Input:
# WPS3784

**Table 1. Poverty Incidence and Poverty Gap in Rwanda**

**Headcount Index (%)** **Poverty Gap**
**P0** **P1**
National 42.92 0.16 Urban 4.09 0.01 Rural 46.06 0.17 Note:

1. The headcount index, P0, is the proportion of population with per capita expenditure below the poverty line
The poverty-gap, P1, estimates the average distance separating the poor from the poverty line, as a proportion of that line 2. Poverty line: 50,145.73 RwF = Income per capita of the last individual in the fourth decile of expenditure ranked by adult equivalent 3. All farm households in urban provinces are considered as "rural"

**Table 2. Determinants of the Probability of being non-poor in Rwanda**

**Standard**

**Wald 95% Confidence**

**Parameter** **Estimate**

**Parameter** **Estimate** **Error** **Limits** **Chi-Square Pr > Chi-Sq**

Intercept -0.3879 0.0178 -0.4229 -0.353 472.47 <.0001 Dummy Access to Road 0.1077 0.0059 0.0962 0.1192 336.26 <.0001 Distance to market -0.018 0.0003 -0.0187 -0.0173 2673.26 <.0001 Electricity dummy 0.2431 0.0075 0.2285 0.2577 1064.67 <.0001 Access to credit dummy 0.0297 0.0035 0.0229 0.0366 72.83 <.0001 H Head's education (years) 0.0885 0.0009 0.0866 0.0903 9077.28 <.0001 Dependents ratio -0.2029 0.0007 -0.2043 -0.2014 74556.8 <.0001 Land area detained 0.1733 0.0017 0.1701 0.1766 10893.6 <.0001 Livestock heads detained 0.1304 0.0006 0.1291 0.1317 41019.7 <.0001 Occupation: Farmers only 0.0663 0.0066 0.0534 0.0793 100.31 <.0001 Occupation: Agri wage worker -0.0851 0.0099 -0.1046 -0.0657 73.77 <.0001 Occupation: Non-farm wage 0.9515 0.0097 0.9325 0.9705 9678.01 <.0001 worker Occupation: Non-farm self 0.9175 0.0122 0.8936 0.9415 5641.28 <.0001 employed Dummy Province: Butare -0.413 0.0088 -0.4302 -0.3958 2219.65 <.0001 Dummy Province: Byumba -0.1436 0.0089 -0.1611 -0.1261 258.35 <.0001 Dummy Province: Cyangugu -0.0079 0.0093 -0.0262 0.0104 0.72 0.3973 Dummy Province: Gikongoro -0.6147 0.0095 -0.6334 -0.5961 4172.71 <.0001 Dummy Province: Gisenyi 0.3932 0.0092 0.3752 0.4112 1835.89 <.0001 Dummy Province: Gitarama 0.087 0.0087 0.0699 0.1041 99.07 <.0001 Dummy Province: Kibungo 0.2772 0.009 0.2596 0.2949 944.15 <.0001 Dummy Province: Kibuye -0.2396 0.0098 -0.2589 -0.2203 593.72 <.0001 Dummy Province: Kigali -0.3153 0.0086 -0.3321 -0.2984 1342.69 <.0001 Dummy Province: Ruhengeri -0.2806 0.0087 -0.2977 -0.2635 1034.2 <.0001

**Note:**
Excluded categories: “No occupation” and “other occupation”; “Kigali City” and “Umutara” 20

**Error**


Input:
# WPS3784

**Table 3. Source of Household Income**
Income Category Rural Urban National Labor Market Income 42.5% 83.6% 58.6% Agriculture/Livestock/Sylvic/Fishing/Hunting 7.9% 0.8% 5.1% Manufacturing Industries 3.3% 6.0% 4.4% Buildings and Public Works 3.1% 5.6% 4.1% Commerce 7.3% 20.3% 12.4% Services a la Collectivite 16.0% 31.3% 22.0% Other Sectors 5.0% 19.6% 10.7% Agricultural Income 41.6% 0.7% 25.5% Industrial Crops: 2.9% 0.0% 1.8% Coffee 2.4% 0.0% 1.5% Tea 0.4% 0.0% 0.3% Food Crops: 33.2% 0.4% 20.3% Beans 3.6% 0.0% 2.2% Sorghum 2.2% 0.0% 1.3% Cassava 3.7% 0.1% 2.3% Sweet Potatoes 2.6% 0.0% 1.6% Livestock Income: 5.5% 0.3% 3.4% Cattle 3.5% 0.3% 2.2% Sheep 1.2% 0.0% 0.7% Transfers 8.9% 3.6% 6.8% Other Income 7.0% 12.1% 9.0% Total Income 100.0% 100.0% 100.0% Note: All farm households in urban provinces are considered rural

**Table 4. Percentage of Farmers Growing Coffee, by Income Decile**

**Expenditure decile** **% of farmers growing coffee**
Poorest decile 8.4 2nd decile 9.7 3rd decile 10.3 4th decile 13.5 5th decile 12.0 6th decile 11.8 7th decile 13.6 8th decile 10.4 9th decile 8.7 Richest decile 1.5 Total 10.0 21


Input:
# WPS3784

**Table 5. Share of Income from Coffee, by Province**
**Province** **Share of Coffee in Total Income (%)**
Butare 3.7 Byumba 2.5 Cyangugu 13.9 Gikongoro 5.5 Gisenyi 6.0 Gitarama 6.7 Kibungo 2.6 Kibuye 9.0 Kigali Ngali 9.2 Kigali Town 1.2 Ruhengeri 2.4 Umutara 0.0 Source: Rwanda EICV 2001

**Table 6. Impact of a 50 Percent Reduction of Rural Transport Costs on Incomes**
Effect on: Reduction transport pass- Headcount Change Poverty Change cost through index Gap coffee farmers 0 0 41.97 0.136 coffee farmers 50% 1 39.60 -5.65% 0.126 -7.12% coffee farmers 50% 0.5 41.31 -1.57% 0.131 -3.72% National 0 0 42.92 0.157 National 50% 1 42.69 -0.53% 0.156 -0.59% National 50% 0.5 42.86 -0.15% 0.157 -0.31% Source: Computed by authors from EICV data 22

EICV data


Input:
# WPS3784

**Table 7. Distributional and Regional Impact of a 50 percent Fall in Transport Costs**
**among Coffee Farmers (assuming 100 pass-through)**
Share of coffee in total income Change in real income
**Expenditure decile**
Poorest decile 6.70% 1.34% 2nd 8.23% 1.65% 3rd 7.16% 1.43% 4th 14.42% 2.88% 5th 4.33% 0.87% 6th 5.06% 1.01% 7th 5.04% 1.01% 8th 3.09% 0.62% 9th 4.16% 0.83% Richest 1.79% 0.36%
**Province**
Butare 3.72% 0.74% Byumba 2.51% 0.50% Cyangugu 13.89% 2.78% Gikongoro 5.50% 1.10% Gisenyi 6.02% 1.20% Gitarama 2.68% 0.54% Kibungo 2.59% 0.52% Kibuye 9.03% 1.81% Kigali Ngali 9.22% 1.84% Ville de Kigali 1.15% 0.23% Ruhengeri 2.42% 0.48% Umutara 0.00% 0.00% 23


Input:
# WPS3784

**Table 8. Composition of household budget by Province**
**Province** **Butare** **Byumba** **Cyangugu** **Gikongoro** **Gisenyi** **Gitarama** **Kibungo**
**Food Bought** 42.2% 48.7% 54.4% 45.8% 49.5% 42.8% 39.8%
**Dry beans** 5.4% 8.5% 3.4% 10.6% 7.2% 4.4% 2.5%
**Irish Potatoes** 2.4% 4.2% 1.9% 4.8% 7.4% 4.6% 1.6%
**Rice** 3.8% 2.0% 4.1% 1.5% 1.7% 2.7% 2.9%
**Beef** 1.4% 1.7% 2.6% 0.8% 2.0% 2.1% 1.7%
**Beverages** 3.4% 5.5% 3.4% 4.0% 5.8% 2.5% 5.1%
**Energy** 2.7% 4.7% 3.7% 2.2% 3.1% 2.6% 3.3%
**Housing** 4.6% 0.9% 1.3% 2.1% 2.3% 0.5% 1.9%
**Cleaning** 6.7% 6.0% 6.7% 5.8% 6.8% 6.4% 7.5%
**Leisure** 1.2% 0.9% 1.9% 1.1% 1.8% 1.1% 0.8%
**Clothing** 6.8% 6.1% 4.8% 6.8% 7.2% 7.3% 10.4%
**Education** 2.7% 3.0% 4.0% 4.4% 2.4% 4.7% 3.9%
**Health** 1.4% 0.9% 1.5% 0.6% 0.9% 1.4% 1.2%
**Transport** 3.0% 1.8% 1.1% 0.6% 2.2% 1.3% 2.8%
**Other** 25.2% 21.4% 17.3% 26.6% 17.9% 29.5% 23.3%

**Total Consumption** 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

**Province** **Kibuye** **Kigali** **Kigali City** **Ruhengeri** **Umutara National**
**Food Bought** 53.7% 46.0% 33.9% 46.8% 38.7% 41.8%
**Dry beans** 12.9% 6.8% 1.7% 7.2% 4.2% 4.6%
**Irish Potatoes** 6.5% 5.3% 2.5% 7.5% 1.9% 3.8%
**Rice** 1.4% 2.3% 3.0% 1.4% 2.6% 2.7%
**Beef** 1.0% 1.2% 2.8% 1.2% 1.1% 2.0%
**Beverages** 4.6% 3.6% 2.9% 3.3% 4.7% 3.7%
**Energy** 2.8% 2.9% 3.6% 2.3% 4.0% 3.3%
**Housing** 2.2% 3.6% 9.0% 1.4% 0.8% 4.5%
**Cleaning** 5.9% 6.4% 6.6% 4.7% 11.9% 6.6%
**Leisure** 1.2% 1.0% 1.7% 0.7% 1.2% 1.4%
**Clothing** 4.4% 5.3% 3.3% 5.9% 5.9% 5.4%
**Education** 2.9% 5.9% 3.7% 3.3% 4.3% 3.8%
**Health** 3.0% 1.7% 1.3% 1.0% 2.1% 1.3%
**Transport** 1.0% 3.0% 5.1% 1.7% 2.2% 3.1%
**Other** 18.3% 20.5% 28.9% 28.9% 24.0% 25.2%

**Total Consumption** 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

24


Input:
# WPS3784

**Table 9. Impact of Quality-Driven Price Premium**

Simulation: doubling Headcount % change Poverty Change in % change in % change coffee price: P0 in P0 gap (P1) P1 expenditure in income _**baseline coffee farmers**_ _**41.88**_ _**0.00%**_ _**0.135**_ _**0.00%**_ _**0.00%**_ _**0.00%**_ 50% of all coffee farmers 39.86 -4.82% 0.124 -8.25% 2.49% 4.85% all large coffee farmers 41.15 -1.74% 0.131 -2.93% 1.34% 3.15% all medium coffee farmers 40.77 -2.65% 0.132 -2.79% 0.87% 1.92% all medium and large 40.05 -4.37% 0.128 -5.72% 2.21% 5.07% coffee farmers small coffee farmers 39.23 -6.33% 0.120 -11.08% 2.93% 4.85% Source: Computed by authors from EICV data

