rafmacalaba/datause-encoder-data huggingface dataset:
Truck stops are often where sex workers congregate, and Busia was identi...ed as a “hot spot” for commercial sex activity due to the high volume of trucks overnighting. A GIS-based study conducted by the Strengthening STD/HIV Control Project in Kenya (SHCP) found that Busia received approximately one-quarter of the trucks overnighting at the Kenya-Uganda border (National Aids Control Council, 2005).<sup>7</sup> Though sex workers in Busia do in fact make signi...cantly more than other self-employed daily income earners, they do not, in general, think of themselves as commercial sex workers (CSWs). Instead, most think of themselves as women who engage in transactional sex in order "to survive," and most (84%) hold jobs outside of sex work.

# 3.2 Identifying Commercial Sex Workers

To obtain a representative sample of women engaged in sex work in Busia Town, and to include more "informal" sex workers who are not typically easy to locate, we identi...ed women through a peer group network which was originally established by the Strengthening STD/HIV Control Project in Kenya (SHCP), a Kenyan organization associated with the University of Manitoba and the University of Nairobi that worked with thousands of formal and informal sex workers across Kenya. SHCP began working in Western Kenya in 1999 by organizing women into peer groups of 15 to 30 women each. Each group is led by a peer educator, and the peer groups within each district are supervised by a trained nurse who serves as a ...eld coordinator. SHCP provided training for the peer educators, and facilitated education in HIV and other STIs for all sex workers. Though SHCP was phased over to the government in October 2005, the peer groups within a district continue to operate essentially as community-based organizations.

By 2005, when this study began, SHCP had recruited approximately 400 women into 30 peer groups in Busia Town. The ...eld coordinator for the district was employed as an enumerator for this project and was assisted by one of the peer educators. To identify a sample of formal and informal sex workers, we used the same de...nition as SHCP: any single, widowed, divorced, or separated woman, aged 18 or older, who had multiple concurrent sex partners. We asked each peer group member to provide a list of all the women living in Busia Town she knew who ...t

> 7 The other major border town is Malaba, which receives about three times as many overnight trucks as does Busia.

7
we assume (quite heroically) that the level of transparency across countries has not changed significantly during our sample.

Table 4 presents the effect of transparency on the probability of a financial crisis. In the first column we test whether poor transparency by itself increases the probability of a crisis, i.e., whether countries with poor transparency are more likely to have banking crises. Thus, we add transparency, using the corruption proxy described above as an explanatory variable, to the probit model in the previous section. The coefficient on transparency variable is insignificant and hence there is no evidence that poor transparency (i.e. corruption) increases the probability of a crisis.

Next we turn to test whether poor transparency increases the probability of a crisis in the period following financial liberalization as the model predicts. We interact the transparency dummy variable with the dummy variable of financial liberalization in the previous _5_ years. That is, we create two dummy variables: One dummy takes value of one if there was a financial liberalization in the previous _5_ years and transparency is poor, and zero otherwise. The other dummy takes value of one if there was a financial liberalization in the previous 5 years and transparency is good, and zero otherwise. Che results are presented in the second column in table 4. Financial liberalization increases the probability of a crisis regardless of the level of transparency. However, much more so in countries with poor transparency. The probability of a crisis in countries with poor transparency that undertake financial liberalization is higher by 10 percent compare to countries with good transparency (21.89 compare to 11.55). The coefficients of the two dummies are significantly different at 14% confidence level.

