mitigation of related risks across households and market makers. Lastly, beyond the demand-side channels emphasized here whereby less price information asymmetry boosts competition and firm performance, there are supply channels that also enhance productivity through relaxation of financial constraints, better functioning factor markets (including the labor market), as well as cheaper and better inputs.

Recently, there is a growing body of literature that examines the effect on economic behavior and outcomes of improvements in information communication technology (ICT) using micro level data. For example, Jensen (2007), based on microlevel survey data from fishing industry in India, reports substantial price reduction as well as welfare gains because of adoption of mobile phones by producers and wholesalers. Based on survey data from Southeast Asian countries (Thailand, Vietnam, the Lao People’s Democratic Republic and Cambodia), Hubler and Hartje (2016) document an economically large positive and significant positive effect of smartphone ownership on household income. Evidence from Sub- Saharan African studies points in the same direction. Muto and Yamano (2009), using data from households in Uganda, find that mobile phone network expansion has led to increased market participation of farmers as well as sales of perishable crops in remote areas. Indeed, sales of a perishable crop (banana) increased relative to a storable one (maize).

These results are attributed to decline in marketing costs of agricultural products because of increased flow of information that comes with the mobile phone network coverage. Furthermore, the results indicate that simple expansion of mobile phone coverage itself (not necessarily ownership and usage of mobile phone)
# **1 Introduction**

There is now abundant evidence from rich counties that heat reduces the formation and utilization of human capital in academic settings.<sup>1</sup> This evidence demonstrates that heat has acute negative impacts, reducing performance on exams on hot days, and longer-term negative effects, reducing the amount that students learn during hotter years. However, how these impacts translate to developing country contexts remains under-investigated. This is especially crucial in low income countries where introducing adaptation measures may be challenging due to generally lower average incomes. The lack of evidence for SubSaharan Africa is particularly acute. In this study, we examine the relationship between high temperatures and learning outcomes in Ethiopia in a high stakes exam setting.

While there exists a large literature documenting the negative impacts of extreme ambient temperatures on human capital formation, there remain gaps. Park et al. (2021) find that heat exposure is associated with reduced rate of skill formation or cumulative learning in both the U.S. and internationally. But their sample includes almost no Sub-Saharan African countries. Graff Zivin et al. (2018) find that short-run changes in temperatures in the US beyond 26 degrees Celsius are associated with lower cognitive performance on math, but not on reading. Cho (2017) and Garg, Jagnani, and Taraz (2020) find similar results in the contexts of the Republic of Korea and India, respectively. Park et al. (2020) use data on students who retook the Practice Scholastic Assessment Test (PSAT) in the US to find that high temperatures during school days inhibit learning and thereby reduce test scores. Further, their findings suggest that air conditioning in classrooms can mitigate the adverse effects of high temperatures.

In the developing country context, there is an evidence gap for high-stakes academic settings among older students. Garg et al. (2020) identify that hot days during the agricultural growing season are associated with both lower agricultural yields as well as lower test scores for younger students in rural India. They
it is currently a global leader in the methodological development of rich and extensive multi-topic questionnaires designed to study various aspects of household welfare. <mark>Within the broader LSMS program, we restrict our attention to the LSMS-ISA household survey program, the explicit a</mark> im of which is to improve the availability, quality, and relevance of agricultural data in multi-topic, multi-purpose household surveys (Carletto and Gourlay, 2019) <mark>. The reason is that the LSMS-ISA has a distinct advantage with respect to standard LSMS surveys when it comes to studying issues specifically related to climate change, as its strong focus on agriculture allows one to study the impacts of weather- and climate-related events on several household welfare outcomes, that might either trigger or prevent migratory flows, as well as exploring farmers’ adaptation responses, especially</mark> _<mark>in situ</mark>_ <mark>ones, that complement migration as coping strategies in the face of climatic shocks.</mark> Nonetheless, most of the reflections and recommendations we propose throughout this paper are also relevant to LSMS surveys more broadly and to multi-topic and multi-purpose household surveys in general.

> 2 For reference to the literature before 2010 see Piguet et al. (2011).

