rafmacalaba/datause-encoder-data huggingface dataset:
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Cameroon’s hydropower resources.

Cameroon has enormous potential within the Central African Power Pool (CAPP) to produce lowcost hydropower and become a major player in regional trade by exporting power to Chad, the Republic of Congo, Gabon, and Equatorial Guinea. To fully develop regional trade through the CAPP, however, Cameroon would need to develop 1,400 MW of hydropower over and above the amounts needed to meet domestic demand, and to develop an additional 831 MW in interconnector capacity. This would also result in commercial gain through exporting over half the domestic power production. A one-time investment of $2.6 billion to develop additional hydropower potential and interconnectors in Cameroon would yield an annual return of 12 percent.

# Information and communication technologies

## **Achievements**

Mobile communications have been the main driver of ICT access in Cameroon, as in most African countries. Cameroon was an early introducer of mobile competition through the licensing of two operators, but the market has remained a duopoly. Mobile subscriptions per 100 people rose from fewer than one in 2000 to 34 by 2009, with 85 percent of the population now covered by a signal (table 12) (France Telecom 2010).

**Table 12. Cameroon’s ICT indicators benchmarked**

|||Camero|on<br>Lower-middle<br>income countries|Sub-Saharan<br>Africa|
|---|---|---|---|---|
|Indicator|Unit|2000|2008/09<br>2008|2008|
|GSM coverage|% population under signal|30|85<br>77|56|
|Mobile phone|subscribers/100 people|0.6|34<br>47|33.3|
|International bandwidth|bits/capita|0.2|13<br>153|34|
|Internet|users/100 people|0.3|6<br>13.9|5.1|
|Landline|subscribers/100 people|0.6|2.2<br>13.6|1.5|
|||Cameroon|Lower-middle income<br>countries<br>Sub-|Saharan Africa|
|||2009|2008|2008|
|Price of monthly mobile basket|US dollars|14.7|8.4<br>11.8||
|Price of monthly fixed-line basket|US dollars|14.7|4.8<br>11.6||
|Price of monthly fixed broadband|US dollars|104|31<br>100.1||
|Price of a 1-minute call to US|US dollars|0.8|—<br>0.9||
|Price of an inter-Africa call per minute|US dollars|0.9|—<br>1.0||

Source: AICD database. — = data not available 36
_registered as living there before the prior occupants died or moved away. This security of tenure was and is highly prized._ These events are especially important in the context of new urban housing construction to address shortages in high demand areas. Responses to the L2CU survey show that public perceptions of demolitions were salient for many respondents. Monthly panel data collected during this time suggest that despite high overall levels of support for free movement, housing construction, and the overall reform agenda, popular sentiment is quite sensitive to perceptions of fairness. Before demolitions reached the center stage, results from L2CU showed a steadily improving trend: a falling share of respondents believed that homeowners were being treated unfairly with respect to new development in cities. However, the trend went sharply in reverse after the demolitions described above, and the protests and media coverage that followed. In August 2019 the share of respondents who felt that urban homeowners were being treated unfairly more than doubled to 18 percent (Figure 15).

