rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5: Reconciling our results with experimental estimates**

|Sample|Training Program|Treatment Effect on<br>Business Practices|Log(Sales)<br>95% confi<br>for treatm|Log(Profits)<br>dence interval<br>ent effect on|Implied Effect from<br>Log(Sales)|Association in Table 2<br>Log(Profits)|
|---|---|---|---|---|---|---|
|Sri Lankan women|ILO SIYB five day course|0.058***<br>(0.016)|[‐0.23, +0.15]|[‐0.12, +0.17]|0.046|0.038|
|Sri Lankan men|ILO SIYB five day course|0.056***<br>(0.010)|[‐0.06, +0.17]|[+0.01, +0.18]|0.045|0.037|
|Kenyan women|ILO GET Ahead five day course|0.042***<br>(0.007)|[+0.00, +0.18]|[‐0.01, +0.16]|0.033|0.028|
|_Related studies_|||||||
|Chilean unemployed (Martínez et al., 2013)|MESP: three weeks intensive training<br>+ 3 months mentoring (+ grant)|0.180***||[+0.08, +0.79]||0.12|
|South African firms(Anderson MacDonald et al,2|014) Business Bridge: 80 hours over 2 months|0.260***|[+0.18,+1.39]|[‐0.00,+1.36]|0.21|0.17|
|Notes:|||||||

Treatment effects on log sales and log profits for Sri Lankan women taken from Table 3 of de Mel et al. (2014). Treatment effects for Sri Lankan men and Kenyan women calculated approximately one year after training. Implied effects from association in Table 2 are calculated by multiplying the treatment effect on business practices by the coefficient in column 4 (sales) and column 8 (profits) from Table 2.

Chile estimates: estimated effect on business practices calculated as 4.493/25 from Table 4 of Martinez et al. (2013); estimated effect on log profits approximated by change in self‐employment income relative to control mean South African estimates: estimated effect on business practices calculated from marketing treatment as average of effect sizes on each of 30 practices measured; effect on profits and sales approximated by percentage change in levels relative to control means.

35
TABLE 3. **Consolidated Nonfinancial Public Sector "Below the Line"** Data (%GDP)

|Ite|m<br>A|B|B1|**Bla**|**Bib**|Blc|<br>C|C1|C2|
|---|---|---|---|---|---|---|---|---|---|
|Year\||||||||||
|61|4.36|2.86|2.86|2.38|0.48|0|-1.5|0.19|1.32|
|62|7.76|4.52|4.52|3.77|0.75|0|-3.24|0.71|2.53|
|63|7|3.15|3.15|2.26|0.89|0|-3.85|1.37|2.49|
|64|6.62|1.09|1.09|0.71|0.38|0|-5.53|0.49|5.04|
|65|4.08|1.2|1.2|0.5|0.7|0|-2.88|0.83|2.04<br>|
|66|4.93|0.31|0.31|0.01|0.3|0|-4.62|0.28<br>|4.35<br>|
|67|2.08|-0.17|-0.17|0.12|-0.29|0|-2.25|1|1.26|
|68|2.23|0.42|0.42|0.47|-0.05|0|-1.81|0.71|1.1|
|69|1.75|0.94|0.94|0.57|0.37|0|-0.81|0.72|0.1|
|70|1.87|0.86|0.86|0.05|0.81|0|-1.01|3.38<br>|-2.36<br>|
|71|4.4|1.92|1.92|0.73|1.19|0|-2.48|2.43|0.06|
|72|5.8|1.93|1.93|0.89|1.04|0|-3.87|1.81|2.06|
|73|7.48|0.91|0.91|0.79|0.12|0|-6.57|4.47|2.1|
|74|8.08|1.87|1.87|1|0.87|0|-6.21|5.6<br>|0.62<br>|
|75|15.08|1.88|1.88|1.77|0.11|0|-13.2|9.74|3.46|
|76|11.69|4.14|0.14|3.03|1.11|0|-7.55|3.41|4.13|
|77|4.71|2.83|2.83|1.54|1.29|0|-1.88|2.44|-0.56|
|78|6.5|5.49|5.49|3.54|1.95|0|-1.01|0.86|0.16<br>|
|79|6.51|5.53|5.53|3.77|1.76|0|-0.98|-0.3|1.27|
|80|7.47|2.99|3.4.|1.53|1.88|-0.42|-4.48|3.59|0.89|
|81|13.26|8.03|8.27-|3.97|4.3|-0.24|-5.23|5.32|-0.09|
|82|15.12|6.3|6.39|5.07|1.32|-0.09|-8.82|7.29|1.51<br>|
|83|15.15|-1.63|-1.34|-1.89|0.55|-0.29|-16.78|16.6|0.19|
|84|11.92|-1.49|-1.48|-0.59|-0.89|-0.01|-13.41|6.19|7.22|
|85|6.02|0.58|0.63|-0.3|0.93|-0.05|-5.44|2.33|3.11|
|86|4.73|-0.02|0.04|-1.01|1.05|-0.06|-4.75|0<br>|4.74<br>|
|87<br>88|5.54<br>7.39|4.17|4.22|1.03|3.19|-0.05|-1.37|0|1.37|

