rafmacalaba/datause-encoder-data huggingface dataset:
grow on average by an additional 2.2 percentage points and East and West African countries by an additional 1.6 percentage points (Calderón and Servén 2010a). Similarly, if each Latin American country would match the average level of infrastructure observed among non-Latin American middle income countries (such as Turkey or Bulgaria), growth in Latin America would rise approximately by 2 percentage points per year (Calderón and Servén 2010b).

35. The Chinese experience illustrates the benefits of infrastructure investments. Between 1990 and 2005, China invested approximately US$600 billion to upgrade its road system. The centerpiece of this investment was the National Expressway Network, which, spanning 41,000 km, was designed to eventually connect all cities with more than 200,000 inhabitants.<sup>23</sup> Only the U.S. Interstate Highway System, with a length of 75,000 km, exceeds its length. Roberts, Deichmann, Fingleton, and Shi (2010) show that aggregate Chinese real income was approximately 6 percent higher than it would have been in 2007 if the expressway network had not been built. Using annual data for the period 1975 to 2007, Sahoo, Dash, and Nataraj (2010) estimate that the output elasticity of infrastructure investment in China is around 0.2 to 0.41 percent. The authors conclude that China’s infrastructure investment strategy was successful since it was embedded in an overall economic policy that focused not only on improving physical infrastructure, but also on enhancing private sector investment and human capital formation.

# **IV. A Global Infrastructure Initiative – For the Benefit of All**

36. The gap between the demand and the supply of infrastructure services is large in developing countries, as it is difficult for developing countries to raise significant amounts of long-term financing, which is required for infrastructure investments. Putting a price tag on this gap requires making heroic assumptions as comprehensive and reliable data, which is required to calculate this gap, is unavailable.<sup>24</sup> Fay et al (2011) estimate annual infrastructure needs in the range of US$1,250 billion to US$1,500 billion and a financing gap in the range of US$175 – 700 billion in

> 23 The length reached 74,100 km by 2010 (National Statistical Bureau 2011).

> 24 See Fay et al (2011) for a detailed discussion. To overcome this constraint, the MDB Working Group on Infrastructure (2010) proposed to the G20 to launch on Infrastructure Benchmarking Initiative that undertakes ongoing data collection on key infrastructure variables that can be compared across countries and over time.

15
Policy Research Working Paper 8379

# **Abstract**

Using online job portal data and probabilistic regression estimations, the paper investigates the explicit gender bias and salary gap in the Indian job market, reflected in more than 800,000 job recruitment advertisements. Exploring formal and informal sector occupations, the study finds high existence of employers’ gender bias in hiring. Explicit gender preferences are highly job specific, and it is common to mention the preferred gender in job ads, which, in general, favor men over women. Although ads for professional occupations exhibit less explicit gender bias, they are not gender neutral. In all types of professional jobs, irrespective of the share of ads with preference for men or women, on average, ads targeting men specify/offer much higher salary. Employers in elementary sectors as well as blue-collar jobs express more segregated gender preference. The findings support the existing research that argues women are more preferred in low-quality, low-status, typically lowpaid informal jobs. Targeting women for low-quality jobs explains half of the mean offered salary gap specified in ads; the rest is direct gender bias. The paper also suggests that, with the rise of new technology and sectors, gender bias in hiring in those new types of jobs is expected to decline.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at achowdhury5@ worldbank.org or afra.rchowdhury@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Annex 3: Installed capacity data**