**Figure 15: Views on Whether Current Urban Homeowners are Treated Fairly**

<!-- Start of picture text --> "I believe that current homeowners in urban areas are being treated fairly with<br>respect to new development"<br>18% 84%<br>16% 82%<br>14% 80%<br>78%<br>12%<br>76%<br>10%<br>74%<br>8%<br>72%<br>6%<br>70%<br>Post<br>4% 68%<br>Demolition<br>2% 66%<br>0% 64%<br>Apr-19 May-19 Jun-19 Jul-19 Aug-19<br>Fully Agree (left) Disagree (left) Fully Disag. (left) Agree (right)<br><!-- End of picture text --> _Source: L2CU, Author’s Calculations._ But beyond directly affecting views regarding the fairness of how urban development policies are implemented, backlash from involuntary demolitions could also potentially diminish support for the government’s larger reform agenda. One way to describe the relationship between sentiment regarding economic reform and the state of the economy is to use simple correlations between views on the fairness of urban development policy on the one hand, and a person’s wider reform views on the 41
_CHAPTER 3. BASIC INDIRECT ESTIMATORS_ 12 _yd_ obtained for an intensive survey conducted in _m_ = 85 of the counties. By considering those counties as the first 85 and regarding _yd_ as the true county median, they assumed the linear regression model with the usual regression assumptions, namely independent errors, _E_ ( _ed_ ) = 0 and var( _ed_ ) = _σe_<sup>2,</sup><sup>_d_= 1</sup><sup>_, . . . , D_.Theyfittedtheregressionmodelusing</sup> the available _yd_ values for the _m_ = 85 counties. The fitted regression parameters were then applied to predict the number of radio stations heard during the day in the remaining 500 _−_ 85 = 415 counties, for which the mail survey estimates _xd_ were available. The resulting predicted values are taken as the small area estimators for the remaining 415 counties where _yd_ was not available. Estimators derived from a regression model with common regression coefficients for all the areas, and where area effects are not estimated, are referred to as _regression-synthetic estimators_ . Note that the regression coefficients in (3.3) are constant for all the _D_ = 500 US counties and are estimated with the data ( _xd, yd_ ) from the _m_ = 85 counties where _yd_ was available, and county effects are not estimated. Moreover, the estimators derived from (3.3) do not account for the fact that _yd_ are subject to sampling error, unlike the SAE procedures described in Chapter 4.

As mentioned earlier, synthetic estimators can have relatively small design variances, but their design bias can be substantial due to the unrealistic assumptions underlying synthetic estimators. Since their design bias is not negligible, design mean squared error (MSE) estimates, which account for both, bias and variance, should be used to complement the synthetic point estimates. Aside from the potentially large bias, a challenge lies in obtaining efficient and area-specific design MSE estimates for these estimators.

Composite estimators, defined as a weighted average of a synthetic and a direct estimator for the same area, were proposed to shrink direct estimators toward the synthetic ones, reducing the design variances of the
photovoltaic modules and lithium-ion battery cells. In this link of the supply chain, value added comes from assembling components into a marketable product that customers value.

**Table 5: Number of HS 6-digit products by value chain and segment**

|VC|Raw Materials|Subcomponents|Processed|End products|Total|
|---|---|---|---|---|---|
|EV|9|43|32|5|89|
|Solar|13|53|22|1|89|
|Wind|5|44|55|3|107|
|Total|27|140|109|9|285|

# 4.2) Description of data sources

Bilateral export flows for 226 countries in 5,000 HS 6-digit products (HS 92 nomenclature) between 1995-2021 come from the BACI international trade dataset, reported by the Centre d’Etudes Prospectives et d’Informations Internationales (CEPII). Gaulier and Soledad (2010) reconcile declarations of the exporter and the importer in the United Nations Commodity Trade Statistics Database (COMTRADE). To smooth out data anomalies such as re-exports and focus on structural patterns, we take 5-year rolling averages of export data. Where 5 years of export data for country-product cells is not available, we take the average over the number of available years.

For the regression analysis, we define control variables - GDP per capita, exports (of goods and services) to GDP ratio, C02 emissions and population - from the World Bank World Development Indicators (WDI), available from <u>https://databank.worldbank.org/source/world-development-indicators.</u>

# 4.3) Key metrics

## _4.3.1) Hirschman-Herfindahl Index_

To study market concentration in products associated with decarbonization technologies, we compute the Hirschman-Herfindahl Index (HHI) for each HS 6-digit product p.<sup>6</sup> It is defined as follows: c p where c is the market share of country c’s export value in total global exports of product p. A HHI of 1 indicates XX that the market is a perfect monopoly (one country exports 100% of the product), while HHI scores approaching 0 XXcc that the market is a perfect monopoly (one country exports 100% of the product), while HHI scores approaching 0 indicate a much more competitive market.

## _4.3.2) Decarbonization Technology Strength (DTS) index_

To define countries’ Decarbonization Technology Strength (DTS) index, we first quantify their strengths in products, using depth and breadth dimensions. To measure breadth, we count the number of HS 6-digit products in each value chain for which a country’s export share is greater than the global average (RCA > 1), as defined in equation (1). To measure depth in decarbonization technologies, we compute a country c’s average market share in export values of HS 6-digit products across value chains (vc):

> 6 For ease of exposition, we leave out the time subscript in all equations.