A. Deficit or Financing Needs B. Net Financing B1. Net use of credit Bla. Net use of domestic credit Bib. Net use of foreigh credit Blc. Net Use of Advances from Suppliers C. Result (B minus A) Cl. Central Bank C2. Increase of Net Financial Liabilities 12
4 6

# 2. **The Design of Subsidies for Pollution Control**

Subsidies for pollution control are of two main types: (i) subsidies for emission reduction or (ii) subsidies for technological improvements.<sup>7</sup> Subsidies for emission reductions are based on the actual quantity of emission reduction. A benchmark emission level is determined for each firm in a given year. For each unit of emission reduction from that benchmark, the firm receives a payment or reward. From an individual firm perspective, this has the same incentive effect as a tax per unit emitted: either the firms gets paid not to pollute or it has to pay to have the "right" to pollute. This equivalence is however misleading. First, the budgetary implications of the two instruments are quite different. Second, an emission based subsidy may be very counterproductive from a full industry perspective and hence for aggregate pollution. It promotes entry into the sector and may maintain financially viable firms that would otherwise have been bankrupt. This sort of subsidy is not commonly considered in developing countries and is then less interesting from a "pragmatic" point of view--in spite of its very impressive coverage in the academic literature.

The second main type of subsidy consists of sharing the costs of investing in pollution control equipment or facilities. It is also the type of subsidy we are assessing in this paper. It is more commonly considered in developing countries as it does not require a recurrent monitoring of pollution sources, a major policy issue in many developing countries. It can take the form of direct grants, tax breaks through credits or accelerated depreciation most commonly or through credit subsidies. The instrument may be a very effective way of ensuring quick reduction in pollution control when the firm was considering to do the investment anyhow

**6 For more details, see OECD (1989), Baumol and Oates** (1988), **Fargeix (1992) or Eskeland and Jimenez (1991).**

**7** A **third type of subsidy combines characteristics of** the two main **categories: subsidies to inputs substitution. It will** often **be** linked to **technological** changes **in the production process,** and **these** will limit some **of** the **distortionary incentive effects of it shares with an emission** based **subsidy.**
<!-- Start of picture text --> 8<br>vt<br>=<br>gS<br>D<br>2£<br>2<br>5<br>g PILI EIT LT JC IL<br>8) (OU GUO YUE WOOL<br>PITI<br>3<br>1 2 3 4 5<br>[) e=-0.1 [7] e=-0.2<br>[dj e=0.3 [| e=0.4<br><!-- End of picture text -->
Table 2.3. A Profile of Poverty and Inequality, 1996-2007

|||Overall|||Urban|||Rural||
|---|---|---|---|---|---|---|---|---|---|
||1996|2001|2007|1996|2001|2007|1996|2001|2007|
|Headcount|53.26|40.18|39.90|41.39|17.88|12.17|59.62|52.08|55.04|
|Poverty Gap|19.09|12.79|12.31|14.67|4.28|2.81|21.46|17.32|17.50|
|Squared Poverty Gap|9.00|5.55|5.03|6.92|1.59|0.96|10.12|7.67|7.24|
|Watts|26.66|17.38|16.11|20.55|5.48|3.51|29.94|23.72|22.99|
|Atkinson (1)|23.84|24.00|21.94|28.72|24.31|18.59|17.81|16.63|15.35|
|Atkinson(2)|38.16|38.82|35.82|45.63|38.25|31.85|30.38|29.72|25.93|
|Gini|40.63|40.41|38.96|44.91|40.71|35.19|34.60|33.15|32.23|
|MLD|27.23|27.45|24.77|33.86|27.85|20.56|19.61|18.19|16.66|
|Theil|31.75|33.75|27.88|37.64|35.39|22.87|21.61|19.36|18.76|

Source: Authors’ Calculations (MLD stands for Mean Log Deviation).