**Table 10. Determinants of the Probability of Being a Coffee Farmer (propensity score)**

**Parameter** **DF Estimate** **Standard**

**Error**

**Wald 95% Confidence**

**Limits**

**Chi-**
**Square**

**Pr > ChiSq**

Intercept 1 -3.1209 0.0318 -3.1832 -3.0586 9651.42 <.0001 Distance to road 1 -0.0353 0.0028 -0.0407 -0.0298 158.7 <.0001 Access to electricity 1 1.0688 0.0372 0.9959 1.1418 824.84 <.0001 Extension services 1 0.1843 0.0049 0.1747 0.1938 1439.03 <.0001 dummy Head's Education 1 0.023 0.0014 0.0202 0.0258 257.34 <.0001 Dependency ratio 1 0.0441 0.0011 0.042 0.0462 1687.25 <.0001 Land area 1 0.0345 0.0012 0.0321 0.037 763.17 <.0001 Livestock heads 1 0.0058 0.0004 0.0051 0.0066 234.77 <.0001 Head is male dummy 1 0.1184 0.0058 0.1069 0.1298 411.64 <.0001 Use fertilizer dummy 1 0.1508 0.0048 0.1414 0.1602 984.59 <.0001 Dummy Butare 1 1.7102 0.0247 1.6617 1.7587 4779.86 <.0001 Dummy Byumba 1 1.1653 0.0251 1.1162 1.2145 2158.49 <.0001 Dummy Cyangugu 1 2.5895 0.0252 2.5401 2.639 10529.5 <.0001 Dummy Gikongoro 1 1.4111 0.0253 1.3615 1.4606 3114.6 <.0001 Dummy Gisenyi 1 1.3244 0.0253 1.2748 1.3739 2740.1 <.0001 Dummy Gitarama 1 1.5732 0.0246 1.5249 1.6214 4078.33 <.0001 Dummy Kibungo 1 1.2965 0.0249 1.2477 1.3453 2712.76 <.0001 Dummy Kibuye 1 1.3999 0.0255 1.35 1.4498 3023.54 <.0001 Dummy Kigali 1 1.0657 0.0255 1.0158 1.1156 1752.61 <.0001 Dummy Ruhengeri 1 0.2822 0.0273 0.2288 0.3357 107.17 <.0001 25

EICV data


Input:
# WPS3784

**Table 11. Income Gains from Switching from Subsistence to Coffee Farming**

Matching Matching method: method:
**Sample: all household heads** Epanechnikov Normal kernel

Observed average agricultural income coffee farmer 99876.3 99876.3 Observed average agricultural income subsistence farmer 74072.8 74072.8 Subsistence farmers' agricultural income had they participated 91256.2 96267.1 Gain from participation in coffee farming 17183.4 22194.4 _Proportionate change in subsistence farmers' income_ 23.2% 30.0% _Impact on the headcount level_ -24.9% -27% _Impact on the poverty gap_ -12.5% -12.9% _Total sample (household heads)_ ~~_1_~~ 2753 _Number of people that switch and gain_ 1073 _Percentage of people that switch and gain_ 39% _Note:Missing values are excluded, so the sample size reported here is smaller than the actual_ _number of head farmers_ _* = Statistically significant as established through bootstrapping._ 26

Epanechnikov Normal kernel


Input:
POLICY RESEARCH WORKING PAPER 2839
# I

Abstract

Emran and Shilpi use survey data from Bangladesh to commodity externality effects in the sale of farm
present empirical evidence on externalities at household households. The vegetable markets in villages with low
level sales decisions resulting from increasing returns to marketable surplus seem to be trapped in segmented
marketing. The increasing returns that arise from thick local market equilibrium. The analysis points to the
market effects and fixed costs imply that a trader is able coordination failure in farm sale decisions as a plausible
to offer higher prices to producers if the marketed explanation for the lack of development of rural markets
surplus is higher in villages. The semi-parametric even after market liberalization policies are implemented.
estimates identify highly nonlinear own and cross

This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to
understand the process of development of rural markets. Copies of the paper are available free from the World Bank, 1818
H Street NW, Washington, DC 20433. Please contact Forhad Shilpi, room MC3-536, telephone 202-458-7476, fax 202522-1151, email address fshilpi@worldbank.org. Policy Research Working Papers are also posted on the Web at http://
econ.worldbank.org. Shahe Emran may be contacted at emran@stanford.edu. May 2002. (31 pages)

_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._

Produced by the Research Advisory Staff

survey data from Bangladesh

survey data


Input:
Marketing Externalities and Market Development [1] 2

M. Shahe Emran
Stanford University and
George Washington University

Forhad Shilpi
World Bank

Key Words: Increasing Returns, Externalities, Marketing, Market **Development,**
Semiparametric Estimation

JEL Classification: D13; D43; **012;** M31

'Emails: emranAstanford.edu, fshilpi@worldbank.org.

**2** We would like to thank Joseph Stiglitz, Hanan Jacoby, Fahad Khalil, Marcel Fafchamps, Gershon Feder, Jose
Canals-Cerda, Takashi Kurosaki, Jishnu Das, Don Larson, Shabbir Jilany and the seminar participants **at** the
NEUDC, 2001 at Boston University, and at BIDS for useful comments. We would also like to thank **Salman** **Zaidi**
and Masako Hiraga for assistance with data, Misuzu Otsuka for superb research assistance.


Input:
Marketing Externalities and Market Development

Introduction

The importance of increasing returns, externalities, and multiple equilibria in explaining the

persistence of underdevelopment has been a perennial theme in the development literature. 1

That there can be multiple Pareto-ranked equilibria due to increasing returns and its associated

pecuniary externalities, and that an economy (or a region) can be trapped in a low-level equilib
rium has gained wide acceptance in recent theoretical literature, ranging from endogenous growth

theory to new economic geography. Different sources of increasing returns and externalities have

been identified and analyzed (for example, fixed costs of production in urban manufacturing sector

as in Krugman (1991) and in subsequent literature on new economic geography, and endogenous

technology adoption in final goods sector in Ciccone and Matsuyama(1996)). 2 However, the

extant literature has not paid adequate attention to the role of **marketing** which is an important

source of increasing returns and externalities in a market economy, especially at an early stage

of development. The increasing returns to marketing arise from thick market effects and from

the existence of fixed costs, for example, in hiring a truck and in renting in a storage space. [3]

Understanding the implications of increasing returns and externalities associated with marketing

for development of markets is of paramount importance, as well-functioning markets are widely

viewed as the _sine_ _qua_ _non_ of economic development. The objective of this paper is to address

this important but largely neglected issue; particularly to provide formal empirical evidence on

the existence and magnitudes of the externalities in farm level sales decisions that result from

increasing returns to marketing.

'The literature can be traced back at least to the presidential address of Allyn Young to the Royal Economic
Society (1928). The recent revival in interest owes much to the work of Murphy et. al. (1989). See also, Matsuyama
(1992), Eswaran and Kotwal (1996), Rodriguez-Clare (1996), Hoff and Stiglitz (2000) and Ray (2000), among others.
Hoff (2000) provides an excellent summary of the theoretical literature focusing on underdevelopment traps.
2 There is a small (relative to the theoretical literature) but rapidly expanding empirical literature on increasing
returns, externalities and multiple equilibria. For instance, in Ciccone and Matsuyama(1996), private return to
investment increases with the aggregate level of investment. Productivity of firms increases with the size/density
of activities e.g. in Ciccone and Hall(1996).
3In their in-depth study on the peasant marketing in Indonesia, Hayami and Kawagoe (1993) report that there
are large economies of scale in long-distance trade. It costs about Rp. 1000- Rp. 2000 to charter a pony wagon
to Garut bazaar, carrying a load up to 200 kg. It costs Rp. 5000 to charter a mini truck for any load, up to the
maximum capacity of 2 tons. Therefore the cost of transportation is Rp. 5/kg or higher if a load of up to 200 kg
of soybean is carried from the study village to Garut Bazaar by pony wagon, but the cost declines to Rp. 2.5/kg if
a two ton load is carried by a mini truck. (P.54)


Input:
The increasing returns to marketing imply that a trader can offer better prices to a farm

household located in a village with high aggregate surplus, as she can achieve significant economies

of scale in marketing. This results in pecuniary externalities at the households level; the sales of

an individual household of any given commodity becomes a positive function of the village level

aggregate marketed surplus of (i) the commodity itself (called _own externality),_ and _(ii)_ all other

commodities produced in the village (called _cross externality)._ The cross externality effect arises

from the fact that, when the marketable surplus of any given commodity is small, a trader needs

to pool multiple commodities for reaping economies of scale.

The existence of fixed costs and externalities also implies that there can be multiple equilibria

which can be identified with different levels of market development. We identify three distinct

stages of niarket development in the rural areas of developing countries. In the first and most

underdeveloped stage, the rural markets are isolated, and the market clearing occurs at the local

level. The marketable surplus is so low that it constrains the emergence and operation of a long
range intermediary class that can connect the local market to the central or urban market(s),

as it is not possible to reap any significant economies of scale in marketing. At this stage of

market development, both the own and cross externality effects in the sales decisions discussed

earlier are absent or insignificant. Moreover, the price an individual farm household gets for the

output is likely to be negatively correlated with the aggregate supply brought to the local market

due to the standard supply shift effect. This, in turn, implies a negative _own_ _externality like_

_effect,_ as a farm household's sales, _ceteris paribus,_ tend to be lower, when the neighbors bring in

more to the market, and thus depress the (local) market price. Observe that the existence of

fixed costs implies that there can be a coordination problem in the sales decisions of the farm

households and a village can be trapped in a segmented local market equilibrium. Given the

local market clearing, it might be individually optimal for the farm households not to produce

more surplus, but when everyone produces enough surplus, this might make long range marketing

intermediary profitable, and thus integrate the market with the urban centers. 4 In the second

stage of the market development, the long range marketing intermediaries are in operation, but

4Observe that the coordination failure can occur at two levels. First, the sales decisions of all the farm households
need to be coordinated. But that might not be sufficient to break out of the local equilibrium, as the increased
surplus will only glut the local market if it can not be coordinated with the entry of the prospective marketing
intermediary.

2


Input:
they need to pool together multiple commodities to cover the fixed costs in transportation and

storage and to reap the economies of scale. So this intermediate stage of market development

is characterized by the presence of both own and cross externality effects. In the last and most

developed stage of market development, the marketable surplus of each commodity is large enough

to permit commodity-wise specialization by the marketing intermediaries. As such there is no

cross commodity externalities at this stage, and it is characterized by the existence of only own

externality effect.

