rafmacalaba/datause-encoder-data huggingface dataset:
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16
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which finances primarily the priority sectors. All in all, the share of the budget allocated to the priority ministries, is generally high. In 2002, the priority sectors represented 74 percent of the capital budget including external resources; this share was about 88 percent in 2005.

<!-- Start of picture text --> Budget Execution Rate by Sector 2002 - 2005<br>120<br>100<br>80<br>2002<br>2003<br>60<br>2004<br>40 2005<br>20<br>0<br>Heath and Education Rural Infrastructures Mines and Total<br>Social Actions development and AT Energy<br><!-- End of picture text --> Sources: Chadian authorities and the World Bank Despite recent progress (simplification of the expenditure circuit, partial computerization), the expenditure circuit remains heavily centralized and cumbersome. Ministries are insufficiently involved in the execution of their budget. This issue is even more severe at the deconcentrated level. There are in Chad, still, significant differences between the voted budget and the budget carried out, and, in particular, inflection of the sectoral priorities in the course of budget implementation. Although it is important to improve the budget preparation and to reinforce the linkages between the PRSP, the sectoral strategies and the program budgets, articulated with the annual budget, it would be necessary also in parallel to: progress toward the implementation of a more unified budget on the basis of an harmonized budgetary classifications and in accordance with the legal provisions governing the management of the oil revenues, of a renovated chart of accounts and an integrated management operating system of public finance so as to enable the tracking of poverty reduction spending.

It is critical to give the greatest priority to protect the budget integrity at the time of the execution phase and to improve for this matter the financial programming; the monitoring of the budget execution during the fiscal year and to lift gradually impediments undermining a smooth budgetary execution, as well with regard to the budget notification, the expenditure circuit (flow of funds) and the procurement process.

Regarding the rules of payment and credit management: Article 19 of Organic Law No. 11/62 of May 11, 1962 states that, notwithstanding the rule on the annualization of appropriations, “appropriations available for capital transactions shall be carried forward by decree of the Ministry of Finance creating an allotment in the same amount over and above the allotments for the following year.” This article is not applied. Its application would prevent projects from being blocked because the work schedule, which 22
above, those without secondary education might have gotten lower earnings in a wage job. In absence of productivity measures, the higher earnings in informal non-farm enterprises compared with agriculture does suggest that the growth of informal non-farm enterprises should raise average labor productivity, especially in rural areas and small towns.

<!-- Start of picture text --> Figure 13 Earnings distributions in Rwanda and Urban Dar es Salam, Tanzania<br>Rwanda Urban Dar es Salam, Tanzania<br>0.6 0.6<br>0.5<br>0.5<br>0.4<br>0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1<br>0.1<br>0.0<br>0.0<br>-0.1 7 10 13<br>1 4 6 9 11<br>-0.1 Log income per hour<br>Log income primary job<br>Agriculture Public sector<br>Wage Private sector<br>Household Enterprise Private HE<br>Density<br><!-- End of picture text --> _Source: World Bank (2011a) and Kweka and Fox(2011)_ Having a HE is associated with higher household welfare in the countries in our sample. Figure 14 seems to suggest that in urban areas having a HE is most common in the urban middle class, while in rural areas the trend is steadily upward. On average about 45 percent of households in the fifth quintile owns a HE, compared to a little more than 30 percent in the first quintile.

**Figure 14: HEs and household welfare**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20 National<br>10<br>0 Urban<br>an HE (%) Rural<br>Households owning<br><!-- End of picture text --> _Source: Annex table 1 and authors‟ calculations. Notes: figures shows weighted average over eight SSA countries and quintiles are defined within urban and rural areas for the urban and rural lines, while the national line is based on national quintiles._ But is having a HE really a good option for households? Table 3 shows the regression coefficients for types of earnings on consumption per capita, for nine SSA countries controlling for education, location, and demographics of the household. Though these regressions do not show causality, they do show the marginal effect on the standard of living of households 25
_ "Jamaica Discusses Its Health Care Problems **"** Market Lette 1/16/1989.

A1bas AA & Walker, GJA. "Determinants of the Utilization of Maternal and Child Health Services in Jordan." International J Epidemiolgy 15(3) 1986:404-407.

Abrars, PS. Handbook of Medical Problems Durinr **PrenMancy.** Norwalk, Cr: Appleton and Lange, 1989.

American Nurses Association. "Access to Prenatal Care: Key to Preventing Low Birthweight." Report of Consensus Conference, 1987.

