rafmacalaba/datause-encoder-data huggingface dataset:
WPS3720

# **International Cooperation and the Reform of Public Procurement Policies**<sup>**1**</sup>

## Simon J. Evenett<sup>2</sup>

University of St. Gallen and CEPR Bernard M. Hoekman<sup>3</sup> World Bank and CEPR

**Abstract** :  The decision not to launch negotiations in the WTO on three of the Singapore Issues in the socalled July 2004 package provides an opportunity to revisit the knowledge base upon which proposals for further international collective action may be drawn. This paper examines the available evidence on public procurement practices in developing countries that could be relevant to further multilateral rule making on state purchasing. Although there is considerable agreement on _ends_ (efficient, non-corrupt, and transparent public purchasing systems), little information is available on _means_ and, in particular, on the _effective_ and _replicable_ strategies that developing countries can adopt to improve their public procurement systems. A concerted effort to substantially add to the knowledge base on public procurement reforms in developing countries, through targeted research and international exchange of information on implemented procurement policies and outcomes, is critical to identifying areas where further binding multilateral disciplines may be beneficial.

### JEL classification: F13 and H57.

Keywords: public procurement, state purchasing, discrimination, WTO, Doha Round.

## World Bank Policy Research Working Paper 3720, September 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Some of the material in this paper draws on a report that Evenett prepared for the World Bank on the available empirical knowledge about public procurement reform in developing countries. That report benefited from the comments of Robert Hunja (World Bank) and his team. Evenett would like to acknowledge his gratitude for these constructive comments and suggestions.

> 2 Email: simon.evenett@unisg.ch.

> 3 Email: bhoekman@worldbank.org. This paper was written while visiting the Groupe d’Economie Mondiale, Institut d’Etudes Politiques, Paris.
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# Introduction.

According to WHO, in Egypt chronic diseases accounted for 78% of all deaths in 2002, well above communicable diseases (18%) and injuries (4%) (WHO, 2005). As a result of the epidemiological transition, the burden communicable diseases has declined, at the expense of an increasing burden of chronic. The transition was helped by massive vaccination campaigns implemented in the past several years: child immunization coverage increased from 86% in 1990 to 92% in 2007, and the under-five-mortality rate was reduced dramatically from 104 to 36.2 in the last two decades<sup>1</sup> . While life expectancy in Egypt has reached 70 years in 2007<sup>2</sup> , the incidence of chronic diseases hinders large proportion of Egyptians to enjoy a long, healthy and prosperous life. Nonetheless, although age and genetic endowment contribute directly to the emergence of chronic diseases, risk factors such as smoking, mal-nutrition, lack of physical exercise play a major role.

In addition to their burden of pain and suffering, chronic diseases bring about important economic consequences. On one hand, they increase the demand of health care and, on the other hand, they may hamper people’s ability to generate income, especially by increasing absenteeism at work or by ultimately impeding people to work.

In this paper we focus on the latter potential consequence. If chronic diseases reduce employment and labor supply, this would cause an efficiency loss to the economy as a whole, as the endowment of labor is not fully used and the number of “efficiency units” per worker remains below its potential. Therefore chronic conditions could contribute to keeping the country’s economy below its production frontier. Whether or not this is the case should be of interest to economic policy-makers, whose objective it might be to maximizing the economy’s production.

The primary purpose of this paper is to investigate whether such efficiency losses do in fact occur in Egypt. More specifically, we measure the causal impact of chronic diseases on the probability of being employed, on the number of working hours supplied per week and on the hourly wage earned in Egypt, among people in their working age 16-64. Although many papers have looked at the impacts of health on the labor market (for recent surveys, see Currie and Madrian, 1999; Rocco and Suhrcke, 2008), few have specifically focused on chronic diseases (Gammon, 2005; Wilson, 2001; Loprest et al. 1995; Suhrcke et al., 2007) and to the best of our knowledge no study has looked at middle income countries in MENA. The reason for focusing on Egypt is that it is one of the largest middle income countries, with a population of more than 70 million and, in many respects, it is representative of the Arab world, a particularly distinguished region around the

> 1 The World Bank, WDI 2010 data:  % of children under 1 year immunized against measles; and under 5 mortality rate per 1,000.