Using household level data from Bangladesh, we find significant evidence of both own and

cross commodity externality effects. While there are evidence of strong own externality effects in

rice markets, the cross externality effect seems to be absent. The evidence thus imply that the

rice markets in Bangladesh have attained the most developed stage of the three stages described

earlier. For a farm household located in a village in the first quartile (sorted by village surplus

of rice), the total sales of rice increases by 47 percent when the average sales of rice by all others

in the village doubles. In contrast, for vegetables, there is a negative own externality like effect

in villages with low marketable surplus, indicating the existence of segmented and local market

equilibrium. The sales of vegetables by a farm household in a village in the first quartile ( sorted

by surplus of vegetables) tends to _decrease_ by 26 percent when the average sales by all others

doubles, _ceteris paribus._ _5_ The vegetables markets in these villages seem to be trapped in the

low-level isolated market equilibrium. The analysis and evidence presented here thus point to a

plausible explanation of the apparently puzzling observation that, in many developing countries,

the deregulation and liberalization policies have largely failed to accelerate the development of

the rural markets (Kherallah et. al. (2000)). To the best of our knowledge, ours is the first

attempt at a formal econometric analysis of the externalities associated with increasing returns

to marketing in the rural markets of developing countries. [6]

The rest of the paper is organized as follows. Section 1 discusses the different sources of

increasing returns to marketing and the many economic functions performed by a marketing

intermediary in developing countries. Section 2 describes a simple model of a marketing interme
5 The elasticities reported here are based on 2SLS estimates.
61t is interesting to compare our analysis to that of Demsetz (1968). Using data from a well developed market,
i.e. the New York Stock Exchange, Demsetz finds evidence of significant scale economies in securities trade. The
scale economies arise from thick market externalities from better matching of buyers and sellers when trade volume
of a given security increases. Thus only a subset of what we call own externality effects is considered.

3

2SLS estimates

household level data from Bangladesh

data from a well developed market

household level data


Input:
diary that guides the empirical analysis. A discussion of the econometric issues involved in the

estimation of externality effects and of the specification of the estimating equation is provided in

the next section. Section 4 gives a brief discussion of the data. The main empirical results on

externalities, using a semi-parametric approach, are presented in section 5. Section 6 concludes

the paper with a summary of the results and a discussion on the implications of the results of

this paper for the broader theme of agricultural market development and poverty alleviation in

developing countries.

Section 1: The Economics of Marketing and Market Development

Several sources of increasing returns to marketing can be identified from existing theoretical

and empirical literature. First, the cost of transacting in a market can decline with the scale of

trading due to _thick market externalities_ from better matching of buyers and sellers, and from bet
ter generation and transmission of information (e.g. Demsetz (1968), Diamond (1982), Rubinstein

and Wolinsky (1987)). Second, as we mentioned above, two of the most important components

of marketing: transportation and storage give rise to natural increasing returns due to fixed costs

involved. These are known as increasing returns to _bulking_ in the marketing literature. Third,

setting up of a marketing network frequently involves substantial sunk investments in "intangible

assets" like trust and reputation. The available empirical evidence from developing countries

indicate that markets in agricultural and many manufacturing inputs and outputs operate on the

basis of relational contracts. The relational contracts facilitate information sharing and informal

enforcement of contracts (Aoki and Hayami(1999)). The intermediaries traders need to incur

significant costs in developing these informal relationships, which can be termed as a trader's

"social capital". _An_ _important implication of the existence_ _of increasing returns to marketing is_

_that,_ _under plausible conditions, it_ _generates pecuniary externalities at the level_ _of individual farm_

_households._ As the aggregate volume of trade increases allowing a trader to take advantage of

the many different sources of increasing returns, part of the gain is passed on to the producers in

the form of higher prices, especially when the entry into marketing is not restricted. [7] Also, there

71n 1960s, there were extensive government interventions in the agricultural markets of developing countries
that often restricted entry by private traders. The markets have, however, been deregulated and liberalized starting
from late 1970s. Although stories of monopsonistic behavior in agricultural markets abound in popular accounts,
the prevailing consensus from more careful studies is that the markets, especially at the local level, are reasonably

4


Input:
are significant cross commodity externalities, especially at the early stages of development, as the

marketed surplus of a single commodity may not be large enough to recover the fixed costs, thus

providing incentives for pooling of different commodities. [8] The lack of specialization in mar
keting, both horizontally and vertically, at early stages of development is well-documented (see,

for example, Hirsch (1961), Hayami and Kawagoe (1993)). The wide prevalence of producers

and traders associations in developing countries probably reflects, at least in part, the outcome of

attempts to capture increasing returns to marketing at low levels of production and marketable

surplus. 9

While the existence of increasing returns to marketing can generate positive externalities, an

increase in the volume of trade, particularly of different commodities, can also increase transaction

costs, and thus eventually limit the number of commodities traded in a given market (Demsetz

_(op_ _cit))._ Market intermediaries play a critical role in inspecting and verifying quality of a

commodity thereby facilitating its exchange even without personal examination by the buyers.10

This is especially important in developing countries because of a lack of standardization. The

marketing intermediary can be looked upon as an expert in judging the quality of a product who

helps resolve the adverse selection problem _d_ _la_ Akerlof's lemon, and thus can stop a market

from unravelling completely (see, for example, Biglaiser (1993)). However, at early stages of

development, any given intermediary usually handles a number of commodities, and she can not

be expected to be equally expert in verifying quality of all of them. The _diseconomies of scope_

in performing quality verification is higher, the larger the number and volume of commodities in

competitive or at least "contestable" (see, for example, Chowdhury (1992) on Bangladesh, Kherallah et al (2000)
on Sub-Saharan Africa). That these markets are essentially contestable was forcefully argued early on by Bauer
and Yamey (1954).
8For instance, in smaller markets, once a truck is hired, traders are often seen to pool different commodities so as
to operate the truck near its capacity (to the extent possible). If a single trader's volume is not large enough, then
multiple traders, possibly dealing with different commodities, may hire a truck in partnership (see, for example,
Belshaw (1965) for evidence on Fiji, and Hayami and Kawagoe _(op_ cit) for Indonesia.)
9There are evidence that the same produce gets better price when marketed through a marketing association in
comparison to individual marketing. For example, while per box of squash marketed through the Ha-ee vegetable
marketing group in Korea got $6.58, the price fetched by individual marketers was $5.92 in 1982 (P.118, Abbot,
1987). This gave an average $800 additional income to a member of the group. Although part of the price
differential might reflect better bargaining power of the group, economies of scale in marketing (transportation,
storage and packaging) arising from fixed costs is also an important factor.
10Even for relatively homogenous commodities like foodgrains, there are multiple dimensions of quality that can
be important: color, taste, appearance of kernels, moisture content, impurity, breakage of kernels, and baking
qualities (Gabre-Madhin, 1999).

5


Input:
the portfolio of a trader. [1 1] With the increase in income and production, as marketed surplus

increases beyond a threshold, a trader can reap the economies of scale without pooling different

commodities together which leads to specialization in marketing and creates experts in quality

verification. The above discussion forms the basis for the three stages of market development

discussed earlier.

We utilize household level survey data from Bangladesh to test for the existence of the exter
nality effects and to estimate them when they are present. The choice of Bangladesh as a case

study is motivated by two characteristics of its agriculture. The average farm size is very small

and economic activity is very densely distributed over geographic space, an ideal combination for

generating pecuniary externalities at the individual farm level.1 [2] If the farm size is large enough

as is the case in much of Latin America, it is easy for individual farmers to reap the benefits of

increasing returns to marketing, and one should not observe any externality effects across different

producers. On the other hand, if the farms are small but geographically isolated, it would be

extremely difficult, if not impossible, to reap any economies of scale in marketing. The high

geographic density and small farm size coupled with the fact that most of the households produce

multiple commodities due to risk diversification reasons, imply that the forces of both own and

cross externalities are likely to be important in Bangladesh agriculture.

Section 2: Price formation in the presence of increasing returns to marketing

At the heart of our analysis of externalities in farm level sales is the formation of producer

price of a commodity at a given market (or at the farm gate) in the presence of increasing returns

to marketing. In this section, we develop a simple model of price formation by considering

the optimization problem of an intermediary/trader. Let qi denote the amount of commodity i

shipped from the local market, and Q be the total volume of all commodities shipped from that

" [1] As pointed out before, even if traders are specialized commodity wise, but deal with relatively small volume, a
number of traders can pool together and hire a truck to transport to the nearest urban market. In this case, there
are no diseconomies from spreading thin the quality inspection skill, but there are costs associated with coalition
formation and collective action. Time and efforts need to be spent to establish such an arrangement and to hold
it together. Private marketing cooperatives, producers' associations fit into this category. The model developed in
the next section of the paper can be easily adapted to represent such an association where the quality verification
costs are replaced by the costs of coalition formation and collective action.
1 [2] With population density of more than 2000 per square mile, Bangladesh is considered as the most densely
populated non-city country in the world. The average farm size is about 1 hectare.

6

household level survey data from Bangladesh

household level survey data


Input:
_**The World’s Largest Open Access Agricultural & Applied Economics Digital Library**_

**This document is discoverable and free to researchers across the**

**globe due to the work of AgEcon Search.**

**Help ensure our sustainability.**

[Give to AgEcon Search](https://shorturl.at/nIvhR) AgEcon Search
[htp://ageconsearch.umn.edu](http://ageconsearch.umn.edu/)

[aesearch@umn.edu](mailto:aesearch@umn.edu) _Papers downloaded from_ _**AgEcon Search**_ _may be used for non-commercial purposes and personal study only._ _No other use, including posting to another Internet site, is permitted without permission from the copyright_ _owner (not AgEcon Search), or as allowed under the provisions of Fair Use, U.S. Copyright Act, Title 17 U.S.C._ _No endorsement of AgEcon Search or its fundraising activities by the author(s) of the following work or their_ _employer(s) is intended or implied._


Input:
AGRICULTURAL ECONOMICS www.elsevier.com/locate/agecon The Green Revolution and the productivity paradox: evidence from the Indian Punjab Rinku Murgai _Development Research Gtvup, The World Bank, 1818 H Street,_ _N. W.,_ _Washington,_ _DC 20433,_ _USA_ Abstract Contrary to a widespread belief, total factor productivity (TFP) growth, as measured by the conventional growth accounting approach, contributed little to economic growth during the Green Revolution in the Indian Punjab. This paper shows that this 'productivity paradox' arises because of a fundamental problem with conventional measures of TFP growth. When technical change is not Hicks-neutral, it is impossible to separate the contribution of technical change from that of factor accumulation.
Simple exercises to assess the magnitudes involved in the Punjab case show that the bias in conventional TFP estimates is severe: 'corrected' measures of productivity growth are between 100 and 200% higher per year during the Green Revolution.
© 2001 Elsevier Science B.V. All rights reserved.

_JEL classification:_ 013; 030; 047; Q16 _Keywords:_ Total factor productivity; Biased technical change; Green Revolution; Agricultural sustainability

1. Introduction

The Green Revolution of the mid-1960s led to a dramatic increase in agricultural production and radically transformed the course of Indian agriculture.
Within India, the state of Punjab has been the star performer.lt was the first state to widely adopt the modem high-yielding varieties (HYVs) of wheat and rice, and has been the most widely cited Indian success story of the Green Revolution. From 1966 to 1974, production of wheat, the primary winter crop, increased at an annual rate in excess of 9%. Rice, which was not widely grown prior to the Green Revolution, grew at a remarkable rate of 18% during this period. Overall, agricultural production increased at a rate of 6%.
This record of growth in production has led many economists to believe that productivity growth must have also been high in Punjab's agricultural sector.

_E-mail address:_ rmurgai@worldbank.org (R. Murgai).