Baer, Edward C. "Prcmoting Breastfeeding: A National Responsibility." Studies in Family Planning 12(4) 1981:198-206.

Bailey, Wilma & Powell, Dorian "Patterns of Contraceptive Use in Kingston and St Andrew, Jamaica, 1970-1977." Social Science & Medicine 16(19) 1982:1675-1683.

Bailey, Wilma "Child Morbidity in the Kingston Metropolitan Area, Jamaica 1983." Social Science & Medicine 26(11) 1988:1117-1124.

Bamisaiye, A.; Ranscme-Kuti, 0; & Famewura, AA. "Waiting Time and its Impact on Service Acoeptability and Coverage at an MCH Clinic in Lagos, Nigeria." J Tropical Pediatrics 32 1986:158-161.

Blake, J Family Structure in Jamaica New York: Free Press, 1961.

Bollag, U. "Primary health care at the commznity level: the involvement of the doctor." Tropical Doctor 10(1) 1980:46-48.

Boyd, Derick C. Economic Manaaement. Income Distribution, and Poverty in Jamaica NY: Praeger, 1988.

Bracken, Michael B. "Contraception and pregnancy after Dropping Out of Family Planning Clinics: A National Interview Survey in Jamaica." Social Biology 23(1) 1976:55-65.

Bracken, Michael B. & Kasl, Stanislav V. "Factors Associated with Dropping out of Family Planning Clinics in Jamaica." American J of Public Health 63(3) 1973:262-271.

Brody, Eugene B. Sex. Contraception. and Motherhood in Jamaica Cambridge, MA: Harvard U Press, 1981.

Campbell, Horace "Rastafari: Culture of Resistance." Race & Class (1) 1980:1-22.

Children's Defense Fund. The Health of America's Children: MAternal & Child 23
Equation 4 estimates the amount of FDI inflow into manufacturing industry i in year t measured at the 2-digit VSIC level. (I) is the set of other industry level characteristics that may affect FDI inflows such as industry wise productivity and industrial tax; μ is industry fixed effects and π is the error term. Similar to equations (1), (2) and (3), we also used fixed effects for dirty and clean industries based on the Mani and Wheeler (1998) classification. Industry wise productivity is measured by net value-added per worker. We also include we also amount of taxes paid by each industry as a control variable. The variable of interest is P which is the industry-wise pollution intensity. If FDI does not show an increase in dirty industries, then =0. δ 2

# **IV. SAMPLE AND DATA**

Annex 1 provides a list of variables used in the industry-level analysis, unit of analysis and their data sources. Data on net turnover, industrial wages, fixed capital, foreign investment and environmental abatement cost come from the General Statistical Office (GSO) of Vietnam. Collection of these data in Vietnam is a recent exercise. As result, detailed data is not available for a longer time frame. The GSO collects these data on a yearly basis since the year 2000 through enterprise surveys and to the extent that this was the first time such an exercise has been conducted in Vietnam, the data seem fairly reliable. Although, ideally we would have liked to have data for much longer timeframe, a most comprehensive industrial survey for Vietnam is available only for the years 20002002. Since this comes on the heels of the bilateral and multilateral trade agreements, it should provide reasonable approximation of the post-openness trends in industrial production.

This data is organized by industry according to the Vietnamese Standard Industrial Classification (VSIC)<sup>11</sup> . Data on exports from Vietnam comes from the Vietnam Trade Database. This data was also organized according to the VSIC.

> 11 The Vietnamese equivalent of ISIC is the Vietnamese Standards Industrial Classification (VSIC) which is identical to ISIC Rev. 3. ISIC – refers to the International Standard Industrial Classification. It has

