rafmacalaba/datause-encoder-data huggingface dataset:
|Transport and Infrastructure Project|Gylfi Palsson|
|---|---|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
|Privatization Technical Assistance Project|Jean-Michel Happi|
|Privatization Technical Assistance Project|Jean-Michel Happi|
|Privatization Technical Assistance Project|Jean-Michel Happi|
|Privatization Technical Assistance Project|Jean-Michel Happi|
|Privatization Technical Assistance Project|Jean-Michel Happi|
|Privatization Technical Assistance Project|Jean-Michel Happi|
|Privatization Technical Assistance Project|Jean-Michel Happi|
|Pub. Sector Reform and CapacityBuildingCredit|Helene Marie Grandvoinnet|
|Pub. Sector Reform and CapacityBuildingCredit|Helene Marie Grandvoinnet|
|Pub. Sector Reform and CapacityBuildingCredit|Helene Marie Grandvoinnet|
|Pub. Sector Reform and CapacityBuildingCredit|Helene Marie Grandvoinnet|
|Pub. Sector Reform and CapacityBuildingCredit|Helene Marie Grandvoinnet|
|Pub. Sector Reform and CapacityBuildingCredit|Helene Marie Grandvoinnet|
|Pub. Sector Reform and CapacityBuildingCredit|Helene Marie Grandvoinnet|
|CapacityBuildingProject for Private Sector Promotion|Korotoumou Ouattara|
|CapacityBuildingProject for Private Sector Promotion|Korotoumou Ouattara|
|CapacityBuildingProject for Private Sector Promotion|Korotoumou Ouattara|
|CapacityBuildingProject for Private Sector Promotion|Korotoumou Ouattara|
|CapacityBuildingProject for Private Sector Promotion|Korotoumou Ouattara|
|CapacityBuildingProject for Private Sector Promotion|Korotoumou Ouattara|
|Livestock Project(01)||
|Livestock Project(01)||
|Technical Assistance Project(01)||
|HighwayProject(04)||
|HighwayProject(04)||
|HighwayProject(04)||
|HighwayProject(04)||
|Cotton Areas Rural Development Project||
|Cotton Areas Rural Development Project||
|Cotton Areas Rural Development Project||
|Cotton Areas Rural Development Project||
|Cotton Areas Rural Development Project||
|Cotton Areas Rural Development Project||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Technical Assistance Project(02)||
|Technical Assistance Project(02)||
From Figure 5, we can see that most small PICs perform better than or at similar levels to what would be expected, when their income and population size is taken into account. The extent to which these countries are disadvantaged by population size is clear, with a population “penalty” of more than one half of a score (the difference between a “C” and a “C+”) experienced by all countries except Fiji.<sup>4</sup> Results presented above test for a constant relationship between population size and PEFA scores. The assumption of linearity of the marginal effect of population on PEFA scores may not be valid if only very small countries are disadvantaged or if only very large countries are advantaged. This indeed seems to be the case when we rerun our regression on subsamples of countries of different sizes. Results are presented in Table 2. We find that the population effect is significant and largest for countries with populations of less than 500,000, whereas it is insignificant and very close to zero for larger countries.

The positive relationship between population and PEFA performance seems to be confined to small countries. We return to potential interpretations of these results in subsequent sections of this paper.

**Table 2: Testing linearity of population effect on PEFA score**

||(1)<br>Pop < 500k|(2)<br>500k > pop > 10m|(3)<br>Pop > 10m|(4)<br>Full sample|
|---|---|---|---|---|
|LGDPit (log income)|0.487*|0.575***|0.526***|0.391***|
||_(0.081)_|_(0.000)_|_(0.007)_|_(0.000)_|
|LPOPit (log population)|0.371***|0.026|0.028||
||_(0.006)_|_(0.784)_|_(0.769)_||
|SMALLi||||-0.803***|
|||||(0.000)|
|MEDIUMi||||-0.478***|
|||||(0.001)|
|Time trend|YES|YES|YES|YES|
|Regional dummies|NO|NO|NO|NO|
|Number of observations|1,799|4,450|4,282|10,948|
|Number of clusters|26|69|61|162|
|Adj. R-Squared|0.010|0.053|0.023|0.022|

Notes: Dependent variable is discrete PEFA score. All regressions use Tobit estimation.

p-values in parentheses are based on robust and clustered standard errors. *** p<0.01; * p<0.1. Constant is included but not reported.