13
Finally, two changes in the approach to development assistance have taken place which also have implications for shelter strategy: first, greater accountability for aid has been embodied in a series of quantitative development goals – the Millennium Development Goals -- which includes a target of improving the lives of 100 million slum dwellers over the next 25 years; and second, the structure of official development assistance (ODA) has changed in both composition and level.<sup>7</sup> In the more globalized world economy, ODA now accounts for a smaller share of international credit flows, and within that lower overall level of assistance, support for infrastructure investments has declined sharply.<sup>8</sup> Therefore, while Bank shelter assistance has not followed the overall trends in ODA or in infrastructure lending, having almost doubled since the last review, it has done so against a background of lower overall development assistance, as well as an assistance that is more quantitatively targeted on directly assisting the poor.

In sum, since the last review much has indeed changed. This assistance is now provided in a very different world environment and uses a variety of new instruments.<sup>9</sup> In addition, a considerable amount of additional research has been undertaken. A more robust understanding of how housing and land markets in both developed and developing countries work has emerged. As argued by Renaud (1999), there is now an active body of research on real estate economics in general and for developing countries in particular. This is a very different situation than existed 18 years ago. It is against this evolving background that we

> 7 The eight MDG goals are: eradicate extreme poverty and hunger, achieve universal primary education, promote gender equality and empower women, reduce child mortality, improve maternal health, combat HIV/AIDS, malaria and other diseases, ensure environmental sustainability, and develop a global partnership for development. The commitment to significantly improve the lives of 100 million slum dwellers is listed under the commitment to ensure environmental sustainability.  For more information on MDGs, see http://www.developmentgoals.org/  For a review of progress on the International Development Goals see <u>www.paris21.org/betterworld.</u>

> 8 The last decade saw a twenty-five percent decline in Infrastructure lending by the World Bank.

> 9 See http://www.worldbank.org/urban/housing/ for a quantitative inventory and summary statistics for the Bank’s shelter lending since 1972

4
Trade Agreement (NAFTA). Both agreements gave rise to a strong growth of exports. The Economic Commission for Latin America and the Caribbean (ECLAC, 2008) showed that the preferential access also contributed to a very marked diversification of exports, generally considered to be a factor supportive of growth and development (Imbs and Wacziarg, 2003; Klinger and Lederman, 2006). The diversification additionally witnessed a sharp increase in the participation of low, medium and high tech manufactured exports, which could provide important opportunities for learning and entrance into new “product spaces” that would be conducive to economic upgrading (Hausmann and Klinger, 2006). But the same ECLAC study also showed that these advantages were not being fully exploited: the expansion of valueadded in low and medium tech manufactures was markedly lower than the increase in the value of exports, reflecting the dominance of import-re-export assembly activities (“maquila”) with the U.S. market. Moreover, in Central America the growth of many exports was in “undynamic” products that were losing shares in world trade.

The story for natural resource exporters in South America also exhibited rough edges. The commodity boom of the 2000s drove a concentration of exports, especially in the Andean area and Chile. These latter countries, which have their manufactured exports based to a large degree on natural resources, exhibit low intensity in engineering even when compared to other natural resource-based exporters such as Australia and New Zealand. Moreover, their exports have been overly represented by relatively undynamic products in terms of their growth in shares of world trade (ECLAC 2008). And while in the 2000s Latin America’s economic growth was the best in 40 years, over-reliance on high commodity prices to drive that growth is a source of vulnerability (Inter-American Development Bank, 2008; ECLAC, 2010). Moreover, that growth has been undistinguished when compared to other developing regions (Table 1).