More recently, in the years following the Green Revolution, there has been growing concern that high rates of productivity growth have not been sustained in the light of heavy water and fertiliser inputs, diminishing growth in the yields of the major crops, and degradation of the water and land resource base.
However, the empirical evidence of productivity growth, as measured by the conventional growth accounting methods, goes directly contrary to expectations. Productivity growth in Punjab was lowest during the Green Revolution years, even as farmers switched from traditional varieties of wheat and rice to hybrid seed varieties and the agricultural sector experienced stellar growth rates in production. Productivity growth increased during a phase of rapid factor accumulation that immediately followed, after adoption of HYVs was essentially complete. This is the puzzle - "why are trends in productivity growth exactly opposite to our expectations?" 0169-5150/01/$ -see front matter© 2001 Elsevier Science B.V. All rights reserved.
PII: SO 169-5150(0 1 )00077 -9


Input:
200 _R._ _Murgail Agricultural Economics 25 (2001) 199-209_ Table I Conventional estimates of productivity growth, 1961-1994 Entire period, 1961-1994 I. Conventional TFP growth (% per year) TFP 1.9 Output 5.0 Input 3.0 II. Contribution of TFP to output growth (%) 38.8 Green Revolution, 1966-1974

1.3
5.9 4.6 22.3 Input intensification, 1975-1985

1.8
4.3 2.4 43.0 Post-Green Revolution, 1986-1994

1.5
4.5 3.0 33.5 This paper shows that the 'productivity paradox' arises because of a fundamental problem with the widely used measure of total factor productivity (TFP) growth. [1 ] TFP is calculated by subtracting the share-weighted growth in factor inputs from the growth rate in output, so that the residual is attributed to technical progress. However, when technical change is not Hicks-neutral, the shares of payments to each factor depend on the rate and bias of technical change. As a result, the contribution of technical change is misattributed to factor accumulation and the derived residual of productivity growth is biased.
Simple exercises to assess the magnitudes involved in the Punjab case show that the bias in conventional TFP estimates is severe; 'corrected' measures of productivity growth are between 100 and 200% higher per year during the Green Revolution. This error has been neglected in the current literature, but is potentially extremely serious in many other regions and sectors that have undergone rapid factor accumulation amidst factor-biased technological change.
Considering the widespread importance placed on measures of productivity growth to monitor the aggregate performance and sustainability of agricultural systems, it is critical that such correction procedures be used to assess and improve the accuracy of TFP estimates.

1 The term 'productivity paradox' is borrowed from the literature that seeks to explain the sudden decline in productivity growth in industrialised countries after 1973, even though there were significant technological advances in some fields. Diewert et a!.
(1999) provide a summary of recent research on the productivity slowdown in the service sectors of industrialised countries.

**2. Conventional estimates of productivity growth**

There are several measures of TFP that use different rules for aggregating outputs and inputs. The method commonly used to minimise the impact of relative price changes when forming the aggregate quantity indices is a Divisia indexing procedure (Alston et al., 1995). Among alternative discrete approximations to the Divisia index, the chain-linked Tornqvist-Theil TFP index is often preferred, since it is exact for the linear homogenous translog production function (Diewert, 1976). TFP is obtained by taking the difference between the growth rates of the aggregate output and input indices.

where _Q/1_ is the Divisia output index, _Xl1_ the Divisia input index, and _Rit_ and _Sjr_ are the revenue shares of output _i,_ and the cost share of input _j,_ at time _t,_ respectively.
For the purposes of computing TFP, annual data on all inputs, outputs and prices were collected at the district level during the period 1961-1994. [2 ] Major 2 Considerable resources were invested to collect data (from the Indian Directorate of Economics and Statistics and various secondary sources) on individual crop and livestock products, inputs and prices at the district-level. This exercise resulted in a more comprehensive data-set than has been previously used in the Indian context. For a detailed discussion of the aggregation and valuing methods, see Murgai (1997).

TFP::::::ln · ( _TFPr_ ) =ln ( -- _Qlr_ ) -In ( -- _Xlr_ ) TFPr-1 _Qlr-I_ _Xlr-1_ =~-(Rit+R;r-I)ln "1 ( _--Q·r_ _1 -_ ) . 2 Qit-1 _l_

-L: ~(Sjt + _Sjr-1)_ In ( _Xjt_ )
. 2 _Xjr-1_ _J_ (1)

Divisia input index
Divisia output index
annual data

Tornqvist-Theil TFP index

Divisia output index

Divisia input index

annual data


Input:
_R._ _Murgai/ Agricultural Economics 25 (2001) 199-209_ 201 farm outputs - 20 crops, fruits and livestock products

- were aggregated into a Divisia output index using
district-specific farm harvest prices for the crops and state-level prices for livestock and fruits. The input categories included land, labour, water, machinery, draught animals, fertilisers and pesticides. To minimise the aggregation bias, inputs of different qualities were valued by the price of each input-quality type.
Land was divided into irrigated and unirrigated land, labour was disaggregated into skilled and unskilled labour based on the rural literacy rate in each district, water was divided into canal and tubewell water, and fertiliser into individual nutrient sources - nitrogen, phosphorous and potassium. [3 ] Growth in TFP was analysed for three periods corresponding to different phases of Green Revolution technical change (Byerlee, 1992). The Green Revolution period (1966-197 4) corresponds to the widespread adoption of input-responsive modern varieties of wheat and rice, which led to a dramatic increase in production. This was followed by an input intensification period (1975-1985) when the use of fertilisers and capital inputs increased rapidly, and a post-Green Revolution period (1986-1994) when input use levelled off.
Table 1 reports the estimates of the Tornqvist-Theil index of TFP growth. Punjab sustained productivity 3 As far as possible, the prices used to value inputs and outputs were those faced by the producers. That is, all taxes on the value of output were removed but all subsidies and taxes on the factors of production were retained (see Young, 1995 for discussion). For example, the fertiliser was valued at the subsidised price paid by the producers. However, using the price paid by producers to compute the factor shares is problematic in the case of inputs that are effectively rationed. In the case of canal water (a subsidised but rationed input), the price does not reflect the true shadow value paid by producers. Consequently, canal water's factor share, when computed at the levied charges, likely understates its true factor share and biases our TFP estimates. The extent of bias depends on the rate of growth of canal water use relative to other inputs and on the share of canal water in factor payments. Simulations suggest that underestimation of the shadow price biases our TFP estimates, but that the degree of bias is small since canal water use grew at a much slower rate than other relatively high priced inputs.
Underestimation of the shadow price of groundwater (which may arise due to electricity shortages in the case of producers who rely upon water purchased from electric tubewells) would bias our estimates upwards. Evidence from simulations suggests that the bias is unlikely to be severe. For example, a 40% increase in the hourly price of water from electric tubewells would lower annual TFP growth rates from 1.9 to 1.8%.

growth rates of 1.3% or more in each period, averaging 1.9% from 1961 to 1994. [4 ] These estimates are comparable to the long-term productivity growth estimates reported for Punjab and at the higher end of those reported for India as a whole. [5 ] The productivity gains explain approximately 39% of the 5% growth rate in aggregate output from 1961 to 1994.
Comparing performance across periods, we see that the Green Revolution was indeed a period of extraordinary agricultural performance. Output growth was most rapid during that period, at nearly 6% per year.
Paradoxically, however, productivity performance was the lowest during the Green Revolution; only 22% of the growth in output was explained by TFP growth.
In the following section, we present evidence on the trends in factor accumulation versus factor shares over the sample period. This evidence, combined with a closer look at the assumptions underlying the TFP calculations, casts doubt on the validity of conventional growth accounting estimates in the Punjab context.

**3. Explaining the productivity paradox**

_3.1. Are conventional TFP estimates biased?_ According to the TFP estimates reported in Table 1, more than three-quarters of the rate of growth in agricultural output during the Green Revolution was explained by factor accumulation. Much of this factor accumulation was in the form of rapid investment in tubewells for accessing groundwater for irrigation, tractors for land preparation, and increased use of pesticides and fertilisers. By contrast, overall labour utilisation declined (rapidly in the case of draught animals, but less so in the case of human labour since 4 To minimise the impact of end-points on the period-specific growth rates, all growth rates reported in the paper are computed by using a spline function with semi-log regressions. Growth rates for the 1961-1965 period are not reported since it is too short.
5 See, for example, Evenson et al. ( 1999), Rosegrant and Evenson (1993), Desai (1994), Kumar and Rosegrant (1994), Sidhu and Byerlee (1992), Dholakia and Dholakia (1993). Ahluwahlia (1996) and Pingali and Heisey ( 1999) provide summaries of TFP estimates for Indian agriculture.

Divisia output index

Tornqvist-Theil index

Divisia output index


Input:
202 _R._ _Murgai/ Agricultural Economics 25 (2001) 199-209_ 600 _<!)_ .8 "' ] 500 400 .g !3 300 ....:1 ~ ...

., ..., • - -•- - Labour Index ~ 200 .......
§< .~~>• ..... - - -• - - - Capital Index u ......

......., __,.,_ " ....
..
........

....:1 300 ~ ~ 200 .~~>• ..... - - -• - - - Capital Index 100 $4..:" **M..;._** .... **............. ._.................** _... ......,., ..... ............................................ ._.._._ .... .

0+-----,------r-----r----~------r-----~--~ (a) 1960 1965 1970 1975 1980 1985 1990 1995 Year

1.00

# e e • ............. .._ . o ··•• ..... ..... ....... ·.:-;; ·.

0.90 ..<:: </.) .... ~ "' 0.80 0.70 ..<:: </.) .... ~ 0.80 0.70 _**.Jij-**_ e _**..**_ .,. **e •** ............. .._ **o** ··•• .....
#### ~ ~ 0.60 ............. ., .. ..,·• ·•· .. • ·- ~ ....... • Labour Share

!3 0 0.50 •. •· ·.. .:!•t _ ... . - '+ ... • • - Capital Share ~ - • - • **···'!...t** ~ ~ 0.60 ~ !3 0 0.50 •. •· ·.. .:!•t _ ... . - '+ ... • • - Capital Share ~ - • - • **···'!...t** ....:1 0.40
**....... f' ... :** **••**
~ .......
~ [0.30 ] §< 0.20 u 0.10

- Capital Share
# ·~-~ . .. ~~ .. ·. .. .................. ._.

0.00 **+------.-----.....,...------,r------r------r----r-----.** 1960 1965 1970 1975 1980 1985 1990 1995 (b) Year Fig. I. (a) Evolution of weighted capital and labour indices, 1961-1994 and (b) evolution of capital and labour factor shares, 1961-1994.

utilisation rates fell but the labour force grew). [6 ] The contrasting patterns of factor accumulation are starkly evident in a comparison of the patterns of evolution of weighted capital and labour indices over the sample period (Fig. la). [7 ] 6 Hazell and Ramaswamy (1991) found a similar decrease in the labour utilisation rates in a study of the effects of the Green Revolution in South India. They attribute the change to increased mechanisation of irrigation and paddy threshing.

7 All non-labour inputs (including machinery, water, land and fertiliser) are aggregated into a capital index, while human and animal labour inputs are aggregated into a labour index.

These patterns of input use are consistent with the differences in relative price movements. Since the beginning of the Green Revolution, the prices of capital inputs and fertilisers declined largely due to government subsidies, while there was a modest increase in real wages. [8 ] However, there was a rather surprising pattern in the movement of factor shares over the same time period (Fig. lb). With rapid capital accumulation and a decrease in the relative price of capital, it seems reasonable to expect a decrease in the share of factor payments that accrue to capital.

