rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Predictions and estimation results

||<br>Information|<br>transmission<br>**H**|**ypotheses**<br>|||**Data**|
|---|---|---|---|---|---|---|
|Outcome variables|Same<br>distribution|Different<br>distribution|Screening|Peer effects|Nepotism||
||(1)|(2)|(3)|(4)|(5)|(6)|
|a. Job search spell|−|−|||−|−|
|b. Observable abilities|||||−|−|
|c. Unobservable abilities|||+||−|0|
|d. Tenure|||+|+||−|
|e. Entry wage|+|−|+|+||−|
|f. Wage growth|||+/−|||0|
|g. Productivity||−|+|+|−|(−)|

_Note_ . Column “Data” indicates our estimation results. + and _−_ mean that use of social networks is, respectively, positively and negatively correlated. 0 means flat or no correlation. ( _−_ ) is negative but statistically insignificant. A blank cell means no or ambiguous prediction.

Table 2: Summary statistics: Establishments

||N|%|
|---|---|---|
|_Industry_|||
|Manufacturing|191|56|
|Commerce|74|22|
|Finance|74|22|
|_Size_|||
|Small (no. employees is 20 and less)|164|48|
|Medium (no. employees is 21-70)|99|29|
|Large (no. employees is 71 and above)|76|22|
|Single establishment frm|207|61|
|Social networks are a common mode of job advertisement|233|69|

_Note_ . _N_ = 339. “ _Personal networks are a common mode of job advertisement_ ” indicates that an establishment answered that personal networks are a primary or secondary mode of advertising job vacancies.

24
connections (in the household or through public standpipes) as “advanced” technology water supply. Open defecation and surface water is the reference category in all estimations and is considered as lack of access to any water and sanitation infrastructure.

This definition is slightly different from the WHO definition of improved and unimproved water and sanitation (WHO, 2009), which is used by the Joint Monitoring Programme (JMP) for Water Supply and Sanitation by the WHO and UNICEF<sup>5</sup> (see column 1 of Table 1). The reason for this difference is simple: a large number of DHS surveys does neither distinguish between protected and unprotected wells, springs and boreholes nor distinguish between private and public flush toilets and latrines. Since we are not primarily interested in identifying the more subtle differences between individual technologies (e.g. between ventilated and non-ventilated latrines or between protected and unprotected wells), we choose a slightly more broad classification, which allows us to estimate the equation of interest across a large number of countries and time, without the need the make any assumptions (see next paragraph). Moreover, we want to explicitly differentiate between surface water and simple water technologies and open defecation and simple sanitation technologies to analyze if already simple - and according to the WHO definition still “unimproved” - sanitation and water technologies are associated with improved child health.

To investigate the degree to which the specific coding rule chosen affects our result, we analyze an alternative definition in a second step, where we follow the official definition of the WHO of improved and unimproved water and sanitation as closely as possible (see column 3 of Table 1). Whenever it was not clear from the data whether a spring or well or a latrine was improved or not had to make the assumption that it is unimproved.

> 5 See http://www.wssinfo.org/.

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# **2.  Methodology: How Do we Define Entrepreneurship?**

In order to measure entrepreneurship and make data universally comparable, we developed a methodology that can be applicable across heterogeneous legal regimes and economic systems. Previous efforts had been made in this regard, but the great majority focused solely on the developed world, and did not take into account differences in legal systems, sectors, and economic structures (see United Nations, 2005).

The definition of entrepreneurship lacks a common language (Outcalt, 2000). Joseph Schumpeter defined entrepreneurship as “the assumption of risk and responsibility in designing and implementing a business strategy or starting a business” (Schumpeter, 1911). J. W. Gough stated that entrepreneurship “refers to a person who undertakes and operates a new enterprise or venture, and assumes some accountability for the inherent risks” (Gough, 1969). For practitioners, entrepreneurship has generally been viewed as the process of creating new wealth. The entrepreneurial process centers on the discovery, creation and profitable exploitation of markets for goods and services<sup>1</sup> . Therefore, and for the purposes of the analysis in this study, entrepreneurship is defined as: _The activities of an individual or a group aimed at initiating economic activities in the formal sector under a legal form of business._ Notably, this definition excludes informal sector initiatives. This exclusion is based on the difficulties of quantifying the number of firms in the informal sector, rather than on its relevance for developing economies (Nielsen and Plovsing, 1997). The only way to measure the informal sector is through economic censuses, which due to their high costs are infrequently collected.