4
**Table 7: Impacts on Business Sales and Profits**

|||**New F**|**irms**|||**Existing**|**Firms**||
|---|---|---|---|---|---|---|---|---|
||||Inverse|Aggregate|||Inverse|Aggregate|
||||Hyperbolic|<br>Index of|||Hyperbolic|<br> <br>Index of|
||Truncated<br>Sales|Truncated<br>Profits|Sine<br>Profits|Sales and<br>Profits|Truncated<br>Sales|Truncated<br>Profits|Sine<br>Profits|Sales and<br>Profits|
|Experimental impacts:|||||||||
|First‐Follow‐up|36.160|‐24.512|2.156***|0.016|50.805|0.074|0.972***|0.080|
||(49.884)|(26.330)|(0.369)|(0.047)|(85.662)|(49.416)|(0.373)|(0.070)|
|Second Follow‐up|297.783***|69.061***|4.154***|0.298***|346.304**|69.234*|2.183***|0.237***|
||(56.494)|(15.150)|(0.326)|(0.036)|(134.728)|(35.420)|(0.401)|(0.060)|
|Third Follow‐up|64.541|20.137|3.962***|0.167***|338.476**|32.035|2.580***|0.213***|
||(92.338)|(21.635)|(0.346)|(0.042)|(142.812)|(40.956)|(0.464)|(0.070)|
|Pooled Second and Third Round Effect|185.142***|45.608***|4.062***|0.235***|336.931***|50.434*|2.357***|0.223***|
||(60.642)|(14.550)|(0.264)|(0.032)|(110.685)|(30.255)|(0.338)|(0.052)|
|PSM impacts for National/Zonal winners|||||||||
|First‐Follow‐up|‐0.065|‐55.926*|2.145***|‐0.023|305.599***|67.872|1.232***|0.324***|
||(69.685)|(32.838)|(0.630)|(0.058)|(96.901)|(70.883)|(0.411)|(0.091)|
|Second Follow‐up|362.469***|152.708***|5.078***|0.442***|548.416***|119.593***|1.975***|0.333***|
||(87.777)|(30.941)|(0.473)|(0.076)|(152.439)|(41.525)|(0.487)|(0.062)|
|Third Follow‐up|279.080*|78.353**|4.617***|0.313***|654.925***|113.103**|2.346***|0.399***|
||(169.253)|(38.705)|(0.578)|(0.085)|(142.289)|(51.802)|(0.584)|(0.105)|
|Control Mean: First follow‐up|509.699|257.025|10.772|‐0.045|509.699|257.025|10.772|‐0.045|
|Control Mean: Second follow‐up|660.535|206.305|9.646|‐0.117|660.535|206.305|9.646|‐0.117|
|Control Mean: Third follow‐up|509.975|192.151|8.565|‐0.108|509.975|192.151|8.565|‐0.108|
|Obs: First follow‐up|995|995|995|995|423|423|423|423|
|Obs: Second follow‐up|1151|1150|1150|1152|497|497|497|497|
|Obs: Third follow‐up|1063|1063|1063|1063|470|469|469|470|

Notes: Robust standard errors in parentheses, *, **, *** indicate significance at the 10, 5, and 1 percent levels. Experimental estimates are ITT estimates and control for randomization strata. Obs is the number of observations in the experimental sample. Sales and Profits are in 1000s of real Naira per month. Aggregate index of outcomes includes monthly sales, truncated monthly sales, annual sales, sales higher than one year ago, monthly profits, truncated monthly profits, profits in the best month, and inverse hyperbolic sine of profits. Note pooled sample conditions on the firm being in operation in both follow‐up 2 and 3. Propensity score matching (PSM) estimates compare non‐experimental winners to experimental control group.

44
is, in fact, the existence of horizontal inequality. This is shown in Figure 2 in the case where payments are progressive on pre-payment income, X, and hence post-payment income, X-T, increases in pre-payment income but at a decreasing rate. The average postpayment income at any level of pre-payment income can be read off the function in Figure 2. There will, however, be variations around this mean. These variations are reflected in a "fan" emanating from the point on the post-payment income function corresponding to the pre-payment income level in question, branching out to the postpayment income axis. For example, a household with a pre-payment income of $1100 might pay $250 in health care payments, ending up in the post-payment distribution behind the average household with a pre-payment income of $1000, which spends only $1000. Thus reranking is _caused by_ horizontal inequity. Given this, it seems unwise to :ry to make too much of the distinction between _R_ and _H._ This is reinforced by the fact that. although in the population at large there will be households on the same pre-payment income; in a household survey such instances are rare. In empirical work, it therefore becomes necessary to define equals by reference to _bands_ of pre-payment income, within which, for the purpose of the exercise, households are deemed to be equal. The choice of bandwidth inevitably affects the computed value of H, but also affects the computed value of _R._ Specifically, it seems to be the case that as the bandwidth is narrowed, _H_ falls and _R_ rises, though their does not seem to change much. In what follows we emphasize the sum of H and _R,_ rather than their individual values.