16
**Table 8**

**Determinants of  Power Sector Performance (Fixed Effects and interactions with Group D)**

|**Interactions with Group D**|**Residential**|**Energy sold**|**Tariff**|**Emission index**|
|---|---|---|---|---|
|<br>**Fixed effects**|**Access (% )**|**(MWh/employee)**|**(US$c/KWh)**|**(CO2 ton/KWh)**|
|**UNBUN**|**DLING (EITHE**|**R PARTIAL OR FUL**|**L)**||
|**Group D**|8.76*<br>(4.72)|-17.83<br>(468.29)|-0.22<br>(2.20)|-0.05*<br>(0.04)|
|**Vertical Unbundling**|1.65<br>(1.13)|-336.29**<br>(174.92)|1.14<br>(0.83)|0.003<br>(.009)|
|**Group D*Vertical Unbundling**|-8.74***<br>|403.85*<br>|1.48<br>|0.03**<br>|
||(2.04)|(222.61)|(1.25)|(0.01)|
|**F**|<br>**ULL VERTICA**<br>|**L UNBUNDLING**<br> <br>|||
|**Group D**|9.76*<br>(5.23)|207.63<br>(438.64)<br>|-1.33<br>(2.05)|-0.08**<br>(0.04)|
|**Vertical unbundling (full)**|3.04*<br>(1.67)|-44.22<br>(174.55)<br>||-0.03***<br>(0.01)|
|**Group D*Vertical unbundling (full)**|-9.47***<br>(237)|138.85*<br>(21428)|2.61***<br>(094)|0.06***<br>(002)|
|**D**|.<br>**EGREEOF DIS**|.<br>**AGGREGATION**|.|.|
|**Group D**|-29.18***<br>(5.96)|-2247.13**<br>(48097.47)||0.15***<br>(0.41)|
|**HHI (reverse)**|0.0002<br>(0.0004)|0.88**<br>(0.44)|-0.002<br>(0.002)|-0.000004*<br>(0.000003)|
|*****|-0.002***<br>|-0.02<br>|-0.0001<br>|-0.00002***<br>|
|**Group D HHI (reverse)**|(0.0007)|(0.04)|(0.0002)|(0.000005)|
||**DEGREE OF**<br>|**PRIVATIZATION**<br>|||
|**Group D**|8.03<br>(5.74)|515.03<br>(391.43)|1.32<br>(2.12)|-0.03<br>(0.04)|
|**Privatization**|4.08***<br>(1.36)|25.95***<br>(8.67)|0.39**<br>(0.19)|0.019**<br>(0.008)|
|*****|-5.67*<br>|-21.36**<br>||0.0004<br>|
|**Group DPrivatization**|(3.49)|(9.14)||(0.023)|
||**REGUL**|**ATION**|||
||6.03|402.67|-1.60|-0.005|
|**Group D**|(4.97)|(380.55)|(2.11)|(0.03)|
|**Regulation**|3.06***<br>(1.12)|73.41<br>(120.71)|4.96***<br>(0.95)|-0.006<br>(0.007)|
|**Group D*Regulation**|-6.92***<br>(1.96)|-397.54**<br>(185.61)|8.51***<br>(2.09)|-0.07***<br>(0.01)|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

21
# Figure 2.3: SOUTH AFRICA, evaluation of performance of the ICA method

I. Kernel<sup>1</sup> estimates of output and input densities in the complete case and in the sample after imputing missing values by the ICA method <!-- Start of picture text --> A. Sales (log) B. Materials (log)<br>5 10 15 20 25 5 10 15 20 25<br>log-Sales log-Materials<br>Complete case ICA method Complete case ICA method<br>C. Capital stock (log) D. Employment (log)<br>5 10 15 20 25 5 10 15 20<br>log-Capital log-Employment<br>Complete case ICA method Complete case ICA method<br>.25<br>.2<br>.2<br>.15<br>.15<br>.1<br>Density Density<br>.1<br>.05<br>.05<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text -->

## II. Table of descriptive statistics and tests of equality of distributions of output and inputs in the complete case and in the sample with imputation by the ICA method

|||**# Obs.**<br>**(# imputed)**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**One-sample K-S**<br>**Test (p-value)**|
|---|---|---|---|---|---|---|---|
|Sales (log)|Complete case|1497|14.24|2.10|7.78|19.40|0.000|
||ICA meth.|1821 (324)|14.30|1.99|7.78|19.40|0.000|
|Materials (log)|Complete case|1293|13.19|2.31|4.33|18.65|0.000|
||ICA meth.|1822 (529)|13.37|2.13|4.34|18.65|0.000|
|Capital (log)|Complete case|1289|11.39|2.26|0.63|19.65|0.000|
||ICA meth.|1816 (527)|11.32|2.05|1.05|19.65|0.000|
|Empl (log)|Complete case|2529|11.63|1.45|7.64|15.42|0.000|
||ICA meth.|2548 (19)|11.63|1.45|7.64|15.42|0.000|

Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. The null hypothesis of the one-sample Kolmogorov-Smirnov Test is that the cumulative distribution differs from the hypothesized theoretical normal distribution.

Source: Authors’ estimations with ICSs data.

