rafmacalaba/datause-encoder-data huggingface dataset:
in 2011 international dollars are constructed to be all the same.

**Table 8: Estimated Middle Class Thresholds, 2003–15**

||**Lowe**<br>|**r Bound**|**Uppe **<br>|**r Bound**|
|---|---|---|---|---|
|**Year**|**Annual Per**<br>**Capita**<br>**Income,**<br>**’000 KZT**|**Income Per**<br>**Day,2011**<br>**US$ PPP**|**Annual Per**<br>**Capita**<br>**Income**<br>**’000 KZT**|**Income Per**<br>**Day,2011**<br>**US$ PPP**|
|2003|211.1|14.0|789.4|52.2|
|2006|263.6|14.0|985.6|52.2|
|2010|393.2|14.0|1,470.1|52.2|
|2013|474.0|14.0|1,772.0|52.2|
|2014|505.8|14.0|1,891.0|52.2|
|2015|539.5|14.0|2,016.7|52.2|

Based on the estimated thresholds, Table 9 reports how many households have qualified within the intervals since 2003, along with the share of overall income the middle class received. The results confirm that since 2003 the middle class of Kazakhstan has significantly expanded—from 3.7 percent of households in 2003 to 44.4 percent in 2015. The trend appears to be almost consistent throughout the period: between 2003 and 2006 there was considerable growth in the percentage of Kazakhstan households that moved from poor/vulnerable to the middle class; between 2006 and 2010 the middle class doubled; from 2010 to 2013 this group continued to grow substantially, continuing to grow slightly into 2015. In terms of income, the middle class had only 12.1 percent of total disposable income in 2003 but in 2006 it shot up to almost one-third, reached half in 2010, and accounted for almost two-thirds in the last few years.

The consistent growth of the middle class parallels that of the per capita GDP and the rapid reduction in poverty experienced by the country after the period of economic turmoil immediately following independence (see Figure 8).

21
PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA The evidence thus suggests that generator **Figure 22 Probability that a firm will own a generator, by** ownership is greatly affected by firm **number of employees** characteristics, such as size, sector, corporate **0.80** structure, and export orientation. Large firms that **0.70** operate 24 hours per day are more likely than **0.60** smaller firms to install backup generation **0.50** capacity compared to smaller firms, which **0.40** operate only during daylight hours and therefore **0.30** are less affected by evening blackouts. Mining **0.20** firms tend to require own power to keep **0.10** elevators, air pumps, and other safety devices **0.00** fully operational regardless of the power supply **0 500 1000 1500 Size (Employees)** from public grid. Petroleum firms have very sensitive and delicate equipment that must be Source: World Bank, Enterprise Survey Database. protected from damage stemming from power outages. Exporters may need to generate their own power to meet ISO standards (e.g., relating to cold chains). Informal firms may be unable to accumulate significant generating capacity because of security concerns, which may include police raids, unstable land or lease tenure, and other factors.

**Figure 22 Probability that a firm will own a generator, by number of employees**

The composite effect of size and reliability of power supply is generally not significant across firms, except for small firms and microenterprises (table 3). The significant negative coefficient on the product of small firms and large power outages indicates that small firms suffer the most from unreliable power supply, since they lack the resources to invest in own generation.<sup>20</sup>

# 5   The costs and benefits of  own generation

## Costs

We use the revealed preference approach to analyze the economic costs of own generation.<sup>21</sup> This approach is based on the presumption that rational, profit-maximizing firms will insure themselves against the risk of frequent power outages. Because insurance contracts for unreliable power supply are not available in developing countries, the only way to minimize losses is to acquire backup generating power. The firm’s problem is to choose the optimal amount of backup power that minimizes the sunk costs incurred by acquiring generation capacity as well as the damage that unsupplied power would cause.

A competitive, risk-neutral firm will maximize expected profits by equating at the margin the expected cost of generating a kWh of its own power to the expected gain due to that kWh. That gain consists of the continued production (even if partial) that the self-generated electricity makes possible,

> 20 Further discussion of this subject becomes complicated because access to electricity and access to finance are frequently simultaneous. For example, in Nigeria small firms may lack internal funds to obtain a generator, and owning a generator may be a prerequisite to secure loan from the bank.

> 21 See earlier citations to the revealed preference approach in the first section of this paper.

16
**Table A.3**

**Reported Age Starting Primary School** Individuals Age 10 to 34 in 2000

|Age|Number|Share|
|---|---|---|
|4|6|0.002|
|5|56|0.021|
|6|530|0.198|
|7|1336|0.499|
|8|639|0.239|
|9|83|0.031|
|10|18|0.007|
|11|6|0.002|
|12|2|0.001|
|13|2|0.001|
|14|1|0.000|

Source: China Center for Agricultural Policy (CCAP) Data Set, 2000. See de Brauw et al (2002) for a description of the CCAP survey.

