rafmacalaba/data-use-mentions-extended huggingface dataset:
<u>(84; 1.311) (55; 0.993) (84; 0.052) (53; 0.091)</u>

* The table reports the average predicted and observed transaction costs in Malawi kwacha per kilogram of maize (constant 2000 prices) in levels and shares (expressed as a percentage of sales prices, all summarized by supplying district). The predicted levels (the first column) are within sample predicted per unit transaction costs from estimated transaction cost equations, and the observed levels (the second column) are averages based on survey data on transaction costs from Fafchamps et al. (2005). Likewise, shares are based on predicted transaction costs and on trader survey data. Numbers of observations and standard deviation are given in parentheses (.) below the mean. The t-test reports the (absolute) t-statistic of the difference in average values.

Predictions of per unit transaction costs by rural region or urban center vary from 1.3 to 1.8, with a standard deviation between 1 and 1.3. In this exercise, all prices are Malawi kwacha per kilogram in constant 2000 prices. The overall average of predicted unit transaction costs is 1.6 (median: 1.3). Survey observations have per unit transaction costs by rural region or urban center varying from 1.5 to 2.3. The overall average of unit transaction costs from the trader survey is 1.7 (median: 1.4). Predicted shares of transaction costs in terms of selling price by rural region or urban center vary from 6 to 22 percent, with an overall average of 9 percent (median 6 percent). Survey observations of transaction costs shares by rural region or urban center vary from 20 to 21 percent, with an overall average of 21 percent (median 20 percent). The equality test confirms equality for the unit transaction costs and rejects equality for shares (both with the exception of the rural north, possibly because
# **Food Insecurity Experience Scale (FIES)**

|**Statement**|**Share of**<br>**choosin**<br>**that ind**<br>**severe o**<br>_PPCS_|**respondents**<br>**g responses**<br>**icate more**<br>**utcomes (%)**<br>_Facebook_|**Difference**<br>**(Percentage**<br>**point)**|**N**<br>**(combined)**|
|---|---|---|---|---|
|You worried that the household would not|42.07|56.67|14.60|5775|
|have enough food|(1.68)|(2.18)|(2.75)||
|You or any household members were not able|43.00|62.36|19.36|5784|
|to eat the kinds of foods you prefer because of|(1.74)|(2.11)|(2.74)||
|a lack of resources|||||
|You or any household member ate a limited|42.71|60.59|17.89|5797|
|variety of food because of a lack of resources|(1.79)|(2.11)|(2.76)||
|You or any household member ate a smaller|31.16|56.11|24.95|5800|
|meal than you felt you needed because there<br>was not enough food|(1.51)|(2.14)|(2.62)||
|You
maps in their area of interest to isolate local cumulative travel dynamics.

# Isochrone analysis

In Figure 18 ‐ Figure 26 the wide spread of municipal averages for every travel time category, in every figure, underlines the diversity of travel patterns at the municipal level and the tendency for well‐served population centers to obscure serious localized shortfalls in remote areas. The results are diverse and mostly specific to the facilities observed, but a few patterns emerge. Wide spreads between municipal averages are most common for travel times under 4 hours (single day round trips). Significant numbers of outliers are clearly visible for most facilities between 4 - 16 hours, with far fewer outliers above 16 hours. These indicate significant service inaccessibility in some remote areas and extreme levels of inaccessibility in a few.

Figure 21 reinforce several trends observed in Table 12 and Figures 15 to 17. Travel times to hospitals and district headquarters are generally higher than travel times to financial institutions, health posts and all health facilities. Drops in accessibility during monsoon season are modest but consistent across all services. The categories of provincial accessibility observed in Table 12 repeat in these figures, with province 2 and provinces 6 & 7 visibly diverging from provinces 1 and 3‐5.

