rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

More than 1.6 billion students have had their schools close temporarily because of the COVID-19 pandemic, over 91% of total enrolled learners (UNESCO, 2020). This has led school systems around the world to attempt to rapidly transition to remote learning. However, it is unclear how prepared students are to respond to this challenge, and whether a lack of availability of electronic devices and internet connections prevents many students from engaging in remote learning. Even when students do have access, there is little available evidence on how much of their time they will devote to learning. Newspaper reports from U.S. schools report high absence rates for lowincome students, with fewer than half of students regularly participating (Goldstein et al, 2020). The concern is that these problems of low access to remote learning technologies and limited attendance may be even greater in developing countries, resulting in limited schooling taking place during school shutdowns.

We examine remote learning of high school students in Ecuador, a country which has suffered one of the worst outbreaks of Coronavirus in the world (Cabrera and Kurmanaev, 2020). Schools in Ecuador were closed on March 13, 2020, and a national lockdown and quarantine period imposed. We began a rapid response phone survey on March 31, 2020 of over 1,500 students who are part of an ongoing evaluation in high schools in the Zone 2 administrative region of the country. Over the month of April we were able to interview 64 percent of our baseline sample, asking them questions about their access and use of remote learning technologies, detailed time-use questions, and questions on their mental health and the main stressors they face because of COVID-19. We then examine heterogeneity in these responses by gender, school grade, wealth quartile, mother’s education, ethnicity, province, and internet access.

We find that most students are engaging in remote learning. Access to remote learning technologies are reasonably high, with 74 percent having internet access at home, and
depth innovation module (long questionnaire) was administered to randomly selected ES respondents. The questionnaire used in the innovation module is also based on the Oslo manual but expanded to capture more details on the innovation introduced and to integrate other important questions on organization and management practices, use of ICT, and innovation inputs.<sup>4</sup> The follow up survey was submitted to firms independently from their answers to the innovation section in the ES. Whenever possible the same respondent was interviewed in the two rounds of data collection.<sup>5</sup>

> 3 More information on the Enterprise Surveys are available on the website http://www.enterprisesurveys.org/ 4 Methodological tests and pilot field work were conducted in preparation to the in‐depth innovation module. The methodological tests was conducted in Ethiopia, Rwanda and Zimbabwe and included preliminary innovation surveys complemented by focus groups and semi‐qualitative interviews. The in‐depth questionnaire also benefited from inputs from experts on innovation in the National Science Foundation (NSF), WB and IDB.

> 5 The World Bank Group Enterprise Analysis Unit also collected data on innovation in Europe and Central Asia (ECA) and Middle East and North Africa (MENA) for a total number of 49 countries with innovation data. Innovation Enterprise Surveys in ECA and MENA are also collected following a two stages approach. The methodology for the

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merit some discussion. First, as a non-weak instrument, the dummy indicating that an owner was active in an activity after 2009 is positive and strongly significant, indicating that all else equal, those taking up activities after stabilization are much more likely to not have an under-contract income in their household. Not surprisingly, those with beyond a secondary education are less likely to have NOWAGE=1, as are those with more employees. Operators with a fixed, but temporary premises (e.g. a market stall) are also less likely to be operating in a situation of few opportunities outside informality.

As mentioned above, the biprobit specification is particularly susceptible to bias from misspecification, including from additional, omitted variables. The rich data from the survey allow for additional co-variates to be included. Table 6 includes several additional controls (in both X’ and therefore Z’), replicating results from Table 5, but including controls for dummies on whether there are children (6 years old or younger) in the owner’s household, a dummy for whether the owner is the primary income earner, a dummy for the marital status of the owner, a dummy if the business uses public services (in the form of use of the electrical grid), and a dummy variable for whether the business was enumerated in an isolated residential area (i.e. no industrial or retail area within 500m).<sup>40</sup> With these additional controls, for NOWAGE, the 2SLS estimates are sufficiently identified with non-weak instruments, and the biprobit estimates are bivariate normal with evidence that a two-stage correction is needed; for NECESSITY, though ρ is significant (and negative), suggesting the appropriateness of the biprobit, the 2SLS fails to reject under-identification (though the instrument is statistically not weak).

