rafmacalaba/datause-encoder-data huggingface dataset:
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- WHO (World Health Organization) and JMP (Joint Monitoring Programme). 2010a. ―Cameroon Estimates for the Use of Improved Drinking-Water Sources.‖ www.wssinfo.org/resources/documents.html.

- ———. 2010b. ―Cameroon Estimates for the Use of Improved Sanitation Facilities.‖ www.wssinfo.org/resources/documents.html.

- World Bank. 2007. Project Appraisal ―Document on a Proposed Credit in the Amount of SDR 53.30 Million to the Republic of Cameroon for an Urban and Water Development Support Project.‖ Report 37979. Africa Region, World Bank, Washington, DC. May 1.

- ———.2010b. ―Project Information Document Appraisal Stage.‖ Project Name: Urban and Water Development Support Project Additional Financing. Project ID P121027, Report AB5822. Africa Region, World Bank, Washington, DC. September 9.

- ———.2011d. ―Project Concept Note on a Proposed Adaptable Program Loan in the Amount $30 Million Equivalent to the Republic of Cameroon for a Cameroon Sanitation Project–Phase 1 (APLl-1) in Support of the First Phase of the Cameroon Sanitation Program.‖ Africa Region, World Bank, Washington, DC.

52
**Table A3:** Inclusion error % by model and number of question for various coverage rates (approx. no. of questions in brackets)

||PMT|Truncated|Early|stop|TrESt|
|---|---|---|---|---|---|
|_10% coverage_||||||
|37.0|–|5.56 (37.0)|–||–|
|30.0|–|5.56 (30.0)|–||–|
|22.8|5.57 (22.8)|5.58 (23.0)|–||–|
|20.0|–|5.59 (20.0)|5.56 (|20.5)|–|
|15.0|–|5.62 (15.0)|–||5.57 (12.1)|
|10.0|–|5.74 (10.0)|5.57 (|10.5)|5.58 (10.3)|
|5.0|–|6.01 (5.0)|5.76 (|5.1)|5.62 (4.9)|
|0.0|–|7.09 (0.0)|7.01 (|0.8)|7.04 (0.1)|
|_20% coverage_||||||
|37.0|–|9.89 (37.0)|–||–|
|30.0|–<br>|9.94 (30.0)<br>|–<br>||–|
|22.8|10.01 (22.8)|9.94 (23.0)|9.89 (|23.0)|–|
|20.0|–|9.97 (20.0)|9.89 (|19.8)|9.96 (19.1)|
|15.0|–|10.04 (15.0)|9.94 (|15.3)|9.92 (14.8)|
|10.0|–|10.27 (10.0)|10.21|(9.8)|9.97 (10.0)|
|5.0|–|10.84 (5.0)|11.47|(5.2)|10.21 (5.0)|
|0.0|–|13.50 (0.0)|–||13.45 (0.1)|
|_30% coverage_||||||
|37.0|–|13.73 (37.0)|–||–|
|30.0|–|13.73 (30.0)|13.73|(29.7)|–|
|22.8|13.86 (22.8)|13.77 (23.0)|13.74|(23.2)|–|
|20.0|–|13.79 (20.0)|13.74|(20.2)|–|
|15.0|–|13.85 (15.0)|13.87|(14.9)|13.77 (15.0)|
|10.0|–|14.24 (10.0)|14.30|(10.2)|13.86 (9.8)|
|5.0|–|15.05 (5.0)|16.00|(4.7)|14.40 (5.0)|
|0.0|–|19.33 (0.0)|–||19.27 (0.1)|
|_40% coverage_||||||
|37.0|–|17.33 (37.0)|–||–|
|30.0|–|17.33 (30.0)|17.33|(29.5)|17.39 (27.2)|
|22.8|17.63 (22.8)|17.39 (23.0)|17.34|(23.2)|17.39 (22.6)|
|20.0|–|17.39 (20.0)|17.37|(20.3)|17.40 (20.1)|
|15.0|–|17.52 (15.0)|17.63|(14.7)|17.46 (15.0)|
|10.0|–|18.01 (10.0)|18.04|(10.3)|17.57 (10.1)|
|5.0|–|19.03 (5.0)|19.02|(5.6)|18.28 (5.0)|
|0.0|–|25.19 (0.0)|–||23.46 (0.5)|
|_50% coverage_||||||
|37.0|–|20.85 (37.0)|–||–|
|30.0|–|20.86 (30.0)|20.85|(30.5)|20.91 (30.2)|
|22.8|21.17 (22.8)|20.91 (23.0)<br>|20.90|(22.5)|20.96 (22.7)|
|20.0|–|20.95 (20.0)<br>39|20.95|(20.3)|20.98 (19.8)|
|15.0|–|21.05 (15.0)|21.21|(15.4)|21.00 (15.0)|
|10.0|–|21.69 (10.0)|22.09|(9.8)|21.17 (9.9)|
|5.0|–|22.89 (5.0)|23.77|(5.2)|22.16 (5.0)|
|0.0|–|31.19 (0.0)|–||28.99 (0.6)|
# **Figure 6 Share of investment in different sectors**

