Price setting behaviour in Lesotho: The role of outlet characteristics and competition

by

Mamello Nchake and Lawrence Edwards

PEDL research workshop, London, United Kingdom

Outline • Background

• Objective of the project

• Retail price data

– Results (price data)

• Survey data

– Preliminary results (survey data)

• Determinant of price setting using merged datasets

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Background • Analysis of firm pricing behaviour using micro price

data has grown rapidly in recent years as micro pricing data has become more available (Klenow and Malin, 2011).

• These studies have drawn on a variety of data sets – micro price data used to compile Consumer Price Index

(CPI) and Producer Price Index (PPI) measures,

– scanner data and firm survey data.

• Most studies, however, have focused on advanced economies. With a few exceptions, the challenge for studies on pricing behaviour in emerging economies has been the availability of price data at the micro level.

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Objective of the project

• Development of integrated retail price and outlet characteristics database for Lesotho. – Build a dataset of monthly product at the outlet

level using data from the Lesotho Bureau of Statistics (BOS)

– Conduct survey of these retail outlets

– Link the product prices database at these outlet survey database

• Use data to analyse characteristics and determinants of price-setting behavour

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183

79

56

66

49

70

83

91

50

47

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The product price dataset 1. Outlet product price data

Monthly product price data (March 2002-Dec 2009)

across the 10 districts of Lesotho

345 retailers

317 in urban centres

28 in rural areas

229 products 91 food (41 perishables, 50 non-perishables)

138 non-food (59 durables, 56 non-durables, 23 services) 12/17/2013 6

Product price dataset

Objectives To identify stylized facts that characterize price setting

behaviour (e.g. The frequency of price change, price increases and price decreases)

To identify the dynamic features of price changes, including the synchronization of price changes and decomposition of inflation

To use data to ‘test’ consistency of theoretical models of price setting behaviour

To compare the stylised facts on price setting behaviour in Lesotho to other countries and South Africa in particular.

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Selected stylized facts - using price data 1. Heterogeneity in the frequency of price change

Heterogeneity across products :

Mean frequency of 37 percent (implied duration of 2.7 months)

The frequency of price change exceeds 40 percent (implied duration of 2.5 months or less) for a quarter of all product items.

For 24 percent of all product items, prices change at most once every 4 months (frequency of 24 percent or lower).

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0 .2 .4 .6 .8Frequency of price change

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Selected stylized facts - using price data

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CPI weighted

Simple average Median

sd frequency

within products Implied duration

Goods 0.386 0.392 0.122 2.593

Food 0.44 0.447 0.131 2.275

Non-alcoholic beverages 0.351 0.383 0.12 2.851

Alcoholic beverages 0.228 0.266 0.092 4.384

Tobacco and narcotics 0.37 0.365 0.144 2.704

Clothing and footwear 0.357 0.376 0.108 2.803

Fuel 0.405 0.384 0.139 2.467

Household furniture and equipment 0.259 0.249 0.097 3.867

Household operations 0.373 0.372 0.121 2.68

Transport equipment 0.277 0.308 0.04 3.607

Personal care 0.314 0.322 0.123 3.187

Services 0.125 0.102 0.057 8.006

Medical care and health expenses 0.135 0.084 0.095 7.407

Recreation and culture 0.218 0.229 0.09 4.587

Education 0.087 0.087 0.048 11.553

Transport services 0.187 0.113 0.033 5.345

Other goods and services 0.293 0.217 0.104 3.412

Total 0.371 0.379 0.119 2.692

Heterogeneity within products across outlets

Selected stylized facts - using price data

• Large average size of price change, but many small price changes

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Selected stylized facts - using price data 2. Dynamic features of price changes: sources of

change in inflation

Gives important theoretical insights;

– In the time-dependent staggered price models of Taylor (1980) and Calvo (1983), the intensive margin (IM) accounts for all the variation in inflation,

– In some state-dependent pricing models, the frequency plays a substantial role (Malin and Klenow, 2011).

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Selected stylized facts - using price data

– Size of price changes (IM) is more closely associated (acc for 94.9 % of inflation) with inflation than the frequency of price changes (EM).

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Selected stylized facts - using price data – …but the frequency of price increases (decreases) is

positively (negatively) correlated with inflation

– Reductions in prices constitute an important source of

variation in inflation (34 percent of inflation variation) 12/17/2013 13

Selected stylized facts - using price data

3. Comparison with South Africa – Product markets in Lesotho are expected to be highly

integrated with those in South African – Frequency of price change much higher in Lesotho (47

percent vs. 17 percent)

– Significant positive association between frequency of price changes in the two countries • Driven by the frequency of price increases, not decreases.