## **Table A3.1: Installed capacity and other parameters in MW in 2014**

|Country|Hydro-<br>power|Natural<br>gas|Diesel and<br>HFO|Coal|Nuclear|Other<br>renewable<br>energy|Total<br>installed<br>capacity|
|---|---|---|---|---|---|---|---|
|Angola|1,126|326|925|0|0|0|**2,377**|
|Benin|0|100|249|0|0|0|**349**|
|Botswana|0|0|160|732|0|1|**893**|
|Burkina Faso|32|0|253|0|0|0|**285**|
|Burundi|25|0|21|0|0|0|**45**|
|Cameroon|732|216|301|0|0|0|**1,249**|
|Cape Verde|0|0|107|0|0|31|**138**|
|Central African Republic|19|0|7|0|0|0|**26**|
|Chad|0|0|125|0|0|0|**125**|
|Comoros|0|0|27|0|0|0|**27**|
|Congo, Dem. Rep.|2,488|28|0|0|0|0|**2,516**|
|Congo, Rep.|234|350|44|0|0|0|**628**|
|Côte d'Ivoire|604|1,028|0|0|0|0|**1,632**|
|Equatorial Guinea|128|136|38|0|0|0|**302**|
|Eritrea|0|0|145|0|0|2|**147**|
|Ethiopia|1,945|0|0|0|0|149|**2,094**|
|Gabon|170|244|93|0|0|0|**507**|
|Gambia, The|0|0|101|0|0|0|**101**|
|Ghana|1,580|1,248|0|0|0|3|**2,831**|
|Guinea|127|0|211|0|0|0|**338**|
|Guinea-Bissau|0|0|11|0|0|0|**11**|
|Kenya|818|173|524|0|0|370|**1,885**|
|Lesotho|72|0|0|0|0|0|**72**|
|Liberia|0|0|22|0|0|0|**22**|
|Madagascar|163|0|406|0|0|0|**569**|
|Malawi|351|0|0|0|0|0|**351**|
|Mali|321|29|270|0|0|0|**620**|
|Mauritania|0|0|155|0|0|15|**170**|
|Mauritius|58|226|312|215|0|0|**811**|
|Mozambique|2,186|7|191|0|0|0|**2,383**|
|Namibia|332|0|35|120|0|0|**487**|
|Niger|0|0|94|32|0|0|**126**|
|Nigeria|1,958|8,584|42|0|0|0|**10,584**|
|Rwanda|69|4|48|0|0|9|**129**|
|São Tomé and Príncipe|2|0|31|0|0|0|**33**|
|Senegal|0|84|569|0|0|0|**653**|
|Seychelles|0|0|98|0|0|6|**104**|
|Sierra Leone|57|0|37|0|0|0|**94**|
|Somalia|0|0|16|0|0|0|**16**|

85
**Table A2: Portuguese emigrants, 2000 (continued)**

|Finland|141|Lesotho|45|St. Vincent and the Grenadines|12|
|---|---|---|---|---|---|
|Panama|137|Somalia|43|Bosnia and Herzegovina|11|
|Kazakhstan|127|Cambodia|42|Jamaica|11|
|Seychelles|123|Paraguay|42|Korea, Rep.|10|
|Niger|122|Rwanda|40|British Virgin Islands|8|
|Iceland|116|Czech Republic|39|Bahamas, The|7|
|Madagascar|115|Grenada|39|Dominica|7|
|Swaziland|113|Bangladesh|32|Anguilla|6|
|Georgia|102|Gabon|31|Albania|6|
|Gambia, The|99|Benin|29|Maldives|6|
|Uganda|99|Bolivia|28|Solomon Islands|6|
|Senegal|98|Hungary|28|Northern Mariana Islands|5|
|Bulgaria|96|Eritrea|27|Botswana|4|
|Oman|92|Tonga|22|Latvia|4|
|Mali|91|St. Kitts and Nevis|21|Macedonia, FYR|4|
|Haiti|86|Korea, Dem. Rep.|21|Slovak Republic|4|
|Armenia|83|El Salvador|20|Samoa|4|
|Trinidad and Tobago|83|Tunisia|20|Antigua and Barbuda|3|
|Gibraltar|81|Comoros|19|Lithuania|3|
|Dominican Republic|75|Cyprus|19|Mauritania|3|
|Afghanistan|71|Mauritius|18|Cayman Islands|2|
|Poland|70|Brunei Darussalam|17|Equatorial Guinea|1|
|Iran, Islamic Rep.|67|Guyana|17|St. Lucia|1|
|Liberia|67|Sri Lanka|16|Monserat|1|
|Malta|63|Marshall Islands|16|St Hellen|1|
|French Polynesia|63|Barbados|15|Azerbaijan|0|
|Peru|57|Cook Islands|15|Central African Republic|0|
|Egypt, Arab Rep.|55|Fiji|15|Federal States of Micronesia|0|
|Djibouti|53|Guatemala|15|Qatar|0|
|Chad|52|Sierra Leone|14|Sudan|0|
|Saudi Arabia|51|Honduras|12|Slovenia|0|
|Papua New Guinea|48|Nicaragua|12|Tuvalu|0|
|Vietnam<br>Note: This table reports the|48<br>number of|Portuguese emigrants in 19|3 countrie|s. The data come from the Global Mig|rant|

<u>Note: This table reports the number of Portuguese emigrants in 193 countries. The data come from the Global Migrant</u> Origin Database and refer to the year 2000.