# _4.2. The sources of redistributive effect of out-of-pocket payments in Vietnam_

_RE_ can be computed simply as the difference between Gx and _Gx-T._ To compute <' (or more precisely the concentration index for out-of-pocket payments, _CT)_ and _CX-T_ one has to decide on appropriate groups of pre-payment equals. In this illustration, prepayment equals were defined by expressing pre-payment income as a multiple of the overall poverty lines for 1993 and 1998. Households below the poverty line _z'_ were divided into eight groups, the first comprising households with a pre-payment income between 0% and 12.5% of the poverty line, the second comprising households with a prepayment income between 12.5% and 25% of the poverty line, and so on. Households with a pre-payrnent income of between 100% and 200% of the poverty line were divided into just four groups, along similar lines, while those with pre-payment incomes in excess of 200% of the poverty line were divided into just three groups. To put this into perspective, nearly 60% of households fell below the poverty line in 1993, and nearly 40% did in 1998. With groups of prepayment equals defined, it is straightforward to compute _CT_ on the grouped data, and to form the ranking variable to compute _CX T._ Using _18_
items). The authors found that the RAI had good but variable reliability, ranging from α=0.6 to α=0.9 among women. Within the domain of household purchases for women and the domain of employment for men, the RAI had poor to unacceptable reliability.

Most recently, to address concerns about poor respondent understanding, the RAI was adapted in the newer 2015 WEAI version as anchoring vignettes and re-piloted in Uganda. Anchoring vignettes are short descriptions of hypothetical individuals or situations meant to convey complicated concepts and ensure that different respondents understand questions similarly across cultures and contexts. They have been generally shown to increase ease of response (Martin 2006). The respondents were read the vignette stories about different types of farmers and their situations (corresponding to autonomous, introjected or external motivation) regarding different agricultural activities, and were asked how similar they are to the farmer in the story. Cognitive testing showed that the vignettes were generally well-understood by respondents (Sproule and Kovarik 2014).

# **Box 2**

# **Motivational Autonomy Vignettes in Uganda (2015 WEAI)**

Example: the respondent is asked about livestock raising, and presented with different stories.

“Asma [Amin] raises the types of livestock she does because her spouse, or another person or group in her community tell her she must use these breeds. She does what they tell her to do.” “Alyea [Omor] buys the kinds of livestock that her family or community expect. She wants them to approve of her as a good livestock raiser.” “Afyee [Anis] chooses the types of livestock that she personally wants to raise and thinks are good for her family and business. She values raising these types. If she changed her mind, she could act differently.” _Responses: Are you like this person? (Yes/No) Are you completely the same or somewhat the same? Are you completely different or somewhat different?_ Further insight into how the anchoring vignettes helped to better capture motivational autonomy can be gained by comparing summary statistics from the RAI vignette pilot and the original RAI adaptation. The original WEAI was conducted in five spatially dispersed rural districts in the northern region (Amuru and Kole), central region (Luwero and Masaka), and eastern region (Iganga). The second pilot was conducted to develop and test a revised version of WEAI in the same districts as the original pilot; sample villages were randomly assigned to receive either the original (1.1) or revised (2.0) versions of the questionnaire. Below, we analyze the Uganda WEAI data and present three key results:

- i. First, the average RAI values are substantially lower in the second (vignettes) pilot.

- ii. Second, we find an ordered correlation pattern within responses in the second pilot, a key test to conceptually validate the vignettes RAI. Specifically, we find a positive relationship

9
**Figure 16: Sources of Russian FDI, 2007–12**

<!-- Start of picture text --> Other EU, 6.7%<br>Rest of world,<br>Sweden, 2.1% 14.1%<br>France,<br>2.3% Bermuda, 5.8%<br>Germany, 5.7%<br>Probable<br>round‐<br>UK, 6.3% tripping<br>Cyprus,<br>origins,<br>18.6%<br>35.0%<br>Ireland,<br>7.3%<br>Caribbean,<br>Luxembourg,<br>9.0% Netherlands, 10.5%<br>11.4%<br><!-- End of picture text --> _Source_ : Central Bank of Russia.

0
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