8 For a more detailed discussion of the trends in input and output use and prices, see Murgai (1997).


Input:
_R._ _Murgail Agricultural Economics 25 (2001) 199-209_ 203 Instead, capital accumulation was accompanied by an increase, not decrease, in its share in total factor payments.
There are two contending explanations for the increase in the capital share despite tremendous capital-deepening. First, the underlying production function for the Green Revolution technologies had an elasticity of substitution between capital and labour that was high enough to compensate for the reduction in the marginal productivity of the capital. Alternatively, there was labour-saving technical change which led to an increase in the relative return to capital and therefore an increase in the share of capital. While we cannot be sure which explanation is true, there are strong reasons to believe the latter.
We know from other empirical sources (Kako, 1978; Hayami and Ruttan, 1985; Thirtle, 1985a,b) that the elasticity of substitution between capital and labour inputs is typically close to one (and usually smaller than one) which is too low to explain the increase in capital's share. [9 ] Moreover, the changes in factor proportions during the Green Revolution were so large that it is highly implausible that these changes represent pure substitution effects along a fixed production function with constant technology.
We also have ample evidence that the seed improvements that spearheaded the Green Revolution were directed towards the selection of more fertiliser and water-responsive varieties. The traditional varieties had equal or higher yields than the improved varieties at lower levels of fertilisation and irrigation, but they did not respond to higher applications of these inputs (Hayami and Ruttan, 1985). It is likely, therefore, that the increase in capital's share reflects the development of crop varieties that were biased toward water and fertiliser use. [10 ] 9 If ordinary factor substitution in response to relative price movements was the only reason underlying changes in the factor share, the observed capital share changes would imply an elasticity of substitution close to 7, if the underlying production function were a scale-neutral CES.

10 Indeed, the extent to which the factor shares would have changed in the absence of relative price changes is precisely how the degree of biased technological change has been measured in the literature (Binswanger, 1974, 1978; Yeung and Roe, 1978; Hayami and Ruttan, 1985).

Since capital is the more rapidly growing input and its share is higher than it would have been in the absence of technical change, the aggregate input growth is over-estimated (see Eq. (1)). As a result, the TFP residual under-estimates true productivity growth. The next sub-section spells out this argument in greater detail.

_3.2. Non-identifiability_ _of factor accumulation_ _and technical progress_ Although distinguishing factor-biased technical change from the price-induced substitution effects has been the focus of a significant fraction of the agricultural production economics literature (see Alston et al., 1995), the implications of biased technical change for index number approaches to productivity measurement have been neglected.
The basic issues are most easily demonstrated in a two-factor case. Consider a production function with two inputs, capital _(K)_ and labour (L), (2) where _A_ is the coefficient of Hicks-neutral technical change, _EK_ and _EL_ are the coefficients of capital-saving and labour-saving technical change, and the production function _(j)_ is linear homogenous and well-behaved. Rewriting Eq. (1) for this simple production function, conventional productivity growth is measured as the residual growth in output, after subtracting the weighted growth in inputs _K_ and _L,_ ln ( T~~~~J =In ( Q~~J _-a_ ln ( K~~J

-(1 -a) ln (~) (3)
_Lr-1_ where _a_ is the average share of capital in factor payments between period _t-_ 1 and _t._ Notice that the rates of growth of the two inputs are weighted by their observed factor shares.
If factors are paid their marginal products, the growth rate of the share of factor payments to capital in total output is
##### a= ( -CJ- [1-CJ) ] (1- at_I)[(EL- A EK)- A (K- A L)] A

(4)


Input:
204 _R._ _Murgail Agricultural Economics 25 (2001) 199-209_ where the hat symbol n denotes the growth rate, _a_ the share of capital in factor payments and _CY_ the elasticity of substitution between capital and labour. As Eq. (4) shows, the evolution of factor shares depends on (1) the form of the production function, as encapsulated by _CY,_ (2) the effect of factor-substitution along a fixed-technology isoquant in response to the changes in relative factor prices and (3) the effect of biased technical change. [11 ] Factor shares are independent of technical change only if the elasticity of substitution between the inputs is unity _(CY_ = 1) or if the technical change is Hicks-neutral _(EL_ _E_ _K_ = 0). [12 ] When it is relatively difficult to substitute capital for labour _(CY_ < 1), an increase in the capital:labour ratio _((K_ - _L)_ - 0), would lead to a decrease in the share of factor payments to capital. However, if there is labour-augmenting technical change _(EL_ - _EK),_ the relative return of capital increases.
Consider the case of the Green Revolution. The increase in capital's share despite capital deepening could be either because of _CY_ that is much greater than 1 (as noted in footnote 9) or because of labour-saving technical change. In the latter case, since capital is the more rapidly growing input and its weight in the Divisia input index is higher than it would be in the absence of technical change, too much of the output growth is attributed to factor accumulation, and too little to the residual (i.e. TFP growth). [13 ] 11 For a non-homothetic production function, factor shares also depend on the scale of production since the expansion path is non-linear (Antle and Capalbo, 1988; Alston eta!., 1995). In this paper, we focus on the homothetic case, since empirical evidence on Green Revolution technologies suggests that the underlying production function is linear homogenous and therefore, homothetic (Ruttan and Binswanger, 1978; Hazell and Ramaswamy, 1991).

12 For the formulation of the decomposition of factor shares into substitution and biased technical change effects based on the cost function (dual) approach, see Binswanger (1974) and Hayami and Ruttan (1985). Rodrik (1997) uses the primal decomposition approach to make the same argument about the difficulty of measuring biased technical change in the context of the east Asian economies.

13 In practice, any chain-linked Divisia index (not just the Tornqvist-Theil index) that uses observed factor shares to aggregate inputs suffers from this problem. Mathematically, the bias arises because of the path-dependent properties of the Divisia index as a line integral (Hsieh, 1998). The TFP index is not path dependent only if technological change is Hicks-neutral.

_3.3. Bias-corrected measures of productivity growth_ How can we correct conventional TFP estimates? In order to separately identify the contribution of factor accumulation from that of technical change, we need to estimate what factor shares would have been in the absence of a technical change. The task is to sort out to what extent the share changes have been due to biased technical change and to what extent to price changes.
However, we need to contend with a fundamental identification problem, related to the Diamond et al.
(1978) 'non-identification theorem' which shows that given the actual observations on input change and factor shares, it is impossible to separately identify the parameters of the underlying production function from the bias of technological change. [14 ] As a result, it is impossible to isolate the contribution of technological change from that of factor accumulation, without making assumptions about the underlying production function.
Given this identification problem, we cannot correct TFP estimates without knowing the 'true' substitution parameters of the production function. However, we can improve the accuracy of the estimates by making assumptions about reasonable values of _CY_ that are available from other empirical studies. [15 ] Assuming a certain _CY,_ and using initial factor shares (in year 0) and growth rates of factor deepening (between year 0 and _t),_ the differential equation for factor share changes (Eq. (4)) can be solved to estimate what factor shares at time _t_ would have been in the absence of 14 Stated differently, given time series data for a single economy with a classical aggregate production function, one finds that the same time series could have been generated by an alternative production function having an arbitrary _C5_ or bias at the observed points. In addition, if the underlying production function were non-homothetic, it may be possible to assign an arbitrary elasticity of substitution and the bias of technical change, and explain the observed time series solely in terms of scale bias.

15 We cannot estimate _C5_ using our data because of the small number of cross-sectional units (districts) in our sample. The parameters of the production function must be estimated with cross-section, rather than time-series data, since the effects of biased technical change are incorporated in the observed evolution of factor shares (Binswanger, 1978; Diamond et a!., 1978). The difficulty with cross-sectional estimation, of course, is that all units should be on the same production function (i.e. there is no unobserved heterogeneity).

Divisia input index
Divisia index
time series data

Divisia input index

Tornqvist-Theil index

time series data

time-series data


Input:
_R._ _Murgai! Agricultural Economics 25 (2001) 199-209_ 205 Table 2 Conventional vs. corrected estimates of TFP growth (two-factor CES) Entire period, 1961-1994 I. Conventionni TFP growth (% per year) TFP 1.9 II. Corrected TFP growth (% per year) _a_ = 0.3 4.8 _a=_ 0.7 3.7 _a_ = 1.3 2.9 III. Bias in TFP ( = II - I) _a_ = 0.3 2.9 _a_ = 0.7 1.7 _a_ = 1.3 1.0 Green Revolution, 1966-1974

1.3

4.0 3.0 2.7 2.7
1.7
1.4

Input intensification, 1975-1985

1.8

2.9 2.6 2.5

1.1
0.8 0.7 Post-Green Revolution, 1986-1994

1.5

2.2
1.2
0.9 0.7
-0.3
-0.6

biased technical change. [16 ] The Divisia input index is then computed using the corrected factor shares, and the residual growth in output gives us a measure of bias-corrected productivity growth. [17 ] Table 2 reports bias-corrected estimates of TFP for three different values of _a_ in a two-factor scale-neutral CES production function. [18 ] For the ease of comparison, the first row in the table reproduces the conventional TFP estimates from Table 1. There are several points of note. First, the bias in conventional TFP estimates is extremely high. Comparing the first and second panels, the conventional TFP estimates under-estimate true productivity growth by 1-3% per year. Putting it differently, depending on the choice of _a,_ the bias-corrected TFP estimates are more than double the conventional estimates, per year. Second, the bias is greater for lower elasticities of substitu 16 This correction procedure is analogous to the approach that has been used to measure the degree of biased technical change (Binswanger, 1974; Hayami and Ruttan, 1985). The approach has been to (l) measure the elasticity of substitution using cross-sectional data and (2) based on these parameters, measure the extent to which the factor shares would have changed had the factor prices remained constant. This approach, although the authors do not state it explicitly, also circumvents the identification problem by making reasonable assumptions about the value of _a_ in the underlying production function.

17 The solution for the factor share differential equation and the bias-corrected Divisia input index is provided in Appendix A.

18 As in Fig. Ia and b, the two factors are 'capital', which is a weighted index of all non-labour inputs, and 'labour', which is a weighted index of human and animal labour. For a detailed discussion of aggregating different input types into a single index, see Young (1995).

tion. Clearly, the lower our priors about the substitutability between factors, greater we must assume the under-estimation in conventional productivity estimates to have been. Third, the under-estimation in conventional TFP estimates is the greatest during the Green Revolution period. This is expectedly because the shift from traditional varieties to modern HYVs was much more drastic in terms of the technological bias than the later shifts from the original Green Revolution varieties to further improved varieties. Finally, comparing the corrected TFP estimates across the periods (and contrary to the trends in conventional estimates), there is a clear decline in TFP growth after the Green Revolution, regardless of the assumptions about the elasticity. For example, when the elasticity of substitution is 0.7, TFP growth declines from 3% per year during the Green Revolution to 1.2% in the post-Green Revolution period. These estimates confirm suspicions about the declining productivity of irrigated agricultural systems that are typical of South Asia. [19 ] The under-estimation in conventional TFP is robust to other specifications of the production function. We repeated the exercise for a multi-factor CES production function, with land and labour as the two primary factors of production, and capital goods decomposed into those that substitute for land and those that substitute for labour (Hayami 19 Murgai et a!. (2000) relate the decline in the productivity of irrigated agriculture in the Indian and Pakistan Punjabs to resource degradation, especially in the rice-wheat cropping system.