11
The abandonment of e-filing by Group A high-risk firms can also explain the result that, unlike for low-risk firms, among high-risk firms no reduction in bribe payments occurs. Since the survey measuring this outcome took place in January/ February 2016, one year after program implementation, we have only a one-time measure of this outcome, which occurs after a sizable proportion of the high-risk firms have dropped out of e-filing. Since the impact on tax payments has dissipated by then, it is reasonable to assume that any collusion arrangements have resumed, such that we see no negative impact of assignment to the treated group upon the prevalence of bribes. Instead, from the list experiment (Table 6), we observe relatively high levels of bribes (10-14 percent) among high-risk firms in both treated and control groups.

# **7.4 Robustness Checks**

One potential concern is that, since the baseline risk profile scores of firms is correlated with other firm characteristics (Table 2), if the impacts of e-filing adoption are heterogeneous across these characteristics then initial differences in these characteristics between lowand high-risk firms may be driving our heterogeneous analysis by risk score. For example, suppose that e-filing has greater impacts on firms in a certain sector and results in those firms paying higher taxes. If firms in that sector also have higher risk scores, this effect could drive the heterogeneous results found by risk profile score.

To examine this possibility, we allow the effect of e-filing adoption to vary with other firm characteristics. To do so, we estimate the following equation for a set of _H_ potential 33
but not significant. Using Sri Lanka’s household survey, we can also employ an alternative measure of working conditions (table 5, columns 5–6): the share of bonuses and allowances over cash earnings and the share of food stamps over cash pay. Favorable working conditions correspond to having a higher-than-average performance pay ratio and a lower-than-average food stamps ratio. Our dependent variable represents an average between those two variables. The analysis shows that this index of working conditions in textile and garment industry compared to other industries worsened in the post-MFA period by 3 percent. However, the working conditions improved for women in the post-MFA period by 8.5 percent relative to men and compared to other industries (see table 5, column 6).

# **5. Conclusions**

Despite gloomy post-MFA expectations, the apparel industries of both Cambodia and Sri Lanka managed to increase export volumes, maintain world market shares, and grow at a healthy rate, outcomes that may eventually have poverty-reducing implications. The resilience of the industries in these two countries can be explained by several factors. The United States and the EU established safeguard quotas against imports from China from 2005 until the end of 2008, which mitigated the impact of the MFA phase-out. Other factors such as proactive policies, industry ownership, and background were also important.

Under the assumption that women are more apparel-specific labor input than men and that the apparel sector is a female-labor intensive sector, the simple theoretical model predicts that a negative price shock will translate into a relative decline in apparel wages compared to other industries in the short run and a relative decrease in female wages compared to male wages across all industries in the long run.

The increased competition in apparel exports after the MFA phase-out drove down the unit values of apparel both in Cambodia and Sri Lanka. The empirical results of the post-MFA changes support the theoretical predictions. Workers, the majority of whom are female, in the apparel industry were receiving a premium comparable to that ofan average worker. This premium decreased in both countries right after the MFAphase-out, but it slightly recovered in the following years. Male-female wage differentials were declining under the MFA in both countries. The gap, however, widened right after the MFA phase-out but later decreased. Finally, in terms of a very narrow measurement of working conditions, we found mixed results. In Cambodia, working conditions—measured as an average index of no child labor and no mandatory or unpaid overtime work—worsened in the post-MFA period. In Sri Lanka results are mixed and depend on how working conditions are measured. If we measure working conditions the same way as in Cambodia, we find that the working conditions in Sri Lanka improved. But if we measure working conditions as an average of the share of bonuses and allowances over cash earnings and the share of food stamps over cash pay, we find that working conditions declined in Sri Lanka.