<!-- Start of picture text --> A. Share of total gross fixed capital formation (GFCF) B. Sectoral GFCF as share of GDP, constant prices<br> % %<br>70 25<br>60<br>20<br>50<br>40 15<br>30 10<br>20<br>5<br>10<br>0 0<br>Housing Infrastructure Government Business Housing Infrastructure Government Business<br><!-- End of picture text --> _Source:_ Author estimates However, the fall in business investment has been offset by a surge in capital formation in other sectors of the economy. Following the introduction of a private housing market at the turn of the century, private capital formation in the residential sector started to increase rapidly from 2004 onwards. By 2015, capital formation in the residential sector was greater than that in the business sector of the economy. Rising demand for new housing generates a strong demand for infrastructure outlays in urban areas as the public finance laws force the profits made by local governments from land sales to be largely invested in urban infrastructure and government-owned land has been used as security for borrowing to finance further infrastructure outlays.

# **_Capital stock_**

The estimates show an acceleration in the growth of capital starting in 2000. This acceleration was most pronounced following the investment package introduced in 2009 (Figure 7). In the five years to 2011, capital grew by 14% annually with similar increases in the business sector and the rest of the economy. As the impact of the stimulus on the economy faded, economic growth slipped and brought a marked decline in the growth rate of the capital stock in the business sector. However, in the remainder of the economy, the very rapid growth of the capital stock did not slacken. The gap between the growth of the capital stock in housing, infrastructure and government and that of GDP rose from 3.7 percentage points between 2006 and 2011 to 6.7 percentage points in the five years ending in 2016.

**Figure 7 Growth of the capital stock by economic sector**

% per year <!-- Start of picture text --> 20.0<br>18.0<br>16.0<br>14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1981 1986 1991 1996 2001 2006 2011 2016<br>Housing Infrastructure & Government Business Total<br><!-- End of picture text --> _Source:_ Author estimates 16
<!-- Start of picture text --> Figure3: Identifying and Classifying Goods Related to the Preservation and Management of the Environment<br>Environmentally Preferable Products (EPPs):<br>Goods for Environmental Management (GEM)<br>Single use<br>(Pollution, Resources)<br>(less harmful for the environment during production, use or disposal)<br>Multiple end-uses<br>(“good” for the environment)<br>--- Filters, pumps, used for air pollution control, Production Use Disposal<br>--- Tanks pipes for sewage treatment, etc. -- Aluminium (Prebake -- Solar stoves --- packaging (glass vs.<br>--- Towers and lattice masts for wind turbines vs Soderberg technology) -- Solar furnaces plastic)<br>-- Organic cotton vs --- Cotton fiber versus<br>--- Measuring instruments -- Energy-efficient<br>conventional cotton; synthetic fiber<br>consumer goods (cars,<br>--Rainfed vs irrigated cotton electrical appliances,<br>lamps)<br>Identification of use Identification<br>Project Approach Relativism to the frontier (static and dynamic)<br>Alternative HS-classification Attribute Disclosure: efficient disclosure mechanism (e.g. certification, harmonization)<br>Processes and Production Methods (PPMs) and the like products at WTO<br>Difficulties to negotiate on agricultural products (e.g. biofuels) and environmental services<br>Lock-in if characteristics are embodied in HS code<br>No coverage in the HS (products and services)<br><!-- End of picture text --> Source : Balineau and de Melo (2011 figure 1) 23
conducted of randomly selected farms. The sampling was clustered in villages to reduce sampling cost. A total of 9597 surveys were administered across the 11 countries in the study: Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, Zambia, and Zimbabwe.

Climate data were gathered from two sources. We relied on satellite observations for temperature and ground weather station observations for precipitation (Mendelsohn et al. 2007). The United States Department of Defense uses a set of polar orbiting satellites that pass above each location on earth between 6am and 6pm every day. These satellites are equipped with sensors that measure surface temperature by detecting microwaves that pass through clouds (Weng & Grody 1998, Basist et al. 2001). The precipitation data came from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association’s Climate Prediction Center, is based on ground station measurements of precipitation.