Figure 2.1 is consistent with the poverty outcomes presented in table 2.3, showing that poverty incidence dropped from about 53.3 percent in 1996 to about 40.2 percent and 40 percent in 2001 and 2007 respectively. The other three measures reported in that same table (the poverty gap, the squared poverty gap and the Watts measure) show a similar decline. These other three measures are members of the _additively decomposable_<sup>10</sup> class of poverty indices defined by the following equation.

where **z** is the poverty line, f(y) is the frequency density function for the welfare indicator **y** , and  ( _y_ | _z_ ) is a convex and decreasing function measuring individual deprivation. This function is equal to zero when the welfare indicator is greater or equal to the poverty line.

To begin to uncover some of the factors that might explain the observed changes in poverty between 1996 and 2007, we start from that fact that poverty indices are computed on the basis of a distribution of living standards which is fully characterized by its _mean_ and the degree of _inequality_ (as represented by the associated Lorenz curve). Any poverty measure therefore is a function of these two factors. Formally we write this as _Pt_  _P_ (  _t_ , _Lt_ , _z_ ) . In other words, poverty at time **t** is a function of the mean,  **t** , the

> 10 The class of poverty measures defined by (2.1) is _additively separable_ because the deprivation felt by an individual depends only on a fixed poverty line and her/his level of welfare and not on the welfare of other individuals in society.  When the population is divided exhaustively into mutually exclusive socioeconomic groups, this class of measures allows one to compute the overall poverty as a weighted average of poverty in each group.  The weights here are equal to population shares.  Thus such indices are also _additively decomposable_

11
**TABLE 3: REAL SECTORAL GROWTH RATES, CONTRIBUTIONS TO REAL GDP GROWTH, GDP SHARES AND SHARE-ADJUSTED CONTRIBUTIONS TO GDP GROWTH, 1995-2013***

||Average<br>Grow|Annual<br>th (%)|Contrib<br>Growt|ution to<br>h (%)|Share of|GDP (%)|Share-<br>Contrib<br>GDP gr|adjusted<br>ution to<br>owth (%)|
|---|---|---|---|---|---|---|---|---|
||1995-<br>2005|2006-<br>2013|1995-<br>2005|2006-<br>2013|1995-<br>2005|2006-<br>2013|1995-<br>2005|2006-<br>2013|
|GDP|**4.5**|**3.3**|**100.0**|**100.0**|**100.0**|**100.0**|**1.00**|**1.00**|
|**Primary Sector**|**2.9**|**2.7**|**9.3**|**9.1**|**16.2**|**13.8**|**0.57**|**0.66**|
|Agriculture|4.8|2.8|7.2|1.3|8.9|7.1|0.80|0.18|
|**Secondary Sector**|**4.6**|**2.4**|**21.9**|**15.0**|**21.3**|**20.6**|**1.03**|**0.73**|
|Manufacturing|3.9|1.6|12.1|5.9|14.0|12.1|0.86|0.49|
|Extraction|3.0|3.8|0.7|-0.1|1.1|1.7|0.64|-0.05|
|Construction|8.7|4.8|6.8|6.1|3.7|4.3|1.86|1.43|
|**Tertiary Sector**|||**68.8**|**75.9**|**62.5**|**65.6**|**1.10**|**1.16**|
|Commerce|4.0|2.5|15.9|12.2|17.3|16.1|0.92|0.76|
|**_Services_**|||**_36.6_**|**_49.9_**|**_26.7_**|**_30.6_**|**_1.42_**|**_1.60_**|
|Transportation|5.3|2.9|4.8|3.3|4.0|4.1|1.20|0.81|
|Post & Telecoms|15.2|11.3|11.4|22.0|3.5|6.7|3.30|3.28|
|Education|3.9|4.0|2.9|4.5|3.4|3.8|0.85|1.17|
|Health/Social Serv.|4.8|4.4|1.7|1.7|1.5|1.3|1.10|1.29|
|Hospitality|3.0|-1.0|0.7|-0.2|1.0|0.8|0.68|-0.23|
|Financial Services|7.0|8.6|4.1|8.2|2.7|3.2|1.52|2.56|
|Real Estate|3.5|2.9|4.5|4.7|5.8|5.5|0.78|0.87|
|Business Services|6.9|3.4|6.0|4.9|4.0|4.8|1.51|1.03|
|**Government**|||**16.0**|**12.0**|**18.5**|**18.9**|**0.86**|**0.63**|
|Public Administration|1.8|3.1|2.8|5.9|6.9|6.3|0.41|0.93|
|Net Taxes|5.3|1.6|13.2|6.1|11.6|12.6|1.14|0.49|