17
|**Table 16 Comparis**|**on of reported**<br>**Uncondition**<br>|**constraints by f**<br>**al means**<br>|**ormal, sm**<br>|**all formal and**<br>|**rural firms**<br>**Survey fixed e**<br>|**ffects and**<br>|**log(L) (center**<br>|**ed on L=5**<br>|**)**<br>|
|---|---|---|---|---|---|---|---|---|---|
||All formal|Small formal|Rural|Diff?|Small formal|Rural|Diff?|Obs (SF)|Obs (R)|
|Electricity|2.575|2.563|1.470|Smaller|2.446|1.828|Smaller|2,234|4,888|
|Telecoms|0.969|0.694|0.318|Smaller|0.602|0.316|Smaller|1,355|4,881|
|Transport|1.081|0.874|0.696|Smaller|0.864|0.725|Smaller|2,166|4,750|
|AccessLand|1.606|1.701|0.100|Smaller|1.613|0.066|Smaller|1,747|4,157|
|InadEducLabor|1.254|0.986|0.183|Smaller|0.929|0.322|Smaller|1,755|4,210|
|MacroInstability|1.978|1.939|1.619|Smaller|1.795|1.785||1,716|2,428|
|GovPolicyUnc|1.868|1.622|0.535|Smaller|1.608|0.395|Smaller|955|1,846|
|PoliticalInstability|2.457|2.290|0.681|Smaller|2.206|0.884|Smaller|1,316|4,029|
|AccessFinancing|1.677|1.655|1.086|Smaller|1.687|1.013|Smaller|2,143|4,827|
|Competit|1.308|1.243|1.394||1.200|1.430||1,766|66|
|TaxAdministration|1.854|1.569|0.099|Smaller|1.328|0.137|Smaller|1,756|4,197|
|TaxRates|1.820|1.669|0.146|Smaller|1.431|0.205|Smaller|1,762|4,195|
|LaborReg|0.904|0.677|0.042|Smaller|0.631|0.056|Smaller|2,143|4,202|
|Customs|1.296|0.811|0.020|Smaller|0.660|0.029|Smaller|1,520|4,091|
|BusLicensing|1.140|1.035|0.177|Smaller|0.933|0.173|Smaller|1,668|1,763|
|Courts|1.585|1.553|0.163|Smaller|1.543|0.195|Smaller|1,000|4,231|
|Corruption|2.137|1.978|0.146|Smaller|1.888|0.159|Smaller|2,211|4,229|
|CrimeTheftDisorder|1.405|1.271|0.791|Smaller|1.199|0.819|Smaller|1,799|67|
|bribe|0.591|0.464|0.288|Smaller|0.404|0.212|Smaller|1,691|52|
|bribe1|0.269|0.205|0.077|Smaller|0.181|0.062|Smaller|1,394|52|
|mng_time|4.467|2.884|0.118|Smaller|2.436|0.286|Smaller|2,170|5,398|
|generator|0.515|0.146|0.062|Smaller|0.056|0.091|Greater|1,631|5,416|
|insufficient_water|0.343|0.302|0.014|Smaller|0.321|0.020|Smaller|1,664|4,031|
|num_insp|12.652|5.809|0.451|Smaller|5.168|1.608|Smaller|1,283|2,970|
|days_customs|8.242|5.204|5.204|n.a.|7.396|7.396|n.a.|142|0|
|m_power_out|0.751|0.727|0.669|Smaller|0.723|0.702||2,014|2,671|

Small numbers of observations are noted in red. Differences are heteroskedasticity-robust tests with a 5% significance level.

