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Brinkman, J. C. 2013. Congestion, Agglomeration, and the Structure of Cities. Federal Reserve Bank of Philadelphia, Working Paper No. 13‐25 Brueckner, J.K. 1979. A model of non‐central production in a monocentric city, Journal of Urban Economics, 6: 444–463.

Brueckner, J.K. 2000a. Urban growth models with durable housing: an overview. In: Huriot, J.‐M., Thisse, J.‐F. (Eds.), Economics of Cities. Cambridge University Press, Cambridge: 263–289.

Brueckner, J.K. 2000b. Urban sprawl: Diagnosis and remedies, International Regional Science Review 23: 160–171.

Brueckner, J. K. 2001. Urban sprawl: Lessons from urban economics, in: W.G. Gale, J.R. Pack (Eds.), Brookings–Wharton Papers on Urban Affairs, Brookings Institution Press, Washington, DC: 65–89.

Brueckner, J. 2007. Urban growth boundaries: an effective second‐best remedy for unpriced traffic congestion? Journal of Housing Economics, 16: 263‐273.

Brueckner, Jan K. and Stuart S. Rosenthal. 2009. Gentrification and neighborhood housing cycles: will America’s future downtowns be rich? Review of Economics and Statistics 91(4): 725–743.

Burger, M.J., F.G. Van Oort, K. Frenken, and B. van der Knaap. 2009. Networks and economic agglomerations. In Tijdschrift voor Economische en Sociale Geographie 11: 139N144.

Carlino, G. and Chatterjee, S., 2001. Aggregate Metropolitan Employment Growth and the Deconcentration of Metropolitan Employment, Journal of Monetary Economics, 48: 549‐583 Carlino, G. and Chatterjee, S., 2002. Employment Deconcentration: A New Perspective on America’s Postwar Urban Revolution, Journal of Regional Science, 42: 455‐475.

Cervero, R. 1989. America's Suburban Centers: The Land Use‐Transportation Link. Boston: Unwin Hyman Cervero, R. and K.L. Wu. 1997. Polycentrism, commuting, and residential location in the San Francisco Bay Area, Environment and Planning A 29: 865–886.

Cervero, R. and K. Wu. 1998. subcentering and commuting: Evidence from the San Francisco Bay area, 1980–1990. Urban Studies, 35: 1059–1076.

Chatterjee, S. and B. Eyigungor. 2014. A Tractable Circular City Model with Endogenous Internal Structure. Federal Reserve Bank of Philadelphia. Paper presented at Conference on Urban and Regional Economics (CURE), Brown University.

28
each simulation, the horizontal axis of Figure 5 shows the 2015 level of consumption per capita in Ghana, while the vertical axis shows the average level of achievement of the non-poverty MDGs considered in this application. The trade-off curve is concave, implying that additional investment in either HD or infrastructure services results in progressively smaller improvements in the relevant indicators. An “optimal” choice is likely to combine appropriate mix of infrastructure and human development spending that will help avoid bottlenecks and sustain balanced, dynamic growth in the long run.

**Figure 5: Infrastructure-human development trade-off in Ghana**

<!-- Start of picture text --> % of HD target achieved in 2015<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>160 165 170 175 180<br>Per capita consumption in 2015 (index, 2004=100)<br><!-- End of picture text -->

# **4. Conclusions**

This paper has relied on the MAMS model to illustrate the economy-wide effects of the pursuit of accelerated growth and MDG achievement strategies in Ghana. Given the assumptions and uncertainty surrounding the future path of some key variables (such as, for example, the availability of international aid), the results from the three scenarios considered in our analysis should be taken with a grain of salt and should not be interpreted as precise forecasts, but rather as long-term implications of alternative growth paths and policy and aid packages. Despite the inherent numeric imprecision in our estimates, several important lessons can be drawn from the analysis.