The contrast between the municipal averages and the national/provincial averages shows the dangers of complacent use of the average figures, the importance of investigating local context for small‐scale projects and the need for a policy focus on remote municipalities and areas. Significant populations in a minority of municipalities face large, overnight travel to access key services. This localization concentrates the impacts on inaccessibility discussed above on specific communities, with possible multiplier effects on human development outcomes where the cumulative effects of inaccessibility to multiple services interact.
has reached. In states that are not fragile, this pattern emerges strongly: the proportion of firms that are pioneers is highest in low-income countries, where the opportunities for harnessing scale and specialization in production abound. Conversely, pioneers decline as income rises, and countries become lower-middle income countries and upper-middle income countries (see Figure 1). This is important because it implies that the impediments are not so overwhelming that feasible public policy interventions would be doomed to ineffectiveness. If pioneering is already common in fragile states, it could be increased to the level appropriate for the state’s level of income.

But fragility systematically undermines the process through which opportunities are discovered. At each income level, fragile states have a substantially smaller proportion of pioneer firms. And, to the extent that fragile states are among the poorer within each income group, then the effect of fragility might be even more pronounced than what the summary statistics suggest. However, while fragility systematically discourages pioneering, it does not kill it off. Despite the disadvantages, the evidence suggests that almost half of firms innovate in one way or another (see Figure 1, left panel). This is important because it implies that the impediments are not so overwhelming that feasible

> 12 The surveys only represent a country's non-agricultural economy. Manufacturing and services are the primary sectors of focus, with services including construction, retail, wholesale, hotels, restaurants, transport, storage, and telecommunications. Mining and financial services, which attract significant investment in fragile states, are excluded. This also excludes firms that employee fewer than five workers, as well as wholly state-owned enterprises.

6
to do transactions with either a branch or an agent. In that sense, they have a choice between at least two alternatives. Still, agents are likely to be physically closer to many potential clients, have longer opening hours and transactions may take less time and be less formal than at a branch. Lacking client survey data, however, we again rely on indirect tests of these hypotheses. Controlling for population and population density (both of which should be positively associated with the frequency and volumes of agent transactions), potential clients in less commercially developed municipalities might benefit less from the convenience of, or have less need for, agents. That is, clients in commercially active municipalities might have greater appreciation for the timesaving convenience of an agent, and thus agent transactions might be more frequent in those areas.<sup>20</sup> While agent transactions are likely to be heavier in commercially developed municipalities, how the general level of local financial development affects agent transactions is harder to predict. On the one hand, the level of financial development may simply be an indication that the local demand for financial services is high, and thus we would expect more transactions from both agents and other providers in financially developed municipalities. But the demand for financial services is likely to be strongly linked to the level of local commercial activity. Since we already control for the level of local commercial development in our regressions, financial development could be negatively related to agents’ transactions to the extent that the financial services provided by agents are substitutes for those from other providers.

> 20 We are assuming that population density serves as a proxy for the number of potential clients that are near an agent. In that sense, density provides information on the convenience associated with reduced travel time to agents rather than branches. In a separate ongoing field experiment, we study how the density of the local FINCA agent network (i.e., the number of agents in close proximity) affects the financial behavior of clients and the profitability of
. relative income, relative GDP growth and trade openness) are defined as deviation from the weighted average of the trading partners.** The weights for trading partners are based on the average importance of each country as an export destination for Turkey and the weights are calculated using the COMTRADE data. The final weights include the 30 largest trading partners.

**Since annual data are used, estimation of single-country determinants puts relatively strong limits on the number of variables to be included in an OLS regression, even in the first step of general-to-specific exercise.** Following Haddad and Nedeljkovic (2012) a preferable solution for confronting parametric model uncertainty and a limited number of observations is model averaging. Model averaging in the present context means that different combinations of the potential current account determinants (not all at once) are used as particular models and each

21
households or use of resident interviewers versus mobile field teams (see, for instance, the analysis of Kilic et al., 2017). In view of these challenges, in this section, we combine (a) the paradata on total time spent on the administration of household and individual questionnaires with (b) the information on survey implementation costs to create the estimated unit cost for a minute of faceto-face multi-topic survey data collection in Cambodia, Ethiopia and Tanzania. This approach enhances the precision with which country-specific unit costs can be compared. These estimates, and comparable measures from other countries, can also be paired with interview-, module- or question-specific average duration measures to create more precise estimates of the costs associated with specific types of data collection in future surveys.