60
|P057996|2000|2001|5/29/2001|3 Closed|Senegal|
|---|---|---|---|---|---|
|P057996|2000|2002|4/26/2002|5 Closed|Senegal|
|P057996|2000|2003|5/6/2003|8 Closed|Senegal|
|P057996|2000|2004|2/13/2004|10 Closed|Senegal|
|P057996|2000|2005|2/6/2005|12 Closed|Senegal|
|P057996|2000|2006|9/19/2005|13 Closed|Senegal|
|P057996|2000|2006|12/20/2005|14 Closed|Senegal|
|P057997|1999|1999||1 Closed|Gambia,The|
|P057997|1999|2000|6/13/2000|3 Closed|Gambia,The|
|P057997|1999|2001|5/10/2001|5 Closed|Gambia,The|
|P057997|1999|2002|4/3/2002|8 Closed|Gambia,The|
|P057997|1999|2003|6/27/2003|13 Closed|Gambia,The|
|P057997|1999|2004|6/29/2004|16 Closed|Gambia,The|
|P057997|1999|2005|5/30/2005|18 Closed|Gambia,The|
|P057997|1999|2006|12/6/2005|19 Closed|Gambia,The|
|P057997|1999|2006|5/30/2006|20 Closed|Gambia,The|
|P057997|1999|2007|11/17/2006|21 Closed|Gambia,The|
|P057997|1999|2021|7/20/2020|22 Closed|Gambia,The|
|P057998|2000|2000|6/22/2000|1 Closed|Cabo Verde|
|P057998|2000|2001|6/12/2001|3 Closed|Cabo Verde|
|P057998|2000|2002|3/18/2002|5 Closed|Cabo Verde|
|P057998|2000|2003|8/23/2002|6 Closed|Cabo Verde|
|P057998|2000|2020|4/27/2020|7 Closed|Cabo Verde|
|P058015|2005|2005|5/13/2005|2 Closed|Kazakhstan|
|P058015|2005|2006|5/8/2006|3 Closed|Kazakhstan|
|P058015|2005|2007|4/3/2007|4 Closed|Kazakhstan|
|P058015|2005|2008|7/28/2007|5 Closed|Kazakhstan|
|P058015|2005|2008|5/30/2008|6 Closed|Kazakhstan|
|P058015|2005|2009|12/29/2008|7 Closed|Kazakhstan|
|P058015|2005|2009|6/9/2009|8 Closed|Kazakhstan|
|P058015|2005|2010|9/15/2009|9 Closed|Kazakhstan|
|P058015|2005|2010|11/30/2009|10 Closed|Kazakhstan|
|P058015|2005|2010|3/31/2010|11 Closed|Kazakhstan|
|P058015|2005|2011|1/9/2011|12 Closed|Kazakhstan|
|P058015|2005|2012|8/14/2011|13 Closed|Kazakhstan|
|P058015|2005|2012|12/25/2011|14 Closed|Kazakhstan|
|P058031|1998|1998||1 Closed|Malaysia|
|P058038|2000|2000|6/29/2000|3 Closed|Rwanda|
|P058038|2000|2001|5/8/2001|5 Closed|Rwanda|
|P058038|2000|2002|5/1/2002|7 Closed|Rwanda|
|P058038|2000|2003|5/1/2003|9 Closed|Rwanda|
|P058038|2000|2004|6/14/2004|11 Closed|Rwanda|
|P058050|2000|2000|3/20/2000|1 Closed|Lesotho|
|P058050|2000|2001|3/13/2001|3 Closed|Lesotho|
|P058050|2000|2002|4/30/2002|6 Closed|Lesotho|
|P058050|2000|2003|4/3/2003|8 Closed|Lesotho|
|P058050|2000|2004|8/11/2003|9 Closed|Lesotho|
# **9. References**

1. Aschauer, D., 1989: Is Public Expenditure Productive? _Journal of Monetary Economics,_ 23, 177200.

2. Baltagi, H. and N. Pinnoi, 1995: Public Capital Stock and State Productivity Growth: Further Evidence from an Error Components Model. _Empirical Economics_ , 20, 351-59.

3. Brenneman, A., 2002: “Infrastructure & Poverty Linkages, a Literature Review.” The World Bank. Washington, D.C. Processed.

4. Briceño-Garmendia, C., A. Estache, and N. Shafik, 2004: Infrastructure Services in Developing Countries: Access, Quality, Costs and Policy Reform. _Policy Research Working Paper 3468_ . The World Bank. Washington, D.C.

5. Canning, D., 1999. The Contribution of Infrastructure to Aggregate Output. _Policy Research Working Paper 2246_ . The World Bank. Washington, D.C.

6. Cashin, P., 1995: Government Spending, Taxes, and Economic Growth. _IMF Staff Papers._ 42 (2), 237-69. International Monetary Fund, Washington, D.C.

7. Calderon, C., L. Serven, 2003a: The Output Cost of Latin America’s Infrastructure Gap. In W. Easterly and L. Serven, eds. _The Limits of Stabilization: Infrastructure, Public Deficit and Growth in Latin America_ . Palo Alto, Calif. and Washington D.C.: Stanford University Press and the World Bank.

8. Calderon, C., L. Serven, 2003b: Macroeconomic Dimensions of Infrastructure in Latin America. _Presented at the Four Annual Stanford Conference on Latin American Economic Development_ , November, 13-15.

