rafmacalaba/datause-encoder-data huggingface dataset:
Table F.10: Productivity and Technology Sophistication, GBF and SSBF EXT Technology, Disaggregated Sectors

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|ln(VAPW)|ln(VAPW)|ln(VAPW)|
|GBF EXT|1.70***<br>(0.32)|||
|GBF EXT<sup>2</sup>|-0.20***<br>(0.05)|||
|GBF MOST||1.89***||
|||(0.45)||
|GBF MOST<sup>2</sup>||-0.27***||
|||(0.09)||
|GBF GAP||0.37***||
|||(0.10)||
|GBF EXT*AGRI|||0.46**|
||||(0.23)|
|GBF EXT*MANF|||0.61***|
||||(0.08)|
|GBF EXT*SVC|||0.49***|
||||(0.09)|
|SSBF EXT*Agriculture|0.34*|0.44**|0.46*|
||(0.21)|(0.20)|(0.26)|
|SSBF EXT*Livestock|0.30|0.37|0.36|
||(0.48)|(0.47)|(0.47)|
|SSBF EXT*Food Processing|0.30*|0.32*|0.28|
||(0.18)|(0.18)|(0.18)|
|SSBF EXT*Apparel|0.44***|0.40***|0.36***|
||(0.11)|(0.10)|(0.11)|
|SSBF EXT*Motor Vehicles|0.64***|0.55***|0.55***|
||(0.17)|(0.16)|(0.16)|
|SSBF EXT*Pharmaceuticals|0.29*|0.25|0.22|
||(0.16)|(0.16)|(0.16)|
|SSBF EXT*Retail and Wholesale|-0.02|-0.07|-0.04|
||(0.11)|(0.11)|(0.12)|
|SSBF EXT*Financial Services|1.15**|1.18**|1.25***|
||(0.47)|(0.48)|(0.47)|
|SSBF EXT*Land Transport|0.38|0.44|0.45|
||(0.33)|(0.33)|(0.33)|
|SSBF EXT*Health Services|-0.62*|-0.65*|-0.70*|
||(0.37)|(0.37)|(0.37)|
|SSBF EXT*Leather|-0.47***|-0.51***|-0.53***|
||(0.16)|(0.15)|(0.16)|
|SSBF EXT*Other Manufacturing|0.07|0.05|0.02|
||(0.05)|(0.05)|(0.06)|
|SSBF EXT*Other Services|-|-|-|
|Var(ABF EXT)|0.28***|0.36***|0.38***|
||(0.10)|(0.10)|(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|_~~√~~_|
|Observations|2,746|2,746|2,746|
|R-squared|0.51|0.50|0.50|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

103
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in non-tradable services in 1998 and this increased to 23 percent by 2013. The share of women-owned firms in non-tradable services similarly increased, albeit from a lower base. The corresponding shares of women workers and women-owned firms was lower in tradable services. Furthermore, the share of employment among small firms (less than workers) in non-tradable services, at more than three-fourths, was consistently large between 1998 and 2013.

Table 1: Summary statistics

||1998|2005|2013|
|---|---|---|---|
|a) **Non-tradable services**||||
|Share of non-tradable in total non-agri employment (%)|55.03|59.80|64.54|
|Share of women employment (%)|14.19|16.96|22.58|
|Share of women ownership (%)|4.18|4.72|8.92|
|Average employment (No.)|2.14|2.04|2.12|
|% Share of employment in small frm ( 1-10)|80.09|82.52|81.13|
|% Share of employment in large frm ( _>_ 10)|19.91|17.48|18.87|
|b) **Tradable services**||||
|Share of tradable in total non-agri employment (%)|2.20|2.41|3.56|
|Share of women employment (%)|7.38|8.13|11.70|
|Share of women ownership (%)|2.58|2.38|6.10|
|Average employment (No.)|2.95|2.55|2.48|
|% Share of employment in small frm ( 1-10)|71.60|81.57|84.96|
|% Share of employment in large frm ( _>_ 10)|28.40|18.43|15.04|

