rafmacalaba/data-use-mentions-extended huggingface dataset:
Improved technology is critical for economic growth and assumes even higher importance in smallholder settings where new technologies can enhance yields, reduce risk, and contribute to better outcomes in a range of other ways. Obstacles to smallholder technology adoption traditionally identified in the literature include human capital, credit, wealth, information and transport constraints, risk aversion, poor tenure security, lack of economies of scale and lack of complementary inputs (Feder _et al._ 1985). Recent literature has focused on models of learning and risk aversion in the presence of credit and insurance market failures (Foster and Rosenzweig 2010) that highlight the importance of prior adoption decisions and outcomes of farmers within an agent’s network.

Cognitive skills are essential to quickly acquiring and processing information not only on the availability of new technologies but also on how to apply them in a given setting. It is thus not surprising that education, as the most commonly available proxy for such skills, emerged as a key determinant of adoption decisions. Skinner and Staiger (2005) show that education and measures of individuals’ social network were the only predictors of adoption significant for all of four key technologies adopted by US farmers over the 20<sup>th</sup> century. Recent literature found that education improves learning (information processing), and increases the adoption of new technologies and the efficiency of their application (Foster and Rosenzweig 2010).

But education itself may be endogenous if it is particularly sought in settings where returns to such skills are high. Indeed, Foster and Rosenzweig (1996) use Indian panel data to show that returns to education are higher in areas where agricultural technologies are available for adoption and that demand for education increased where, due to availability of new agricultural technology, returns to education had increased (an effect limited to land-owning households). This is consistent with the finding of many studies that, beyond basic numeracy, education is a statistically insignificant determinant of adoption decisions (Huffman 2001). For example high levels of unobserved heterogeneity are found
some developed countries in terms of modal choice, distance, and travel purpose. Data shows that women travel shorter distances, have more complex multimodal trips, and walk and rely on public transport more than men. Women have less access to cars and motorcycles, while the lack of proper infrastructure widens the gender gap in the usage of bicycles. Existing data from San Jose, Costa Rica, and Mexico City, Mexico, support these findings. Gender differences are also prevalent in terms of travel time. Except for Bogota (Colombia) and Montevideo (Uruguay), cities in Latin America show longer travel times for men, suggesting that men travel longer distances and that women tend to prefer to, or are forced to, work from home or close to home, being constrained in their access to economic opportunities (Perez, 2019).

**Women’s mobility constraints can be disproportionally affected by external shocks.** The available evidence on the impact of the COVID-19 pandemic on mobility reveals that it affected women’s mobility through less economic capacity for transport and less time available for journeys (González-Sánchez _et al_ ., 2021). The pandemic and the related restrictions mainly impacted the economic activities where women have higher representation, such as the hospitality industry and low-paid and informal jobs, which led women to either stop working altogether or turn to part-time jobs. At the same time, women’s mobility seems to have been higher during the pandemic than men’s, as they are disproportionately more employed in sectors that require physical presence such as health and social services. Based on mobile data analysis from Barcelona, low-income people, the majority of them women, were not able to reduce their travel due to being employed as frontline workers (Mejía-Dorantes _et al_ ., 2021). This “mandatory” travel that particularly affected women also includes household related activities (e.g., medical visits, shopping), which increased during the pandemic. In addition, women bearing higher responsibilities at home, derived,
constraint for nonfarm enterprise growth in Yemen. Wang (2008) finds that an exogenous shock/loosening of credit constraints allowed households in urban China to switch from salaried state employment to self-employment.

We attempt to document how access to finance affects microenterprise profitability and growth in Bangladesh. We also examine the role of finance in the participation of microenterprise and return on enterprise. We address a set of pertinent questions for raising microenterprise growth and productivity: Do the returns on such micro-investment justify the cost of capital? How do such returns vary across various microenterprise activities? What underlying factors might be affecting the profitability of microenterprises?