66
asked separately about domestic and foreign markets). The 2004 and 2005 BEEPS surveys asked a retrospective question about levels of competition faced three years earlier. The responses suggest, if anything, a further catching-up of the TEs to levels of competition seen in the pre-2001 EU: of the firms that faced moderate competition in 2002 (1-3 competitors), 34% in TEs stated they faced strong competition (4+ competitors) in 2005, vs. 22% in the pre-2001 EU; and of the firms that faced strong competition in 2002, 18% in TEs said they faced less competition in 2005, vs. 15% in the pre-2001 EU.

A closer look at variation across country regions shows that firms generally reported an intensification of competition across all transition country regions between 2002 and 2005. The least competitive environment is reported by firms in the low-income CIS countries (V), whereas the most competitive environment is in the cohesion countries (II) and the EU8 members (III). The fastest change in the market structure (towards more competition) is observed in the middle-income CIS countries (IVb) followed by the SEE countries (IVa) and low-income CIS countries (V). The interpretation of this result is straightforward: (new) firms are filling in niches in markets characterized by low competition. The data also show that the EU8 member states are fairly close to the cohesion countries in terms of market structure and market structure change.

The price elasticity of demand data show a somewhat different picture (Table IV.2). Over the entire 1999-2005 period, the overall degree of price elasticity is similar to what is seen in the pre-2001 EU countries. However, the share of firms in TEs reporting inelastic demand grew between 1999 and 2005, and the share of firms facing highly elastic demand shrank. The picture is therefore somewhat different from the degree of competition results: firms in TEs in 1999 faced elasticities of demand that were slightly higher than in the market economy benchmark (EU pre-2001), and in 2005 faced elasticities that were slightly lower than the benchmark.

A possible explanation for this is that responses to questions about elasticity of demand are affected by the business cycle and business environment much more than estimates of 25
# **1. Introduction**

Poverty is heavily concentrated in rural areas, where 68.8 percent of Yemen’s total population resided in 2009. According to a poverty assessment undertaken by the World Bank in 2007, urban poverty rates in Yemen have declined while poverty reduction in rural areas is not robust (World Bank 2007d). The proportion of the rural population living on less than US$1 a day decreased only slightly between 1998 and 2005, from 42.5 percent to 40.1 percent. In urban areas, this proportion decreased more significantly, from 32.2 percent in 1998 to 20.7 percent in 2005 (figure 1.1). The GINI coefficient reflects worsening income inequality during this period, increasing from 33.4 in 1998 to 37.7 in 2005.<sup>1</sup> In addition to poverty and income inequality, the gender inequality index for Yemen is the worst of 146 countries and improved only slightly, from 0.853 in 2008 to 0.796 in 2011 (UNDP 2010; 2011).<sup>2</sup>

**<u>Figure 1.1 Poverty Ratios (Below $1 ppp per day) and GINI Index</u>**

<!-- Start of picture text --> 45 42.5<br>40.1<br>40<br>33.4<br>35 37.7<br>GINI index<br>30<br>32.3<br>25<br>Poverty headcount ratio at rural<br>20 poverty line (% of rural<br>20.7 population)<br>15<br>Poverty headcount ratio at urban<br>10<br>poverty line (% of urban<br>5 population)<br>0<br>1998 2005<br><!-- End of picture text --> _Source_ _<mark>:</mark>_ <mark>The United Nations MDGs Report 2011 (http://mdgs.un.org/unsd/mdg/Default.aspx?q=poverty); WDI 2010</mark> Policy makers should consider reducing the dependency on oil exports and diversifying economic activities by creating more jobs.<sup>3</sup> Especially for rural poverty reduction, income

> 1 A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

> 2 Gender inequality index is measured by a composite measure reflecting inequality in achievements between women and men in three dimensions: reproductive health, empowerment, and the labor market. An index of 1 represents perfect inequality, whereas an index of 0 represents no inequality.

> 3 The country’s financial institutions and its integration within world markets also improved over the past decade. This can be reflected by raising a share of total trade to GDP ratio from 61 percent to 66 percent during this period (IMF 2010). However, recent global financial crisis and the collapse of international oil prices during 2008-09 hit hard on Yemen’s vulnerable economy, which depends heavily on the oil industry. Although oil production has fallen over time, income from oil exports remains the single most important source of government revenue. A sharp decline in average oil prices from $98 per barrel in 2008 to $60 per barrel in 2009 and contraction of oil production led to a significant drop in government oil exports. As a result, Yemen had a record high fiscal deficit in 2009,

2
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

## **Table 17. Intraroaming networks in ECOWAS**

|**Network**|**Countries**|
|---|---|
|Orange Zone|Available for subscribers in Côte d’Ivoire, Guinea, Guinea-Bissau, Mali, Niger, and Senegal|
|Zain One|Available for subscribers in Burkina Faso, Ghana, Niger, Nigeria, and Sierra Leone|
|One World of MTN|Available for subscribers in Benin, Côte d’Ivoire, Ghana, Guinea, Guinea-Bissau, and Nigeria.|

_Source:_ Derived from Ampah and others (2009).