If the assumptions of the a favorable medium-term outlook hold––and this has been the case as of early 2008––Ghana is likely to faces good growth prospects and is likely to achieve the poverty MDG well ahead of schedule. Our micro-simulations show that the proportion of Ghanaians living below the poverty line could fall to 11.4 percent, an impressive achievement for a country where more than half of the population lived in poverty in 1990. In addition, given the public expenditure plans reflected in the budget, spending on infrastructure varies exogenously from 10 to 190 percent of the expenditures committed in the baseline, and MDG achievement is endogenous.

20
TABLE 45: EFFECT OF CONTRACT VALUE ON TENDER PREPARATION COSTS FOR BIDDERS

||Logari<br>|thm of tend<br>|er preparati|on cost|
|---|---|---|---|---|
|Dependent Variable|OT|M|LT|M|
||Paper|eGP|Paper|eGP|
|Model|OLS|OLS|OLS|OLS|
|L f C Vl|0.295***|0.239***|0.219***|0.159***|
|n o ontract aue|(0.03)|(0.03)|(0.03)|(0.03)|
|Constant|3.475***|4.272***|4.294***|5.256***|
||(0.44)|(0.46)|(0.50)|(0.48)|
|R<br>2|0.182|0.125|0.081|0.048|
|N|540|533|519|537|

Note 1: + p<0.10 * p<0.05 ** p<0.01 *** p<0.001 The total bidder tender preparation cost savings was estimated to be negative using both models indicating the overall cost increased due to the transition to electronic bids. The increased overall cost for about 69,000 tenders was estimated to be Tk. 51.1 crore (US$ 6.01 million – using model 1a in Table 7) when matching by procuring entity, and Tk. 60.0 crore (US$ 7.06 million - model 2a in Table 7) when matching by financial year of tender. The increased cost is obtained because the effect of higher number of bids overshadows the lower bid preparation costs.

c. Security Cost Savings: One of the primary reasons for the introduction of the electronic government procurement system was frequent coercive incidents while physically submitting the bid at the procuring entity office. This was especially true when the bid was being submitted by a non-local firm. These firms would therefore spend to ensure their safety while submitting these bids. The security cost savings is estimated by multiplying the number of bids had the tender been a manual tender with the average security cost incurred for submitting a bid, as obtained from the responses to the bidders’ survey. The average security cost for submitting a bid was Tk. 2,950 (US$ 35). The bidder security cost savings for about 69,000 tenders was estimated to be Tk. 105.9 crore (US$ 12.46 million – using model 1a in Table 7) when matching by procuring entity, and Tk. 97.2 crore (US$ 11.4 million – using model 2a in Table 7) when matching by financial year of tender.

We observe that the security costs are on average higher for firms that submit or win a larger proportion of their bids, which indicates that security expenses are higher for non-local firms.

76
# Network Proximity and Business Practices in African Manufacturing

_Marcel Fafchamps and Måns Söderbom_ .

## **JEL classification codes** : O1, D2, D4.

## **Keywords** : Networks, technology diffusion, business practices, manufacturing firms, Africa.

Marcel Fafchamps is a professor at the University of Oxford and a Co-Director of the Centre for the Study for African Economies; his email address is <u>marcel.fafchamps@economics.ox.ac.uk. Måns Söderbom (corresponding</u> author) is a professor at the University of Gothenburg; his email address is mans.soderbom@economics.gu.se.

The authors thank Matt Jackson, Yann Bramoullé, Alain de Janvry (Editor), three anonymous referees, and participants at seminars at the University of Gothenburg, Trinity College Dublin, the University of Wageningen, the Centre for the Study for African Economies conference Economic Development in Africa, Oxford, 2010, and the Nordic Conference on Development Economics, Copenhagen, 2011, for their very helpful comments. The authors also thank Magdi Amin for his encouragements and support, Gebrehiwot Ageba for his help with the geographical and social network data in Ethiopia, the World Bank for providing the data, and Rowena Chu and Michele Valsecchi for their high-quality research assistance. Måns Söderbom thanks Sida for financial support. A supplemental appendix to this article is available at http://wber.oxfordjournals.org.
# **Table of Contents**