The monthly data were organized into three month seasons. We define the winter in the northern hemisphere as the average of November, December and January. February, March and April are spring, May, June and July are summer, and August, September and October are fall. The seasons in the southern hemispheres are assumed to be 6 months apart from the northern hemisphere seasons. For example, the winter in the southern hemisphere is May, June and July (Kurukulasuriya et al. 2006).

Soil data were obtained from the Food and Agriculture Organization’s digital soil map of the world (FAO 2003). The FAO data provide information about the major and minor soils in each location as well as slope and texture. Data concerning the hydrology were obtained from the results of an analysis of climate change impacts on African hydrology (Strzepek and McCluskey 2006). Using a hydrological model for Africa, the authors calculated flow and runoff for each district in the surveyed countries. Data on elevation at the centroid of each district were obtained from the United States Geological Survey (USGS 2004). The USGS data are derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).

# **4. Empirical Results**

Table 1 presents the fraction of the farms that have adopted livestock for Africa as a 7
Table 4. The <u>effects of COVID-19 exposure on educational outcomes by year (</u> _<u>continued</u>_ <u>)</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_C. Switched to public school_|||||
|Warning index x 2020|0.002|0.002|0.002|0.002|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Warning index x 2021|-0.001|-0.001|-0.000|-0.000|
||(0.000)***|(0.000)***|(0.000)|(0.000)|
||[0.001]|[0.001]|[0.131]|[0.130]|
|Pr(2020=2021)|0.000|0.000|0.000|0.000|
|Mean outcome|0.010|0.010|0.010|0.010|
|Adjusted R<sup>2</sup>|0.005|0.013|0.101|0.100|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|_D. Switched to private school_|||||
|Warning index x 2020|-0.002|-0.002|-0.002|-0.002|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Warning index x 2021|-0.001|-0.001|-0.002|-0.002|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Pr(2020=2021)|0.000|0.000|0.026|0.014|
|Mean outcome|0.032|0.032|0.032|0.032|
|Adjusted R<sup>2</sup>|0.003|0.053|0.066|0.100|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|Controls|No|Yes|No|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports difference in difference estimates of the effects of differential exposure to COVID-19 (see Section 4 for details). Each column presents results from a separate regression for each outcome. Clustered standard errors by municipality are shown in parentheses. Columns 1 and 3 estimates Equation (1) with no controls, Columns (2) and (4) include controls for gender, urban residence, level of schooling, grade, and whether the school provides bilingual instruction. We present q-values that adjust for multiple hypothesis testing in brackets, calculated using the method by Benjamini and Hochberg (1995) that controls for the false discovery rate described in Anderson (2008). Significance levels: ***p<0.01; **p<0.05; *p<0.10.

21
revenue product. Below, by restricting our analysis to enterpriseis within single industries with similar technologies (i.e. aimilat a,s), we use average produicts as proxies for marginal products. If the technologies of enterprises within the same industry are identical, then a, in Equation (1), fixed for all j, can be factored out of the numerator (standard deviation) and denomi-nator (mean) in the coefficient of variation. Once the a1s have been canceled in the numerator and denominator, the coefficient of variation is bimply the ratio of the standard deviation and mean of the average products of the srinple enterprises.

4.4 For a sample of enterprises drawn from a wide range of industrial types, such as steel, textiies, apparel and food products, the factor output elasticities are unlikely to be uniform. Within one branch, however, the assumption of a uniform technology is more plausible, especially since we limit our sample to large and medium size enterprises. By investigating patterns of factor returns among large and medium enterprises within each of China's major industrial branches, we avoid the strong assumption, relied upon by previous studies, of a single homogeneous production technology for large and smali firms in many branches of industry.

# _5._ The Convergence olf Factor Returns

5.1 The data we use consist of a sample of 226 of China's large and medium-sized industrial enterprises.'<sup>2</sup> As such they allow for investigating the tendency for factor returns to converge in China's larger state-owned enterprises. The 226 enterprises are distributed over 10 industrial branches. The branch with the smallest number of enterprises, apparel, includes three enterprises, while our sample from the chemical industry includes 62 enterprises. Coefficients of variation are reported both for returns to labor in physical terms (Q/L) and in nominal terms (Q/W). For purposes of consistency with nominal measures of capital and materia inputs, and because the nominal measure is more consistent with the ec.onomic logic of hiring labor up to the point which equalizes labor's marginal re';enue product and wage, we limit our attention to the nominal measure of labor input.