**Table 18. International call charges**

|US$|**Call within region**<br>**C**|**all to the United**<br>**States**|**Internet**<br>**dial-up**|**Internet ADSL**|
|---|---|---|---|---|
|Without submarine cable|1.34|0.86|68|283|
|With submarine cable|0.57|0.48|47|111|
|Monopoly on international gateway|0.70|0.72|37|120|
|Competitive international gateway|0.48|0.23|37|98|
|_Source:_AICD|||||
|_Note:_ADSL = asymmetric digital subscriber line.|||||

Access to submarine cables generally reduces costs, particularly if there is competition at the gateway (table 18). Therefore, the fiber-optic infrastructure that Senegal has put in place will serve it will in the long run.

## **Challenges**

The main challenge in the telecommunications sector remains the need for more vigorous competition in the domestic markets. As modern ICT services develop, it is necessary for the regulator to ensure broadband access to independent service providers in addition to the three established carriers. Furthermore, Senegal has access to international gateways but only SONATEL/Orange has access to the existing submarine cables, even though Sudatel or Millicom could build their own cables. Prices of the international capacity/bandwidth sold by SONATEL have been relatively high compared to other SAT-3 members. New cables are unlikely to change the situation since neither Sudatel or Millicom would have access to these planned cables. Effective regulatory oversight is therefore needed to enforce open access to the submarine cable.

# Financing Senegal’s infrastructure

To meet its most pressing infrastructure needs and to catch up with developing countries in other parts of the world, Senegal needs to expand its infrastructure assets in key areas (table 19). The targets outlined in table 19 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting targets, which can be modified or delayed as needed to achieve financial balance.

34
households of human capital h = hA is n ∈ [0, 1] in the city, and x ∈ [0,1] in neighborhood 1. The welfare of an individual with human capital h and a fraction x of high-human capital neighbors is a function w(h, x). The welfare of a neighborhood is thus: The city's social optimum is reached at the maximum of W (x) + W (n − x/2). The city's optimum will be to segregate individuals if such total welfare is a convex function of x. Crucially, the optimality of segregation thus depends on two empirically measurable parameters: (i) whether high human capital individuals' welfare benefits more from high-human capital peers than lowhuman capital individuals, and (ii) whether welfare is a concave function of peers' human capital.

There is evidence in some dimensions that more segregated metropolises have lower average welfare, and that low-human capital individuals benefit more from their peers than high-human capital individuals. Estimates of peer effects in schools and neighborhoods suggest that low ability peers are more affected by their peers than high ability peers (Sacerdote, 2011). Card and Rothstein (2007) find that higher segregation arguably causes a higher Black-White test score gap.

A city where high human capital households are constrained in their ability to borrow could lead to higher average welfare, e.g. a third-best allocation with greater welfare than at market equilibrium. Controversially, relaxing borrowing constraints may lead to greater segregation and lower welfare, even absent a credit bust.

Ouazad and Rancière (2016b) estimate the impact of the relaxation of lending standards during the housing boom of 2000-2006 on changes in racial segregation across neighborhoods within metro areas. The paper first estimates such impact for the 935 metropolitan and micropolitan statistical areas of the United States. At the metropolitan area level, a common measure of racial segregation is the set of exposure indices. For instance, the exposure of Blacks to Whites is the average fraction of White individuals in the neighborhood of an average Black resident. Despite 21
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- 1053 Y. Kurachi, H. Morishima, H. Kawata, R. Shibata, K. Bunya, and J. Moteki. Challenges for Japan’s 1054 Economy in the Decarbonization Process. Research Paper, The Bank of Japan, June 2022.

- 1055 S. Kverndokk, K. E. Rosendahl, and T. F. Rutherford. Climate Policies and Induced Technological 1056 Change: Which to Choose, the Carrot or the Stick? _Environmental and Resource Economics_ , 27 1057 (1):21–41, Jan. 2004. ISSN 0924-6460. doi: 10.1023/B:EARE.0000016787.53575.39. URL `http:` 1058 `//link.springer.com/10.1023/B:EARE.0000016787.53575.39` .