Table 2: List of tradable and non-tradable services

|**Tradable services**|**Non-tradable services**|
|---|---|
|Sea and coastal water transport|Wholesale trade|
|Inland water transport<br>|Retail trade|
|Air transport|Land transportation activities|
|Warehousing|Postal and courier activities|
|Support activities for transportation|Accommodation and food service|
|IT services|Financial and insurance activities|
|Picture, video and television programme|Real estate activities|
|Broadcasting and programming activities<br>|Legal and accounting activities<br>|
|Architectural and engineering activities|Rental and leasing activities|
|Technical testing and analysis|Employment activities|
|Scientifc research and development|Travel agency, other reservation services|
|Advertising|Education|
|Photographic activities|Health|
|Creative, arts and entertainment activities|Residential care activities|
|Libraries, archives, museums and cultural|Personal service activities|
|Sports activities|Repair of computers, personal and household goods|
|Other amusement and recreation activities|Veterinary activities|
|Activities of business, employers, professional member organizations||

8
fully accurate about this specific type of corruption. Information about the statutory requirements for business entry regulation need not reflect the actual practice of how these requirements are implemented on the ground, nor are they informative about regulatory burdens in other areas. Information about freedom of the press is only one of many factors contributing to the accountability of governments to their citizens. Notwithstanding some clear advantages that specificity of an indicator may have for some purposes, one should be careful not to interpret them as sufficient statistics for broader notions of governance.

How important is this measurement error quantitatively? Unfortunately, the vast majority of existing governance indicators do not explicitly acknowledge the extent of measurement error present in them. One of the few exceptions to this is the Worldwide Governance Indicators (WGI) project that we discuss further below. Fortunately, however, some simple calculations can shed some light on the likely magnitude of measurement error in individual governance indicators as well. The key to doing so is to identify pairs of indicators that arguably are measuring similar concepts, up to an unavoidable measurement error component. For example we have discussed above the correlation between the World Bank and African Development Bank's CPIA assessments of transparency and corruption. A useful way to interpret this imperfect correlation is that both of these sources are measuring the same concept of transparency, accountability, and corruption, but they do so with a degree of measurement error. Intuitively, the less measurement error there is in these two sources, the more correlated they should be. Thus we can interpret the correlation between them as telling us something about the degree of measurement error that is present.

More formally, think of the observed scores from two organizations, y1 and y2, as a combination of a signal about unobserved governance, g, and source-specific noise, ε1 and ε2, i.e. y1 = g + ε1 and y 2 = g + ε 2 . Suppose we assume that the variance of measurement error in the assessments of the two organizations is the same, and without loss of generality assume that the variance of governance is one.<sup>17</sup> Then some simple

> 17 The assumption of a common error variance is necessary in this simple example with two indicators in order to achieve identification.  In this example there is just one sample correlation in the data that can be used to infer the variance of measurement error:  we thus can identify just one measurement error variance.

25
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- **Clemens, Michael, Cindy Huang, and Jimmy Graham.** 2018. “The Economic and Fiscal Effects of Granting Refugees Formal Labor Market Access.” CGD Report No. 496. Washington DC: Center for Global Development.

- **Clemens, Michael, Cindy Huang, Jimmy Graham, and Kate Gough.** 2018. “Migration Is What You Make It: Seven Policy Decisions that Turned Challenges into Opportunities.” Center for Global Development, May 2018 Note.

- **Colletta, Nat J, Teck Ghee Lim, Anita Kelles-Viitanen, et al.** 2001. _Social cohesion and conflict prevention in Asia: managing diversity through development._ World Bank Publications.

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22
**<u>Table 7: Characteristics of newly acquired parcels land in rural areas</u>**

||**Registered during**<br>**LTR**|**Not officially**<br>**registered**|**Subsequent**<br>**registration**|
|---|---|---|---|
|Year acquired|2012|2013|2013|
|Land area in ha|0.13|0.11|0.09|
|Purchased|0.35|0.67|0.65|
|Registered land price in USD|290.12|365.01|698.98<sup>a</sup>|
|Inherited|0.56|0.22|0.26|
|Government allocated|0.05|0.02|0.00|
|Subjective land value in USD|479.46|447.95|665.59|
|Registration feepaid(US$)|2.14<sup>b</sup>||30.06|
|Number ofparcels|587|537|23|

Source: World Bank 2015 third round follow up survey **(restricted to those within the cell boundary).** aThis is after dropping one outlier parcel, otherwise, the mean price would have been USD 1614. bNote that 90% of the cases paid only the official rate of USD 1.42.