20
# Table 12:. Sequential Probit Results - Urban Areas

## **First Stage: P1** = **Probability of going to school and not working**

||Probit|Standard|Probability|
|---|---|---|---|
||Coefficent|Error|Derivative|
|l_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _|_ _ _<br>_ _ _ _ _ _ _ _ _ _ _ _|_ _ _ _ _ _ _ _ _ _ _ _|<br>(% po in ts)|
|**Intect_**|-0.5812|0.9413||
|**Child Characteristics**||||
|AGE|-0.1566|0.1224|-5.45|
|AGESQ|-0.0010|0.0053|-0.03|
|FEMALE|-0.8563*|0.2131|-29.80*|
|**Parent Characteristics**||||
|EDUCFA|0.0424*|0.0152|1.48*|
|EDUCFA X FEMALE|-0.0065|0.0224|-0.23|
|EDUCMO|-0.0232|0.0178|-0.81|
|EDUCMO X FEMALE|0.0313|0.0250|1.09|
|EMPFA|-0.1140|0.1579|-3.97|
|EMPFA X FEMALE|0.0612|0.1999|2.13|
|EMPMO|0.3055*|0.1324|10.63*|
|EMPMO X FEMALE|-0.2677|0.1744|-9.32|
|**Household Characteristics**||||
|HEADAGE|0.0837*|0.0266|2.91*|
|HEADAGESQ|-0.0008*|0.0003|-0.03*|
|HEADFEMALE|0.1214|0.1767|4.22|
|#BOYS 0-5|-0.0631|0.0460|-2.20|
|#BOYS 6-9|0.0544|0.0609|1.89|
|#BOYS 10-15|0.1049*|0.0650|3.65*|
|#BOYS 16-17|0.1058|0.1370|3.68|
|#GIRLS 0-5|-0.0090|0.0458|-0.31|
|#GIRLS 6-9|0.0598|0.0602|2.08|
|#GIRLS 10-15|0.1262*|0.0652|4.39*|
|#GIRLS 16-17|0.1213|0.1385|4.22|
|FARM|-0.2151|0.1412|-7.48|
|BUSINESS|-0.2885*|0.1154|-10.04*|
|POOR|-0.2480*|0.1123|-8.63*|
|**Cost of Schooling**||||
|COST|0.0031|0.0024|0.11|
|DISTANCE 1-5|0.0939|0.1005|3.27|
|DISTANCE 5+|0.2547|0.1630|8.86|
|**Location**||||
|OTHERCITIES|0.1116|0.1160|3.88|
|Log. Likelihood|-603.9|||
|Restricted Log. Likelihood|-795.6|||
|Chi-Squared|383.4*|||
|**%**Correct Predictions|72.5|||

49
Industry Restucturing and Regulation -Building a base for sustainable growth

# _2.2.2 Telecommunications_

## **Multi-country study**

In a recent study, Wallsten (1999), considers information from 30 countries in Africa and Latin America from 1984 to 1997. Using a range of physical quality indicators, such as main line penetration, telecom employees per main line and the price of a three-minute local call, the impacts of liberalization are studied through competition, privatization and conduct regulation.

The findings of this research indicate that:

- competition (structural regulation) has tangible benefits on performance;

- privatization by itself has a negative impact on performance; and

- privatization combined with the establishment of conduct regulation has a positive impact on performance.

A key finding here is that structural regulation improves company performance and, as such, governments that are considering granting a period of exclusivity to the incumbent as part of the privatization process should think very carefully.

What this study does not consider is the depth of the conduct regulation that is established. Box 2 provides some evidence from a study that did address this specific issue.

Anaysi ofte: r~rac -0 of th gteIe 0oisse tor0 befor mid030000 aft r. codc 0 ;;00 guar reor appears j to 1 Threeireafinddit*we uaton

* prics!ing* isses * iforAtion asnmety _ah"nd #a N # kqiyk_

## **Chile**

Although telecommunications reforms in Chile started after those in electricity, great advances can be seen. Consider Figure 2, which summarizes several quality indicators.

It can be seen from this figure that the demand for telephones was being met, accompanied by a reduction in the waiting list toward the end of the period. Furthermore, this increase exceeded the natural growth of the population, as evidenced by the increase in the density of telephones, as measured by the number of telephone lines per 100 people. From this information, however, it is not possible to establish whether all the gains were in urban areas or whether rural areas were also benefiting.

12
# 1 Introduction

The Africa Infrastructure Country Diagnostic (AICD) has conducted extensive data collection and analysis of the infrastructure situation in Africa, including East Africa. The results have been presented in a variety of continental reports covering different areas of infrastructure— information and communication technology (ICT), irrigation, power, transport, water, and sanitation—and in different policy areas—including investment needs, fiscal costs, and sector performance.

The purpose of this regional report is to pull together in one place the key AICD findings relevant for East Africa. The main value in doing so is that it makes it possible to benchmark the infrastructure situation in the region against that of other African peers, to identify the main gaps in the regional infrastructure backbones, and to quantify the costs and benefits of regional integration, as well as their distribution across member states.

A number of methodological issues should be borne in mind.

First, due to the cross-country nature of the data collection, there is inevitably a time lag in the data. The period covered by AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Given the fast pace of regional integration, the snapshot presented here does not necessarily correspond to today’s situation but rather represents the 2006 baseline against which subsequent progress can be measured.

Second, given the need to make comparisons across countries, indicators and analysis had to be standardized and made consistent. That means that some of the indicators may be slightly different from those routinely reported and discussed at the country level.

Third, the database on which the analysis is based was designed to give a national and continental picture of infrastructure, as opposed to an explicitly regional picture. But national infrastructure provides the basic building blocks for regional integration, and hence can be used to build up a picture of the regional situation. Nevertheless, some specifically regional issues—particularly of the regulatory and institutional variety—may not have been explicitly addressed in the national data collection effort.