3
Table 3: Gender differences in risk appetite and performance

||Leverage|Profts/<br>Revenue|TFP|Log(Sales/<br>Worker)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_A. All countries_|||||
|Female|0.005|0.144|0.002|-0.112|
||[0.049]|[0.048]***|[0.017]|[0.035]***|
|Obs|21,411|21,411|21,411|21,411|
|R2|0.026|0.034|0.865|0.165|
|_B. By social perceptions about women_|||||
|Female in less traditional countries|0.02|0.141|-0.019|-0.148|
||[0.069]|[0.069]**|[0.025]|[0.042]***|
|Female in more traditional countries|-0.01|0.147|0.023|-0.076|
||[0.064]|[0.064]**|[0.021]|[0.058]|
|Obs|21,411|21,411|21,411|21411|
|R2|0.026|0.034|0.865|0.166|

Sources: World Bank Enterprise Surveys and World Value Surveys. Notes: The sample includes 61 countries, of which 28 are classified as more traditional and 33 as less-traditional. All models control for manager experience, indicators of firm age, legal status, size, whether the firm exports,
WPS3768

# **Road Freight Logistics, Competition and Innovation: Downstream Benefits and Policy Implications**<sup>**1**</sup>

Mark Dutz The World Bank

## **Abstract**

This empirical paper sheds light on a significant element of the debate of whether infrastructure services have a strong impact on economic development by exploring the impact of innovative road freight services on downstream business users. The paper uses a new and purpose-specific survey of 165 logistics service providers and 493 user enterprises in food processing, food distribution and the automotive industry in the Czech Republic, Hungary and Poland. The main findings are that there are substantial downstream benefits from innovations in road freight services, both dampening cost increases and raising sales revenues of business users. The additional finding that increased intensity of competition in road freight services is significantly associated with the provision of innovative services suggests that easing any remaining barriers to competition in upstream business sectors should be a priority.

World Bank Policy Research Working Paper 3768, November 2005 _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 I am grateful to Maria Vagliasindi for helpful suggestions throughout the research project, and to Professor Remy Prud’homme for useful feedback. This paper and the underlying enterprise data were prepared under funding from World Bank research grant ‘Road freight transport, competition, and innovation’ (RPO 683-44).
sides of the same variable. Are people less bonding also invariably more bridging (and vice-versa) or can people be more (or less) bonding and bridging at the same time? To address this question we carried out a factor analysis with the variables selected in Step 2. The results are shown in Table 2. It can be seen (top panel) that only the first factor has an Eigenvalue sufficiently high to be retained.<sup>4</sup> In other words, the analysis suggested that our proxies for bonding and bridging attitudes were jointly related with only one latent variable in some meaningful way. It can also be seen (bottom panel) that the five variables that we considered as proxies for bonding attitudes all load positively against the first factor while the four variables that we considered as proxies of bridging attitude all load negatively against the same factor.<sup>5</sup> This finding suggests that the two sets of variables that we identified could in fact represent two sides of the same latent variable. This is a new result in contrast with those in the existing literature. For example, Knudsen et al. (2010) find, on the basis of US data, that two factors should be retained, and that the proxies for bonding and bridging attitudes correspondingly load on the two factors, thus concluding that bonding and bridging are two separate latent variables (see section 2).

# [TABLE 2]

_Step 4_ . As a further test for the preliminary finding of step 3, we carried out a third factor analysis exercise (Table 3). This time we assumed that bonding and bridging attitudes were two sides of the same variable and we constructed proxies for a single latent variable by building variables with low scores corresponding to bonding attitudes and high scores corresponding to bridging attitudes. We constructed five of these variables using some of the same variables we used in step 3 as well as some of the variables discarded in step 2 but useful for this last exercise. The
- Van der Weide, R., Rijkers, B., Blankespoor, B. and Abrahams, A. (2018). Obstacles on the road to palestinian economic growth. Policy Research Working Paper 8385.

- Van Donkelaar, A., Martin, R. V., Brauer, M. and Boys, B. L. (2015). Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. _Environmental health perspectives_ , **123,** number 2, 135–143.

- Van Veelen, M. and van der Weide, R. (2008). A note on different approaches to index number theory. _American Economic Review_ , **98,** 1722–1730.

- Van Velthuizen, H., Huddleston, B., Fischer, G., Salvatore, M., Ataman, E., Nachtergaele, F. O., Zanetti, M. and Bloise, M. (2007). Mapping biophysical factors that influence agricultural production and rural vulnerability. Woking Paper 11. FAO, Rome.

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58
br>98 and 2017. The o|Y<br>utcome variable is a d|Y<br>ummy variable indicat|Y<br>ing that the grid-c|Y<br>ell experienced a ba|Y<br>ttle event in a given|

Each observation corresponds to a 50km x 50km grid-cell observed in a given year between 1998 and 2017. The outcome variable is a dummy variable indicating that the grid-cell experienced a battle event in a given year, as reported in the ACLED dataset. Irrigated area is measured as the percentage share of an average plot of land that is equipped for irrigation, as documented by Siebert et al (2015). Controls include log of population at the start of the period as well as dummies to indicate the presence of wetlands, croplands, pasture lands, rivers and the homelands of ethnic groups that historically relied on agricultural livelihood strategies. Standard errors in parentheses clustered at the grid-cell level. Climate controls include mean temperature and log of total rainfall in the wet and dry seasons. * p<0.1, ** p<0.05, *** p<0.01" 29