> 40 This was done using OpenSourceMaps, which include coded data on land usage, each informal business can be coded according to the near-area surrounding where they were interviewed.  Using a 500m radius, an estimated
precision in multiple contexts before they can be applied. To do that, we compare estimates from these models to ground-based “truth” derived from unit-level census data in seven countries: Burkina Faso, Madagascar, Malawi, Mozambique, Sri Lanka, Tanzania, and Vietnam. While these countries were selected because of the availabiltiy of census data with either enumeration area geocoordinates or sub-area identifiers with corresponding shapefiles, they also cover a wide variety of contexts, varying in incomes as well as geography.<sup>5</sup> The official administrative boundaries available in shapefiles differ across countries. In general, we pull geospatial data for the lowest administrative level possible. For example, this is the Fokontany in Madagascar, while in Malawi it is the Enumeration Area. We refer to these levels as “subareas” throughout this paper.

Table 3 presents the number of areas and subareas for each country. Sub-areas, in particular, vary greatly across countries, from as few as 8,763 in Burkina Faso to as many as 67,239 in Mozambique.<sup>6</sup> Areas, on the other hand, show less variation, ranging from 169 (Tanzania) to 1,515 (Madagsacar). The table also shows the number of households in the unit-level census data. We have the full census for Madagascar, Mozambique, and Sri Lanka, but a sub-sample of the full census for the other countries.<sup>7</sup>

# **2.1 Outcomes**

We focus on two separate measures of welfare: an asset index and poverty rates. We calculate an asset index for all countries and estimated poverty rates for Malawi and Tanzania, both of which have a near-concurrent household survey that allows us to impute poverty into the census. Using survey data, we predict household per capita expenditures for all households in the census using assets. We then classify households as poor (or not) based on the quantile of their imputed expenditures per capita and the poverty rate
# **5.2 Evidence on the role of livelihood transformation and poverty**

# **reduction**

Without panel data, analysis of the role of Uganda‘s livelihood transformations in poverty reduction suffers from inherent problems of endogeniety. Nonetheless, the evidence strongly suggests a relationship. Despite the enormous problems of comparing earnings in wage and non-wage sectors, all evidence points to the non-farm sector offering higher earnings than subsistence farming – not only because hours worked over the year tend to be higher but also real earnings per day are on average higher. The diversification of household livelihood portfolios into non-farm sources of income thus increases income and household welfare.

To quantify the role of household livelihood expansion in poverty reduction, we estimate determinants of household welfare. We run the standard OLS regression, but we include type of income source and their interactions as explanatory variables and controlling for other confounders. Household welfare is proxied by the natural log of monthly consumption per adult equivalent. This analysis is based on the UNHS 2005/06, which was chosen ahead of the UNHS 2009/10 due to previously mentioned data problems in the labor module that renders the later survey inadequate for a complete livelihood analysis. The results from the consumption regressions are presented in Table 2 and summary statistics of the explanatory variables used in the regression are presented in Table 3 in the appendix.

Regression results show that households with income from a household _enterprise_ have a higher income by at least 14 percent in rural areas and 23 percent in urban areas, while those with non-agriculture _wage income_ have higher per capita welfare by at least 12 percent in rural areas and 13 percent in urban areas (columns 21
4%|
|_Totals_|_100.0%_|_100.0%_|_100.0%_|_100.0%_|_100.0%_|

_Source: DANE survey, 2003_ 25. **More people seek loans in the informal than in the formal sector.** The Bogotá survey reveals that the banked population is more likely to seek loans than the unbanked - by a factor of six to one (see Figure 3.12). Among the population surveyed 13.6% of the total population successfully sought loans with the informal sector in the past thirty six months while another 8.6% successfully sought loans from the formal sector. This proportion is higher than that reported in other studies. Only 10% of those surveyed in Mexico City reported requesting a loan in the past 36 months from either formal or informal sources.

