rafmacalaba/data-use-mentions-extended huggingface dataset:
labor force surveys (LFS) and multi-topic household surveys – to differentiate between individuals engaged in own-use production work and individuals engaged in employment. While own-use production of goods can take many forms (e.g. collection of firewood or water, food preservation, etc. for family use), the revised standards have the largest implications for the measurement of agricultural work, where surveys need to distinguish between a farmer producing for own/family use or for sale. This, however, is difficult to measure, and historically, there has been scant empirical evidence and guidance on measurement practices. While the 2007 Standard Classification of Occupations (ISCO-08) distinguishes “subsistence farmers, fishers, hunters and gatherers” (sub-major group 63) and “market-oriented skilled agricultural workers and forestry, fishery and hunting workers” (sub-major groups 61 and 62) (ILO 2013a), this separation has had limited practical relevance, as occupational breakdowns rarely go below the level of major groups (i.e. combining all agricultural

> 1 The International Conference of Labour Statisticians (ICLS) is a standard-setting body in labor statistics, hosted every five years by the International Labour Organization (ILO). As per the tripartite structure of the ILO, its participants include experts from governments (typically officials from the ministries dealing with labor issues and national statistical offices), employers' and workers' associations.

> 2 The 19th ICLS also introduced a new ‘forms-of-work’ framework.

2
higher rates in the rural North-East. This discrepancy may be related to differential access to conflict affected areas of the rural North-East by the survey teams of the GHS and the MICS survey, which may suggest that the temporarily inaccessible areas display notably higher education deprivation rates than the more accessible ones. Such a discrepancy would confirm findings by Bertoni et al. (2018) that the conflict does notably effect schooling of children in the North-East. It is also worth noting that while children in the rural South-West similar to the children in the northern regions, appear to start schooling later and used to have slightly higher education deprivation rates than the other southern regions, towards GHS wave 3 also in the South West almost all children have been at some point in school by the time they reach the oldest child category.

Figure 6: National Level Severe Education Deprivation

> 22 UNICEF (2017) evaluates for instance the unconditional cash transfer program ranging from 2014-2016 with the primary objective of increasing the enrolment, retention and completion of basic education of girl’s in specific areas of Niger and Sokoto States, presented in.

19
living in a household with such an account<br><!-- End of picture text --> Note: Individual-level sampling weights applied. Statistics capture whether any household member had an account or had savings. Source: EHCVM and World Bank estimates.

There is no clear evidence that households take out loans or are able to sell livestock to cope with the lean season. Across the EHCVM sample, around 16.2 percent of households had outstanding loans at the time of the survey, but the dates when these loans were agreed do not reveal any clear seasonal patterns. Additionally, despite the hypothesis that households may use sales of livestock as a buffer to insulate against losses induced by shocks or seasonality, existing evidence from both the Sahel and other countries suggests that this mechanism is not frequently observed in practice.<sup>20</sup> In-keeping with this, in the EHCVM data, the number of animals that households own in fact appears to be very slightly higher in the wave corresponding to the lean season, taking Burkina Faso, Mali, Niger, and Senegal together. These results persist even when disaggregated by animal type – large ruminants, small ruminants, pigs, poultry, and

> 18 The financial institutions considered are regular banks; postal banks; rural savings associations or microfinance institutions; mobile banking; and stored-value or prepaid cards.

> 19 This may partly reflect patterns of mobile phone ownership. According to the EHCVM assets module, around 95.3 percent, 93.7 percent, and 98.8 percent of households own at least one mobile phone in Burkina Faso, Mali, and Senegal respectively, compared to 72.7 percent and 64.4 percent of households in Niger and Chad.

> 20 See Hoogeveen (2003), Fafchamps and Lund (2003), and Kazianga and Udry (2006).

10
total consumption increases to nearly 50% when each individual is attributed their cell per capita consumption level (and that of non-food consumption to 67.2%). This level of inequality would place Senegal just below Honduras in the countries ranking, another 8 ranks hike from where the country stands when intra-household inequalities are not accounted for.

How important are intra-household inequalities in the building of inter-personal inequalities? To answer this question we compute the Theil T index, which we decompose between intra- and inter-household inequalities (Table 4). The decomposition indicates that more than 18% of total inequalities in non-food expenditures occur within households. As expected, in the case of food expenditures the share of intra-household inequality is much lower, reaching only 6% of the total.

Assuming equal sharing of food between household members eating together underestimates actual intra-household inequality in food consumption. In fact, although we cannot directly observe it, there are some indications in the literature that in developing countries contexts there exist intra-household inequalities in the access to nutritional inputs (see e.g. Dercon and Krishnan, 2000, D’Souza and Tandon, forthcoming, or Brown et al., forthcoming). Further, the intra-household inequality we measure is also likely to be underestimated by the fact that vulnerable household members are assigned the per capita consumption level of the cell they belong to, even though it is likely they consume less than other members of the cell. This could for example be the case of the elderly widowed mother of the household head, attached to his cell for the purpose of our consumption survey.