Divisia input index

cross-sectional data


Input:
206 _R._ _Murgail Agricultural Economics 25 (2001) 199-209_ Table 3 Corrected estimates of TFP growth, 1961-1994 (multi-factor CES)a _a=_ 0.2 _a=_ 0.5 _a=_ 0.9 Average annual TFP growth (%) _a A= aL_ = 0.2 5.07 4.98 4.94 _a A= aL_ = 0.7 4.99 4.87 4.84 _a A_ = 0.8; _a L_ = 0.7 4.98 4.85 4.81 _a A_ = 0.7; _a L_ = 0.8 4.97 4.86 4.82 a Note: _a A_ is the elasticity of substitution between labour and labour-saving inputs (machines), _a_ _L_ the elasticity of substitution between land and land-saving inputs (fertiliser and water) and _a_ the elasticity of substitution between land and labour aggregates.

and Ruttan, 1985). Land-saving capital is identified with biological, chemical and water control investments, while the labour-saving capital consists of machinery, particularly tractors. Technical change is classified as land-saving or labour-saving according to whether it increases the productivity of the land-saving or the labour-saving capital goods.
Table 3 reports the bias-corrected TFP growth rates for the period 1961-1994. For all reasonable values of the elasticities of substitution, the estimated growth rate of TFP lies between 4 and 5% per year. [20 ] For these values, the conventional TFP estimate of
1.9% per year, therefore, under-estimates the contribution of technical progress by approximately 2%
per year.

4. Conclusions India's Punjab exemplifies the Green Revolution of the mid-1960s, when it achieved dramatically high rates of growth in agricultural output. Growth during the Green Revolution can be attributed largely to improvements in the yields of wheat and rice and to the expansion of area under wheat and rice HYV s.

20 The elasticities used for the simulations in this paper are the ones reported by de Janvry et a!. (1989) as the average of those reported by several empirical studies. From a survey of various empirical estimates of the Allen partial elasticities of substitution between the inputs in the same sub-function and between the inputs in different sub-functions, de Janvry eta!. (1989) report that elasticities within sub-functions are higher than elasticities across the sub-functions and that they are all systematically lower than unity.

However, most yield improvements resulted from rapid factor accumulation, especially in fertilisers and capital inputs. Contrary to a widespread belief, productivity growth, as measured by the conventional growth accounting approach, contributed little to the economic growth.
A discussion of the conventional method of growth accounting helps to explain why measured productivity growth was much lower than expected. When technical change is not Hicks-neutral, it is impossible to separate the contribution of technical change from that of factor accumulation. The difficulty arises because when technical change is biased, the share of payments to each factor depend on the rate and bias of the technical change. As a result, when observed factor shares are used to compute an aggregate Divisia input index, part of the effects of technical change are captured in the index of factor accumulation.
In the case of the Indian Punjab, the bias in conventional TFP estimates is severe. Conventional estimates of TFP growth are biased downward during the Green Revolution because some of the contribution of labour and land-saving technical change is attributed to factor accumulation. Corrected TFP estimates also show that, contrary to the trends observed in the conventional estimates, there has been a marked decline in the productivity since the Green Revolution, raising concerns about the sustainability of irrigated agricultural systems.
More generally, in this paper, we have used the Tornqvist-Theil index as an example to illustrate the problem with conventional growth accounting in the presence of biased technical change. In practice, any chain-linked Divisia index that uses observed factor shares to aggregate the inputs needs to be corrected if there is a likelihood that technical change has been biased towards one or more factors. Indeed, if we believe in the role of induced innovation in maintaining the productivity growth, we should start incorporating its impact into our measures of productivity growth.

Acknowledgements I am grateful to Dr. M.S. Bhatia at the Indian Directorate of Economics and Statistics without whom the

Tornqvist-Theil index

Divisia input index

survey of various empirical estimates


Input:
_R._ _Murgai/ Agricultural Economics 25 (2001) 199-209_ 207 data collection would not have been possible. I wish to thank Pranab Bardhan, Derek Byerlee, Calogero Carletto, Alain de Janvry, Navroz Dubash, Chang-Tai Hsieh, Nancy McCarthy, Prabhu Pingali, Elisabeth Sadoulet, Paul Winters and seminar participants at the University of California, Berkeley, the World Bank, and the XXIV International Conference of Agricultural Economists for comments. All the remaining errors are mine. The views expressed in this paper do not necessarily represent those of the World Bank or any of the affiliated organisations.

**Appendix A. Correcting factor shares and factor**
**accumulation for technical change bias**

_A. I. Corrections for a two-factor CES production_ _function_ A scale-neutral CES production function with two inputs, capital _(K)_ and labour (L) can be written as _Q=[y(ELL)(a-1)/a_ +(1-y)(EK _K)(a-1)/a]aj(l-a)_ (A.1) where _EK_ and _EL_ are the coefficients of capital-saving and labour-saving technical change, _y_ is a distribution parameter between 0 and **1**, and _O"_ the elasticity of substitution between capital and labour. Under profit maximization (when factors are paid their marginal products), the rate of growth of the share of capital in factor payments is given by Eq. ( 4) (see Section 3.2).
Given the initial factor share for capital observed at time 0 (1961), by solving the differential Eq. (4), the factor shares that would have been observed in the time period _T_ ( 1994) in the absence of Hicks-biased technical change can be computed as _ao_ exp[(O"- 1)/0"](K- _L)T_ _aT_ = --------~------------~--~1 - _ao_ + _ao_ exp[(O"- 1)/0"](K- _L)T_ (A.2) TFP growth is computed by subtracting the bias-corrected growth rate of aggregate inputs (Eq. (A.3)) from the observed growth rate of aggregate output. The sensitivity of the corrected TFP growth rate to different assumptions about _O"_ depends on the growth rate of the capital:labour ratio and on the initial shares of capital and labour in total income.

_A.2. Corrections for_ _a multi-factor CES_ _production function_ A multi-factor production function that makes a distinction between labour, land, labour-saving and land-saving factors can be represented as _Q_ = [(XA)(a-1)/a + (1 _ _y)(XL)(a-1)/af!(l-a),_ _XA_ = _[8(EAA)(ar1)/aA_ +(1 _ _8)(EF F)(arl)/aA ]aA/0-aA),_ _XL=_ _[f3(ELL)(aL-1)/aL_ +(1- _f3)(EMM)(aL-I)faLfL/(I-aL)_ (A.4) where _XA_ (·)is the 'land' input index, XL(·) the 'labour' input index, _A_ and _L_ are land and labour use, respectively, _F_ the land-saving capital (fertiliser), _M_ the labour-saving capital (machinery), and the _Ei_ s are indices of biased technical change (see Hay ami and Ruttan (1985) for a discussion of the advantages of the two-level CES function over the translog function in the context of characterising biased technical change in agriculture). When factors are paid their marginal products, the rate of growth of factor shares is given by _aF_ A _-aA'_ A = ((JA-1)( ----- 1-- _aF)_ _O"A_ _aA'_
#### aM A -au A = (0"£ ----- 1) ( 1 - - aM)

_O"L_ _au_ X _[(M-_ L) - _(EL_ - _EM)]_ (A.5) where the hat symbol () denotes growth rate, _a F_ and _aM_ are the shares of land-saving and labour-saving inputs in total income. The shares ofthe land (XA) and labour (XL) aggregates in total income are given by _a A'_ The bias-corrected Divisia input index, using the corrected factor shares is then given by A A [0"/(1-0")]ln(aT/ao) _I_ = _K_ + .::..__:_~ ___ __:_T=-----'------_ (A.3)

Divisia input index


Input:
208 _R._ _Murgai/ Agricultural Economics 25 (2001) 199-209_ and _au,_ respectively (i.e. _a A'= ap +a A_ and _au=_ _aM+ aL)._ As in the two-factor case, given the initial factor shares in time period 0 (1961), the factor shares (more precisely, _ap faA'_ and _aM /au)_ that would have been observed in time period _T_ (1994) in the absence of Hicks-biased technical change can be computed for different values of the elasticities of substitution.
The Divisia input index using these bias-corrected factor shares is then given by where (A.7) The TFP residual is computed by subtracting the bias-corrected growth rate of aggregate inputs (Eq. (A.6)) from the observed growth rate of the aggregate output. The sensitivity of the computed TFP growth rate to different assumptions about _CJ,_ _CJ A,_ and _CJ L_ depends on the growth rate of the fertiliser:land ratio and machines:labour ratio, and the initial shares of fertiliser and land in the total income.

**References**

Ahluwahlia, D., 1996. Agricultural growth in India: trends, sources and challenges. Manuscript. World Bank Resident Mission, New Delhi.
Alston, J.M., Norton, G.W., Pardey, P.G., 1995. Science under scarcity: principles and practice for agricultural research evaluation and priority setting. Cornell University Press, Ithaca, London.
Antle, J.M., Capalbo, S.M., 1988. An introduction to recent developments in production theory and productivity measurement. In: Capalbo, S.M., Antle, J.M. (Eds.), Agricultural Productivity: Theory and Measurement. Resources for the Future. Washington, DC.
Binswanger, H.P., 1974. The measurement of technical change biases with many factors of production. Am. Econ. Rev. 64 (6), 964-976.
Binswanger, H.P., 1978. Measured biases of technical change: the United States. In: Binswanger, H.P., Ruttan, V.W., et a!. (Eds.), Induced Innovation: Technology, Institutions, and Development.
Johns Hopkins University Press, Baltimore, MD.
Byerlee, D., 1992. Technical change, productivity and sustainability in irrigated cropping systems of South Asia: emerging issues in the post-Green Revolution Era. J. Int. Dev.
4, 477-496.
de Janvry, A., Sadoulet, E., Fafchamps, M., 1989. Agrarian structure, technological innovations and the state. In: Bardhan, P. (Ed.), The Economic Theory of Agrarian Institutions. Oxford University Press, Oxford.
Desai, B., 1994. Contribution of institutional credit, self-finance and technological change to agricultural growth in India. Indian J. Agric. Econ. 49 (3), 457-475.
Dholakia, R.M., Dholakia, B.M., 1993. Growth of total factor productivity in Indian agriculture. Indian Econ. Rev. 28 (I), 25-40.
Diamond, P., McFadden, D., Rodriguez, M., 1978. Measurement of the elasticity of factor substitution and bias of technical change.
In: Fuss, M., McFadden, D. (Eds.), Production Economics: A Dual Approach to Theory and Applications, Vol. 2.
North-Holland Press, Amsterdam.
Diewert, W.E., 1976. Exact and superlative index numbers. J.
Econometr. 4, 115-146.
Diewert, W.E., Nakamura, A.O., Sharpe, A., 1999. Introduction and overview. Canadian J. Econ. 32 (Suppl. 5), 5-15.
Evenson, R.E., Pray, C.E., Rosegrant, M.W., 1999. Agricultural research and productivity growth in India. Research Report 109, International Food Policy Research Institute.
Hayami, Y., Ruttan, V.W., 1985. Agricultural development: an international perspective. Johns Hopkins University Press, Baltimore, MD.
Hazell, P., Ramaswamy, C., 1991. The Green Revolution reconsidered: the impact of high-yielding rice varieties in South India. Johns Hopkins University Press, Baltimore, MD.
Hsieh, C.T., 1998. Measuring biased technological change.
Working Paper, Princeton University.
Kako, T., 1978. Decomposition analysis of derived demand for factor inputs: the case of rice production in Japan. Am. J. Agric.
Econ. 60, 628-635.
Kumar, P., Rosegrant, M., 1994. Productivity and sources of growth for rice in India. Econ. Pol. Weekly 29 (53), 183-188.
Murgai, R., 1997. Diversity in economic growth and technical change: a district wise disaggregation of the Punjab and Haryana growth experience. Ph.D. Dissertation, University of California, Berkeley.
Murgai, R., Ali, M., Byerlee, D., 2000. Productivity growth and sustainability in post-Green Revolution Agriculture in the Indian and Pakistan Pun jabs. World Bank Research Observer (in press).
Pingali, P.L., Heisey, P.W., 1999. Cereal crop productivity in developing countries: past trends and future prospects.
CIMMYT Economics Paper 99-03, Mexico DF, CIMMYT.
Rodrik, D., 1997. TFPG Controversies, Institutions, and Economic Performance in East Asia. NBER Working Paper 5914, Cambridge, MA.
Rosegrant, M.W., Evenson, R.E., 1993. Agricultural productivity growth in Pakistan and India: a comparative analysis. Pakistan Dev. Rev. 32 (4), 433-451.
Ruttan, V.W., Binswanger, H.P., 1978. Induced innovation and the Green Revolution. In: Binswanger, H.P., Ruttan, V.W., et a!. (Eds.), Induced Innovation: Technology, Institutions and Development. Johns Hopkins University Press, Baltimore, MD.