**Figure 3. 12  Loan request from formal institutions**

<!-- Start of picture text --> 97%<br>100%<br>91%<br>90% 82%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>18%<br>20%<br>9%<br>10% 3%<br>0%<br>Banked Unbanked Total population<br>Requested a formal loan in the past 3 years<br>Did not request a formal loan in the past 3 years<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 26. **Higher loan approval rates help explain the relatively higher importance of credit obtained outside the banking system.** Table 3.5 shows the approval rate, according to the would-be borrowers surveyed, 56
# 3. Base scenario

The base scenario is designed to generate a plausible business-as-usual path for Yemen’s economy during the period 2020-2030, i.e., a path that assumes that fighting continues along the lines of the past eight years. As a result, economic growth is slow and living conditions do not improve. The subsequent analysis compares the base and non-base scenarios during the period 2022-2030. The solution for 2020 replicates the information in the SAM while, in 2021 and 2022, GDP grows at rates that are estimated or projected by the IMF (2022).<sup>6</sup> The assumptions are kept as simple and transparent as possible. Most importantly, it is assumed that (a) all international (export and import prices) are constant in real terms; and (b) for most institutional payments, including receipt and spending items in the government budget, the GDP shares are constant, kept at shares computed from the 2020 SAM. At the macro level, it is assumed that the government budget is cleared via tax adjustments, that the real exchange rate clears the balance of payments, and that household savings adjust to help maintain domestically financed private investment with an unchanged GDP share.

Figures 3.1-3.7 summarize results from the base scenario (covering indicators related to the macroeconomy, sectors, household welfare, and poverty); Tables 3.1-3.6 provide additional information about both base and non-base scenarios, including macro data (growth and GDP shares), the government budget, the balance of payments, and data on economic structure in 2022 and for the base scenario in 2030.

As intended, to facilitate comparisons to non-base scenarios, the picture is one of stable growth starting from 2023. GDP growth is simulated at an annual rate at 2.0 percent between 2022 and 2030 (Figure 3.1).<sup>7</sup> Absorption (the sum of domestic final demands) is boosted by Yemen’s trade deficit, permitting
capital accumulation decisions. The model combines the labor supply model with worker mobility costs of Artuc, Chaudhuri and McLaren (2010) with the labor demand model with capital adjustment costs of Cooper and Haltiwanger (2006) and Bloom (2009). The labor supply side is characterized by a rational expectations optimization problem of workers facing mobility costs and a time-varying idiosyncratic shock. The labor demand side is characterized by the rational expectations intertemporal profit maximization problem of firms facing costs for adjusting their capital stock.

Bet, Brambilla and Porto (2011) fit the model to plant-level panel data and household survey data from Argentina. They estimate large costs of adjusting both capital and labor. For capital, the fixed cost of adjustment is around 14.5 percent of the firm’s capital stock, and the quadratic cost parameter is 0.11. The partial irreversibility cost parameter is 0.91, suggesting that the selling price of installed capital is 9 percent lower than the buying price of capital. As discussed in section 5.1, the labor mobility costs are equivalent to around 9 times the annual value of wages.

The authors also study transitional dynamics following a tariff cut in the Food and Beverages sector. Figure 4 reports the evolution of employment in the manufacturing sectors (top left panel), employment in the non-manufacturing sector (top right panel), real wages (bottom left) and workers’ present values (bottom right). Overall, Bet, Brambilla and Porto (2011) observe a sizeable effect of trade liberalization on labor markets and a sluggish adjustment of employment. For instance, labor reallocation towards the new steady state is complete after more than 15 years. In particular, labor allocation in the food and beverages sector decreases from a steady-state value of approximately 10 percent to a new steady-state of around 6 percent. Labor reallocates to all other sectors, with the largest increases in Nonmanufactures and Other Manufactures.

