1. Vahram Ghushchyan, Ph.D., AIPRG Mher Baghramyan, AIPRG
Implications of Armenian Dram Appreciation for the Competitiveness of Armenian IT, Tourism, and Food Processing Industries
Vahram Ghushchyan, Ph.D., AIPRG
Mher Baghramyan, AIPRG
This study was made in 2007, using data from survey conducted by AIPRG within IT, Tourism and Food Processing companies.
Sponsors: CAPS, NUPI
Conference Looking Forward: Global Competitiveness of the Armenian Economy
Washington, DC
May 17-18, 2008

2. AMD/USD Nominal Exchange Rate, Armenia
1997
2002
2003
2004
2005
2006
2007
May 2008
Nominal Exchange Rate, AMD/USD, (year average)
490.8
573.4
578.8
533.5
457.7
416
342
310
Change in Nominal Exchange Rate, % to previous year
3.3%
0.9%
-7.8%
-14.2%
-9.1%
-17.8%
-9.4%
Source: NSS of Armenia
Starting from 2004, the Armenian currency, Dram, has been experiencing dramatic appreciation.
Theoretically, domestic currency appreciation can have extremely negative effect for all three industries under investigation.
For tourism, along with appreciation, domestic prices (when denominated in foreign currency) become more expensive for foreign visitors and their number may decrease in favor of alternative cheaper destinations.
In case of IT Industry, most of Armenian IT companies either export their products or operate as outsourcing contractors. Most of their costs are denominated in Armenian drams, labor being the largest cost category, which makes cost-cutting almost impossible. In the competitive market, the entire burden of dram appreciation can be offset only by an increase of dollar price, if that company operates at the possible highest efficiency level, which again makes them less able to compete with other outsourcing destination.
The most common official explanation of that phenomenon are high inflows of remittances from abroad, possible undervalued position of the real exchange rate prior 2003, rapid growth in income and productivity, as well as a process of de-dollarization of the economy following new banking and legal regulations and the depreciation of dollar with respect to other major world currencies (Euro, Yuan, Yen, etc). Despite high public pressure, Central Bank of Armenia continues to follow the policy of prioritizing low inflation rate rather than supporting stable exchange rate. Instead of developing state programs for assisting businesses to overcome the negative impact of the appreciation, the common reaction of Armenian officials to the complaints of Armenian producers and exporters regarding the negative impact of the appreciation has been advice to increase the productivity. However, few appear concerned with the feasibility of such productivity growth.
Moreover, according to a recent study by the World Bank (World Bank, 2007), Armenia has achieved significant improvements in the labor productivity. It is quite possible that many Armenian enterprises that already have modernized their technology and have applied effective management and quality control systems, have already completed the catch-up process in productivity, and sustaining further productivity growth might not be feasible for them.
In order to account for the growth in productivity and efficiency and reveal the “pure” impact of exchange rate appreciation, we try to estimate the degree of the technical efficiency at which Armenian companies operate, and then measure the possible impact that the exchange rate appreciation had for technical efficiency and thus for the overall performance of the companies.

3. Methodology: Stochastic Frontier Model
Outputs .
Production frontier
Actual production
Source: Wu, 1996, modified by authors
Technical Inefficiency
Allocative Inefficiency
The method used in our analysis is called Stochastic Frontier Model which we will use to estimate the degree of technical efficiency (TE) of Armenian companies.
In real life two types of inefficiencies exist: i) technical efficiency that allows to produce the maximum level of output given the level of inputs (ENTER) and ii) allocative efficiency that requires production of given level of output as cheaply as possible (ENTER).
To understand the level of efficiency of the firm, we need to have the level of output of absolutely efficient firm, which is known as production frontier, and then compare the output of the firm with the frontier. The stochastic models helps us to estimate the production frontier and level of inefficiency.
Inputs

4. Methodology (continued)
Exchange Rates
Technical Efficiency
Exports,
IT and Food Processing
Profits,
Tourism and Food Processing
After obtaining the levels of technical efficiency, we regress them on exchange rates to see whether the appreciation of the Armenian currency had affected the technical efficiency or not. Having that, we use technical efficiency estimates as independent variables in the regression models where exports and profits are dependent variables. We finally come out with the estimate of the impact that exchange rates have for profits and exports through affecting the technical efficiency. In our model, TE is used as an intermediary link affected by exchange rate which in its turn can bring about important changes into the levels of profit and exports.

5. 23 Food Processing Companies 13 IT companies
Collected Data
23 Food Processing Companies
13 IT companies
15 Incoming Tour Operators
7 Hotels
The data used for this study was obtained during the survey of Armenian food processing companies conducted by AIPRG among IT companies, tour operators, hotels, and food companies during June-September, Initially, data of 50 companies from each sector of economy was intended to be studied; however, only 23 food processing companies, 15 incoming tour operators, 7 hotels, and 13 IT companies agreed to provide their firm-level data.
The data for Revenue, Capital, Wages and other monetary variables is adjusted for the inflation by using GDP Deflator of Armenia with base year of 1996.
Since the data for each company was not sufficient enough to run our analysis for each industry separately, we had to pool IT and tourism companies together, at the same time introducing dummy variables for each industry. The analysis for food industry was conducted separately.