After defining our measure of entrepreneurship, we need to create a standard unit of measurement. Generally, entrepreneurial activities are carried out in the form of a “business.” Statistical agencies around the world define “business” in many different ways based on the sources of available administrative data (Vale, 2005). Due to the lack of a universally-agreedupon definition of what constitutes a business, agencies have formulated either an economic,

> 1 See Shane and Venkataraman, 2000, and Venkataraman, 1997  for a discussion of  alternative definitions.

3
## **Figure 5. Decomposition of services value added in world gross exports, by industry, percent, 2011**

<!-- Start of picture text --> Mining<br>Agriculture<br>Coke, petroleum<br>Fabricated metals<br>Total manufacturing<br>Utilities<br>Nonmetallic minerals<br>Machinery<br>Basic metals<br>Other transport<br>Wood<br>Electrical machinery<br>Other manufacturing<br>ICT and electronics<br>Rubber and plastics<br>Textiles and apparel<br>Food products<br>Paper, print, publishing<br>Motor vehicles<br>Chemicals<br>0 5 10 15 20 25 30 35 40 45<br>Distribution Transport Finance Business services Other<br><!-- End of picture text --> _Source_ : Miroudot (2016) based on OECD-WTO TiVA 2015.

The decomposition of services value added by type of service seems to show similar patterns across manufacturing industries (see figure 5). Distribution represents about a third of services value added in exports, as do business services, which includes telecommunication services, computer services, professional services, R&D services, consulting, advertising and marketing services, technical testing services, and environmental services. The last third is split among transport, finance, and other services (a category covering construction, hotels and restaurants, government services, health and education, entertainment, and audiovisual services).

# **_Why is the share of services in value-added exports increasing?_**

The information in figures 2 and 3 reveals an increasing share of services in value added exports, suggesting that the increase may be explained in part by an increase in services value added embodied in exported manufactures.

The growing importance of services in the economy has been a matter of discussion for a long time. Bhagwati (1984) set out the main reasons: “splintering” (outsourcing services formerly provided in-house by manufacturing firms), the high-income elasticity of the demand for services, and relative price shifts due to the lower growth of productivity in services than in goods. Splintering was seen as the spontaneous result of the specialization opportunities arising from growth and technical change.

12
Table 4a: District traits and unorganized employment changes

||Manufacturing<br>1989-2005|Manufacturing<br>2000-2005|Services<br>2001-2006|
|---|---|---|---|
||(1)|(2)|(3)|
|A. Correlation with change in unorganized sector sh|are of employment|||
|Log population|-0.028|-0.023|-0.002|
|Log population density|0.086|-0.030|0.035|
|Age profile|-0.030|0.007|0.021|
|Share of population in scheduled caste/tribe|-0.101*|-0.006|-0.047|
|Female labor force participation rate|-0.063|0.008|0.022|
|Educated worker share|0.068|0.002|-0.017|
|Literacy rate|0.003|-0.007|-0.032|
|Infrastructure: electricity access|0.009|-0.009|-0.001|
|Infrastructure: paved roads|-0.054|-0.121*|0.041|
|Travel time to nearest of India's ten largest cities|-0.065|-0.072|0.020|
|Strength of household banking sector|0.117*|0.071|-0.017|
|B. Correlation with unorganized sector log employm|ent growth rate|||
|Log population|0.057|-0.007|-0.147*|
|Log population density|0.041|-0.002|-0.010|
|Age profile|0.096*|0.109*|-0.005|
|Share of population in scheduled caste/tribe|0.047|-0.028|0.045|
|Female labor force participation rate|-0.031|-0.034|0.004|
|Educated worker share|0.170*|0.050|-0.094*|
|Literacy rate|0.105*|0.073|0.032|
|Infrastructure: electricity access|0.126*|0.106*|0.027|
|Infrastructure: paved roads|-0.030|0.037|0.109*|
|Travel time to nearest of India's ten largest cities|-0.070|-0.142*|0.051|
|Strength of household banking sector|0.123*|0.130*|0.081|