For total consumption, the share of intra-household inequality is nearly 14% of total inequality, and, as explained above, this should be considered a lower bound. Whether this is a large share or not is difficult to assess without a comparison point. We can think of one external comparison, that given by Klasen and Lahori (2016), who estimate intra-household inequality 16
traffic per freeway lane – leads to a decrease in productivity growth per worker of up to 0.033%. With a panel of similar geographical scope, Jin and Rafferty (2017) show that an increase in congestion growth by one percent – measured by an index of traffic delays – furthermore causes a decrease in employment growth of 0.08%.

Traffic disruptions and congestion have been shown to have negative productivity effects in developing countries too. Based on a survey with commuters in Kumasi, Ghana, congestion has been estimated to result in an average loss of daily productive hours of 9% per worker (Harriet et al. 2013). In the Greater Cairo Metropolitan Area in Egypt, traffic congestions cause direct costs of US$ 5.1 billion annually as of 2010, an amount that is only expected to increase (World Bank, 2013). The upside to these numbers is that addressing congestion does not have to be costly. A recent modeling study in Grand Casablanca, Morocco, finds that a combination of demand‐side instruments – a fuel tax, a parking fee, and a bus fare – increases income by 0.8% through decreasing travel times and thus freeing up travelers’ time for other activities. These gains can be further increased by adding bus services or constructing new roads (Anas, De Sarkar and Timilsina, 2018).

4. Firms adapt to unreliable infrastructure – often at considerable cost As infrastructure directly affects firms’ productivity, firms take infrastructure quality into account when making business decisions. Through adaptation measures, firms can influence to what degree they will be affected by disruptions.

This can be illustrated when considering the effects of a large shock, such as an earthquake. Figure 4 plots the recovery of a directly affected firm’s production capacity. The recovery from damages to the firm’s productive assets is slowed down by the loss of electricity, water, and gas supply (subsumed under ‘lifelines’). Here, two factors determine the impact of the infrastructure
4|0|0|
|**Share of Assets**|0|0.99|2.2|0|0|

Source: Central Bank of Kenya data

# **4. Banking Sector Efficiency**

In this section we describe how interest spreads, our proxy for the efficiency of financial intermediation, have evolved over the past ten years in Kenya, and relate those developments to our discussion of the government strategy for the development of the financial sector and the accompanying changes in the legal and regulatory framework discussed above. We then present simple arithmetic decompositions of the interest spreads to explain the factors that have contributed to their relatively high levels and also to their decline over time. We also examine how spread levels and their determinants differ by bank size and ownership type. Finally, we offer regressions that better enable us to test whether the determinants of spreads differ by bank ownership type and if such differences can be explained by the types of activities that different owners pursue.

Headline indicators produced by the Central Bank of Kenya indicate that spreads declined from 1999 to 2003 and have since remained stable (Figure 1). The relatively sharp decline in spreads in 2003 owes much to improvements in Kenya’s fiscal situation and general macro-management, which led to substantial declines in both the volume of government securities issued and the interest rates paid. As government securities became a less attractive investment option for banks, they turned to new lending opportunities, and the competition 10
# **Introduction**

The World Bank has declared that data are the next deprivation to end; they argue that the lack of data causes many of the world’s poorest populations to be overlooked when resources are allocated to address their essential needs [1]. Data deprivation is a pressing challenge with as many as 74% of the global and 97% of the Sub-Saharan African population living in countries without adequate vital registration [2]; one-third of countries lack any poverty statistics [1]; and only 17% of the estimated road traffic deaths are reported in official figures of low-income countries [3]. Without data to inform national and urban policies, the gap between low- and high-income countries will worsen [4]. However, while official statistics are poor, data in the hands of private providers are plentiful, populated by the rapid expansion of mobile phones and social media. Globally, phone penetration reached 67% in 2019 [5], and social media penetration is almost 50% [6]. This provides an opportunity for using crowdsourced data to study major urban and development policies [7–11].

In this project we test the hypothesis of whether privately maintained data can be transformed into a resource to better understand development challenges. Private data have been used to characterize populations from determining poverty to understanding public emotions [12–17]. Here, we use private data to describe the urban environment that affects those populations, specifically analyzing events reported on social media that affect people’s safety such as road traffic crashes, crime or floods. We focus on road traffic crashes (RTCs). Despite being the number one cause of death for children and young adults aged 5-29 years, the lack of adequate data on RTCs is a recognized and unmet challenge <mark>[18]</mark> . The objective is to improve RTC data for urban planners so they can contribute to addressing the high toll of road deaths, estimated globally at 1.35 million a
28 Figure A2.3 and Figure A2.4 show the same calculations as those for Figure 3. However, in interpreting the results, it is important to bear in mind that, unlike national statistics, the population in each quintile is not the same once people are disaggregated into urban and rural areas, and hence the differences show only qualitative trends. Importantly, for any energy source, the difference taking the national sample is not a weighted average of the differences in urban and rural areas.