– No association between size of price change

Dependent variable: Lesotho

Independent variable: South Africa Coef Std. Err Significance

Adj R2

Frequency price change 0.130 0.024 *** 0.42

Frequency price increase 0.187 0.026 *** 0.23

Frequency price decrease 0.044 0.033 0.28

Inflation 0.084 0.030 *** 0.06

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Concluding remarks - using price data

• We also look at many other stylized facts

– Duration and size, distribution of size, hazard rates

• What is missing in the price data analysis is the role of outlet characteristics. We use survey of retail outlets to unpack this.

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Administering survey

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The dataset Outlet survey data across the 10 districts of Lesotho

District Number of

outlets

Total

Sample

Number of

respondents

Response rate

Maseru 183 128 83 64.8

Butha-Buthe 56 39 29 74.4

Leribe 79 70 69 98.6

Berea 70 50 43 86.0

Mafeteng 83 39 29 74.4

Mohales' Hoek 91 41 36 87.8

Quthing 50 48 44 91.7

Qachas' Neck 47 42 39 92.9

Mokhotlong 66 39 35 89.7

Thaba-Tseka 49 39 34 87.2

Total 774 535 441 82.4

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Survey dataset

Objective

Identify role of location, competition and outlet level characteristics in determining price setting behaviour in Lesotho

Two channels to analyse price setting behaviour

Analyse the main price setting strategies of retail and services outlets

Analyse factors that prevent outlets from increasing prices of main product/service

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Preliminary results –survey data

1. What are main price setting strategies across outlets?

– Decision making process of price setting mostly

takes place within the outlet itself.

– Dominance of mark-up consistent with imperfectly competitive markets

Share of total responses (percent)

Variable Retail Services Total

Mark-up over costs 74.6 61.5 71.8

Government 2.4 6.6 3.3

Business council 3.3 7.7 4.3

The parent company/ Head quarters 15.5 11.0 14.5

Main competitor's price 4.2 13.2 6.2

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Preliminary results –survey data

2. Which factors delay increasing the price of the main product/service?

Main factors of price rigidity Share of all responses (percentage)

Important Very

important

Total

Coordination failure 16.9 27.7 44.6

Explicit contracts 5.8 8.0 13.7

Attractive thresholds 8.6 6.2 14.8

Menu costs 9.1 8.1 17.2

Implicit contracts 16.9 38.9 55.8

Bureaucratic rigidities 5.6 6.6 12.2

Other 5.0 11.7 16.7

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Merging the two datasets

Link the survey data with the retail price database

Analyse the main determinants of frequency of price change

– outlet characteristics and market structure

Use price data from January 2010-December 2012 which closely map the outlets in that sample

– Final sample has 370 mapped outlets and 14 main products

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Preliminary results

• Descriptive analysis: Unconditional relationships

1. The frequency of price changes is higher for large firms (31 %) than for small firms (29%)

2. Retail outlets change prices more frequently (30%) than services (24%)

3. The frequency is higher for supermarkets than other outlets and lower for chain stores than other outlets

4. Outlets with more competitors change prices more frequently (30%) than outlets with low competition (29%)

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Preliminary results – conditional relationships

The competition variable may be biased due to omitted variable, e.g. demand in that market

VARIABLES (1)

Outlet level

Number of competitors -0.003**

(0.002)

Outlet factors

Size of outlet 0.039***

(0.009)

Services -0.097***

(0.020)

Supermarket -0.038**

(0.017)

Chain 0.005

(0.013)

Incl. other controls(location, assets) Product fixed effects No

Area fixed effects Yes

Observations 280

Adj. R-squared 0.27

Columns 1 are basic regression and columns 2 are basic regressions with area fixed effects. Robust standard errors in

parentheses are clustered at outlet level. *** p<0.01, ** p<0.05, * p<0.1

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Results – Frequency price change • Instrumental variable approach

(1)

VARIABLES Outlet level

Number of competitors 0.020**

(0.009)

Outlet factors

Size of outlet 0.022**

(0.011)

Services -0.106***

(0.027)

Supermarket -0.053**

(0.023)

Chain -0.023

(0.016)

Plus controls (location and assets)

Observations 280

Adj. R-squared 0.83

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Concluding remarks • Most outlets set the prices of their main product based

on mark-up over costs

• Size of outlet and level of competition are important determinants of the frequency of price change – large outlets change prices more frequently than smaller

outlets – Outlets faced with more competition change prices more

frequently

• Implicit contracts or long-term relationships with

customers and coordination failure are important sources of price stickiness

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Way forward • Price database: extend the analysis to look at:

– product market integration, role of borders in driving price differences

– links between inflation, monetary policy and price setting behaviour

• Survey database: Unpack outlet characteristics in more

systematic way. – This involves investigating the endogeneity of competitive

measures in price setting decisions

• NB: We also want to raise finance to run survey again in

2015 to develop panel to analyse price dynamics at outlet level.

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Thank you!!!