Fourth, for the purposes of this review, an extended East Africa is discussed. This includes the East African Community (Burundi, Kenya, Rwanda, Tanzania, and Uganda), as well as Sudan and Ethiopia. This group of seven countries is referred to in this report as East Africa.

Fifth, while water resource management is an important aspect of regional integration in Africa, this report does not explore water resource issues. The reason is that the AICD project did not cover water resources per se, but rather the specific water resource needs associated with the power, irrigation, and water supply sectors.
subsidiary jobs. There was also a decline in the share of casual jobs for women in rural areas, accompanied by a slight increase in the share of non-farm regular jobs under the PS definition, for both men and women. But the increase took place mainly in urban areas and was not large enough to offset the decline in farming jobs.

Figure 6: The decline in farming jobs in recent years was not offset by an increase in non-farm jobs <!-- Start of picture text --> Females Males<br>30 30<br>2004-05 2004-05<br>25 25<br>2011-12 2011-12<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>Usual Status Principal Status Usual Status Principal Status<br>% of working-age population<br>% of working-age population<br>Farmers Non-Farm Self Non-Farm Regular All Casual Farmers Non-Farm Self Non-Farm Regular All Casual Farmers Non-Farm Self Non-Farm Regular All Casual Farmers Non-Farm Self Non-Farm Regular All Casual<br><!-- End of picture text --> The change in the structure of employment at local levels was different along the rural-urban gradation. While most analyses (including the discussion in the introduction to this paper) consider a rural-urban divide, this dichotomous view results in an important loss of granularity. Urbanization in developing countries has been driven to a large extent by the rapid development of peri-urban areas, beyond formal municipal boundaries, and the densification of officially rural areas. This is even more so in India, where relatively weak city governance has resulted in a particularly “messy” urbanization process.

To better capture the granularity of the rural-urban gradation, this paper introduces a novel approach to classify NSS sampling units along a continuum. Its implementation is described in more detail in section 4. Based on this approach, rural areas fall under one of three ranks based on the average population of a village in the corresponding sampling substratum (below district): 0-999 inhabitants, 1,000-4,999 and 5,000 and above. The selection of these ranks is motivated by the 5000 population size criterion used for the identification of Census towns. Urban areas can be classified into three groups: below 50,000 inhabitants, between 50,000 and one million, 11
**Figure 3:  Percent of Households with Electricity, 2003-2007**

# **a. Rural**

<!-- Start of picture text --> Latin America/ LAC South & SE SASSEA Sub-Saharan SSA<br>Caribbean Asia Africa<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text -->

# **b. Urban**

<!-- Start of picture text --> Latin America/ LAC South & SE SASSEA Sub-Saharan SSA<br>Caribbean Asia Africa<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: DHS (2009) 31
A corollary of the negative relationship between SPEI and conflict incidence would be that investments in infrastructure and technologies that reduce exposure to such climate-induced variability in the soil water, such as irrigation infrastructure, may reduce the risks of conflict. However, this section adds a nuance to this argument, presenting evidence documenting that such investments may make land a more valuable target and thus result in increased conflict incidence during times of heightened instability, especially in high-scarcity settings such as the Sahel.

This section exploits an exogenous increase in conflict in Western Africa in the post-2011 period, sparked in by events coinciding with the Arab Spring.<sup>7</sup> Trends in battle events from ACLED data in Western Africa over the period 1998 – 2017 show a sharp uptick in conflict events in Western Africa from 2011 onwards, in the aftermath of the Arab Spring (Figure 3, Panel A). It has been suggested that the Arab Spring, and in particular the fall of the Ghaddafi regime which supported various political and business interests in the Sahel region and other African countries, created a shift in the region’s economic and political equilibrium _Figure 3: Conflict in Western Africa (1998 – 2017)_ <!-- Start of picture text --> Panel A<br>Conflict in West Africa<br>2000 2005 2010 2015 2020<br>year<br>.1<br>.08<br>.06<br>Share of Cells with Battles<br>.04<br>.02<br><!-- End of picture text --> <!-- Start of picture text --> Panel C<br><!-- End of picture text --> <!-- Start of picture text --> Panel B Panel C<br>Rest of West Africa G5 Sahel<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>year year<br>Irrigation Exists No Irrigation Irrigation Exists No Irrigation<br>.2 .15<br>.15<br>.1<br>.1<br>.05<br>Share of Cells with Battles .05 Share of Cells with Battles<br>0 0<br><!-- End of picture text --> 7 Note that in the previous section, the analysis of conflict and SPEI data covered the period starting in 1998 and ending in 2011, i.e. before the post-Arab Spring induces a change in the political equilibrium of the region.

10
- Shleifer, Andrei, and Robert Vishny (1994). "Politicians and Firms." _Quarterly Journal of Economics_ 109: 995-1025.