# **Figure A2.3  Difference between the top and bottom quintile in the percentage of urban residents citing different forms of primary energy for cooking**

<!-- Start of picture text --> Electricity<br><!-- End of picture text --> <!-- Start of picture text --> Biogas+solar<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

_Note_ : For each energy category, the number shown is obtained by subtracting the share of the population in the bottom quintile from that in the top quintile. For example, 64% for LPG in Angola represents the difference between 90% of the top quintile and 26% of the bottom quintile citing LPG as the primary cooking fuel. Wood combines firewood and charcoal in Botswana. Others = mostly agricultural residues and dung.

# **Figure A2.4  Difference between the top and bottom quintile in the percentage of rural residents citing different forms of primary energy for cooking**

<!-- Start of picture text --> Electricity<br><!-- End of picture text --> <!-- Start of picture text --> Biogas+solar<br><!-- End of picture text -->
_~ ~_ fiveFormostthefrequently _male − female_ occurringdimension,in our corpus.we usedWordsvariousforgender-specific _career~ − family~_ wordsandwhich _good~_ were _− bad_<sup>_~_</sup> foundwereoutchosento beinthea similar fashion. Only five words were chosen for each because, given the relatively small size of the corpus, the inclusion of too many words could result in invalid measures of slant. The word used are displayed in table A2.

Table A2: Words used for each vector dimension

|Vector dimension<br>|Words|
|---|---|
|_~_<br>_MaleNames_<br>|john, joseph, peter, james, david|
|_~_<br>_FemaleNames_<br>_~_|faith, mary, rose, jane, margaret|
|_Male_<br>|his, he, him, mr, himself|
|_~_<br>_Female_<br>|her, she, ms, mrs, herself|
|_~_<br>_Good_<br>|competent, strong, power, serious, professional|
|_~_<br>_Bad_<br>|frivolous, vain, incompetent, unreasonable, incapable|
|_~_<br>_Career_<br>|company, service, pay, business, work|
|_~_<br>_Family_|family, wife, mother, father, brother|

Each dimensions includes the five most common relevant words in the corpus. Only five words were chosen for each because, given the relatively small size of the corpus, the inclusion of too many words could results in invalid measures of slant.

To apply this process to the data, we first preprocessed the entire Kenya Law corpus of judgements by removing punctuations (but retaining hyphenated words). To avoid case sensitivity, we transformed all our words to lower case. We then retained only the most common 50,000 words in all judicial
assets financed through non-bank financial institutions. The third variable is the percentage of fixed assets financed through other sources, including credit/advances from suppliers/customers.

# _2.4 Other variables_

We account also for additional firm characteristics that may affect the decision of and the extent to which a firm innovates. _Size_ is measured through the natural logarithm of the number of employees. In principle, larger firms are better equipped to innovate more because of larger cash flows, lower fixed costs, and a wider range of human capital skills and knowledge. Yet small size allows for more flexibility in the undertaking of innovative projects (Rogers 2004). We also include a labor productivity variable as firms with a skilled workforce are more likely to pursue innovative projects. The literature documents that innovation of all types tend to have a positive impact on firm-level productivity, as per the surveys of 7
. Using pictures of different types of spending and plastic chips, we first asked grant recipients to indicate how they used the grant. Table 5 reports self-reported allocations of the grant by treatment arm. We see little effect of the recent therapy on allocations. Little of the grant seems to have spent on drugs, alcohol, gambling and prostitution. Even if men underreport these expenses, we see no difference between cash recipients who did and did not receive therapy.

We can also look at expenditure data, which included a range of business investments in the two weeks prior to the 2- and 5-week surveys. As reported in Table 4, those who received only cash reported $56 more investment in each 2-week period. Thus the total 5-week investment treatment effect is at least $112—around 60% of the grant. Meanwhile, the therapy only group resembled the control group in terms of investment. Labor inputs also rise, as total hours of work per week rise by 20% in the short run.

These short run investments did not last. In the cash only group, the stock of business assets after a year is only $19 greater than in the control group, not statistically significant. We also see no long run difference in total work hours.<sup>38</sup> What happened? From qualitative interviews, insecure property rights were a major barrier to capital accumulation. A large number of men reported the theft of all their assets, or all their wares, on a regular basis, by criminals or (for market wares) the police.<sup>39</sup> At each

> rising about 36% relative to the control mean.

> 38All three treatments caused individuals to substitute from illicit work to non-agricultural low-skill business in the short term, but the effects were most pronounced and longer lasting for the group that received both cash and therapy (see Appendix E.7).