2 The World Bank, WDI 2010 data: Life expectancy increased from 55 years in 1978 to 70 in 2008. 2
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Finally, the Engel curve deflator also biases assessment of progress in reducing poverty, in this case showing much faster progress between 2010 and 2012 than is likely (table 4). Recall that the Engel curve implied lower inflation (and even deflation) for most regions and sectors than what the WCPD indexes show (figure 3). In fact, only the rural Southeast and the urban Central Highlands—containing just 8% of Vietnam’s people—had Engel curve inflation higher than WCPD inflation. Consequently, much of the growth in nominal consumption between 2010 and 2012 is treated as real growth, and so the fall in poverty seems faster than it actually was. For example, the headcount poverty rate appears to fall by eleven percentage points between 2010 and 2012, compared with a seven percentage point decline when either WCPD index is used. For the other poverty measures shown in table 4, the change in poverty using the Engel curve deflated consumption is twice as large as the change using the other two deflators. These other poverty measures include the average exit time measure of Morduch (1998), which shows the expected number of years to escape poverty with constant and uniform growth (assumed 3% per annum here).<sup>18</sup> Use of the Engel curve deflator would lead one to find a three-year fall between 2010 and 2012 in the average time expected to escape poverty, and such progress may induce a false sense of achievement for Vietnam’s policy makers when compared with the actual record (based on either WCPD deflator) of just over a one-year reduction in expected poverty exit time.

> 18. The exit time measure has the same properties as the poverty severity index (sensitivity to distribution amongst the poor) but allows an intuitive interpretation. It is calculated as: _Tg_  1 _N_  _qj_  1 ln ( _z_ )  ln ( _y j_ ) _g_ , where constant and uniform growth rate _g_ would see person _j_ below the poverty line take _tg_ years to reach the poverty line (the expected value, _Tg_ , includes an exit time of zero for the nonpoor). For the average poor person, it takes _Tg H_<sup>years to escape</sup> poverty.

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Since the sum of the sector-level GHGE is lower than the national-level GHGE (both reported by CAIT), this paper assigns the difference to sectors without GHGE data in CAIT (e.g. financial intermediation) following these steps: (i) calculate the participation of these sectors in the national production; (ii) calculate a residue as the total country-level emissions (excluding land-use change, as explained above) minus the emissions for which the source is known (emissions from CAIT sectors); and (iii) assign each sector a level of emissions corresponding to their participation in national production times the country’s residue of total emissions. We then use the sector-level GHGE from CAIT and the imputed GHGE for non-CAIT sector, as well as the IOT, to calculate the total emissions by sector (CAIT and non-CAIT).