|**1.**|**Introduction .4...............................................4**|
|---|---|
||1.1. Wheat Price Series Selection .................................................<br>5|
|**2.**|**Wheat Price Variation in Poland in Years 1990-95 .................................................**6|
||2.1. Variation over Time ................................................<br>6|
||2.2. Spatial Variation ................................................<br>7|
|**3.**|**Basis Risk Analysis ................................................**<br>**7**|
||3.1. Relationships between Polish Wheat Cash Prices and CBOT<br>and LIFFE Wheat Futures Prices .................................................<br>8|
||3.1.1. Correlation of Prices in US Dollar Terms .................................................<br>8|
||3.1.2. Correlation of the Lagged Prices in US Dollar Terms ........................................9|
||3.1.3. Cointegration Analysis ................................................<br>10|
|**4.**|Assessment of the Price Basis over Time**................................................**<br>10|
|**5.**|**Discussion of the Primary Reasons for Lack of Stable Basis .........................................**10|
||_5. 1._Currency Exchange Rate Risk .........<br>**1**..|
||5.2. Market Inefficiencies ................................................<br>11|
||5.3. Seasonality Differences .........................<br>11|
||5.4. Policies of Western Governments .........................<br>12|
||_5.5._Polish Government Intervention Policies .12|
||_5.5.1._Mechanics of ARR Policies ........................................................<br>13|
||6.5.2. Impact of ARR Policies .........................................................<br>14|
||6.5.3. ARR Policy Objectives and Methods .........................................................<br>14|
||6.5.4. Policy Lessons Learned ........................................................<br>15|
||6.5.5. Is Price Support Crucial for Agricultural Development in Poland? .................... 16|
||6.5.6. Price Support and Future EU Accession ........................................................<br>16|
|**6.**|**Conclusions and Discussion ........................................................**<br>17|
||**References**|

# **List of Exhibits**

q
# **Appendix S2. Tariffs, Data and Descriptive Statistics**

The main source is the reform dataset compiled by the Research Department of the IMF (Prati, Onorato, and Papageorgiou 2013; Giuliano, Mishra, and Spilimbergo 2013) which cover an unbalanced sample of 151 countries from 1964 to 2004. We extend the data to 2014 using tariff data from the World Integrated Trade Solution (WITS) and World Development Indicators (WDI). Tariff rates at the country-level are computed by aggregating product-level tariff data using weighted averages, with weights given by the import share of each product, measured as fractions of value. We do extensive checks of the data to ensure that they do not contain spurious jumps, by checking each jump is supported by country and policy reports.

Advanced economies comprise 28 percent of the sample, emerging markets around 44 percent and low-income economies around 28 percent.

**Table S2.1.** Descriptive Statistics for Tariffs

||**Number of**<br>**observations **|**Mean**|**Standard**<br>**Deviation **|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|**Tariffs (%)**||||||
|All|5042|11.2|13.8|0.0|161.6|
|AM|1427|5.7|7.5|0.0|99.2|
|EM|2161|12.5|11.6|0.0|91.4|
|LIC|1422|15.1|19.2|0.0|161.6|
|**Annual Tariff Changes (ppt)**||||||
|All|4702|-0.3|3.6|-52.0|41.2|
|AM|1392|-0.3|2.2|-43.6|16.1|
|EM|1960|-0.4|3.5|-24.3|27.6|
|_Source:_See Table S1.2.<br>LIC|1320|-0.2|4.9|-52.0|41.2|

_Note:_ AM = advanced economies, EM = emerging economies, LIC = low income developing countries.