> 4 Influence of population and GDP are as compared to the median country in the sample.

13
<!-- Start of picture text --> Number of firms remaining in each entry year cohort<br>o 15900 to 1952 1954 to 1561 1962 to 1966 1967 to 1971 1972to 1973<br>2<br>= OOrtt thse senscce<br>o 1974 to 1976 1977 to 1978 1979 to 1980 1981 to 1981 1982to 1983<br>2<br>2 0080-0000 session<br>o 1984 to 1584 1985 to 1985 1986 to 1987 1988 to 1986 15985to 19590<br>2<br>2<br>eer<br>1985 1990 1995 2000 2005<br>o 1991 to 1992 1993 to 1994 1995 to 1996 1997to 1999<br>2a Fa re aetee, 4<br>= 1985 1990 1995 2000 20051985Se ee 1990 1995 2000 20051985 1990 1995 2000 20051985 ooo 1990 1995 2000 2005<br>year<br>—*— 000s firms per cohort == Fitted values<br>Graphs by cohort_name<br><!-- End of picture text -->
10 10 the trade data, the country of origin corresponds to the place of origin of the good or service.<sup>6</sup> The indicators of protectionism can apply to the nationality of firms, goods and services, or both. For instance, the (lack of) national _treatment_ indicator applies to both firms and goods/services, while provisions for preferential treatment apply to firms. _Domestic sourcing_ obligations, on the other hand, refer to the nationality of goods and services and are consistent with the definition of nationality used in the trade data. The composite indicator includes rules that depend on the nationality of both firms and goods and services. By investigating also the impact of each type of protectionist measure, we can provide suggestive evidence on the role of the different nationality principles.

Because of these data limitations, our empirical strategy relies on simple cross-country regressions and gravity models of bilateral trade. Our estimates are interpreted as correlations suggesting the existence (or lack thereof) of a relationship between trade openness and the protectionism measures. Reverse causality and omitted variables can introduce biases that prevent us from interpreting the estimates as causal. Countries whose markets have been historically open to foreign firms might have designed their law in ways that accommodate such a trend, thus creating a link going from trade openness to the (low) protectionism level of the law. Furthermore, unobservable country characteristics (for instance, legal aspects that are not measured by the coding of national laws) that correlate with both the coding of the law and the trade flows can influence the estimates of the relationship of interests. For these reasons, the estimates found in the econometric analysis are suggestive of the relationship between trade openness and protectionism of the law, without indicating causal effects.

# IV. Results on the relationship between trade openness and protectionism in national laws on government procurement

We move beyond descriptive evidence and examine the relationship between protectionism in government procurement, as measured by a coding of the national law, and trade flows. We first estimate this relationship in cross-country regressions while controlling for potential confounding factors that could correlate with both protectionism and trade flows, as demonstrated in the descriptive analysis. In a second step of the econometric analysis, we estimate how the protectionist indicators relate to bilateral trade flows in a gravity model.

> 6 This is the case also for services sold by affiliates of multinational firms (what is commonly referred to as “commercial presence” or “Mode 3” of service trade). This means that, for instance, the purchase of business services by the Brazilian government from the Brazilian affiliate of a U.S. multinational would be recorded as domestic procurement.