In the bottom panels, as expected, real wages in the food and beverages sector decrease as a result of
energy and their unit energy provision to 2,430 kcal per AME produced the desirable macronutrient intake used in Figure 2.

Desirable micronutrient intake (Annex 6b) for benchmarking in Figure 3 is from the study Desirable Dietary Pattern for Bangladesh, which adapted recommended nutrient intake (RNI) of micronutrients from FAO 2004 (Nahar et al. 2013). Regarding Ca, Vit. A, Vit. B12, Folate, we chose RNI for males aged 19-65; we also took the lowest bioavailability (5 percent) for micronutrients Fe and Zn for adult males between 19 and 65.

# _2.4.3 Data on trade_

Two trade data sets were used to allocate shares of imported consumption — FBS and TM data sets from FAOSTAT.<sup>10,11</sup> FBS 2016 and TM 2016 were used for GHG emission calculations in 2016 whereas FBS 1998 and TM 1998 were used for year 2000, due to the unavailability of FBS and TM in 2000. FBS and TM have their own food coding systems which are different from HIES. FBS differs from TM in that FBS defines food items by their primary equivalents and thus it contains fewer food items than HIES and TM. Moreover, FBS does not show COOs for imported items. Both datasets contain data for imports and exports whereas FBS also has data for population, domestic production, stock variation, and domestic supply. As shown in section 2.3.2, FBS together with TM are essential for calculating shares of production by COO.

> 8 U.S. Department of Agriculture. Food Data Central. Available at: https://fdc.nal.usda.gov/.

> 9 The recommended energy intake of 2,430 kcal per AME is from FBDG of Bangladesh in 2013, which is lower than 2,854.1 kcal per AME (age between 30 and 59) with a moderate activity level, found by Waid and others in their study in 2017.

> 10FAO. FAOSTAT Trade Matrix database. Available
# **1 Introduction**

The marginal value of consumption is very high for poor households. For this reason, one expects households in low-income countries to be especially mindful of ways to arrange purchases so as to maximize consumption per dollar. This paper studies the surprising, contrary finding that many households in Tanzania purchase non-perishable goods in small increments, multiple times, over a two-week period. If price schedules were linear and transaction costs minimal, frequent purchasing of small quantities would have no impact on the budget set. If bulk discounts were available, but only for large quantities representing several weeks or months of consumption, there could be many reasons not to buy in bulk. However, we find that many items exhibit bulk discounts over modest quantity ranges. Households appear to be systematically over-paying for consumption goods in order to maintain a pattern of frequent, small-quantity purchases.

The goal of this paper is to better understand this phenomenon. We aim to answer two questions. First, how much consumption is foregone because of small-quantity purchasing? Second, why do households make purchases in this way?

To answer the first question we analyze transaction diaries maintained by 1,493 Tanzanian households over a two-week period. Diary respondents recorded the date, quantity, price, and detailed description of every purchase. From these data we calculate the total quantity purchased at the household-item level over two weeks, the total amount paid, and the counterfactual cost if the household had purchased the total quantity in one transaction. This last step makes use of expenditure schedules that we estimate from the data (and validate with market price surveys). We limit our analysis to 19 standardized items that are non-perishable over the relevant quantity and time ranges. The item list includes numerous staples: maize, rice, cooking oil, kerosene, onions, dried sardines.