- 1059 F. Lecocq and Z. Shalizi. The economics of targeted mitigation in infrastructure. _Climate Policy_ , 1060 14(2):187–208, Mar. 2014. ISSN 1469-3062, 1752-7457. doi: 10.1080/14693062.2014.861657. URL 1061 `http://www.tandfonline.com/doi/abs/10.1080/14693062.2014.861657` .

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- 1069

- 1070 D. Lemoine and I. Rudik. Managing Climate Change Under Uncertainty: Recursive Integrated 1071 Assessment at an Inflection Point. _Annual Review of Resource Economics_ , 9(1):117–142, Oct. 1072 2017. ISSN 1941-1340, 1941-1359. doi: 10.1146/annurev-resource-100516-053516. URL `http:` 1073 `//www.annualreviews.org/doi/10.1146/annurev-resource-100516-053516` .

52
# **Classification Results: Subjects**

|Date|Count|Distrust<br>institutio|ns<br>Irrelevant|Lack of<br>Informat|ion<br>Other|Partisan-<br>ship|Personal<br>economic<br>impact|
|---|---|---|---|---|---|---|---|
|Jan 2011|275|6.9|4.7|10.9|52.0|18.5|6.9|
|Apr 2011|863|14.1|7.9|9.0|47.0|6.0|15.9|
|May 2011|310|14.5|12.6|7.4|43.2|7.7|14.5|
|Aug 2011|118|11.9|27.1|7.6|34.7|8.5|10.2|
|May 2012|570|5.4|36.1|3.3|39.8|11.1|4.2|
|Aug 2012|168|7.1|20.8|10.1|50.0|4.2|7.7|
|Sep 2013|680|3.8|27.5|4.6|58.4|3.7|2.1|

Table 9: The classification results for the categories of all the tweets. Source: Author’s calculations.

information”, and “personal economic impact” categories. As public sentiment is shifting in favor of the subsidy reform, people mention these categories less. As we see below, the appearance of these tweets that fall within these categories are almost always negative, so “no news is good news” here.

The results in Table 11 for the sentiment analysis are not as clear. Given the extraordinarily high class imbalance at the expense of the positive category, the classifier is unable to predict even one positive category in- or out-of-sample.This is likely an artifact of using the SGD algorithm, which will not do well on highly imbalanced classification tasks with rare events and also the lack of discriminating information in the small number of positive training examples. However, we do notice a decline in overall negative sentiment coinciding with the change in the survey sentiment. However, as we see in Table 12, this is mainly due to the increase in the “other” and “irrelevant” categories, which generally have fewer tweets that express any sentiment.

One of the benefits of using Twitter data is that we can take a deeper look and identify what exactly is driving these results. Given the nature of surveys, it is often prohibitively costly if not impossible to ask different questions after a general picture emerges from the collection of an original survey. That is, with surveys it is much more important to get it right the first time. Figures 2-9 give a general picture of the coefficients that are important in predicting whether or not a tweet belongs to a certain category or expresses a certain sentiment. These are the coefficients in Equation 4. The use of words with positive coefficients suggests that the tweet belongs to that class. The negative coefficients suggest that the tweet belongs to some other class in the OvA scheme.

24
Table A2: Robustness to different community matching definitions

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. Var.:Growth inper capita|income(1996-|2011)|||||
|Homeland (=1)|0.660|-0.032|-0.297|-0.206|0.866|-0.888|
||[0.454]|[0.970]|[0.539]|[1.136]|[0.574]|[1.001]|
|Share Employed mining1996|-0.447*|-0.103|-0.677**|-0.363|-0.306|0.013|
||[0.258]|[0.226]|[0.302]|[0.230]|[0.227]|[0.237]|
|Homeland*Share Employed|0.840***|0.510**|0.941***|0.692***|0.671***|0.461*|
|mining1996|[0.284]|[0.241]|[0.314]|[0.255]|[0.249]|[0.261]|
|Observations|9,583|9,558|7,094|7,071|6,072|6,049|
|R-squared|0.634|0.711|0.614|0.714|0.630|0.702|
|1996 Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|
|2011 Controls|-|Yes|-|Yes|-|Yes|
|Geographical match type|Centroid|Centroid|Area>75%|Area>75%|Area>75%|Area>75%|
|Other restrictions|-|-|-|-|Unique|Unique|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). Definitions for Geographical match type: (1) Centroid: each community from 2011 is matched to the nearest 1996 community centroid. (2) Area>75%: If over 75% of the area of a 2011 community is contained in a single 1996 community, these are matched (note that several communities from 2011 can match a single 1996 community). Unique: restrict sample to one-to-one matches only. The dependent variable is the difference in log per capita income for 2011-1996. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1