10
# **Table T1.U: SUMMARY STATISTICS (URBAN SAMPLES)**

||**F**|**ull**|**Sons**|**Daughters**|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**<br>**SD**|**Mean**<br>**SD**|
||||**CHINA**||
|CFPS2016 (18-35)|N=6|433|N=3192|N=3241|
|Father's Sch.|7.66|4.27|7.65<br>4.32|7.67<br>4.23|
|Children's Sch.|11.17|3.88|11.17<br>3.85|11.18<br>3.9|
|CFPS2016 (18-30)|N=4|435|N=2230|N=2205|
|Father's Sch.|7.8|4.2|7.8<br>4.23|7.79<br>4.17|
|Children's Sch.|11.27|3.77|11.23<br>3.73|11.31<br>3.8|
|CFPS2010 (6-22)|N=2|784|N=1425|N=1359|
|Father's Sch.|9.35|4|9.24<br>4|9.48<br>4|
|Educ. Exp.|3178.11|4760.34|3014.98<br>4761.47|3349.16<br>4754.89|
|No. of Children|1.2|0.72|1.18<br>0.67|1.21<br>0.76|
|CFPS2010 (8-23)|N=2|435|N=1251|N=1184|
|Father's Sch.|9.28|3.99|9.17<br>4.02|9.39<br>3.95|
|Educ. Exp.|3261.35|4990.48|3101.34<br>5093.39|3430.41<br>4875.83|
|No. of Children|1.17|0.75|1.15<br>0.69|1.19<br>0.8|
||||**INDIA**||
|IHDS2012(18-35)|N=1|8519|N=9449|N=9070|
|Father's Sch.|6.54|5.05|6.64<br>4.96|6.44<br>5.14|
|Children's Sch.|9.69|4.52|10.01<br>4.2|9.36<br>4.8|
|IHDS2012(18-30)|N=1|4140|N=7314|N=6826|
|Father's Sch.|6.81|5|6.9<br>4.89|6.72<br>5.1|
|Children's Sch.|10|4.35|10.2<br>4.06|9.78<br>4.63|
|IHDS2005 (6-22)|N=1|3086|N=6917|N=6169|
|Father's Sch.|8.55|4.68|8.52<br>4.71|8.59<br>4.65|
|Educ. Exp.|3798.29|5349.04|4060.57<br>5657.13|3504.2<br>4965|
||241|143|236<br>143|246<br>143|
|No. of Children|.|.|.<br>.|.<br>.|
|IHDS2005 (8-23)|N=1|1051|N=5883|N=5168|
|Father's Sch.|8.58|4.68|8.54<br>4.7|8.61<br>4.66|
|Educ. Exp.|4082.12|5721.06|4368.01<br>6025.75|3756.67<br>5335.11|
|No. of Children|2.42|1.46|2.35<br>1.46|2.5<br>1.46|
|NSS1995 (6-13)|N=1|8689|N=10071|N=8618|
|Father's Sch.|5.09|2.46|5.03<br>2.47|5.16<br>2.43|
|<br>Educ. Exp.|1101.84|1237.90|1145.16<br>1293.68|1051.22<br>1167.40|
|No. of Children|2.67|1.11|2.61<br>1.10|2.73<br>1.13|
|NSS1995 (6-18)|N=2|7819|N=15152|N=12667|
|Father's Sch.|5.24|2.44|5.16<br>2.46|5.33<br>2.42|
|Educ. Exp.|1338.27|1529.59|1382.00<br>1588.18|1285.96<br>1454.75|
|No. ofChildren|2.67|1.14|2.60<br>1.12|2.75<br>1.17|

Notes: CFPS stands for China Family Panel Survey, IHDS stands for Indian Human Development Survey, and NSS stands for National Sample Survey.
**_Table 7: Effects on taking diabetes, hypertension, and at least one screening test_**