We find that across items purchased multiple times over two weeks, the value of forgone consumption is equal to 8.7% of the value of expenditure. By simply bundling purchases made over a short period
|Gibson &<br>Carnavale<br>(2015)|Milan, Italy|2008-2012|Traffic, pollution and<br>weather|OLS||
|---|---|---|---|---|---|
|Kreindler<br>(2018)|India|2017|GPS location data for trips<br>of car and motorcycle<br>commuters and survey|Randomized<br>Experiment|The costs of rescheduling<br>travel to inconvenient<br>times will almost entirely<br>offset the benefits of the<br>saved travel time, resulting<br>in only a small consumer<br>welfare gain|
|**_Subsidies for Ad_**<br>|**_option of Altern_**<br>|**_ative Fuel V_**<br>|**_ehicles_**<br>|||
|Huse &<br>Lucinda (2014)|Sweden|2004-2009|Car Registrations,<br>Characteristics, Fuel Data,<br>Mileage Data|Structural model to<br>examine a number of<br>counterfactuals to the<br>actual policy.|Substantial share of FFV<br>consumers in would have<br>purchased FFVs in the<br>absence of the cash rebates<br>because of the vehicles’<br>lower operational costs|
|Xing et al<br>(2019)|United States|2010-2014|Characteristics of gasoline<br>and electric vehicle<br>models|Simulation|Cost effectiveness is<br>greater for policies that<br>that eliminate or reduce<br>subsidies for high-income<br>households, and provide<br>more generous subsidies<br>for low-income households|
|Muehlegger &<br>Rapson (2018)|United States|2012-2018|Disadvantaged community<br>designations, program<br
15

- old low-rise units saw the greatest increase in privatization (Table 1.9);

- tenants of standard modem panel buildings still showed no eagemess to privatize (Table 1.9);

- potentially mobile households (e.g., couples with children) displayed more interest in privatizing (Tabie 1.1 1);

- the main distinction from previous years was that the high-end income categories were now more active than the others (Fig. 1.2). The greatest changes in the attitudes towards housing privatization occurred in the 20% highest-income group. The share of apartment owners among the middle-income group fell (this can be explained, in part, by redistribution of incomes among social groups).

The trends described here have positive implications for the process of housing privatization, and consequently, for the fledgling housing market, as the entrance of younger, more mobile and wealthier families into the housing market strengthens its development. As the market develops, it in turn affects the tenants' inclination to privatize their apartments. That is, the two processes of housing privatization and the development of the housing market exert reciprocal, mutually beneficial influences on each other.

**Fig.** 1.2. Moscow households who privatized their dwellings By deciles based on total household income

<!-- Start of picture text --> 70 1<br>50 e<br>1047 1<br>60 ~ ~ ~ ~~ 61.1 3. 29<br>1 2 3 ~ 403 6 B 7 8 S 140.3<br>40 3.2-37.7 -<br>30 - 31.2<br>27.5<br>20 -<br>1 2 3 4 5 6 7 8 9 10<br>1-01994 1 6<br><!-- End of picture text -->

**Source: The Moscow LDngitudinal Household Survey in Dec-1994 and Jan-1996. Note:** 1 **
# **Appendix C. Estimating TFP**

Here we describe how we estimate the firm-level TFP in three ways.

We use a standard log-linear Cobb-Douglas production function to estimate the firm-level TFP. Specifically, the TFP of firm _i_ in year _t_ is the estimated residual from the regression: i 0 i i i (A1) where i is the logarithm of value-added, and kk i and ll i are the logarithms of capital and labor, yy = ββ + ββ kk + ββ ll + DD respectively. To allow for different factor intensities across industries, we estimate equation (A1) yy kk ll separately for each two-digit industry. TFP can be interpreted as the relative productivity of a firm within its industry.

Real value added is obtained by subtracting the real input from the real output. We use the twodigit ex-factory price index from _China Urban Living and Price Statistics_ to deflate the output. The input deflator is calculated based on the available output deflators at the two-digit industry level and information from the National Input-Output (IO) tables in 1997, 2002, and 2007. From the IO tables, we know how much inputs are needed to produce a unit of output. Then the average input price index is the weighted average of the price indices of those inputs. Thus, to obtain the input deflator for each industry, we calculate a weighted average of the input deflators, using as weights the coefficients in the IO table.<sup>23</sup> In the ASIF dataset, firms report the total annual employment, but they do not report the real capital stock. Instead, the firms report the value of their fixed capital stock at the original purchase prices. As these book values are the sum of the nominal values for different years, they are not equal to the real capital stock and are not comparable across time and across firms. Since we do not have all past investments of a firm to construct the real capital stock, we follow Brandt, Van Biesebroeck and Zhang (2012)