**Table 5 – Sector classification based on CAIT, OECD, and SEDLAC classifications**

|**Sector**|**Included sectors**<br>**CAIT**|**Included sectors OECD**|**Included sectors SEDLAC**|
|---|---|---|---|
|Agriculture,<br>hunting,<br>forestry, and fishing|Agriculture, forestry,<br>and fishing|Agriculture, forestry, and fishing|Agriculture, hunting and<br>forestry<br>Fishing|
|Mining and quarrying|Energy<br>–<br>Fugitive<br>Emissions|Mining and extraction of energy producing<br>products<br>Mining and quarrying of non-energy<br>producing products<br>Miningsupport service activities|Mining and quarrying|
|Manufacturing,<br>Construction<br>&<br>Industrial processes|Energy<br>–<br>Manufacturing<br>&<br>Construction<br>Industrial Processes|Food products, beverages, and tobacco<br>Textiles, wearing apparel, leather, and<br>related products<br>Wood and of products of wood and cork<br>(except furniture)<br>Paper products and printing<br>Coke and refined petroleum products<br>Chemicals and pharmaceutical products<br>Rubber and plastics products<br>Other non-metallic mineral products<br>Manufacture of basic metals<br>Fabricated<br>metal<br>products,<br>except<br>machinery and equipment<br>Computer, electronic and optical products<br>Electrical equipment<br>Machinery and equipment n.e.c.<br>Motor vehicles, trailers, and semi-trailers<br>Other transport equipment<br>Other<br>manufacturing;<br>repair<br>and<br>installation of machinery and equipment<br>Construction|Manufacturing<br>Construction|
|Electricity,<br>gas,<br>and<br>water supply|Energy – Electricity &<br>Heat<br>Waste|Electricity, gas, water supply, sewerage,<br>waste, and remediation services|Electricity, gas, and water<br>supply|
|Wholesale and retail<br>trade|-|Wholesale and retail trade; repair of motor<br>vehicles|Wholesale and retail trade|
|Transport, storage, and<br>communications|Energy – Transport|Transportation and storage|Transport, storage, and<br>communications|

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powerlooms, want the state government to promote the powerloom sector, because in southern India there are not enough powerlooms and spinning mills have to sell more than half their spun yarn to powerlooms in the western region. Interestingly, some powerlooms have formed a political alliance to pursue their common interests with southern spinning mills. For example, in order to alter the 1985 Textile Policy, which was against the interest of powerlooms, the western powerlooms led by the All-India Powerloom Federation and the SIMA organized by the southern mills lobbied jointly in order to make the central government alter the policy, although it was not very successful.

# **_Handloom Sector_**

The handloom sector is one of the poorest groups in India. Product-wise, the Indian handlooms are basically categorized into two groups - those that produce high value-added luxury fabric with unique traditional designs, and those that weave low value-added standardized cotton fabric. The former type, which accounts for roughly a quarter of all the handloom units, has their niche market where neither mills nor powerlooms can compete. Some of them tend to be operated using very primitive technology. Conversely, the latter type - handlooms that produce low-quality plain cloth - can barely survive with low wages and state subsidies.

Scattered in the rural areas all over the country, handloom weavers are the least well-organized group in the Indian cotton sector. Although handloom weavers tend to be hit harder than mills and powerlooms when cotton prices go up, most of them are politically silent. There are of course some organizations such as the Handloom and Handicrafts Association, but like the powerloom organizations, they are far less efficient in terms of lobbying than the ICMF. Olson's proposition also holds here.

However, despite their disorganization and lack of money for political contribution, handlooms can have an influence on policymakers when the cotton prices go up. This is partly because of the number of their votes, and partly because of the nationwide sympathy for them. Although the number of handloom weavers is roughly the same as powerloom weavers, the votes of handloom weavers are easier to control than workers in mills and powerloom, because many handloom weavers belong to the low caste in rural areas, where higher caste community leaders are often very influential. The nationwide psychological sympathy for poor handlooms is another important factor in explaining the pro-handloom policy in India. Often the handloom sector has been used as a political tool for politicians to attract popularity, votes and raise money. There are many politicians who own handlooms and head handloom co-operative societies in India.

6 The analysis in this section is based on Leadbeater (1993). 14
ln( _Y_ 05 _i_ )<sup>is 0.216, with a White standard error of 0.013 (n=144);18 Figure 3 plots the data from the</sup> 2005 ICP. For the 1993 round, the regression coefficient is 0.293 (s.e.=0.012; n=134), while for the 1985 round it is 0.275 (s.e.=0.024; n=56). The attenuation of the Penn effect between 1993 and 2005 is consistent with the fact that the proportionate increases tended to be larger in initially poorer countries, as can be seen in Figure 4.<sup>19</sup> Turning to the changes over time, let us begin by testing the homogeneity restriction that   1 . The estimate of the unrestricted model in equation (3) is (with White standard errors in parentheses):<sup>20</sup> The restriction clearly performs well. Given that this is a regression for changes rather than levels, it is also notable that almost 99% of the variance is accounted for.