||(1)<br>|(2)<br>|(3)<br>|
|---|---|---|---|
||Diabetes test|Hypertension test|At least one test|
|Intervention Group One|.163***<br>|.167***<br>|.174***<br>|
||(.023)|(.026)|(.027)|
|Intervention Group Two|.169***|.17***|.182***|
||(.023)<br>|(.026)<br>|(.026)<br>|
|Intervention Group Three|.17***|.162***|.182***|
||(.023)|(.025)|(.026)|
|Intervention Group Four|.324***<br>|.331***<br>|.343***<br>|
||(.026)|(.028)|(.029)|
|Small Facility|-.137|.001|.026|
||(.099)|(.116)|(.117)|
|Medium Facility|.072|.086|.119|
||(.092)|(.098)|(.099)|
|Age|0|.001|.001|
||(.001)|(.001)|(.001)|
|Male|-.04**|-.07***|-.078***|
||(.018)|(.019)|(.019)|
|Urban|-.166|-.056|-.009|
||(.116)|(.132)|(.133)|
|No education|.081<br>|.066<br>|.051<br>|
||(.126)|(.134)|(.132)|
|Technical Vocation, College|-.045**|-.025|-.03|
||(.02)|(.022)|(.023)|
|Higher Education|<br>-.03|<br>-.031|<br>-.033|
||(.026)|(.028)|(.028)|
|Single|-.065**|-.062*|-.063*|
||(.029)|(.032)|(.033)|
|Divorced|<br>-.021|<br>-.036|<br>-.052|
||(.053)|(.056)|(.056)|
|Widowed|.015|0|-.002|
||(.053)|(.058)|(.058)|
|State Employee|.019<br>|.013<br>|.028<br>|
||(.026)|(.028)|(.028)|
|Private Sector Employee|-.032|-.027|-.037|
||(.023)|(.024)|(.024)|
|Self Employed|-.042|-.049|-.055*|
||(.031)|(.033)|(.033)|
|Income not sufficient for food|-.055|-.044|-.044|
||(.038)|(.043)|(.044)|
|Income sufficient but not for clothes|.038*|.027|.024|
||(.02)|(.021)|(.022)|
|Income sufficient but not for savings|<br>.004|<br>.008|<br>-.005|
||(.034)|(.037)|(.038)|
|Income sufficient for everything|-.06<br>|-.076<br>|-.096<br>|
||(.063)|(.064)|(.066)|
|Y Control Mean|.043|.08|.061|
|SE Control|(.10)|(.14)|(.144)|
|Observations|<br>1997|<br>1997|<br>1997|
|R-squared|.089|.086|.089|
|p ig1=ig2|0.824|0.911|0.791|
|<br>p ig1=ig3|0.804|0.868|0.791|
|p ig1=ig4|0.000|0.000|0.000|
|p ig2=ig3|0.979|0.779|0.998|
|<br>p ig2=ig4|<br>0.000|<br>0.000|<br>0.000|
|p ig3=ig4|0.000|0.000|0.000|
|_Standard errors are in parentheses_<br>||||
|_*** p<.01, ** p<.05, * p<.1_||||
|<br>Facility and Province effects on all models (not show|n) and robust standard errors|||

20
|Table B.5: Fiscal (pro)cyclicality of corporate tax|rates, whole sample (|2000-2016): Impac|ts of level playing|field, natural resou|rces, and other fact|ors|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable: Individual income tax|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Cyclicality of real GDP|-0.258**|-1.359*|0.788|-0.416|-0.441|-0.416|-0.401***|-0.043|-31.549|-0.146**|
||(0.104)|(0.770)|(1.492)|(0.425)|(0.440)|(0.424)|(0.047)|(0.281)|(23.212)|(0.058)|
|**Equality of conditions**|||||||||||
|Crgdp*power.distribution|0.098<br>(0.172)||||||||||
|Crgdp*resource.distribution||1.740<br>(1.243)|||||||||
|Crgdp*ethnicandrel.stability|||-0.372<br>||||||||
||||(0.539)||||||||
|**Natural resources**|||||||||||
|Crgdp*resource.dependant (bd)||||0.083<br>(0.420)|||||||
|Crgdp*resource.rich (bd)|||||0.110<br>(0.435)||||||
|Crgdp*resource dependant and rich||||||0.083<br>|||||
|||||||(0.420)|||||
|**Other factors**|||||||||||
|Crgdp*elections|||||||0.208**<br>(0.102)||||
|Crgdp*debt.ratio||||||||-0.021<br>(0.024)|||
|Crgdp*rigidity|||||||||5,727.836<br>(3,927.079)||
|Crgdp*fiscal space||||||||||-8.396***<br>(1.575)|
|Constant|39.587***|40.695***|59.060***|59.537***|59.537***|59.537***|59.533***|58.880***|38.483***|59.068***|
||(2.361)|(9.174)|(5.738)|(1.138)|(1.138)|(1.138)|(1.133)|(1.180)|(2.696)|(1.195)|
|Adjusted R-squared|0.723|0.723|0.716|0.732|0.732|0.732|0.732|0.737|0.819|0.743|
|Rmse|3.705|3.705|4.465|4.371|4.371|4.371|4.374|4.359|3.021|4.288|
|Joint significance (p-value)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.139|0.000|
|<br>CD test (p-value)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|<br>Order of integration|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|
|Cluster|country|country|country|country|country|country|country|country|country|country|
|Covariates|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|Observations|700|700|751|831|831|831|831|819|407|828|
|# countries|47|47|48|48|50|50|50|50|26|50|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. Coefficients attached to cyclicality of GDP or its interactions are multiplied by 10000. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.3 for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