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**Table AII-1 – Post-matching standardized bias of selected covariates (in %)**

|(complete sample)|_New_<br>_Credit_|_Formal_<br>_Credit_|_Informal_<br>_Credit_|_Training_|_Taxes_|_Guild_|
|---|---|---|---|---|---|---|
|Matching using_H_|||||||
|_ln Capital Stock*_|51.0|80.4|39.9|44.2|89.4|39.4|
|_ln Workers*_|51.4|72.6|38.6|31.4|62.7|32.9|
|_Time in Business*_|14.5|24.6|6.2|-0.4|18.8|21.7|
|_Age_|-0.2|-1.6|0.0|-7.8|0.5|-0.4|
|_Prim_|-1.3|-9.5|-1.8|-10.0|-1.1|2.5|
|_Sec_|0.2|3.5|2.3|6.3|-3.8|0.1|
|_High_|1.3|10.0|0.4|5.6|4.4|-2.7|
|Matching using_(H,Z)_|||||||
|_ln Capital Stock_|8.0|25.5|9.3|10.4|3.9|3.0|
|_ln Workers_|2.2|10.3|7.3|0.2|-0.1|-2.8|
|_Time in Business_|1.5|0.8|0.7|-0.6|3.7|-1.6|
|_Age_|0.8|-3.4|0.4|-6.7|2.7|-0.8|
|_Prim_|0.2|-3.3|-1.1|-9.0|2.8|2.5|
|_Sec_|0.8|2.7|1.9|4.9|-2.0|0.6|
|_High_|-0.6|3.2|-0.2|6.1|-2.0|-3.0|
|Matching using_(H,Z, F)_|||||||
|_ln Capital Stock_|7.6|24.8|8.9|8.7|3.4|2.4|
|_ln Workers_|2.7|10.5|7.3|-1.5|0.6|-3.0|
|_Time in Business_|1.5|1.0|0.6|-0.6|3.4|-1.8|
|_Age_|0.8|-3.1|0.5|-7.0|2.6|-0.7|
|_Prim_|0.1|-3.4|-1.1|-8.8|2.2|-8.5|
|_Sec_|1.0|3.1|1.9|5.6|-2.1|0.7|
|_High_|-0.9|2.7|-0.3|5.1|-1.1|-3.2|

_Notes: * not used for matching. All the results correspond to Nearest Neighbor matching procedure._ 24
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Under trade expansion, the pattern of spending shifts. Kenya, Burundi, and Egypt would need to spend less than they do under trade stagnation to meet the regional power infrastructure needs. Ethiopia, Sudan, Tanzania, and Uganda would have to spend more under trade expansion to meet their infrastructure needs. In the extreme case, Ethiopia would have to spend as much as 6 percent more of its GDP under trade expansion than under trade stagnation. This reflects the important role that Ethiopia would assume as an exporter of hydropower for the region under trade expansion.

**Figure 3.2 . Regional spending needs as a percentage of GDP**

<!-- Start of picture text --> 20%<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Rwanda Kenya Sudan Uganda Tanzania Burundi Egypt Ethiopia<br>Trade expansion Trade stagnation<br>Spending as a percentage of GDP<br><!-- End of picture text --> Source: Derived from Rosnes and Vennemo 2009.

In 2005, power trade flows in the EAPP were just 0.28 terawatt-hours of imports and 0.18 terawatthours of exports, about 2.1 percent of the electricity generated. Although EAPP is the third-most active regional power pool in Africa, after SAPP and WAPP, the volumes of power involved are very small. The main flows involve Egypt importing power, and Uganda exporting small amounts of power to Rwanda and Tanzania (figure 3.3a).

Under trade stagnation, future trade volumes would be 12 terawatt-hours per year up to 2015, and the pattern of trade would shift somewhat. Most notably, under trade stagnation, gas-fired power capacity and production would rise in Egypt and Kenya, and less hydropower would be developed in Ethiopia, Sudan, Uganda, and Rwanda. Because the existing transmission network remains in the trade stagnation scenario, there would be very little trade (figures 3.3b, 3.4b).

Under trade expansion, the volume of power traded in EAPP could increase substantially to 162 terawatt-hours by 2015. The key change under trade expansion is that Ethiopia and Sudan would fully develop their hydropower potential and become the major power exporters of the region, sending power into neighboring countries, particularly Egypt. Under this scenario, Ethiopia could export as much as 200 percent of its domestic consumption and Sudan more than 100 percent of its domestic consumption. Rwanda and Uganda would also become power exporters. While the volume of their exports would be 40
# **2. What aspirations are**

We understand aspirations as people’s desires for the future with respect to various aspects of their life and well-being. Aspirations are about life goals, which can refer to any specific long-term outcome— such as education level, type of job, wealth, health, political power, and social status—or a mix of them (Ray 2006). Aspirations can be for oneself or for someone else, such as parents’ aspirations for their children.