To estimate the unit cost for a minute of survey data collection, one can divide the total survey budget with the total interview length of the survey. The total adjusted interview length has been cleaned to include only key aspects of the interview process (i.e., certain events are excluded in the time calculation process, see earlier section for how the data was cleaned). The disaggregated survey budgets that we have access to are comparable in terms of the main budget categories, including household listing, piloting, recruitment of staff, training and field practice, fieldwork implementation (including renumeration and transportation costs), and purchases of equipment and office materials. Table 6 reports our cost estimates. In Cambodia, the cost of a minute of survey data collection is estimated at $0.87, while the comparable figure is $1.71 in Ethiopia, and $3.94 in Tanzania. With the estimated unit cost, we are able to calculate the average cost of a household interview, which is $71 in Cambodia, $131 in Ethiopia. $472 in Tanzania. Each additional individual interview per household, on average, costs $22 in Cambodia, $23 in Ethiopia, and $80 in Tanzania.

16
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complete primary education. The results from the earnings functions can be used to estimate the

# earnings premium associated with each level of education and the private rate of return to each

# level of schooling.

Table 6: Vietnam: Earnings Functions with Schooling Levels

|Variables|All|Males|Females|Public|Privatea|
|---|---|---|---|---|---|
|Constant|0.870|1.181|0.617|0.845|1.220|
|Primary|0.134|0.035|0.214|-0.278|0.233|
||(2.2)|(0.5)|(2.3)|(2.0)|(3.2)|
|Secondary|0.325|0.269|0.423|0.404|0.260|
||(3.8)|(2.4)|(3.1)|(3.7)|(1.9)|
|Vocational|0.207|0.240|0.259|0.276|---|
||(2.8)|(2.3)|(2.4)|(3.4)||
|University|0.437|0.414|0.488|0.429|---|
||(3.8)|(2.8)|(2.7)|(3.5)||
|Experience|0.064|0.057|0.066|0.053|0.072|
||(9.9)|(6.6)|(6.7)|(5.1)|(8.3)|
|Experience-squared|-0.001|-
are combined into a measure of expected learning‐adjusted years of school, using the conversion metric proposed in the 2018 World Development Report ( **Figure 3** ).

<u>Component 3: Health There is no single broadly‐accepted, directly‐measured, and widely‐available</u> metric of health that is analogous to years of school as a standard metric of educational attainment. In the absence of such a measure, two proxies for the overall health environment are used to populate this component of the index: (i) adult survival rates, defined as the fraction of 15 year‐olds that survive until age 60, and (ii) the rate of stunting for children under age 5 ( **Figure 4** ). Adult survival rates are calculated by the UN Population Division for 197 countries. In the context of the HCI they are used as a proxy for the range of non‐fatal health outcomes that a child born today would experience as an adult if current conditions prevail into the future. Stunting serves as an indicator for the pre‐natal, infant and early childhood health environment, summarizing the risks to good health that children born today are likely to experience in their early years – with important consequences for health and well‐being in adulthood. Data on the prevalence of stunting is reported in the UNICEF‐WHO‐World Bank Joint Malnutrition Estimates. This dataset contains 132 countries with at least one estimate of stunting in the 3
# **Table 8**