On imposing   1 , the regression coefficient of ln( _P_ 05 _i_ /<sup>_P_</sup> 93 _i_ ) on ln( _Y_ 05 _i_ /<sup>_Y_</sup> 93 _i_ ) is <sup>ˆ</sup>  ˆ 0.283 (s.e.=0.054; n=132) with    .0019 (s.e.=0.043) and _R_<sup>2</sup> =0.212. This is close to the crosssectional estimate of the Penn effect, suggesting that latent country characteristics are not an important source of bias in past tests for the Penn effect using cross-sectional data.

Figure 5 plots the relationship between changes in price levels and growth rates. The price level does not start to rise until there is sufficient growth. The expected change in ln( _P_ 05 _i_ /<sup>_P_</sup> 93 _i_ ) is zero when ln( _Y_ 05 _i_ /<sup>_Y_</sup> 93 _i_ )  .0066, although the latter number is not significantly different from zero (s.e.=0.140). Thus one cannot reject the null hypothesis that the price level index conditional on GDP per capita is unchanged between the 1993 and 2005 ICP rounds.

Recall that this sample includes both developed and developing countries. The data suggest that the DPE is stronger in poorer countries, as can be seen from the following regression indicating a significant negative interaction effect: ln( _P_ 05 _i_ / _P_ 93 _i_ )  .0008  (.0604  .0049 ln _Y_ 93 _i_ ) ln( _Y_ 05 _i_ / _Y_ 93 _i_ )   ˆ _i R_ 2  .0246; _n_  132 (5) (.0046) (.0143) (.0020)

> 18 Using GDP at PPP instead, the regression coefficient of ln( _P_ 05 _i_ ) on ln( _Y_ 05 _PPPi_ )

> <sup>is 0.237</sup>

> (s.e.=0.020), while for the 1993 ICP it is 0.343 (s.e.=0.027). Note that White standard errors are used when relevant in this paper.

> 19 The slope of the regression line is -0.059 with a standard error of 0.014.

> 20 The regression was very similar for the non-OECD sample.

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complementary improvements in other aspects of the business environment. Finally, note that the results using two year lagged values of the EBRD index are qualitatively similar to those shown in Table 1.

**_Table 1: Effects of EBRD Index on Services Sub-Sector Performance_**

Panel A. Dependent Variable is Growth in WIIW Labor Productivity

||OLS|Fixed Effects|
|---|---|---|
||(1)|(2)|
|One Year Lagged EBRD Index|0.034<br>[0.084]|0.018<br>[0.066]|
|Year Dummies|Yes|Yes|
|Country Dummies|Yes||
|Sub-Sector Dummies|Yes||
|Year Dummies*Sub-Sector Dummies|Yes||
|N. Observations|149|149|
|Panel B. Dependent Variable is Growth in KL|EMS Labor Productivity||
||OLS|Fixed Effects|
||(1)|(2)|
|One Year Lagged EBRD Index|0.056<br>[0.022]**|0.041<br>[0.035]|
|Year Dummies|Yes|Yes|
|Country Dummies|Yes||
|Sub-Sector Dummies|Yes||
|Year Dummies*Sub-Sector Dummies|Yes||
|N. Observations|329|329|

Notes: Robust standard errors in parenthesis. ** indicates significance at the 5% confidence level.