> 39In some cases this was theft by a friend, family member, or stranger
---|---|---|---|---|---|---|---|---|---|
|**_concept_**||**_Girls_**|**_Boys_**|**_Within_**<br>**_-group_**|**_Between_**<br>**_-group_**|**_Rural_**|**_Urban_**|**_Within-_**<br>**_group_**|**_Between_**<br>**_-group_**|
|Market<br>income|0.320|0.330|0.310|0.320|0.001|0.262|0.302|0.276|0.044|
|Disposable<br>income|0.310|0.317|0.301|0.309|0.001|0.257|0.299|0.272|0.038|
|Consumable<br>income|0.312|0.320|0.303|0.311|0.001|0.260|0.303|0.274|0.037|
|Final income|0.267|0.273|0.261|0.267|0.000|0.228|0.268|0.241|0.027|

**_Source:_** _Authors’ calculations; Data from ESS 2018/19._ **_Note:_** _Inequality is computed as a Theil’s index._

In summary, this analysis suggests that the overall fiscal system is not well calibrated to reduce monetary poverty, with poverty rates increasing for all groups between market income and consumable income. Only the significant in-kind transfers for education and health result in a decrease in the poverty
of gender gaps in both entrepreneurship and labor force participation on GDP, using a Lucas span-of-control type model of occupational choice.<sup>38</sup> Their estimates are available for 139 countries (33 OCED, 106 non-OECD), which is about 75% of the basic GEGI and 87% of the full GEGI (there is no interpolated data). CT define GDP losses relative to a higher benchmark without gender gaps—rather than as a fraction of the lower status quo for the GEGI (and MGI 2015)—so for comparability, CT’s gender gaps need to be scaled up by . I compare the basic GEGI at the country level with CT estimates of losses due to participation only (CT-Participation), which I calculate as their total losses (Variable 4 in Appendix Tables B1 and C1) 1/(1 −HHCC%) less losses only due to gender gaps in entrepreneurship losses (Variable 3 in Appendix Tables B1 and C1).

Figure 11 shows that the estimates of GDP losses from the basic GEGI are highly correlated with those from CT and for some countries they are almost identical (such as the Republic of Yemen, the Arab Republic of Egypt, Fiji and Tunisia). In general, the CT-Participation measure is about 10%-20% smaller than the basic GEGI.<sup>39</sup> Most of the differences are likely due to definitional difference between employment (for the GEGI) and participation (for CT2016), as well as different vintages of labor-market data. CT’s full measure—including entrepreneurship gaps in addition to participation gaps—is about 10ppts larger than the basic GEGI for low-GEGI countries, but almost identical for high-GEGI countries (Appendix Figure 6).<sup>40</sup>

**Devadas and Kim (2020)** use the World Bank Long Term Growth Model (as in Section 5) to calculate the effects on growth of closing gender gaps in labor force participation.<sup>41<
**|**0**|
|**10134 -16669**|**40**|**20**|**2643**|
|**16670 - 26864**|**30**|**30**|**6228**|
|**26855 +**|**10**|**40**|**14661**|
|**Weighted average**||21|4391|

_Source: Author's estimates using 1992 Individual Budget of households Survey, NSI._
export experience and productivity varies across industries. The volume of industry exports and the proportion of industry exports going to high-income countries contribute to this cross-industry variation.

The paper is organized as follows. In Section 2, we review the learning-by-exporting literature. Our empirical strategy and data are described in Sections 3 and 4. Our results are presented in Sections 5 and 6. Section 7 concludes.

# **2. The Measurement of Learning-by-Exporting Effects**

Following the influential papers of Bernard and Jensen (1999) and Clerides et al. (1998), the literature has used two main methods to measure learning-by-exporting effects. The first method consists of separating the sample into mutually exclusive groups, such as exporters and nonexporters, to assess differences in plant performance between these groups. Consider the following equation explaining a measure of performance for firm _i_ at year _t_ :<sup>3</sup> ' ' ln _Yit_ = α 0 + α 1 _Di_ 0 + α 2 _Z io_ + β 0 _t_ + β 1 _Di_ 0 _t_ + β 2 _Z i_ 0 _t_ + ω _i_ +η _it_ , where _D_ i0 is a dummy variable equal to one if the plant belongs to the “treatment” group and equal to zero if the plant belongs to the “control” group during the baseline year _t =_ 0; _Z_ i0 is a vector of observable plant characteristics, such as size or industry affiliation in the baseline year; _t_ is a time trend; ω _i_ is an unobservable, time-invariant plant effect; and η _it_ is an i.i.d. disturbance. Taking average annual differences between _t =_ 0 and _T_ , we obtain:

> 3 We use the terms firms and plants interchangeably in the paper, but our empirical analysis relies on plant-level data.

5