Source: Senegal Country Sources and authors’ own estimates Noticeably, the critical importance of services remains strong, after making this adjustment, with communications and financial services attaining the most outstanding contributions. Thus, recent growth is indeed services-biased, but the dynamism of the services sector, led by the two sectors is likely to extend into the longer term (figure 4). The communications subsector’s contribution to GDP rose from 11.4 percent during 1995-2005 to 22.0 percent during the second period 20062013, representing 16.6 percent and 29.0 percent of the services sector’s total contribution to GDP growth, respectively. The adjusted index reveals similar indicators, with communications accounting for the most dominant share of the service sector’s contribution to economic growth.

12
Figure 1. Survey-based measures of inflation expectations: Country evidence A. 1-year-ahead inflation expectations, selected advanced economies, average <!-- Start of picture text --> Percent Professional<br>5 Household<br>4<br>3<br>2<br>1<br>0<br>United States Japan New Zealand<br><!-- End of picture text --> C. Volatility of 1-year-ahead inflation expectations, selected advanced economies <!-- Start of picture text --> Professional<br>2<br>Household<br>1<br>0<br>United States Japan New Zealand<br><!-- End of picture text --> E. 5-year-ahead inflation expectations, selected advanced economies, average <!-- Start of picture text --> Percent<br>Professional<br>5 Household<br>4<br>3<br>2<br>1<br>0<br>United States Japan New Zealand<br><!-- End of picture text --> B. 1-year-ahead inflation expectations, selected EMDEs, average <!-- Start of picture text --> Percent Professional<br>12 Household<br>10<br>8<br>6<br>4<br>2<br>0<br>India Philippines South Africa<br><!-- End of picture text --> D. Volatility of 1-year-ahead inflation expectations, selected EMDEs <!-- Start of picture text --> Professional<br>3 Household<br>2<br>1<br>0<br>India Philippines South Africa<br><!-- End of picture text --> F. 5-year-ahead inflation expectations, South Africa <!-- Start of picture text --> Percent<br>7.0<br>Analysts Business Labor<br>6.5<br>6.0<br>5.5<br>5.0<br>2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Source: Bank of Japan; Bloomberg; Bureau of Economic Research, South Africa; Central Bank of the Philippines; Consensus Economics; Haver Analytics; International Monetary Fund; Reserve Bank of India; Reserve Bank of New Zealand; University of Michigan; World Bank.

Note: EMDEs = emerging market and developing economies.

A.C. The sample period is 2006H1-2018H1.

B.D. The sample period is 2007H2-2018H1.

C.D. Volatility is measured by standard deviation.

- E. The sample period is 2009H1-2018H1.

18
the World Bank’s Global Development Finance publication, and is available since 1970. We rely on a new dataset on the present value of debt constructed by Dikhanov (2003). He applies currency-, maturity-, and time-specific market interest rates to the flow of debt service obligations on a loan-by-loan basis, using data from the World Bank’s Debtor Reporting System database to arrive at a historical series of present value of debt for all developing countries since 1980. We also consider a number of other indicators, including the face value of debt relative to exports, debt service relative to current government revenues, and debt service relative to non-gold reserves.

Our second group of explanatory variables is intended to measure the quality of policies and institutions in the country. In our core specifications we will rely on the World Bank’s Country Policy and Institutional Assessment (CPIA) ratings, which are available on an annual basis since 1977, although with some methodological changes over time. In some of our regressions, we extend this variable back to 1970 by imputing missing values with the fitted values of a simple OLS regression of the CPIA on the logarithm of one plus the inflation rate. Since the CPIA is not publicly available, we also confirm that our results hold if we use a publicly-available measure of institutional quality constructed by Kaufmann, Kraay and Mastruzzi (2002). This variable measures property rights enforcement or the rule of law, which we treat as time-invariant and so use only a cross-section of data for 2002.