- **2** The data set was constructed by splicing data for large and medium-siz **e** nterprises in the CESRRI (Chinese Economic System Reform Research Institute) panel data set covering 1986-89 with the same enterprises for which data are reported in the Industrial Census of 1985 for 1980 and 1985 [Zhongguo renmin gonghepuo 1985 nian gongye pucha ziliao vol. 2 (a) and (b)]. The original set consisted of 661 enterprises from which enterprises missing observations for 1980, 1985, or 1989 were excluded. From the remaining 230 observations, one additional observation was omitted, because of the appearance of gross measurement error, and an additional three were omitted because they exceeded by 4 standard deviations the means of capital productivity (Q/K), labor productivity (QOL, Q/W) or materials productivity (Q/M).

**7**

_Assessing_ **_G3wns_** _in_ **_Efficient_** _Prodwcdon Anwng China's IndwslrloJ Enterprises,_ by Gauy H. Jefferson and Xu **Wcnyi**
# **Table 2:1**<sup>**st**</sup> **Step estimates: School decisions and intermediate outcome**

|||Expenditur<br>_bk08__|e on student<br>_as03E_|Number o<br>teac<br>_T_no_|f non-PNS<br>hers<br>_nPNS_|Teacher<br>r<br>_Teache_|attendance<br>ate<br>_r_attend_|
|---|---|---|---|---|---|---|---|
|VARIABLES||coef|se|coef|se|coef|se|
|_Top down:_||||||||
|_Ibk05_ao01a4|Dummy, =1 if very influential district/sub district over planning and<br>allocating school's budget|4.201e+06**|(2.078e+06)|0.0878|(0.460)|3.702|(3.675)|
|_Ibk05_ao01c3|Dummy, =1 if somewhat influential district/sub district over determining<br>the content of teacher professional development|1.647e+06|(2.357e+06)|-0.336|(0.523)|7.359*|(4.175)|
|_Ibk05_ao01c4|<br>Dummy, =1 if very influential district/sub district over determining the<br>content of teacher professional development|-1.128e+06|(2.550e+06)|-0.352|(0.566)|9.723**|(4.518)|
|_Ibk05_ao01d2|<br>Dummy, =1 if little influential district/sub district over selecting textbooks<br>and instructional materials|-2.088e+06|(1.870e+06)|0.592|(0.415)|-5.588*|(3.312)|
|_Ibk05_ao01d3|Dummy, =1 if somewhat influential district/sub district over selecting<br>textbooks and instructional materials|-1.340e+06|(1.719e+06)|0.680*|(0.381)|-2.998|(3.045)|
|_Ibk05_ao01d4|Dummy, =1 if very influential district/sub district over selecting textbooks<br>and instructional materials|-1.403e+06|(2.052e+06)|0.868*|(0.456)|-6.011*|(3.636)|
|bk05_ao03x|Dummy, =1 if principal have attended a meeting with the district education<br>office staff|4.834e+06|(4.212e+06)|1.961**|(0.935)|-1.277|(7.462)|
|_Bottom up:_||||||||
|_Ibk05_ks03a1|Dummy, =1 if the roles of school committee is to provide inputs in the<br>allocation of BOS funds|2.172e+06|(1.534e+06)|-0.254|(0.340)|4.795*|(2.718)|
|bk05_pt07A|Dummy, =1 if school send all parents written information about school<br>performance|-706,430|(1.274e+06)|0.377|(0.283)|-<br>6.539***|<br>(2.256)|
|bk05_pt07B|<br>Dummy, =1 if school send all parents written information about use of BOS<br>funds|2.432e+06*|(1.450e+06)|0.00623|(0.321)|0.712|(2.566)|
|bk05_pt07C|Dummy, =1 if school send all parents written information about school<br>committee activities|1.637e+06|(1.488e+06)|-0.565*|(0.330)|1.829|(2.637)|
|bk06_ks20A|Dummy, =1 if School Committee received information on the school's<br>teachers and their performance|-2.048e+06|(1.357e+06)|-0.0605|(0.300)|4.061*|(2.396)|
|bk06_ks20B|Dummy, =1 if School Committee received information on students and<br>their performance|2.029e+06|(1.770e+06)|0.713*|(0.393)|-4.641|(3.137)|
|age_chair|<br>Age of School committee Chairman|-27,396|(57,469)|-0.0109|(0.0128)|0.207**|(0.102)|
|D_edu_chair|Dummy, =1 if School Committee Chair has education senior secondary or<br>above|2.158e+06*|(1.227e+06)|-0.0288|(0.272)|4.001*|(2.172)|
|bk06_ks08xC|Dummy, =1 if School Committee held meetings with parents and/or<br>community members|-256,234|(1.333e+06)|-0.515*|(0.296)|3.624|(2.360)|
|_School_<br>_characteristics and_<br>_capacity_||||||||
|bk08_as03R|School total budget|0.0141***|(0.00243)|9.13e-10*|(5.38e-10)|8.21e-<br>09*|(4.29e-<br>09)|

20
experience and counseling in a protected environment. Qualified applicants were assigned to either training or control group _randomly_ . Members of the treatment group received job training for 9 to 18 months depending on their profile and the site. The nonexperimental data for the comparison group are contained in the file PSID_CONTROLS.TXT. This file holds relevant information on 2490 individuals drawn from the Population Survey of Income Dynamics (PSID). This sample consists of all male heads of household less than 55 years old who were continuously observed in the PSID, and not classified as retired in 1975.