**Regressions of Monthly Earnings (In) Full Time, Male, Salaried Workers General and Vocational School Completers Israeli Census, 1983 (n=13879)**

||Direct M|atchings|Wider|Matchings|
|---|---|---|---|---|
|Independent<br>Variables|Coefficient|t-Statistic|Coefficient|t-Statistic|
|YRS.SCH|0.017|1.34|0.017|1.29|
|EXP<br>|0.034|3.52|0.034|3.49|
|EXP<sup>2</sup>|-0.0008|7.32|-0.0008|7.31|
|EXP*YRS.SCH|0.001|1.79|0.001|1.82|
|WEEKS(ln)|0.312|16.58|0.312|16.57|
|HOURS (in)|0.312|9.60|0.313|9.64|
|ETHNIC|-0.131|13.59|-0.129|13.43|
|Economic Sector|:||||
|IND|0.072|2.40|0.069|2.30|
|ELECT|0.260|6.55|0.261|6.59|
|COMM|-0.028|0.86|-0.027|0.84|
|FIN|0.128|3.82|0.130|3.88|
|TRANS|0.073|2.29|0.074|2.32|
|PUB|-0.029|0.94|-0.026|0.83|
|PRIV|-0.072|1.99
; Damm & Dustmann, 2014) and those using observational data exploiting samples of movers (Alesina et al., 2020, 2021; Chetty & Hendren, 2018a; Deutscher, 2020; Lalibert ́e, 2021; Ward, 2020). Third, this paper relates to the set of papers studying the different drivers of intergenerational mobility with the help of data at the sub-national level and the application of quasi-experimental methods (see for example, Card, Domnisoru, & Taylor, 2022; Derenoncourt, 2021; Sharkey

> 2Important recent contributions are Narayan et al. (2018); Van der Weide, Lakner, Gerszon Mahler, Narayan, and Ramasubbaiah (2021) documenting IGM in education at the country-level for 153 economies.

3
## _Productivity-enhancing technology in rural areas_

On economic mobility, another important area for future work is developing individual-level questions on use of technologies, including frequency and purpose of use, that can help expand economic opportunities and access to markets. Mobile phones, for example, can reduce costs of business and farm-related transactions, provide information on market prices and job opportunities, broaden personal and business networks, and ease access to financial services, including mobile money accounts that allow women better control over their income (Demirgüç-Kunt et al, 2017). Among the LSMS, the 2016-17 Malawi and 2013-14 Uganda LSMS-ISA ask about individual ownership of a mobile phone (in the household roster for Malawi and the module on asset ownership for Uganda), but not questions about how they use their phones that can improve policy design; only the 2015-16 Ethiopia LSMS-ISA asks about individual mobile money accounts in the module on savings. The link between mobile phone use and agriculture is also missing. The 2017 Global Findex provides guidance on use-related questions that can be policy-relevant (for example, how individuals have used their mobile phones in financial transactions, whether these transactions are for agriculture, and with what frequency).

Another important issue within rural areas is the use of improved agricultural technology and inputs. Recent LSMS-ISA agriculture modules ask about the use of improved seeds and other inputs at the plot level, that could be compared across men and women plot owners/managers (along with jointly managed/owned plots), and linked to indicators of production and income. However, because women also make up a much smaller share of independent farmers, and are often not equally targeted by agricultural extension and technology initiatives, simply looking at use of improved technologies without understanding who is left out — and why — can skew an understanding of demand for technology. Direct questions on reasons for/against adoption that have been documented in more focused country-level studies are lacking in broader socioeconomic surveys, including skills/knowledge (Udry, 2018); information networks (Munshi, 2004; Moser
wor**<br>|**kers**<br>||||||||||||
|-Manufacturing|42.6|42.4|47.3|47.5|||||||||
|-Agriculture|4.4|3.4|3.0|3.7|17.0|17.7|14.8|13.9|20.7|18.9|9.1|11.0|
|-Industry|2.5|2.0|2.6|2.7|13.2|12.8|13.2|13.9|36.0|32.2|38.8|41.2|
|-Services|50.5|52.2|47.2|46.0|52.8|51.4|56.7|58.3|27.2|25.6|34.7|35.9|
|-Public Sector|||||16.9|18.1|15.3|14.0|16.1|23.3|17.4|11.9|
|_P-value of Pearson chi-square_||_94%_||_97%_||_98%_||_97%_||_27%_||_52%_|

Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.