23
PROVISION OF WATER TO THE POOR IN AFRICA

**Figure 3.4   Prices of water tankers and water vendors**

<!-- Start of picture text --> 12,00<br>10,00<br>8,00<br>6,00<br>4,00<br>2,00<br>0,00<br>Water vendors Water tankers<br>Burkina Faso Niger Senegal Madagascar Tanzania Sudan Zambia Cote d'Ivoire Kenya Ethiopia Rwanda Uganda Nigeria Ghana Cape Verde<br>Price of water (US$/m3)<br><!-- End of picture text --> The high price charged by mobile distributors is **Table 3.6 Operating accounts for mobile** generally justified by high transport costs. The **distributors in three cities** literature reveals that carters do not yield large profits Tanker Vendor on the water that they sell; actually, the salaries that _USD/m3_ Luanda Accra Khartoum they earn are among the lowest in their cities Sale Price 5.46 5.46 3.00 (Collignon and Vézina 2000; Kariuki and others Transport costs 3.85 4.09 1.53 2003). Table 3.6 illustrates this reality in Accra, Water costs 0.44 0.55 0.84 Khartoum and Luanda, where water supply systems Profit 1.17 0.82 0.63 for the unconnected are mainly based on mobile Profit (% income) 21 15 21 distributors, either as wholesalers or retailers. The Source: For Luanda, Development Workshop 1995; for Accra, profits that these distributors make are not very high; <u>Sarpong and Abrampah 2006; for Khartoum, Gadir 2006.</u> in the case of carters, sales are relatively low level, and carters barely make a subsistence salary. Even at zero profit, the water prices are very high in volumetric terms.

|Tan|ker|Vendor|
|---|---|---|
|_USD/m3_<br>Luanda|Accra|Khartoum|
|Sale Price<br>5.46|5.46|3.00|
|Transport costs<br>3.85|4.09|1.53|
|Water costs<br>0.44|0.55|0.84|
|Profit<br>1.17|0.82|0.63|
|Profit (% income)<br>21|15|21|
|Source: For Luanda, Development W<br>Sarpong andAbrampah 2006;for K|orkshop 19<br>hartoum, Ga|95; for Accra,<br>dir 2006.|

Two of the places where there is a buoyant mobile water sector are in Luanda, Angola, and Accra, Ghana; in these cities, empirical studies show that the market is quite competitive. However, this does not necessarily imply low prices compared to other water service providers. Almost half of the cities that have water tankers in operation to supply water have regulations in place (table 3.7).

The utility emerges as a minor player in the operation of water tankers. The formal and informal private sectors are the main operators in 4 out of 9 cities with water tanker supply. When the formal private sector is the main operator, the market is more competitive than when the informal sector is the main operator. These results should be taken with certain reservations because they are based on impressions by utility staff, and not necessarily based on empirical analysis.

31
where _i_ refers to the remittance recipient country _i_ . For our dependent variable, _REMi_ , we will try three different variables, namely the ratio of remittances to GDP, the logarithm of GDP, and the logarithm of remittances in per-capita terms. _MIGi_ is the level of migration from country _i_ . We express this either as the logarithm of migrants abroad or as the ratio to population size of home country _i_ . _FDi_ is financial sector development variables of country _i_ , which are either the ratio of bank deposit or credit to GDP. As for _GDPi_ , both the logarithm of GDP and of GDP per capita are employed. _GDPgrowthi_ is the growth rate of GDP of country _i_ and _Edui_ is the difference between the ratio of people with tertiary education among migrants and the ratio among the working population in their home countries. _αi_ and _βi_ are parameters to be estimated and _u_ i is the error term. Table 2 summarizes all the variables employed for the estimation.<sup>2</sup> We start by estimating equation (1) by Ordinary Least Squares (OLS) by ignoring any possible endogeneity problems. We will then estimate it through the IV estimation, first only instrumenting the migration variables and then instrumenting both the migration and financial development variables.

# **4. Estimation Results**

The estimation results are reported in Tables 3-6. All the columns headed by (1) imply that the ratio of bank deposit to GDP is used as a variable for financial sector development, while those headed by (2) imply that the ratio of bank credit to GDP is employed for the estimation. In order to check the validity of our set of instruments, the Hansen J statistics for the overidentification test are reported at the bottom of the tables (Tables 4, 5 and 6). The results support the validity of our instruments, i.e., they are uncorrelated with the error term and excluded instruments are correctly excluded from the estimated equation.<sup>3</sup>

> 2 These dependent variables are constructed based on the average value of remittances to country _i_ during the period between 1998 and 2002.

> 3 The regression presented in column 8 of Table 5 marginally fails the overidentification test and this should be taken into account when discussing the results.

9