Overall, the messages regarding the link between performance, ICT, skills, and liberalization in the services sector in ECA countries are as follows: (i) labor productivity levels are higher for ICT producers or ICT users and for high skill intensive sub-sectors during 1997-2004, and (ii) productivity growth rates are higher for ICT users and for high skill intensive sub-sectors in most EU-8 countries after 2000. These findings suggest a progressive penetration and efficient use of ICT in the services sectors of EU-8 countries during 1997-2004. However, the penetration and efficient use of ICT in services are still in progress. ICT can potentially translate into further productivity gains and be a crucial factor in reducing the large services productivity gap between ECA countries and EU-15 countries, especially if combined with the continuation of structural reforms, namely further liberalization of the services sector and its opening to FDI. The development of efficient, low-cost, and broadly diffused telecommunication networks should therefore rank high in the policy-makers’ growth agenda across ECA countries. Such development can be fostered by improvements in competition and continued liberalization of the 33
The output gap is measured as the difference between seasonally adjusted real GDP and its trend, obtained via the HP filter (as in Patra and Kapur 2012) and expressed as a percentage of seasonally adjusted real GDP.<sup>27</sup> Summary statistics and correlations for these variables are presented in the Appendix Table A4.<sup>28</sup> The policy rate is positively and significantly correlated with the output gap and exchange rate depreciation. It is also positively correlated with inflation, although the correlation is weaker and significant only at 10 percent level.

We estimate the following baseline specification of the reaction function using OLS: epr 0 1gap 2inflation tt tt tt tt where, epr is the effective policy rate; gap= αα + αα denotes the output gap expressed as a percentage of + αα + εε GDP; and inflation denotes the inflation measure as targeted by the RBI. We assess if the policy rate is tt tt different after controlling for output gap and inflation once the country moved to inflation targeting by tt including a dummy for the period since inflation targeting in the regression.

In all variants in Table 7 the output gap has a positive and significant coefficient, as anticipated. Inflation also has a positive coefficient.<sup>29</sup> When we add the lagged policy rate as an explanatory variable (the Woodford 2001 version of the Taylor Rule), its coefficient indicates significant inertia. The output gap and inflation remain positive as before.

**Table 7: Monetary policy reaction functions**

**<u>(Dependent variable is effective policy rate)</u>**

||**I**|**II**|**III**|**IV**|
|---|---|---|---|---|
|Inflation|0.11|0.06|0.08<sup>***</sup>|0.08<sup>***</sup>|
||(1.25)|(0.62)|(3.23)|(2.86)|
|Output gap (% of|0.36<sup>***</sup>|0.40<sup>***</sup>|0.20<sup>***</sup>|0.21<sup>***</sup>|
|GDP)|(4.43)|(4.96)|(2.65)|(2.64)|
|IT||-0.82<sup>***</sup>||-0.07|
|||(2.98)||(0.75)|
|Lagged Effective|||0.85<sup>***</sup>|0.85<sup>***</sup>|
|Policy rate|||(18.00)|(17.85)|
|Constant|6.17<sup>***</sup>|6.56<sup>***</sup>|0.48<sup>*</sup>|0.53<sup>*</sup>|
||(13.27)|(11.29)|(1.73)|(1.80)|
|Observations|91|91|90|90|
|Adjusted _R_<sup>2</sup>|0.11|0.13|0.89|0.89|

Note: Robust t statistics in parentheses. *, **, ***, indicate significance at 10, 5 and 1 percent respectively.

> 27 The quarterly real GDP series is seasonally adjusted using the X-11 algorithm (of the US Department of Commerce).

> 28 The policy rate is most persistent (or inertial), followed by inflation. The output gap and exchange rate are least persistent, as per their AR (1) coefficients.

> 29 It is significantly less than the standard Taylor rule benchmark of 1.5. The coefficients on the output gap and inflation are similar to those in other papers estimating reaction function for India, including Hutchison et al 2013 Patra et al 2012, RBI Study 2010 (Singh), and Mohanty and Klau 2004. Bhoi et al (2019) estimate a reaction function for 2000-2018, using weighted average call money rate as the policy rate, and similarly find the coefficient on the output gap to be larger post inflation targeting.

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