Divisia input index


Input:
_R._ _Murgai/ Agricultural Economics 25 (2001) 199-209_ 209 Sidhu, D.S., Byerlee, D., 1992. Technical change and wheat productivity in the Indian Punjab in the post-Green Revolution period. CIMMYT Economics Paper 92-02, Mexico, DF.
Thirtle, C., 1985a. The microeconomic approach to induced innovation: a reformulation of the Hayami and Ruttan model.
Manchester Sch. Econ. Social Stud. 53 (3), 263-279.
Thirtle, C., 1985b. Induced innovation in United States field crops, 1939-1978. J. Agric. Econ. 36, 1-14.

Yeung, P., Roe, T.L., 1978. A CES test of induced technical change: Japan. In: Binswanger, H.P., Ruttan, V.W., et al.
(Eds), Induced Innovation: Technology, Institutions and Development. Johns Hopkins University Press, Baltimore, MD.
Young, A., 1995. The tyranny of numbers: confronting the statistical realities of the East Asian growth experience. Quart.
J. Econ. 110 (3), 641-680.


Input:
Chong, Alberto; Galdo, Virgilio

**Working Paper**
## Should State-Owned Firms Change CEOs Before Privatization? The Case of the Telecommunications Industry

Working Paper, No. 481

**Provided in Cooperation with:**
Inter-American Development Bank (IDB), Washington, DC _Suggested Citation:_ Chong, Alberto; Galdo, Virgilio (2003) : Should State-Owned Firms Change CEOs Before Privatization? The Case of the Telecommunications Industry, Working Paper, No. 481, InterAmerican Development Bank, Research Department, Washington, DC This Version is available at:
[https://hdl.handle.net/10419/88052](https://hdl.handle.net/10419/88052)

**Standard-Nutzungsbedingungen:**

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.

Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.

Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.

**Terms of use:**

_Documents in EconStor may be saved and copied for your personal_ _and scholarly purposes._ _You are not to copy documents for public or commercial purposes, to_ _exhibit the documents publicly, to make them publicly available on the_ _internet, or to distribute or otherwise use the documents in public._ _If the documents have been made available under an Open Content_ _Licence (especially Creative Commons Licences), you may exercise_ _further usage rights as specified in the indicated licence._

EconStor


Input:
Inter-American Development Bank Banco Interamericano de Desarrollo (BID) Research Department Departamento de Investigación Working Paper #481

# Should State-Owned Firms Change CEOs Before Privatization? The Case of the Telecommunications Industry

by

### Alberto Chong Virgilio Galdo

#### Inter-American Development Bank

February 2003


Input:
**Cataloging-in-Publication data provided by the**
**Inter-American Development Bank**
**Felipe Herrera Library**

Chong, Alberto.

Should state-owned firms change CEOs before privatization : the case of the telecommunications industry / by Alberto Chong, Virgilio Galdo.

p. cm. (Research Department Working paper series ; 481) Includes bibliographical references.

1. Telecommunication. 2. Government business enterprises.  3. Government business
enterprises--Employees. 4. Privatization. I. Galdo, Virgilio. II. Inter-American Development Bank. Research Dept. III. Title. IV. Series.

384 C889--------dc21 ©2003 Inter-American Development Bank 1300 New York Avenue, N.W.
Washington, D.C. 20577 The views and interpretations in this document are those of the authors and should not be attributed to the Inter-American Development Bank, or to any individual acting on its behalf.

The Research Department (RES) produces the Latin American Economic Policies Newsletter, as well as working papers and books, on diverse economic issues.

To obtain a complete list of RES publications, and read or download them please visit our web site at: [http://www.iadb.org/res](http://www.iadb.org/res/32.htm) 2

Cataloging-in-Publication data

Cataloging-in-Publication data


Input:
**Abstract** **[*]**

Should state-owned enterprises change chief executive officer before privatizing?
We test competing views on this question by complementing a recently released database with newly collected data. We are able to cover 77 telecommunications privatizations, which account for nearly 80 percent of the sector in terms of value.
We find that CEO replacement will improve performance in the telecommunications industry before privatization as measured by penetration, operating efficiency, and profitability. CEO change before privatization does appear to have real consequences in firm performance before privatization.
Moreover, findings are consistent with previous research that links CEO replacement and an increase in privatization prices.

**JEL Classification** : G32, H10, J45, O1
**Word Count** : 2,195
**Key Words** : Privatization, Corporate Governance, Telecommunications

- Research Department, Inter-American Development Bank. Luisa Zanforlin provided comments and suggestions.
The usual disclaimer applies. The findings and interpretations of the authors do not necessarily represent the views of the Inter-American Development Bank or its executive directors. Corresponding author: Alberto Chong, Research Department, Inter-American Development Bank, Stop W-0436, 1300 New York Ave, NW, Washington, DC 20577, USA. Fax: (202) 623-2481, Tel: (202) 623-1536. E-mail: _albertoch@iadb.org_ 4

recently released database

newly collected data

newly collected data


Input:
#### **1. Introduction**

Recent research shows that the change in CEO before privatization has a bearing on privatization prices. In fact, when taking endogeneity problems into account, López-de-Silanes (1997) shows that replacing the CEO before privatization is linked with a 54 percent increase in the net privatization price in the case of Mexico. Buyers may be willing to pay higher prices for firms whose CEOs have been replaced, as profitability of the firm may have improved due to restructuring before privatization. The new owners may have to do less tweaking with the firm after privatization and may reap benefits relatively faster. This finding is particularly important as CEO replacement before privatization has become a relatively common policy issue in developing countries, and it tends to receive relatively broad political support (Chong and López de-Silanes, 2002a). Still, taking the findings by López-de-Silanes (1997) at face value, there is still some lack of understanding on the mechanisms by which CEO replacement may affect the productive process of a firm before privatization.

In this note we seek to complement the research by López-de-Silanes, by exploring the two key possible views by which replacement of top management before privatization may impact the productive process of the firm and thus increase privatization prices. The first view is that a change in CEO before privatization sends an unequivocal signal to prospective buyers on the seriousness of the privatization process. This eliminates incentives for corruption on the part of the old management that otherwise, would have tried to maximize rents before the firm is privatized. According to this signaling view, top management replacement before privatization will contribute to the increase in privatization prices once the firm is on sale, regardless of any restructuring done before privatization (Kikeri, 1999).

On the other hand, according to the restructuring view, the managers of state-owned enterprises are good in terms of political abilities but are not good at actually running firms. The current management has the wrong human capital to face competition and the new market conditions that the firm will soon face once it is privatized. A new chief executive officer, with the appropriate human capital, is thus needed in order to implement all necessary restructuring and run the firm efficiently until privatization takes place (Rosen, 1992; Barberis, Boycko, Shleifer, and Tsukanova, 1996). Thus, replacing the chief executive officer before privatization provides the firm with a step forward towards adapting the firm to the forthcoming market conditions before actual transference of ownership to private hands takes place. This, it is 6


Input:
believed, helps smooth out the transition of the firm to a competitive market economy.

According to this view, top management replacement before privatization will yield higher privatization prices, as prospective buyers will value the restructuring done before privatization. [1] We build on a recent data set by Chong and López-de-Silanes (2002b) who collect information on privatization characteristics of state-owned enterprises for firms around the world. We focus on the sub-group of firms from the telecommunications sector. As is well known, until recently this sector has been typically considered among the “jewels” of privatization. Focusing on one single sector allows us to minimize potential problems related with industry heterogeneity. Also, the gathering of additional data necessary for this research is relatively less complicated, as several data sources are available for the case of telecommunications. We are thus able to expand the data set from Chong and López-de-Silanes and further minimize sample bias problems, if any. [2] Our paper is organized as follows. Section 2 describes the data employed. Section 3 presents our empirical findings. Finally, the last section concludes.

#### **2. Data**

Our sample is based on a data set by Chong and López-de-Silanes (2002b) who obtained information for 400 privatizations around the world for the period 1984-2000, of which around 74 were from the telecommunications sector. The sample covers about 75 percent of the privatizations carried out in the telecommunications sector in the world and account for an estimated 80 percent of the revenues brought in by privatizations in the sector worldwide during the 1990s (World Bank, 2001). [3] Furthermore, we are able to expand their sample for telecommunications firms by around five percent by using three additional sources: the firms themselves, the regulatory agencies, and the International Telecommunications Union (2002). To 1 Barberis, Boycko, Shleifer, and Tsukanova (1996) use a survey of Russian shops to measure the importance of alternative channels through which privatization promotes restructuring. While they find that the presence of new managers in privatized firms raises the likelihood of restructuring the firm after privatization, they show that keeping old managers but adding equity incentives in such firms do not promote restructuring after privatization. According to these researchers, their findings provide empirical support to the view that management replacement after privatization is crucial in the restructuring process of the firm.
2 Still, Chong and López-de-Silanes (2002b) provide evidence that their sample of firms is unbiased.
3 These researchers prepared a detailed questionnaire addressed to the CEO with a recommendation to direct it to the chief financial officer and the director of human resources of the corresponding firms. They also used additional sources extensively, in particular, documents from multilateral organizations, regulatory agencies, ministries, and international publications (Chong and López-de-Silanes, 2002b).

7

survey of Russian shops
data set by Chong and López-de-Silanes

survey of Russian shops

data set by Chong and López-de-Silanes

survey of Russian shops

data set


Input:
achieve this, we followed a procedure analogous to that in Chong and López-de-Silanes (2002b).