Our paper also examines the impact of the adoption of microenterprises on income, expenditures, and poverty. We examine the trend of productivity in rural microenterprises by sector for households that are credit and non-credit constrained to predict how the release of credit and other constraints will allow non-farm economies to contribute more by increasing income growth and reducing poverty. Thus we provide an explanation of how finance, RNF growth, and poverty reduction are interrelated.

This paper analyzes the nationally-representative Household Income and Expenditure Surveys (HIES) from Bangladesh, consisting of three rounds of HIES (2000, 2005 and 2010) data sets. There are 5,027 rural households included in the 2000 HIES round, 6,027 rural households in the 2005 round, and 7,830 rural households in 2010. The central theme of the paper is to critically examine the sources of growth, focusing on the role of finance in enhancing microenterprise growth and productivity.

The paper is organized as follows. Section two presents a discussion of the role of microenterprise in rural economy, and trends and determinants of microenterprise expansion. Section three discusses why growth in microenterprise matters. Section four shows average 5
in _Red_ zones also spent more time at home than people in either _Orange_ or _Green_ zones. We notice that starting in August 2020, a few months after the centralized lockdown was over, these differences reduced, and by December 2020, these differences, especially in workplace mobility, become negligible.

# - 3 REDUCED FORM EVIDENCE

In this section, we outline a simple empirical specification to provide evidence showing the role of lockdown policies on key outcome variables for firm-to-firm trade. We show that the sudden Covid-19 lockdown policies between March and May 2020 led to a rise in unit values and a fall in the monthly number of transactions between firms.<sup>18</sup> In the next section, we exploit this variation to estimate firm-level elasticities of substitution across intermediate suppliers of the same product.

> 17 See the Google Covid-19 Community Mobility Reports.

> 18To see a similar application of this empirical strategy for domestic violence and economic activity in India, see Ravindran and Shah (2020) and Beyer et al. (2021).

9
Figure 4: Distribution of Total Season Rainfall, by Remote Sensing Source and Agro-ecological Zone <!-- Start of picture text --> Tropic-warm/semi-arid (n = 9,095) Tropic-cool/semi-arid (n = 2,840)<br>.003 .0025<br>.002<br>.002<br>.0015<br>.001<br>.001<br>.0005<br>0 0<br>0 500 1000 1500 2000 0 500 1000 1500 2000 2500<br>Tropic-warm/sub-humid (n = 9,009) Tropic-cool/sub-humid (n = 5,886)<br>.003 .0015<br>.002 .001<br>.001 .0005<br>0 0<br>0 1000 2000 3000 4000 0 2000 4000 6000 8000<br>Tropic-warm/humid (n = 3,280) Tropic-cool/humid (n = 2,960)<br>.004 .002<br>.003 .0015<br>.002 .001<br>.001 .0005<br>0 0<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Total Season Rainfall (mm) Total Season Rainfall (mm)<br>CHIRPS CPC MERRA-2<br>ARC2 ERA5 TAMSAT<br>Density<br>Density<br>65<br>Density<br><!-- End of picture text --> _Note_ : The figure presents rainfall distributions pooled across all countries and years, disaggregated by agro-ecological zones.
This study focuses on the dissemination and use of _Mirt_ stoves in rural Ethiopia. _Mirt_ (translated as “best”) stoves are a locally developed technology specifically designed to cook _injera,_ the staple bread of Ethiopia. The stove was designed by the Ethiopian Ministry of Water and Energy, is produced from locally available raw materials, and sells at a market price of 100-250 birr (approximately $10.00 USD at the start of the study). Repeated controlled cooking trials done is a laboratory setting suggest the _Mirt_ reduces fuelwood use by 50.7 percent, carbon monoxide emissions by 92.3 percent, and particulate matter by 41 percent compared to traditional open-fire or three-stone technology (Teshome, 2007). Given the estimated fuel savings and a typical _injera_ baking frequency of two to three times per week, the break-even period of two months compares favorably with the stove’s five-year life expectancy.