Our third set of variables measures shocks that countries experience. Our most comprehensive measure of shocks is simply real GDP growth in constant local currency units, which reflects the confluence of all domestic and external shocks the country experiences. We also attempt, although with limited success, to identify the proximate causes of these shocks, by separately looking at real exchange rate movements and fluctuations in the terms of trade. In particular, we construct the growth rate of the real exchange rate relative to the US dollar using changes in the nominal exchange rate and GDP deflators. Positive values of this variable correspond to real depreciations. We measure the income effect of terms of trade changes as the current local currency share of exports in GDP times the growth rate of the local currency export deflator, minus the 13
Table 4 Means Difference for Supported and Non-supported Groups

|**Industry**|||**Bevera**|**ges**||||**Food**||||**Garmen**|**ts**||**M**|**etals and**|**machinery**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Firms**|**Supp **|**orted**|**Non- Su**|**pported**|**Difference**|**Suppo **|**rted**|**Non- Su**|**pported**|**Difference**|**Supported**|**Non- Su**|**pported**|**Difference**|**Supported**|**Non- S**|**upported**|**Difference**|
|**Variable**|**Obs.**|**Mean**|**Obs.**|**Mean**|**T-Test**|**Obs.**|**Mean**|**Obs.**|**Mean**|**T-Test**|**Obs.**<br>**Mean**|**Obs.**|**Mean**|**T-Test**|**Obs.**<br>**Mean**|**Obs.**|**Mean**|**T-Test**|
|Age|41|25.12|94|16.03|-1.8097**|28|18.54|88|15.14|-0.9826|55<br>16.33|188|14.95|-0.5961|133<br>19.84|382|17.99|-1.0110|
|**Variable**|**Obs.**|**%**|**Obs.**|**%**|**T-Test**|**Obs.**|**%**|**Obs.**|**%**|**T-Test**|**Obs.**<br>**%**|**Obs.**|**%**|**T-Test**|**Obs.**<br>**%**|**Obs.**|**%**|**T-Test**|
|Dummy Large<br>firm|13|31.71|21|22.34|-1.1501|5|17.86|15|17.05|-0.0982|20<br>36.36|29|15.43|-3.4739***|43<br>32.33|74|19.32|-3.1099***|
|Dummy Medim<br>firm|15|36.59|32|34.04|-0.2832|8|28.57|36|40.91|1.1687|11<br>20.00|59|31.38|1.6420*|40<br>30.08|118|30.81|0.1580|
|Dummy small<br>firm|13|31.71|41|43.62|1.2974*|15|53.57|37|42.05|-1.0642|24<br>43.64|100|53.19|1.2457|50<br>37.59|191|49.87|2.4541***|
|Dummy high/<br>upper-middle<br>income country|10|24.39|28|29.79|0.6374|20|71.43|55|62.50|-0.8561|30<br>54.55|120|63.83|1.2449|101<br>75.94|304|<br>79.37|0.8292|
|Dummy<br>domestic<br>owners|5|12.20|21|22.34|1.3741*|10|35.71|23|26.14|-0.9740|16<br>29.09|76|40.43|1.5254*|25<br>18.80|112|29.24|2.3583***|
|Note:<br>* significant at 10<br>Dummyvariables|%; **signi<br>take on the|ficant at 5<br>value of 1|%; and ***<br>if the firm|significant<br>has the sta|at 1%.<br>ted characteristi|c and 0 other|wise.||||||||||||

Source: Authors‘ calculations 17
# **4 Analysis**

## **4.1 Empirical Strategy**

To estimate the impact of big sisters on child development, we assume that child gender is plausibly exogenous.<sup>18</sup> We estimate the regression equation: where _Sisteri_ is an indicator equal to one if the older sibling in household _i_ is female. Parents cannot control the sex of any given child, and households in our study area have little access to sex-selection technologies — so gender is not explicitly endogenous. Nevertheless, our estimates of the treatment effect of older sisters will be biased if _Sisteri_ is correlated with any (observed or unobserved) covariates that also predict outcomes. For example, if adolescent girls were more likely to live at home in wealthier households, _β_<sup>ˆ</sup> would not capture the causal impact of having an older sister on child development. We test for this by comparing the observable characteristics of households with and without an older sister.