32
# **<u>Table 1.10. Short-Term Income Mobility, RLMS 1994-2015 (percentage)</u>**

||<br>**Unconditional**|**Conditional**|
|---|---|---|
|**Panel A: 1994-1998**|||
|Upward mobility|26.0|37.2|
|Immobility|53.2|53.2|
|Downwardmobility|20.8|32.8|
|**Panel B: 2000-2004**|||
|Upward mobility|25.8|37.2|
|Immobility|53.2|53.2|
|Downwardmobility|21.0|32.8|
|**Panel C: 2005-2009**|||
|Upward mobility|21.4|31.0|
|Immobility|55.7|55.7|
|Downwardmobility|22.9|35.3|
|**Panel D: 2010-2015**|||
|Upward mobility|22.9|33.2|
|Immobility|57.3|57.3|
|Downwardmobility|19.8|31.4|

**Note** : Estimation results are obtained based on total household income per capita. The distribution by terciles is based on the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 6303 panel individuals in 1994-1998, 5901 panel individuals in 2000-2004, 5100 panel individuals in 2005-2009 and 7498 panel individuals in 2010-2015.

58
Yes|No|No|

Sources: 2018 National Census of Guatemala, 2017 and 2018 municipality statistics (FUNDESA and INE), 20172018 Census of Human Resources in Central Government (INE), 2017 Latinobarometro survey.

Notes: Women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves. Robust standard errors clustered at the municipality level in columns 1 and 3 and at the department level in columns 2, 4 y 5 between brackets. *** p<0.01, ** p<0.05, * p<0.1.

33
decaffeinated|92,601,372|Cereals|121,872,560|
|7<br>Men's trousers,bib & overalls|89,106,353|Coffee,tea,mate and spices|96,788,256|
|8<br>Wood sliced/peeled|78,479,760|Electrical machineryand equipment|35,518,933|
|9<br>Wood(strips & friezes forparquet)|66,503,849|Inorganic chemicals|32,351,639|
|10<br>Wood sawn/chipped length wise|58,368,114|Sugars and sugar confectionery|30,679,037|
|11<br>Men's/boys' shirts of cotton|53,272,747|Mineral fuels and mineral oils|25,945,920|
|12<br>Men's/boys' trousers synthetic|52,852,007|Manmade staple fibers|23,618,003|
|13<br>Posts & beams of copper|40,585,513|Tobacco and manufactured substitutes|21,543,518|
|14<br>Piezo-electricquartz|39,192,681|Footwear, gaiters and the like|19,271,164|
|15<br>Jerseys, pullovers,cardigans,coats|35,222,709|Nuclear reactors,boilers,machinery|18,269,723|

_Source:_ Authors‟ calculation based on Lao PDR customs data We show in Table 3 the top destination countries for the same period. One sees the traditional trade partners and neighbors (Thailand, Vietnam and China) as well as some of the big economies (Japan, the UK, South Korea) but also Swaziland, whose presence is probably due to data entry errors (the confusion with Switzerland). Table 3 also shows the breakdown of export USD values by checkpoint. Checkpoints have been aggregated at the province level (except for
Rohner, and M. Thoenig (2017). This mine is mine! How minerals fuel conflicts in Africa. _American Economic Review 107_ (6), 1564–1610.

- Bernal, C., M. Prem, F. Vargas, and M. Ortiz (2024). Peaceful entry: Entrepreneurship dynamics during Colombia’s Peace Agreement. _Journal of Development Economics 166_ (103119).

- Besley, T. and H. Mueller (2018). Predation, protection, and productivity: A firm-level perspective. _American Economic Journal: Macroeconomics 10_ (2), 184–221.

- Blumenstock, J. E., T. Ghani, S. R. Herskowitz, E. Kapstein, T. Scherer, and O. Toomet (2024). Insecurity and firm displacement: Evidence from Afghan corporate phone records. _Mimeo_ .

- Boehm, C. E., A. Flaaen, and N. Pandalai-Nayar (2019). Input linkages and the transmission of shocks: Firm-level evidence from the 2011 T ̄ohoku earthquake. _Review of Economics and Statistics 101_ (1), 60–75.

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