While GIZ, a German governmental organization, has supported the stoves since 1998, very few households in rural settings have access to the _Mirt_ technology or other improved cookstoves. No improved stoves were present in our study households at baseline, and data from the 2013 Ethiopian Rural Socioeconomic Survey indicate that 98.4 percent of households in rural areas cook exclusively over traditional open-fires (Central Statistics Agency of Ethiopia, 2013).

# **2.2 Initial randomized controlled trial and adoption estimates**

In 2013 we conducted an initial one-year randomized evaluation of _Mirt-_ stove adoption, its fuel savings, and users’ willingness to pay for the product. The baseline of the study surveyed households from 36 communities ( _Gots_ or sub- _Kebeles_ ) randomly selected to represent the forest cover in Amhara, Oromiya, and Southern Nations, Nationalities, and Peoples’ (SNNP) regional states. The three states cover approximately 70 percent of the land area and 80 percent of the population of Ethiopia.

Within each enumeration area, we randomly drew 14 households from a local census, and assigned them into treatment and control groups at the household
shocks and refugee crises (World Bank, 2016). This literature remains in its infant stage but provides a baseline for further macroeconomic studies built on the very rich tradition of trade models.

# 4. Data issues

As already illustrated, the forcibly displaced are essentially represented by two large groups: Refugees and IDPs. Data collection, data management and data dissemination are historically different for these two groups.

## Refugees

Data on refugees have been collected since the creation of the UNHCR in 1950. The UNHCR’s main mandate is the protection of refugees, and since its early days the UNHCR has collected data on refugees by registering individuals and households seeking asylum and refugee status. Once granted, refugee status provides access to rights and assistance. For this reason, the UNHCR keeps and updates records of asylum seekers and refugees on a continuum basis.

These records are used for a variety of purposes, such as legal protection purposes, identification of beneficiaries of social assistance programs, or production of statistics on the people of concern to the UNHCR. Records included in the UNHCR registry are organized in different levels depending on urgency of the information required. Refugee crises often result in thousands of people crossing the border daily, and the first priority is to register large numbers of people quickly. Therefore, the first set of information recorded includes only a few key individual socioeconomic characteristics such as name, age, education, former occupation, place of origin and destination. In a second stage, the UNHCR conducts a more formal interview where existing records are verified and other records are added, trying to reconstruct, for example, family structure and relations, types of special assistance and protection needed and other information necessary for protection and assistance purposes.

Because of the very protection mandate of the UNHCR, registry data are very rarely shared. A few exceptions to this rule occur when UNHCR implementing agencies need information for running programs with refugees or if the UNHCR needs to carry out a study on a particular group of refugees. Recently, the UNHCR
|‐23.7|
|Uzbekistan|‐37.1|‐38.3|‐20.1|‐14.1|
|Belarus|‐52.2|‐33.9|‐37.9|‐44.5|
|Moldova|‐50.4|‐31.0|‐4.6|‐3.9|
|Russian Federation|‐33.0|‐17.7|‐13.8|‐28.4|
|Ukraine|‐55.6|‐25.0|‐20.7|‐34.7|
|FSU countries*|‐37.6|‐21.8|‐15.2|‐24.9|
|Remittance receiving FSU countries|‐30.0|‐14.7|‐6.3|‐10.7|
|FSU Hydrocarbon exporters|‐38.8|‐25.8|‐18.6|‐33.1|

*Excludes the three Baltic States

A positive number denotes an under-valuation of the RER, a negative number an over-valuation Source: authors’ calculations using data from WDI The estimated BSA RER over or under-valuations for each of the eight FSU countries in CASC along with other FSU countries are reported in table 8.<sup>5</sup> Each of the four remittance receiving countries in CASC had an undervalued BSA RER in each of the four sub-periods. In all four countries, the undervaluation of the real exchange rate was at its largest during 2000-04, but the trends since then varied. The Kyrgyz Republic experienced a consistent decline in

> 5 We did not compute real exchange rates for the three Baltic states because of difficulties arising from a break in the data series when these countries adopted the euro.