Table 3.1. Descriptive Statistics for the Underlying Data

|**Variable**|**Treated**|**Comparison**|
|---|---|---|
|Age|25.82 (7.16)|34.85 (10.44)|
|Education|10.35 (2.01)|12.12 (3.08)|
|Black|0.84 (0.36)|0.25 (0.43)|
|Hispanic|0.06 (0.24)|0.03 (0.18)|
|Married|0.19 (0.39)|0.87 (0.34)|
|No High School Degree|0.71 (0.46)|0.31 (0.46)|
|Real Earnings in 1974|2095.57 (4886.62)|19428.75 (13406.88)|
|Real Earnings in 1975|1532.06 (3219.25)|19063.34 (13596.95)|
|Real Earnings in 1978|6349.14 (7867.40)|21553.92 (15555.35)|
|Zero Earnings in 1974|0.71 (0.46)|0.09 (0.28)|
|Zero Earnings in 1975|0.60 (0.49)|0.10 (0.30)|
|Sample Size|185|2490|

Source: Author’s calculations For both groups, the variables used in the analysis include the following: (1) outcome of interest, represented by real earnings in 1978 (RE78); (2) participation indicator (TREAT =1 if participant, and 0 otherwise); (3) a set of pretreatment covariates including age (AGE), education (EDU), marital status (MARRIED=1 if married and 0 Corporation on ten sites across the United States including Atlanta, Chicago, Hartford, Jersey City, Newark, New York, Oakland, Philadelphia, San Francisco, and Wisconsin (Lalonde 1986).

21
competing claims about the factors associated with high levels of conflict.<sup>5</sup> Other intermediate approaches to surveys or local fieldwork for the systematic mapping of violence and conflict include the use of national and, increasingly, local media sources (Tadjoeddin 2002; Welsh 2003).<sup>6</sup>

# **_3.1. The PODES Data_**

The Central Bureau of Statistics’ (BPS) Village Potential series (PODES) is a longstanding tradition of collecting data at the lowest administrative tier of local government. It collects detailed information on a range of characteristics – ranging from infrastructure to village finance – for Indonesia’s current 69,000 villages and neighborhoods.<sup>7</sup> The latest PODES was surveyed at the end of 2002 as part of the 2003 Agricultural Census.

5 Other sources of information on conflict include the police, health care providers, morgues, and the Indonesian army (TNI). However, it is very difficult to use these data sources for cross-provincial comparisons, in part because of large gaps in the data but also because common definitions (for example of violence types) are not used in different areas.

6 In the absence of any primary data, the United Nations Support Facility for Indonesian Recovery (UNSFIR) attempted to create such a dataset by compiling newspaper reports on violent conflicts (Tadjoeddin 2002). However, in large part because of the use of national media, which often do not record localized conflicts, the resulting data massively underreports levels of conflict. The UNSFIR dataset captured only 1,093 incidents of conflict over an eleven-year period (1990-2001). In contrast, PODES documented almost 5,000 villages as reporting conflicts in 2002 alone. Newspaper reports will also be subject to biases. For example, newspapers may only report high profile violent conflict. At the same time, coverage may differ significantly across regions. Areas with high readership or local correspondent penetration may also yield regional over-reporting biases. The PODES for example suggests that local newspapers are only available in 55.9 percent of villages. While readership may not be a proxy of correspondent coverage, clearly urban areas may be subject to more intense scrutiny. However, in large part because of the use of national media, which often do not record localized conflicts, the resulting data massively underreports levels of conflict. While further work is being conducted by UNSFIR, UNDP, the World Bank and independent researchers using _local_ media sources, which appear to more accurately report conflict, the creation of a national dataset by such means is still a long way away.

> 7 The module was apparently initiated by the BPS Directorate of National Resilience ( _Ketahanan_ ) under Drs. Suharno.  The PODES is typically conducted in conjunction with the Population Census (e.g., 1990 and 2000), the Economic Census (1986 and 1996), and the Agricultural Census (1993 and 2003) (Kaiser and Molyneaux 2000).

12