Aspirations influence behavior as an evolving reference point, affected by one’s situation. In theory, aspirations motivate people to make efforts and choices towards achieving them (Dalton, Ghosal, and Mani 2016; Ray 2006; Genicot and Ray 2017). Aspirations can evolve over time as an individual’s situation and information change (Appadurai 2004; Ray 2006; Dalton, Ghosal, and Mani 2016).

Aspirations depend on information and socially influenced preferences thus they do not necessarily align with one’s potential. While aspirations can be hard-but-plausible motivating goals, they can also be low, restricted goals. Indeed, since the awareness of opportunities affects aspirations — consciously or unconsciously — living in isolation or having a limited knowledge of available opportunities can dwarf aspirations (Appadurai 2004; Ray 2006; Bernard and Taffesse 2014). Likewise, aspirations also capture preferences for specific opportunities, which, as explained further in this review, form in close interaction with social groups, based on what people perceive as appropriate given social norms and experiences (Appadurai 2004; Ray 2006).

Several concepts related to beliefs and mental models are sometimes confused with aspirations. These concepts capture a range of dispositions, including optimism towards the future and the degree to which one feels in control of his life, which strongly influence aspirations but are distinct from them. These beliefs include hope (how optimistic people are about their future; Lybbert and Wydick 2018), locus of control (the degree to which people believe that their lives are under their control rather than determined by external factors; Rotter 1966), and self-efficacy (belief that one is able to achieve his objectives; Bandura 2001), among others.<sup>1</sup> While they are related and sometimes coincide, aspirations are also different from expectations, which are the outcomes people feel they will most likely achieve (Bernard et al. 2014; La Ferrara 2019). Annex 1 defines these concepts and a few others belonging to the semantic field of aspirations.

Economic studies on aspirations generally align to this basic conceptualization of aspirations. While most studies do not define explicitly their concept of aspirations, they tend to align—at least

> 1 For examples of studies measuring these beliefs, see Bloem et al. (2018) and Lybbert and Wydick (2018) for hope; Lybbert and Wydick (2016) for locus of control; and Bernard, Dercon, and Taffesse (2011) and Esopo et al. (2018) for self-efficacy.

5
Figure 3 captures this importing of technical ability, first in the 1870s and 80s as Britain expanded in the nitrate and coal industry, and then after 1905 when major foreign copper companies took over the copper industry. The census data shows the density of foreign entrepreneurs increased from almost zero in 1870 to 80 in 1920, the last census which permitted disaggregating by nationality/profession, or roughly 4 times Chile’s domestic density in 1900. Local capacity was increasing as well, however, the share of foreign engineers in the country total increased sharply from 1% of total engineers in the country to around 40% across this period. These movements emerged concomitant with Chilean production reaching new highs and market share of an expanding global supply staging and important recovery. As in Mexico, below, and consistent with Howitt & Mayer-Foulkes (2005)’s education externality, both the quantity and the quality of the engineering graduates produced by local universities were thought inferior to the talent imported from Europe and the US (Serrano, 1993; Bazant de Salda ̃na, 1993). Increasing frustrated by the creeping influence of foreigners across the major industries, it is perhaps not surprising that Chileans developed a self-perception that they were perhaps “unfit for the modern era”(Monte ́on, 1982, p. 62 and 35).<sup>24</sup> By 1918, American interests controlled 87% of Chilean copper output (O’Brien, 1989).

Similar stories of an inability to exploit new technologies leading to decline in the mining industry can be found throughout the region. The engineering data in Figure 1 supports the historical evidence that in Mexico local entrepreneurs lost share in the industry they had dominated for centuries precisely due to lacking the capacity to master emerging periods of bonanza. If the process had been initiated and maintained adequately, without doubt it would have created the means to confront more challenging tasks, such as those posed by copper mining when it was necessary to exploit less rich veins. However, faced with the technological revolution, the local mining companies did not have either sufficient accumulated resources or organizational and administrative capacityboth of which were indispensable. In these circumstances, there was no other option but the introduction of foreign capital and expertise.”Pinto Santa Cruz (1959)(p 71)

> 24Prominent intellectual Tancredo Pinochet Le-Brun(1909), granting that Chileans were inferior to Europeans, still wondered, “Don’t we have minds in this country that can go to Europe to learn what professors, whom we have imported and continue importing, have studied? Are we truly incapable of steering our own ship?” _La Conquista de Chile en el Siglo XX_ , Santiago, La Ilustracion, page 81 cited in Monte ́on (1982)).

27