5
industries in the 2000 Census, we classi...ed business sectors into male-dominated industries, identi...ed as construction, repair services, manufacturing, and shoe making and repair; femaledominated industries, identi...ed as hair and beauty care, and food and restaurant sales; and mixed industries, identi...ed as trade and retail, and sewing and tailoring. These industries cover the vast majority of the industries in which the self-employed work in Ghana. The 4.6 percent of those screened who worked in other industries such as communication services, pharmacy, photography, ...shing, and agriculture were not included in the sample.

Our aim was then to arrive at a sample of roughly 900 baseline ...rms strati...ed by gender and sector. In order to minimize the spillovers from the treatments to be carried out, we limited the sample from each EA to no more than 5 males in male-dominated and 5 males in mixed industries, and no more than 3 females in female-dominated and 3 females in mixed industries. We also ensured that only one individual was chosen from any given household. This resulted in an initial sample of 907 ...rms, consisting of 538 females and 369 males. A baseline survey of these ...rms was conducted in October and November 2008. The ...rm owners were asked for details of both their ...rm and their household.

A second pre-treatment survey of these ...rms was conducted in February 2009. The purpose of a second pre-treatment round was to eliminate ...rms most likely to attrit. In particular, 55 of the initial 907 ...rms could not be found on at least three attempts, 15 ...rm owners refused this second round, 24 ...rm owners were no longer operating a business, and 20 ...rms that did not provide details on their ...rm pro...ts, expenses and sales were eliminated. This left a ...nal sample for the experiment of 793 ...rms, comprising 479 females (248 in female-dominated industries and 231 in mixed industries) and 314 males (146 in male-dominated industries and 168 in mixed industries).

# 3.2 Experimental design

The design of the experiment closely followed that used in Sri Lanka by de Mel et al. (2008, 2009a). Firms which completed the ...rst two survey rounds were randomly allocated into three groups: a control group of 396 ...rms, a treatment group of 198 ...rms which would receive 150 Ghanaian cedis (approximately US$120 at the time of the baseline) in cash which they could use for any purpose, and a treatment group of 198 ...rms which would receive 150 cedis in equipment, materials, or inventories for their business. In the case of the in-kind treatment, the equipment or materials were selected by the ...rm owner and purchased directly by our research assistants 15
In March 1991, the 50% rate was removed, leaving only two tariff levels, 15% and 25%. The reform also advanced at the institutional level:

- a) The Prior Import License, the No Competition Ruling, the Unified Import Registry, the Andean Group Import Registry and other quantitative restrictions were formally eliminated.<sup>6</sup>

- b) The monopolies of state enterprises in food products and inputs were eliminated. c) INDECOPI<sup>7</sup> was created as the entity in charge of overseeing free competition.

- d) A total reform of Customs was launched with exceptional success. Total revenue, including sales taxes collected at Customs, rose from 23% of imports to 35.8% in 1999, and the average length of the Customs clearance process was reduced from several weeks to 24 - 48 hours.

Additionally, the foreign currency market and the assets account of the balance of payments were freed, and restrictions on foreign currency transactions and holdings were eliminated, including the requirement that exporters sell their foreign currency to the Central Reserve Bank (BCR)<sup>8</sup> at a the unified exchange market rate (MUC)<sup>9</sup> .

Business pressures channeled through Congress succeeded in abolishing the announced tariff reduction schedule, which was understood as a setback and generated doubts about the continuity of the reform. In this sense, it is evident that the so-called civil coup of 1992 was a determining factor in deepening the reform. In 1995, the official proposal for a flat tariff and the position of some business groups in favor of a tiered tariff motivated a public debate which indefinitely postponed the new tariff changes. From 1993 to June 1998 the reform became deeper through new changes in the tariff structure, with some setbacks. But in all, the average rate fell from 16.3% in June 1993 to 13.5% in March 1998.

This ambitious reform sets a milestone in the trade history of the country. Neither the extent of liberalization nor the speed of the change has antecedents in the history of the country.