Notes: Table documents correlations between district traits and unorganized sector employment changes. The top panel considers the change in the unorganized sector's share of district total employment. The bottom panel considers the log growth in unorganized sector employment across the period as measured by log (unorganized employment end / unorganized employment start). District traits in Column 1 are from the 1991 Population Census; district traits in Columns 2 and 3 are from the 2001 Population Census. District traits are expressed in log values or percentage point values as indicated. In both panels, a negative correlation indicates that the district trait is associated with a decline in relative unorganized activity across the period. An asterisk denotes a correlation is statistically significant at the 10% level.
# **3.2 Variable**

We begin by examining whether workers stopped working after the pandemic set in. Workers are classified as having stopped work if they answered yes to the question “Was the respondent working before the pandemic?” (working pre-pandemic) and no to the question “Did the respondent work in the last week?” (currently working). However, surveys in most countries did not ask respondents if they were working before the pandemic if they reported that they are currently working. Hence, it is not possible to estimate the number of workers prior to the crisis. This in turn means that it is not possible to construct the share of workers who were working prior to the pandemic that stopped working, because the denominator is unknown. We therefore approximate the share of workers who stopped working by dividing the number of people who stopped working by the sum of the number of people who stopped working and those that were currently employed at the time of the survey. In the LAC countries, however, the questionnaires asked respondents about their past work and whether they were currently working or not. Across the 12 LAC countries where both measures can be constructed, the two measures have a correlation of 0.99, suggesting that this approximation is highly accurate.

In addition to stopping work, we consider several other variables from the HFPS surveys to measure economic impacts. These include _partial or no payment for work performed among wage workers_ (“For the work that you did in the last week, will you be paid/were you paid?”), _changing jobs_ (“Has the respondent changed jobs since the beginning of the pandemic?”), as well as _income change_ (“Has your household income changed since the pandemic started?”). For income change, we consider four income sources: (i) total; (ii) family farming, livestock, or fishing; (iii) non-farm family business; and (iv) wage employment. Table 4 summarizes the questions used and our variable definitions.

13
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A. Sanghi and D. Johnson roughly the same as its exports to countries similar to Kenya; neither the year dummies nor the Kenya year dummies are significant (Table C7).

Table 2: Estimates of China’s Trade Flows (1948-2014). Dependent variable: China’s exports

|Poisson|Quasi Maximum Likelihood|
|---|---|
|Constant|8.82(15.22)<br>|
|China GDP per capita|0.00(0.00)<br>|
|Importer GDP per capita|0.00(0.00)<sup>_∗∗∗_</sup>|
|Population China|_−_0.01(0.03)|
|Population Importer|0.00(0.00)<sup>_∗∗∗_</sup>|
|Distance|_−_0.00(0.00)<sup>_∗∗∗_</sup>|
|Contiguous|0.09(0.06)|
|Common Language|1.94(0.06)<sup>_∗∗∗_</sup>|
|GATT China|0.90(18.60)|
|GATT importer|0.79(0.08)<sup>_∗∗∗_</sup>|
|Colonial History|_−_1.45(0.91)|
|Kenya dummy|4.42(32.00)|
|AIC||
|BIC||
|Log Likelihood||
|Deviance|13045625.79|
|Num. obs.|8215|

> _∗∗∗ p <_ 0.001, _∗∗ p <_ 0.01, _∗ p <_ 0.05. Standard errors in parentheses.

**Gravity model suggests room for Kenyan export growth** The results suggest that Chinese exports to Kenya are essentially normal, but Kenya under exports to China compared to other larger markets. Since China has specialized in manufacturing exports, it is simply a large exporter in global value chains; Kenya imports the same amount from China as countries at similar income levels do. Kenya, however, has some room for growth of exports. Leather products and cutflowers have shown some promise, and Kenya could reap the benefits of reorganizing its leather sector and negotiating for duty free access of its cut-flowers in China.