34
Comparison of different types of disaster management investments (100% coverage, policy financed 100% from investment) <!-- Start of picture text --> GDP compared to baseline<br>—— Adaptation<br>5 —— Insurance<br>—— Contingency fund<br>0 —— Fiscal space<br>—— No policy (only shocks)<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Consumption compared to baseline<br>—— Adaptation<br>4 —— Insurance<br>—— Contingency fund<br>0 —— Fiscal space<br>—— No policy (only shocks)<br>4<br>-8<br>-12<br>-16<br>-20<br>2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text --> <!-- Start of picture text --> Potential GDP compared to baseline<br>—— Adaptation<br>0 ——— Insurance<br>—— Contingency fund<br>5 —— Fiscal space<br>—— No policy (only shocks)<br>-10<br>-15<br>-20<br>-25<br>-30<br>2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Debt/GDP<br>—— Adaptation<br>160 —— Insurance<br>—— Contingency fund<br>140 —— Fiscal space<br>—— No policy (only shocks)<br>L0<br>100<br>80<br>60<br>40<br>20<br>2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->
# **Table 2: Water shortages and labor productivity**

|||**Labor Prod**|**uctivity (logs)**||
|---|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Total duration of water shortages last month (in days)|-0.033**|-0.037***|-0.036***|-0.037***|
||(0.015)|(0.012)|(0.011)|(0.011)|
|Log of total number of employees in regular month||-0.506***|-0.560***|-0.560***|
|||(0.114)|(0.110)|(0.111)|
|Largest owner has secondary education or higher: Y/N||0.296***|0.278**|0.286**|
|||(0.114)|(0.124)|(0.135)|
|Manager experience (years)||0.017**|0.016**|0.016**|
|||(0.007)|(0.007)|(0.007)|
|Largest owner has a job in formal sector or looked for one over last 2 years Y/N||0.033|0.033|0.021|
|||(0.144)|(0.137)|(0.136)|
|Largest owner started another business in the last three years Y/N||-0.129|-0.136*|-0.128|
|||(0.080)|(0.079)|(0.079)|
|Firm uses electricity for activity Y/N||0.099|0.069|0.078|
|||(0.096)|(0.087)|(0.080)|
|Firm uses machinery in its current operations Y/N||0.041|0.022|0.037|
|||(0.089)|(0.092)|(0.090)|
|Firm uses vehicles in its current operations Y/N||0.467***|0.455***|0.464***|
|||(0.079)|(0.089)|(0.095)|
|Manufacturing firm Y/N||0.180**|0.150*|0.144*|
|||(0.071)|(0.080)|(0.084)|
|Produces or sells under contract from another business or person Y/N|||0.361***|0.379***|
||||(0.122)|(0.136)|
|Firm uses bank account to run business Y/N|||0.179|0.188|
||||(0.131)|(0.121)|
|Firm has a loan at this time Y/N|||-0.017|0.000|
||||(0.071)|(0.073)|
|Firm pays informal payment to remain unregistered Y/N||||-0.251|
|||||(0.168)|
|Firm experienced crime last month Y/N||||0.062|
|||||(0.231)|
|Firm pays for security Y/N||||-0.079|
|||||(0.181)|
|Constant|5.205***|5.812***|6.045***|6.146***|
||(0.012)|(0.196)|(0.191)|(0.198)|
|Location (within country) Fixed Effects|YES|YES|YES|YES|
|Number of observations|710|710|710|710|
|Adjusted R2|0.274|0.332|0.341|0.341|