9. Calderon, C., L. Serven, 2004a: Trends in Infrastructure in Latin America, 1980-2001. _Policy Research Working Paper 3401_ . The World Bank. Washington, D.C.

10. Calderon, C., L. Serven, 2004b: The Effects of Infrastructure Development on Growth and Income Distribution. _Policy Research Working Paper 3400_ . The World Bank. Washington, D.C.

11. Calderon, C., L. Serven, 2006: Is infrastructure Capital Productive? The World Bank, Washington, D.C. Processed.

12. Demetriades, P. and T. Mamuneas, 2007: Intertemporal Output and Employment Effects of Public Infrastructure Capital: Evidence from 12 OECD Economies. _The Economic Journal_ , 110, 687-712.

13. Escobal, J., and M. Torero. 2004: “Análisis de los Servicios de Infraestructura Rural y las Condiciones de Vida en las Zonas Rurales de Perú.” Grupo de Análisis para el Desarrollo (GRADE), Perú.

43
- 43 -

**<u>Table 6. Estimation of bidder participation decision</u>**

||Zero tru<br>negative b|ncated<br>inomial|1st stag|e of IV|Pro|bit|
|---|---|---|---|---|---|---|
||Coef.|Std. Err.|Coef.|Std. Err.|Coef.|Std. Err.|
|_bdnum_|-0.002|(0.004)|0.104|(0.058) *|||
|_bdcost_|-0.0003|(0.0001) ***|0.178|(0.121)|0.0002|(0.0001)|
|_bidpreptime_|0.025|(0.010) ***|0.321|(0.092) ***|-0.009|(0.009)|
|_securitybefore_|0.005|(0.008)|-0.022|(0.050)|-0.024|(0.007) ***|
|ln_dist_|||-0.060|(0.027) **|-0.190|(0.050) ***|
|D(_ethnicity_)|||-0.089|(0.136)|-0.222|(0.207)|
|ln_cost_|0.362|(0.112) ***|0.018|(0.078)|-0.102|(0.116)|
|ln_length_|0.052|(0.099)|0.029|(0.070)|0.161|(0.075) **|
|ln_lane_|-0.051|(0.370)|0.001|(0.158)|0.028|(0.412)|
|ln_thickness_|0.072|(0.060)|-0.014|(0.043)|0.100|(0.058) *|
|ln_gravel_|-0.010|(0.027)|-0.001|(0.012)|-0.081|(0.034) **|
|ln_bitumen_|-0.010|(0.026)|0.014|(0.015)|-0.055|(0.024) **|
|ln_earth_|0.124|(0.038) ***|0.105|(0.016) ***|0.054|(0.032) *|
|ln_brick_|-0.022|(0.028)|-0.045|(0.014) ***|-0.021|(0.037)|
|ln_gabion_|0.054|(0.024) **|0.031|(0.013) **|0.038|(0.024)|
|ln_excavate_|0.006|(0.023)|0.002|(0.010)|-0.007|(0.016)|
|ln_cement_|-0.126|(0.032) ***|-0.079|(0.011) ***|-0.071|(0.028) **|
|D(_postqualify_)|-0.752|(0.218) ***|-0.716|(0.161) ***|0.653|(0.245) ***|
|Constant|-4.501|(1.330)***|-1.468|(0.763)*|0.570|(1.661)|
|Obs.|118||591||2119||
|Wald chi2|1498.88||||436.39||
|R-squared|||0.779||||
|_F_-statistics|||305.25||||
|Pseudo R2|||||0.180||
|No. of dummy variables:|||||||
|Project districts|18||18||17||
|Bidders' home districts|0||25||28||

Note: The dependent variable is the number of bidders, _num_ , for the zero truncated negative binomial regression. For the 1st stage of the instrumental variable regression, the first four independent variables are in logarithm. In the probit model, the dependent variable is a binary variable which is set to 1 if a potential contractor participates in competitive bidding, and zero otherwise. Robust standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.

**Table 7. Implied marginal effect** **<u>on the number of bidders</u>**

||Marginal<br>effect|Std.<br>Err.||
|---|---|---|---|
|_bdnum_|-0.051|(0.088)||
|_bdcost_|-0.506|(0.184)|***|
|_bidpreptime_|0.640|(0.250)|***|
|_securitybefore_|0.053|(0.086)||
|_cost_|0.362|(0.112)|***|