# **2.6 Brighter prospects for services exports as China rebalances**

Kenya is unlikely to expand exports to China at the moment, but it may look forward to real benefits towards 2030. A recent World Bank Africa’s Pulse report (2015) shows that China will import more from Kenya once it rebalances its economy to a consumption-driven path. Focusing on domestic consumption, China’s GDP growth will slow from 7 percent to 6 percent per year between 2016 and 2030, eventually reaching 4.6 percent per year in 2030. Initially, Kenya’s GDP will fall by 1.3 percent compared to if China continued with seven percent yearly growth. Kenya’s GDP will suffer from weak demand for commodity exports, lower world commodity and food prices, and 16
- 27 -

Constructing the first index (S) and the third index (T) - indices of compatibility of trade flows - is a relatively straightforward procedure. Data of trade flows, classifled by a uniform scheme-the SITC (Standard International Trade Classification)-are almost universally available. A minor problem encountered when comparisons with earlier periods are concerned (such as the 1960's) is that the precise classification scheme has slightly changed over the years; but in the present context these changes are of very little significance-the use of one scheme and not the other would be almost immaterial for the size of the index. A more general problem is the selection of degree **of detail** of the classification scheme. Obviously, adopting the "two-digit" level (which distinguishes among 69 items) would group together goods which are widely different from each other."<sup>3</sup> We have experimented with the next two levels of classification -the "three-digit" (with 239 items) and the "four-digit" (with 790 items). Although the indices constructed under the two alternatives are naturally different from each other, it was found that in general the differences were minor; and, moreover, that they are immaterial for the **relative levels of the** indices in comparisons of the countries. We have therefore selected just one scheme-the "three-digit" level; and all the indices presented are based on data thus classified.

Construction of the other index (C), of compatibility between trade and production, is a much bigger challenge. To start with, the index is to match a flow of trade with a flow of production of tradables (which are the component of the national product subject to replacement by imports). A determination of what is or is not "tradable" is subject to to a large degree of arbitrariness, due to both conceptual problems (e.g. a "non-tradable" activity 13. For example, coffee, tea, cocoa, chocolate, and spices would be classified as one good.
22 A series of diagnostic tests as well as overall regression evaluation have been performed for each regression. Table 5 presents the results.

Table 5: REGRESSION EVALUATION AND TESTS

||Model I|Model II|**Model III**|
|---|---|---|---|
||0.995|0.996|0.995|
|DW|<br>2.05|1.53|2.11|
||Tests<br>|on the Overall Significance<br>||
|Wald Test|<br>Chi<sup>2</sup>(6)=3130.7[0.0000] ***|Chi<sup>2</sup>(5) =4731.1 [0.0000] ***|Chi<sup>2</sup>(5)=2127.5 [0.0000]***|
|F Test|<br>F(12, 18)= 248 [0.0000]***|F(10,20) = 498 [0.0000]***|F(13, 17) = 135 [0.0000]***|
||<br>N|<br>ormality of Residuals<br>||
||2(2)= 0.0092849[0.9954]<br>Tests on th|X<sup>2</sup>(2) = 0.45765 [0.7955]<br>e Significance of Each Variable|X2(2)= 2.8375 [0.2420]|
|_Constant_|F(1,18)=38.256 [0.0000]***|F(1, 20) =64.953[0.0000] ***|F(1,17)= 35.861 [0.0000]***|
|_M2_|-|F( 1,20)=102.13 [0.0000]***||
|_DC_|F(2, 18) =4.4713 [0.0265]**-|||
|w|F( 1,18)=18.188 [0.0005]***||F(3,17)= 7.9094 [0.0016]***|
|_rer_|F(2,18) =44.779 [0.0000]***|F( 3, 20) =99.978[0.0000] ***|F(2,17) = 18.243 [0.0001]***|
|_Tsd_|F(3,18) =14.293 [0.0001] ***|F( 2, 20) =77.89 [0.0000]***|F(3,17) =18.766 [0.0000]***|
|_Tsk_|F(1, 18) =5.0635 [0.0372]**|F(1, 20) =20.205 [0.0002]***|F(2,17) = 5.2678 [0.0166]**|
|_CSeason_|F(3, 18) =1.8298 [0.1779]|F( 3, 20) =3.7858 [0.0267]**|F(3,17)=<br>2.2897 [0.1151]|