note: *** p<0.01, ** p<0.05, * p<0.1, Hubert-White robust standard errors clustered at the region-sector level in brackets 25
- 22 -

the equilibrium bid function still looks pro-competitive with respect to _n_ ; more bidders lead to lower procurement costs.

|Table 7. IV an|d Treatment E|ffect Regres|sions on Bid|s|
|---|---|---|---|---|
|Estimation method|2SLS||Treatment ef|fect|
||1st stage|2nd stage|1st stage<br>|2nd stage|
|Dependent variable|ln_n_|ln_b_|_D _<sup>_TE_</sup>|ln_b_|
|ln_n_||–0.495 <sup>**</sup>|0.274|–0.420 <sup>***</sup>|
|||(0.231)|(0.245)|(0.070)|
|_D _<sup>_TE_</sup>|–0.059|0.498 <sup>**</sup>|<sup>*</sup>|0.709 <sup>***</sup>|
||(0.406)|(0.140)||(0.196)|
|_D _<sup>_DAM_</sup>|–0.301 <sup>**</sup>|–0.005||0.018|
||(0.140)|(0.107)||(0.092)|
|_D _<sup>_CIVIL_</sup>|0.528 <sup>***</sup>|0.270 <sup>*</sup>||0.226 <sup>***</sup>|
||(0.132)|(0.140)||(0.082)|
|ln_GCAP_|0.001|0.008 <sup>**</sup>|0.049 <sup>***</sup>|0.005|
||(0.006)|(0.004)|(0.014)|(0.004)|
|ln_LINE_|0.030 <sup>***</sup>|0.002|–0.006|–0.0004|
||(0.005)|(0.008)|(0.016)|(0.004)|
|ln_SUBS_|0.002|0.012 <sup>**</sup>|<sup>*</sup><br>0.026 <sup>*</sup>|0.011 <sup>***</sup>|
||(0.006)|(0.004)|(0.015)|(0.004)|
|ln_COST_|0.129 <sup>*</sup>|1.021 <sup>**</sup>|<sup>*</sup><br>–0.357 <sup>***</sup>|1.032 <sup>***</sup>|
||(0.068)|(0.053)|(0.120)|(0.048)|
|ln_WAID_|1.028||||
||(2.143)||||
|ln_CAID_|0.443 <sup>***</sup><br>(0.157)||||
|Constant|–10.419|14.309 <sup>**</sup>|<sup>*</sup><br>1.829 <sup>***</sup>|14.009 <sup>***</sup>|
||(19.369)|(0.328)|(0.609)|(0.301)|
|Obs.|131|131|131|131|
|_R_-squared|0.713|0.972|||
|_F_-statistics|6.48||||
|Wald chi2||4536.80||4014.84|
|Recipient country dummy|Yes|Yes|No|Yes|
|Bidder nationality dummy|Yes|Yes|No|Yes|
|Donor dummy|Yes|Yes|No|Yes|
|_ρ_||||–0.521|
|||||(0.316)|
|LR test statistics:_ρ_=0||||0.70|
|Note that the standard errors a|re shown in paren|theses. *, **,|and *** indica|te the 10%,|
|5% and 1% significance level|s, respectively.||||

# **V. CONCLUSION**

Infrastructure procurement is a challenging task for governments. Infrastructure projects are often technically complicated and highly customized. Because potential bidders that are competent for large-scale infrastructure works are relatively limited, procurement
# **9. References**

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