- Svejnar, Jan, and Katherine Terrell (1991). "Reducing Labor Redundancy in State-Owned Enterprises." Policy Research Working Paper 792. Development Research Group, World Bank, Washington, D.C.

- Topel, Robert (1990). "Specific Capital and Unemployment: Measuring the Costs and Consequences of Job Loss." _Carnegie-Rochester Conference Series on Public Policy_ 33: 181-224.

- Vickers, John, and Yarrow, George (1991). "Economic Perspectives on Privatization." _Journal of Economic Perspectives_ 5: 111-132.

- World Bank (1995). _Bureaucrats in Business: The Economics and Politics of Government Ownership._ World Bank, New York: Oxford University Press.

World Bank (2001). World Bank Privatization Database. Washington, D.C.

World Bank (2001 a). _World Development Indicators._ Washington, D.C.

22
# **1. Introduction**

A central feature of development is the changing nature of the production process of goods and services. Typically, as countries become richer, production and trade patterns change with a higher share of domestic value added being generated through more sophisticated processes – both within agriculture, services and industries as well as due to a shift away from agriculture, accompanied by increasing production taking place in cities. The technology revolution, witnessed over the past two to three decades, has only accelerated this process and impacted on the technology content of modern service and industrial production significantly. Production in industrial countries itself has undergone unprecedented changes within mature industries while some developing economies have been able to make inroads into hightechnology intensive service sectors – with the Indian success in developing its indigenous information technology industry one, often cited, example.

The mirror image of such shifting economic production is a change in occupations and, embedded, a transformation of the types of human skills used in an economy. Occupations which require intensively manual skills, associated with many jobs in low-machine intensity farming but also with specific trades as well as mass-manufacturing, give way to occupations requiring more cognitive skills. Such cognitive skills include verbal ability, working memory, numeracy, and problem solving abilities – teachers will need these intensively as well as computer operators. The more modern the economy and the more sophisticated the division of labor in production processes, the more important are also interpersonal skills which underlie behaviors such as teamwork, reliability, discipline, and work effort.

This paper derives the embedded human skill content of aggregate economic production for thirty countries, ranging from low-income to high income ones. We follow the methodology proposed by Autor, Levy and Murnane (2003) and updated by Acemoglu and Autor (2010). The approach conceptualizes and measures skills by assessing the specific tasks associated with different occupations rather than measuring the educational credentials of workers (e.g., years of education, diploma) performing those tasks. Five different skills are assessed: routine manual, non-routine manual physical, routine cognitive, non-routine cognitive analytical, and non-routine cognitive interpersonal skills. We then apply the occupation-skill grid to household labor force surveys contained in the World Bank’s International Database on Income Distribution (I2D2)

**2**
quantities measured in kilograms, and destination markets. Although the information is limited, because it does not provide information about domestic sales or other variables that would be needed to calculated standard indicators of firm productivity, it does allow us to decompose export growth to enable analyses about the role of firm turnover in and out of exporting activities, and about the main drivers of export growth across firms of different export sizes (in terms of export value).

The main findings are as follows. First, the rate of firm turnover into and out of exporting activities is quite high, and this turnover is dominated by firms exporting lower volumes than incumbent firms. On average, about 30 percent of incumbent firms in each year tend to exit export activities while a similar percentage of firms enter. The exiting and entering firms tend to be significantly smaller than incumbent firms in terms of export value (e.g., entrants export about 30 percent of the average exports of incumbent firms). Over time, by the end of 1997-2007, the main driver of export growth in Costa Rica was the introduction of new products by incumbent firms. New product-firm combinations (i.e., product-firm combinations not present in 1997) account for almost 60 percent of the value of exports in 2007, but the contributions of new firms and new products separately explain no more than 8 percent of total exports. As in Eaton et al. (2007), export growth is negatively correlated with export value across firms – in particular, the smallest quintile of firms experienced the fastest growth rates of exports.<sup>1</sup> Hence the main obstacle to export growth does not seem to be an issue related to size, but rather the challenge seems to be the survival of firms in export activities. In fact, over 40 percent of firms exit exporting after one year, and the exit rate thereafter hovers around 20 percent.<sup>2</sup> Finally, the entry of new and incumbent firms into new products seems to be associated with reductions in the median real unit value of exports. In addition, and consistent with existing literature, the decline in median unit values seems associated with increases in the effect of distance on export outcomes across destinations. More specifically, the effect of distance on the value, the number of products, and to a lesser extent the average unit value of exports to each destination appears to have risen during the period of declining unit values. All this is borne

> 1 See Arkolakis (2009) for an explanation of why small exporters experience faster growth.

> 2 These numbers were calculated by the authors, based on data provided by PROCOMER and cleaned by the authors – see Section III below.

3