_source: author's calculations using GUS and IFS data_ Specification search in all three regressions (I, II and III) yielded statistically significant estimates in high explanatory power equations. Both F and Wald tests strongly confirm the overall significance of the postulated models. Stability of thie modeled relationships was tested by various Chow tests (not quoted here), none of which indicated the danger of instability at the 5% significance level. Residuals were checked for normal]ity by x2 tests which reported no significant departures from normality in any case. Visual inspection of residual plots did not indicate the presence of heteroscedasticity (no formal tests for heteroscedasticity were available because of the scarcity of observations). Even though some seasonal coefficients in the above
with faster and lower‐cost access to outside markets.<sup>135</sup> However, this also could leave the territory more vulnerable to future climate change related disruptions as agricultural intensification makes the region more vulnerable to extreme weather events. Economic development and poverty reduction policies need to factor in climate change risks to deliver both economic progress and reduced vulnerability.<sup>136</sup> The World Bank and other international development partners are increasingly active in encouraging client countries to pursue more holistic assessments of life‐cycle benefits and costs of more resilient and efficient investment, and greater emphasis on risk reduction as well as recovery capabilities.<sup>137</sup> Nevertheless, experience highlights the importance of better understanding why so many pro‐ development, pro‐resilience initiatives encounter resistance in developing countries.

> 135 <mark>Blankespoor, Brian, Shahe M. Emran, Forhad Shilpi and Lu Xu. Bridge to Bigpush or Backwash? Market Integration, Reallocation, and Productivity Effects of Jamuna Bridge in Bangladesh. 2018. SRN: https://ssrn.com/abstract=3162451.</mark>

> 136 Bowen, Alex, Sarah Cochrane, and Samuel Fankhauser, Climate Change, Adaptation and Economic Growth. _Climatic Change_ 113, 2012, pages 95–106.

> 137 World Bank Group, _Action Plan on Climate Change Adaptation and Resilience:  Managing Risks for a More Resilient Future._ Washington, DC:  World Bank, 2019. <u>http://documents.worldbank.org/curated/en/519821547481031999/The‐World‐Bank‐Groups‐Action‐Plan‐on‐ Climate‐Change‐Adaptation‐and‐Resilience‐Managing‐Risks‐for‐a‐More‐Resilient‐Future.pdf.</u>

38
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Under these market conditions, and given the technical incompatibilities of the various gauges, the case for further integration of railway networks is quite limited. Although the average haul of African networks is long relative to their size, it is comparable to the average haul for road transport. Tanzania Railway Corporation and Tazara both haul freight an average distance of 1,000 kilometers, and some smaller railways in East Africa—such as Uganda Railway—act as feeders to other systems that carry traffic a few hundred kilometers further.

Before contemplating further extensions to the rail network, a turnaround in the performance of existing railways is sorely needed to regain competitiveness with road transport. The poor quality of service provided by East Africa’s railways makes it increasingly difficult for them to compete with road freight services. Most railways in

**Figure 2.6 . Traffic density on African railways**

<!-- Start of picture text --> 5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Million Traffic Units per route km<br>Sudan, SR Tanzania, TRC Tanzania, TAZARA East Africa Kenya, KRC Uganda, URC Central Africa West Africa Southern Africa North Africa<br><!-- End of picture text --> _Source:_ Bullock 2009.

Note: Density is normally expressed as traffic units per route-km. The traffic units carried by a railway are the sum of the passenger-km and the net tonne-km.

East Africa operate at the standard at which they were originally constructed and now face major problems with competing modes. The rails can accommodate only relatively light and slow-moving trains. Poor maintenance over extended periods has many sections of track to deteriorate beyond repair and resulted in a loss of competitiveness and rolling-stock productivity. While such inefficiencies can be tolerated on low-volume feeder lines (and may be necessary for some lines to be viably operated), they are a major handicap when competing against the modern roads being built in major corridors.

Competition from road transport is often cited as one the of the reasons for declining railway traffic. Yet Tanzanian railways (Tazara and TRC) both record relatively high volumes of passenger traffic, but only because the competing road network is in poor condition or does not exist. Tazara is one of the few railway networks that records cross-border flows of passenger traffic.

East Africa has several binational railway systems. The rail lines serving landlocked Uganda, Zambia, and Ethiopia via Kenya, Tanzania, and Djibouti respectively have been developed as integrated operations—the Kenya-Uganda railway, Tazara, and CDE—that link the landlocked capitals to coastal ports (except for CDE). This configuration smoothes the passage of goods across national borders and avoids the lengthy delays otherwise associated with switching of locomotives as freight is shifted from one national network to another.

The overall operational efficiency of most East African railways is very poor, with the exception of TRC and Tazara, which demonstrate reasonably good operational efficiency. There have been two 20