Summary statistics comparing households with an older sister to households with an older brother are presented in Table 2. Households are broadly similar in terms of family structure, parental characteristics, and living conditions; and younger children are similar in terms of gender, age, and school enrollment. Older sisters and older brothers are also similar in age, suggesting comparable patterns of fertility and birth spacing in the two types of households. Since families with older sisters and older brothers look similar in terms of observable characteristics, we treat the gender of the older child as plausibly exogenous in our subsequent analysis.<sup>19</sup>

> 18See Washington (2008) for a similar estimation approach.

> 19Our sample is also demographically similar to families sampled in the 2014 Kenya Demographic and Health Survey (DHS) (Kenya National Bureau of Statistics 2015). For example, in the DHS, among women age 15-49 in the former Nyanza province who had given birth at least once, the average number of years of schooling was 7.8, the average age was 31.7 years, and 82.6 percent had a latrine or toilet (author’s calculations); this is similar to the present sample, in which, on average, mothers have 7.9 years of schooling, are 30.5 years old, and have a latrine or toilet 79.3 percent of the time.

20
|Kerinci-Seblat Integrated Conservation and Development Project|Asmeen M. Khan|
|---|---|
|Kerinci-Seblat Integrated Conservation and Development Project|Asmeen M. Khan|
|Kerinci-Seblat Integrated Conservation and Development Project|Asmeen M. Khan|
|Kerinci-Seblat Integrated Conservation and Development Project|Asmeen M. Khan|
|Kerinci-Seblat Integrated Conservation and Development Project|Asmeen M. Khan|
|Strategic Urban Roads Infrastructureproject|Edward B. Dotson|
|Strategic Urban Roads Infrastructureproject|Edward B. Dotson|
|Strategic Urban Roads Infrastructureproject|Edward B. Dotson|
|Strategic Urban Roads Infrastructureproject|Edward B. Dotson|
|Strategic Urban Roads Infrastructureproject|Edward B. Dotson|
|Strategic Urban Roads Infrastructureproject|Edward B. Dotson|
|Strategic Urban Roads Infrastructureproject|Edward B. Dotson|
|Strategic Urban Roads Infrastructureproject|Edward B. Dotson|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|UniversityResearch for Graduate Education Project|HalseyL. Beemer|
|Flores Earthquake Reconstruction Project|Kodaganallur G. Sundar|
|Flores Earthquake Reconstruction Project|Kodaganallur G. Sundar|
|Flores Earthquake Reconstruction Project|Kodaganallur G. Sundar|
|Flores Earthquake Reconstruction Project|Kodaganallur G. Sundar|
|Flores Earthquake Reconstruction Project|Kodaganallur G. Sundar|
|Flores Earthquake Reconstruction Project|Kodaganallur G. Sundar|
|Flores Earthquake Reconstruction Project|Kodaganallur G. Sundar|
|Flores Earthquake Reconstruction Project|Kodaganallur G. Sundar|
|AccountancyDevelopment Project(02)|NancyChen|
|AccountancyDevelopment Project(02)|NancyChen|
|AccountancyDevelopment Project(02)|NancyChen|
|AccountancyDevelopment Project(02)|NancyChen|
|AccountancyDevelopment Project(02)|NancyChen|
|AccountancyDevelopment Project(02)|NancyChen|
|AccountancyDevelopment Project(02)|NancyChen|
|Kabupaten Roads Project(05)|Hatim M. Hajj|
|Kabupaten Roads Project(05)|Hatim M. Hajj|
|Kabupaten Roads Project(05)|Hatim M. Hajj|
|Kabupaten Roads Project(05)|Hatim M. Hajj|
|Kabupaten Roads Project(05)|Hatim M. Hajj|
|Kabupaten Roads Project(05)|Hatim M. Hajj|
|Kabupaten Roads Project(05)|Hatim M. Hajj|
|Second Power Transmission and Distribution Project|Noureddine Berrah|
|Second Power Transmission and Distribution Project|Noureddine Berrah|
|Second Power Transmission and Distribution Project|Noureddine Berrah|