Thus, we organized a questionnaire in two different areas. The first area covered pre privatization firm characteristics, such as sales, profits, liabilities, CEO change, and the presence of unions. The second area focused on the privatization process, and in particular, on shares sold, the type of sale, and foreign participation. The key explanatory variable of interest is a dummy variable that equals one if the firm changed CEO up to three years prior to privatization, and zero otherwise. [4] We complemented our firm-specific information with country-level macroeconomic data, mainly from the World Bank (2001) and Wallsten (2001). In particular, we used the rate of growth, the rate of inflation, the gross domestic product, and the presence of an independent regulatory agency at the time of CEO replacement, among other measuress. Additionally, we gathered information on economic performance at the firm level from Bloomberg, Economatica, Worldscope, and the International Telecommunications Union (2002). [5] Following related work on the telecommunications literature, three basic outcome measures were collected: change in telephone lines per inhabitant before and after CEO change (penetration), change in telephone lines per employee before and after CEO change (operational efficiency), and change in returns on sales before and after CEO change (profitability). Table 1 presents exact definitions of all the variables used in this paper. [6]

#### **3. Evidence**

Along the basic lines of the methodology by La Porta and López-de-Silanes (1999), Table 2 provides empirical evidence on the link between CEO change before privatization and change in economic performance after replacement. The evidence points towards a confirmation of the view that CEO replacement before privatization is associated with firm restructuring, rather than with firm signaling. Not only is the coefficient of our variable of interest positively associated with change in firm performance before privatization, but it is also statistically significant at 1 4 In fact, according to our sample, CEO change before privatization varies widely from region to region. While it has been common in Africa, Latin America, and developed countries, it has been much less used in Asia and Transition Economies.
5 Subscription-based Bloomberg, Economatica, and Worldscope deliver international financial information in a standardized format that facilitates comparisons between companies, countries, regions, and industries. Worldscope, for instance, covers approximately 90 percent of the worlds stock market value and includes records on more than 20,000 active companies representing over 50 emerging and established markets.

8

country-level macroeconomic data

country-level macroeconomic data


Input:
percent with respect to change in penetration, and at 5 percent with respect to both change in operating efficiency and change in profitability. In fact, the results suggest that CEO change has a considerable positive impact on changes in performance, as it adds 35 percentage points to mean changes in penetration, 57 percentage points to mean changes in operating efficiency and 7 percentage points to mean changes in profitability. Interestingly, these results are stable to the inclusion of a dummy variable that accounts for the presence of an independent regulatory agency at the time of CEO replacement. The latter is also shown in Table 2.

Furthermore, according to our results, CEO replacement is the only statistically significant variable that is robustly linked with change in firm economic performance among the privatization characteristics considered in this research. In fact, while foreign participation in the privatization process yields a positive and statistically significant sign with respect to change in firm profitability, it does not have any bearing on either change in penetration or change in operating efficiency. Similar results are obtained in the case of the percentage of shares sold, as such a variable is only statistically significant when associated with change in profitability, but not when linked with change in penetration or change in operating efficiency. [7] Similarly, most of the firm characteristics considered in this paper do not appear to have a bearing on change in economic performance either. For instance, a dummy that accounts for net total liabilities before privatization usually does not yield a statistically significant link with the change in performance variables considered, and when it does, it is at ten percent and with the wrong sign (operating efficiency). On the other hand, average sales before privatization is statistically significant with change in profitability only, while a dummy that accounts for the presence of unions has no bearing on performance change. However, some country-specific variables do appear to have a bearing on change in firm performance. In particular, a dummy that accounts for whether the country has pursued economic reforms yields a positive sign, which is statistically significant at five percent or better with respect to change in penetration and change in profitability. However, such a variable is only weakly significant with respect to change in operating efficiency in the absence of a regulatory agency dummy.

6 Summary statistics for all the variables used are presented in Appendix 1.
7 Moreover, we do not obtain the expected sign.

9


Input:
#### **4. Conclusions**

In a context in which state-owned enterprises still represent a sizable fraction of the world’s gross domestic product, investment, and employment (Kikeri, 1999) and in light of the current political sensitivities towards privatization in several regions, we provide evidence on the benefits of CEO replacement before privatization that complements López-de-Silanes (1997) by showing that actual firm restructuring may be the missing link between CEO replacement and privatization prices. From a policy perspective, our research helps provide a better understanding of what works and what does not work in privatization programs, and thus may help improve the design of future programs. In fact, as actual firm reform appears to be the mechanism at play between CEO replacement and privatization prices, governments should weigh the trade-off between political costs and economic benefits of restructuring the firm.

10


Input:
#### **References**

Barberis, N., M. Boycko, A. Shleifer et al. 1996. “How Does Privatization Work? Evidence from the Russian Shops.” _Journal of Political Economy_ 114: 764-790.

Chong, A., and F. López-de-Silanes. editors. 2002a. “Costs and Benefits of Privatization in Latin America.” Washington, DC, United States: Inter-American Development Bank.

Manuscript.

----. 2002b. “Privatization and Labor Force Downsizing Around the World.” New Haven, United

States: Yale University. Forthcoming as NBER Working Paper.

International Telecommunication Union (ITU). 2002. _Yearbook of Statistics Telecommunication_ _Services 1991-2000_ . Geneva Kikeri, S. 1999. “Privatization and Labor: What Happens when Governments Divest?” World Bank Technical Paper 396. Washington, DC, United States: World Bank.

La Porta, R., and F. López-de-Silanes. 1999. “The Benefits of Privatization: Evidence from Mexico.” _Quarterly Journal of Economics_ 114(4): 1193-1242.

López-de-Silanes, F. 1997. “Determinants of Privatization Prices.” _Quarterly Journal of_ _Economics_ 112(4): 965-1025.

Rosen, S. 1992. “Contracts and the Markets for Executives.” In: L. Werin and H. Wijkander, editors. _Contract Economics_ . Oxford, United Kingdom: Blackwell.

Wallsten, S. 2001. “An Empirical Analysis of Competition, Privatization, and Regulation in Africa and Latin America.” _Journal of Industrial Economics_ 49(1): 1-19.

World Bank. 2001. World Development Indicators. Washington, DC, United States: World Bank. CD-ROM.

11

World Development Indicators


Input:
Variable _**Privatization Characteristics**_

**Table 1. Definition of Variables**

Description CEO change Dummy variable equal to 1 if the company changed CEO up to three years prior to privatization, 0 otherwise (Chong and Lopez-de-Silanes, 2002).
Foreign participation Dummy variable equal to 1 if foreign participation was allowed in the privatization process, and 0 otherwise (Chong and Lopez-de-Silanes, 2002).
Share sold Percentage of firm's shares sold in privatization (Chong and Lopez-de-Silanes, 2002).
Independent agency Dummy variable equal to 1 when an independent regulatory agency was established, and 0 otherwise. An independent agency means that the country has a regulatory agency not directly under control of a ministry (Wallsten, 2001) .

_**Firm Characteristics**_ Penetration Percentage change between the two-year average of firm lines per 100 inhabitants before CEO change and the two-year average after CEO change (WorldScope, 2001; International Telecommunications Union, 2002).
Operating eficiency Percentage change between the two-year average of firm lines per employee before CEO change and the two-year average after CEO change (WorldScope, 2001; International Telecommunications Union, 2002).
Profitability Change between the two-year average of return on sales before CEO change and the two-year average after CEO change (WorldScope, 2001; International Telecommunications Union, 2002 ; Bloomberg and Economatica).
Net total liabilities Dummy variable equal to 1 if net total liabilities are greater than zero up to three years prior to privatization, and 0 otherwise (Chong and Lopez-de-Silanes, 2002).
Sales The net present value of the three-year average of firm sales before CEO change. The sales value were denominated in US$ of the initial year (Chong and Lopez-de-Silanes, 2002).
Presence of unions Dummy variable equal to 1 if firm had unions or strikes up to three years prior to privatization, and 0 otherwise (Chong and Lopez-de-Silanes, 2002).

_**Country-Specific Variables**_ Economic growth Average rate of growth of the country three years prior to privatization (World Bank, 2001).
Gross domestic product Gross Domestic Product (US$ PPP) in logs. Average of the three years prior to privatization (World Bank, 2001).
Urban population Average percentage of urban population three years prior to privatization (World Bank, 2001).
Expropriation risk Index that can take values between 0 and 10, with lower scores indicating a lower risk of expropriation (ICRG’s risk ratings).
Economics reforms Dummy variable equal to 1 if telecom privatization took place in an economics reform context (World Bank).
Continental dummies Dummy equal to 1 if to account for the following regions: Latin America, Middle East and Africa, Transition Economies, 0 otherwise.

12


Input:
**Table 2. CEO Change and Pre-Privatization Performance**

Variables Penetration Operating Efficiency Profitability _**1.- Privatization Characteristics:**_ CEO change 0.3583 *** 0.3574 *** 0.5800 ** 0.5776 ** 0.0722 ** 0.0722 ** (0.095) (0.095) (0.274) (0.266) (0.033) (0.033) Foreign Participation -0.1128 -0.0722 -0.3208 -0.2179 0.0669 - 0.0679 (0.123) (0.123) (0.261) (0.259) (0.039) (0.040) Share Sold -0.0023 -0.0007 -0.0097 -0.0055 -0.0022 - -0.0021 (0.004) (0.004) (0.011) (0.009) (0.001) (0.001) Independent Agency -0.2393 ** -0.6067 ** -0.0063 (0.097) (0.229) (0.027) _**2.- Firm characteristics:**_ Net total liabilities 0.0875 0.0488 0.4488 - 0.3505 0.0367 0.0357 (0.107) (0.104) (0.256) (0.222) (0.033) (0.034) Sales 0.0026 0.0011 0.0064 0.0025 -0.0039 ** -0.0039 ** (0.004) (0.004) (0.015) (0.015) (0.002) (0.002) Presence of unions -0.0011 -0.0972 -0.1631 -0.4068 0.0230 0.0204 (0.251) (0.244) (0.364) (0.372) (0.049) (0.053) _**3.- Country-Specific Variables:**_ Economic Growth 0.0222 0.0220 0.0624 0.0617 *** 0.0049 0.0049 (0.018) (0.016) (0.072) (0.068) (0.005) (0.005) Gross Domestic Product -0.0092 0.0069 -0.0790 -0.0381 -0.0256 - -0.0252 (0.034) (0.031) (0.093) (0.092) (0.012) (0.012) Urban Population -0.0178 *** -0.0175 *** -0.0199 *** -0.0191 *** 0.0013 0.0013 (0.003) (0.003) (0.006) (0.005) (0.001) (0.001) Expropriation Risk 0.0160 0.0265 -0.0040 0.0227 -0.0728 ** -0.0725 ** (0.051) (0.050) (0.123) (0.117) (0.017) (0.017) Economics Reforms 0.2653 ** 0.1919 - 0.4510 - 0.2648 0.1087 *** 0.1068 ** (0.118) (0.112) (0.244) (0.226) (0.040) (0.040) Latin America 0.5677 *** 0.5879 *** 1.4845 ** 1.5358 *** -0.0580 -0.0575 (0.174) (0.154) (0.586) (0.558) (0.054) (0.054) Asia 0.7435 ** 0.7280 - 0.3214 0.2822 -0.1492 *** -0.1496 *** (0.307) (0.315) (0.543) (0.559) (0.052) (0.052) Africa and Middle East 0.2725 0.3020 -0.4543 -0.3795 -0.1276 ** -0.1268 ** (0.253) (0.243) (0.388) (0.393) (0.056) (0.056) Transition Economies 0.3140 *** 0.2869 ** 0.3731 0.3044 -0.1038 ** -0.1045 ** (0.142) (0.138) (0.351) (0.350) (0.044) (0.045) Constant 1.3200 0.9251 3.7332 2.7318 1.2463 *** 1.2359 *** (0.900) (0.930) (2.573) (2.662) (0.371) (0.376) Observations 77 77 77 77 77 77 R-squared 0.73 0.76 0.50 0.54 0.62 0.62 F 12.81 15.54 5.47 6.59 3.85 3.62 Prob > F 0.000 0.000 0.000 0.000 0.000 0.000 Robust standard errors in parentheses

- significant at 10%; ** significant at 5%; *** significant at 1%

13