# **II. Antidumping and Safeguards**

# **A.  Institutional Reform**

A great part of the reform was materialized through new laws that regulate aspects of market behavior, such as Monopoly,<sup>10</sup> , Foreign Investment<sup>11</sup> , Consumer Protection<sup>12</sup> , Unfair Competition<sup>13</sup> , Competitiveness of Economic Agents<sup>14</sup> , Micro and Small

> 6 A relation of these restrictions is presented in Table 2.10, Op.Cit., p. 56.

> 7 National Institute for Competition and Intellectual Property Defense

> 8 Central Reserve Bank.

> 9 Single Exchange Market, official exchange rate.

> 10 Legislative Decree 701- Oct 1991

> 11 Legislative Decree 662 - Aug 1991

> 12 Legislative Decree 716 - Oct 1991

> 13 Law 26122

> 14 Law 28032

6
who draw income from farm sources as well was 69.5 percent 2000, 81.5 percent in 20005, and 82.0 percent in 2010. In this section, we estimate the net contribution of microenterprise in income generation and poverty reduction using the three rounds of HIES survey data. Table 7 presents the relationships between microenterprise participation and household welfare (measured in terms of per capita income and expenditure as well as moderate and extreme poverty) over time.<sup>6</sup> As Table 7 shows, the percentage of households engaged in microenterprise activities besides farm and other activities has declined from 25.6 percent in 2000 to 21.6 percent in 2010. Yet we find consistently higher growth in per capita income and expenditure among the households engaged in microenterprises, besides farming and other nonfarm activities. For example, in 2010, households with microenterprises as additional sources of income had 36 percent higher per capita income and 12 percent higher per capita expenditure than those without a microenterprise activity as an additional source of income and their differences are statistically significant at more than the 5 percent level.

We also find that both the moderate and the extreme poverty rates are substantially and significantly lower for households with microenterprises than those for households without a microenterprise activity. For example, as Table 7 suggests, in 2010 moderate poverty is 26.5 percent for households with microenterprises compared to 35.1 percent for those who do not have a microenterprise activity. Extreme poverty follows the same trend.

This leads us to ask whether participation in a microenterprise activity indeed causes income and expenditure to rise and poverty to fall. The relationships reported in Table 7 are not causal in the sense that it is not clear whether income or expenditure have increased because of

> 6 The calculation of moderate poverty is based on the official poverty line, which includes the food poverty line and an allowance for nonfood expenditures. The food poverty line is calculated by estimating the cost of a food basket needed to maintain the per capita daily caloric requirement (2,120 calories) recommended by Food and Agricultural Organization (FAO) and the World Health Organization (WHO) (FAO/WHO 1973). For Bangladesh, the food basket contains mostly rice, as well as other food items including pulse, milk, meat, fish, fruits, and vegetables in specific quantities. The cost of the food basket is calculated according to local prices. Extreme poverty, in contrast, is defined by the household’s total consumption expenditure on food and nonfood falling short of the food poverty line.

11
economic institutions (which also vary in design). Moreover, a continuous process of what might be termed “shocks” for want of a better word, such as changes in external trade, natural events, technology, or health, may provide windows of opportunity for changing certain institutions (for example economic institutions) before they have an impact on other types of institutions.

So we ask to what extent aid can affect economic institutions. This is an important question since donors attempt to raise growth in recipient countries not just by providing capital for investment but by advising on policies and institutions designed to raise the productivity of investment. We find no empirical evidence to suggest that aid has a robust and significant impact on institutional quality using several measures of institutions. The only exception to this was aid’s impact on regulation as defined by one indicator from the Fraser Institute (aid is seen to favor more regulation).

These results suggest that in general aid cannot be said to have affected institutional quality. One caveat is that the data could be refined much further. For example, the influence of aid targeted to a particular sector could be assessed with measures of institutional quality in that sector. For this, more work on clearing up the aid data is needed and better estimates of institutional quality would help. We tried to assess the impact of aid in a particular sector, the financial sector, but found no effect.

34
# O Mode 3: exports

O Mode 3: domestic sales <!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text -->