12
segment using a vehicle given current traffic conditions. The speed is calculated by dividing the distance of the segment by the estimated travel time. We query this information every 30 minutes. We verify these measures using speed information from Uber Movement data. Uber provides speed data aggregated from Uber trips at the road segment level across Nairobi. Average speed is available hourly, and data is made available through March 2020, after which time Uber stopped producing these statistics. Given data is not available beyond this time, the Uber data is only used for cross-validating speed data from Google and is not used for the analysis. An overview of data sources and variable construction is shown in Table B.2.

For all the analyses, across all outcomes and indicators, we analyze 3 time intervals: (1) Daytime, including 5:00 to 17:00 hours, (2) Pre-Curfew time, including 17:00 to 20:00 hours, and (3) Curfew time that covers 20:00 to before 5:00 hours. While the curfew started at 19:00, we include the 19:00-20:00 hour interval in the pre-curfew time given the combination of: (1) the likely behavior with people rushing and getting in crashed around and beyond the curfew threshold at 19:00, and (2) the delay in the reporting of crashes by the police while they arrive to the crash scene, particularly at the time they are enforcing the curfew starting time.

When working with data derived from smartphones, it is important to consider the potential for biased measures (Milusheva et al., 2021). The traffic and congestion data from Waze and Google Maps are likely to be minimally

> 2See Appendix B for a description of how these predictions were generated.

> 3For example, while there are data on vehicle registrations, data is not collected on vehicles that are no longer in commission, which hinders an accurate picture of the volume of active vehicles.

6
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective"

# 1. **Introduction**

This paper presents a global perspective on infrastructure coverage and the poor that many people will think they have seen before but in fact have not.<sup>4</sup> It is widely assumed that the poor in developing countries have fewer infrastructure services than middle and upper-income households, but there is surprisingly little information on the actual empirical relationship between household income and infrastructure service coverage in different countries. The available coverage statistics are typically country-wide averages. These are widely used to assess the scope and magnitude of infrastructure problems in developing countries, and they are often the only global, cross-country data available about infrastructure services. When such coverage statistics reveal that many households do not have service (i.e., are "not covered"), it is generally assumed that such households are poor. Global coverage statistics are often compiled by international organizations such as the World Hlealth Organization and the World Bank, and have profoundly shaped the way many people conceptualize infrastructure policy problems.<sup>5</sup> Despite their widespread use and influence, there are in fact numerous problems with tble country-wide infrastructure coverage statistics currently available. The data on household coverage typically come from general-purpose household surveys (such as censuses) that include a few questions designed to determine whether a household has various infrastructure services. For example, a member of a household may be asked whether the house has an in-house piped water connection or electricity. The global statistics from such surveys are usually self-reported by countries and are of varying quality. In many cases the wording of questions in the different surveys

> 4 By "coverage we simply mean whether or not a household has an infrastructure service such as electricity or piped water supply; if a household does have a particular service, it is said to be "covered."

> 5 Coverage data can aid in the description of an existing infrastructure situation, but they
|**Household level data**<br>_Panel C: Village Voucher Committee (VVC) member survey_|||
|---|---|---|
|Number of changes in VVC composition since its<br>creation|Baseline|Number of VVC members that have been changed or replaced since the committee<br>was formed.|
|||Number of responsibilities correctly named by VVC member (up to 7). VVC<br>responsibilities are: (i) inform village farmers about the targeting criteria and<br>selection of beneficiaries, (ii) prepare list of beneficiaries, (iii) select beneficiary<br>|
|Responsibilities correctly named by VVC member|Baseline|farmers from the list given number of vouchers allocated to village, (iv) submit list<br>of beneficiary farmers to the Village Government and Council for approval, (v)<br>inform approved farmers and request applications, (vi) distribute vouchers and<br>(vii) monitor the use of inputs by voucher recipients|
|Correctly named program eligibility criteria|Baseline|Equals 1 if VVC member correctly listed the program eligibility criteria, namely<br>(i) cultivate no more than 1 hectare of maize or rice, (ii) not have used chemical<br>fertilizer or improved seeds in the previous 5 years and (iii) top-up affordability|
|**Village level data**|||
|Number of agrodealers in the village|Baseline|Average number of agrodealers in the village reported by VVC members in each<br>village|
|_Panel D: Household survey_|||
|**Household level data**|||
|Raven's index|Follow-up|Number of correct answers given by the household head in a Raven's test.<br>Assumes value of 0, 1, 2 or 3.|
|PCA food security index|Follow-up|Index of food security
, J. S., and Walker, R. (2018). Regulating Mismeasured Pollution: Implications of Firm Heterogeneity for Environmental Policy. _AEA Papers and Proceedings_ , 108:136–142.

- Martin, R., Muˆuls, M., de Preuxe, L. B., and Wagner, U. J. (2012). Anatomy of a paradox: Management practices, organizational structure and energy efficiency. _J. Environ. Econ. Manage._ , 63(2):208–223.

- Ryan, N. (2018). Energy productivity and energy demand: Experimental evidence from Indian manufacturing plants. _NBER Working Paper 24619_ .

- Schweiger, H. and Stepanov, A. (2019). Energy Subsidies, Energy Intensity and Management Practices. _SSRN Electronic Journal_ .

- Syverson, C. (2004). Market structure and productivity: A concrete example. _J. Polit. Econ._ , 112(6):1181–1222.

- Syverson, C. (2011). What determines productivity? _J. Econ. Lit._ , 49(2):326–365.

- Taylor, M. (2020). Energy Subsidies: Evolution in the Global Energy Transformation to 2050. Technical report, International Renewable Energy Agency, Abu Dhabi.

- U.S. Census Bureau (2015). Management and Organizational Practices Survey (MOPS). `https://www.census.gov/programs-surveys/mops.html` .

- World Bank (2020a). Enterprise Surveys. `https://www.enterprisesurveys.org/en/ enterprisesurvey` .

- World Bank (2020b). Exchange Rate. https://databank.worldbank.org/source/worlddevelopment-indicators.

- World Bank Data (2020). World Bank Country and Lending Groups Classification. `https://datahelpdesk.worldbank.org/knowledgebase/articles/ 906519-world-bank-country-and-lending-groups` .

33
exit year.

**Differences from income and institutions** To better characterize the variation in returns across countries, we estimate the _β_ coefficients in Equation (1) including several country characteristics, beginning with (the log of) real GDP per capita and indicators for British and Socialist legal origins.<sup>19</sup> Since sector, vintage year, and exit year explained relatively little residual variation in Table 5, we exclude their fixed effects from subsequent analysis. The inclusion of real GDP per capita allows us to test the hypothesis from the neoclassical growth model that returns are higher in lower-income economies if markets are segmented (Lucas 1990). The inclusion of indicators of legal origins tests the hypothesis that the protection of property rights leads to higher returns. Protection of property rights is presumed greater in countries with British legal origins, and worse in countries with Socialist legal origins (La Porta et al. 1999). The omitted category is French or German legal origins (only one country in the sample, the Republic of Korea, is classified as having German legal origins, so we pool it with French origins). Results of this regression are reported in Table 6, which presents OLS estimates in Column (1) and quantile regression estimates at the 10th, 25th, 50th, 75th, and 90th percentiles in Columns (2)-(6). Panel A reports the specification for direct investments, and Panel B reports the specification for fund investments.

For GDP per capita, a nuanced pattern emerges. For direct investments in Table 6, Panel A, the OLS specification yields a positive association between GDP per capita and performance, though the standard error is large, such that the confidence interval includes a zero or negative association as well. The quantile regressions reveal that the average positive association is driven by investments at the 25th percentile, where a 1 percentage point increase in GDP per capita is associated with a PME increase of 0.05, an effect which is statistically significant at the