6. Mean Annual Values per Company, 2006IT
Hotels
Tour Operators
Food
Revenue, USD
489,942
761,072
170,209
1,346,085
Profit, USD
57,466
303,894
26,209
155,653
Capital Assets, USD
348,788
3,295,673
44,524
415,954
Labor, persons 51 71 13
Monthly wage of productive workers, USD
329
111
173 81
Wage of admin. workers, USD
380
250
183
158
The table presents data for 2006 on some variable, such as Revenue, Profits, Capital Assets, Labor, and wages.

7. Model 1: Translog Production Function
Model 2: Cobb-Douglas Production Function
where capital (K), labor (L) time (t) are input factors used to estimate the stochastic frontier model, and Y is the output.
Dependent Variables
Independent Variables

8. Technological Progress (TP) :
TP is calculated as a derivative of the production function with respect to time
If TP is positive (negative), then the production frontier shifts up (down).

9. Food Processing companies
Summary Statistics of Technical Efficiency (te1) by Company Type, Average
Obs
Mean
Std. Dev.
Min
Max
Hotels 22
0.43
0.21
0.86
IT companies 27
0.50
0.27
0.06
Tour Operators
0.52
0.23
0.10
0.87
Food Processing companies 73
0.26
0.25
0.03
0.82
Tables provide the summary statistics of the estimated parameters of TE by company type. Hotels are about 15-20% less efficient than tour operators. In case of IT companies, their mean TE is almost at the same level of about 50%, however this industry has the largest spread in terms of technical efficiency (with largest standard deviation) showing that while some IT companies are successful in improving their efficiency, others are lagging far behind.
The smallest TE is observed within food processing industry.

10. Mean of Estimated Parameters, IT and Tourism Industries, 2003-06,
year
te1
te2
tp1
tp2
2003
0.44
0.47
-0.23
-0.08
2004
0.49
0.52
0.02
2005
0.50
0.55
0.18
2006
0.27
We estimate the technical efficiency and technological progress for each firm, for every year. The mean TE and TP for IT and Tourism industries by year are presented in this Table.
The positive sign of TE shows that in both models the degree of technical inefficiency is decreasing over time. An interesting observation is that the technical efficiency is increasing for but the rate is slowing down in The Technological Progress (TP) indicates the direction of change of the production frontier. In the first model, starting from 2004, the frontier is shifting up. In case of second model, the TP is constant and can be interpreted as an average progress during the last 4 years.
Note: te – technical efficiency, tp = technological progress. 1 and 2 refer to the Translog and Cobb-Douglas production functions respectively.

11. Mean of Estimated Parameters, Food Processing Industry, 2003-06
year
te1
tp1
2003
0.263
-0.538
2004
0.260
-0.162
2005
0.268
0.100
2006
0.267
0.238
For food processing industry, TE is marginally fluctuating from year to year during this 4 year period.
Note: te – technical efficiency, tp = technological progress. 1 refers to the Translog Production Function.

12. Regression Outputs, using Nominal AMD/USD Exchange Rate
te1IT&TOUR = *exch*** – *infa – *inff *exp** e-07*marketr *tour – *hotel
te2IT&TOUR = *exch*** – *infa *inff *exp** e-07*marketr *tour *hotel
te1FOOD = *exch*** *infa *inff *exp – 4.10e-09*marketr
* significant at 10%; ** significant at 5%; *** significant at 1%.
We want to estimate how the change in exchange rate affects the technical efficiency of the firms. In regressions, the calculated firm and time specific technical efficiency, TE1 and TE2, are dependant variables. Two measures of exchange rate are alternatively considered: i) nominal AMD/USD exchange rate (exch) together with domestic inflation rate (infa) and trade weighted foreign inflation rate (inff), and ii) Real Effective Exchange Rate (reer) calculated by Central Bank of Armenia. The foreign inflation rate for each year is calculated using weighted average of the inflation rates of ten largest Armenian trade partners; it is weighted by share of the trade of each country in the total foreign trade of Armenia.
Since we are trying to estimate the changes in the competitiveness of Armenian producers, in addition to macroeconomic variable we also include firm specific variables such as marketing expenses in thousands of drams (adjusted by GDP Deflator) and average work experience of the employees expressed in years. We also wanted to include data on trainings, but unfortunately it was not reliable and consistent and thus was not included into the model.
From both specifications, for all three industries, we can see that the nominal exchange rate has statistically significant impact on the level of technical efficiency. The positive sign shows that the effect of the appreciation of Armenian currency for the technical efficiency and thus competitiveness of the companies is negative. The positive sign is robust to changes in the model specification. We run similar regressions by using consumer price index (CPIa and CPIf) instead of inflation rate as a measure of domestic and foreign price levels, and we found similar results.
The coefficients for work experience are positive and significant at 5% significance level for IT and tourism industries. It means that work experience is one of the important determinants of technical efficiency.
The coefficients of domestic and foreign price level, as well as marketing expenses are not significant and are sensitive to the model specifications.
The industry dummies also are highly insignificant, meaning that the determinants of technical efficiency don’t differ between industries.

13. Regression Output, IT and Tourism, using Real Effective Exchange Rate
te1IT&TOUR = – *reer*** *exp* e-07*marketr +
*tour – *hotel
te2IT&TOUR = – *reer*** *exp** e-07*marketr +
*tour – *hotel
* significant at 10%; ** significant at 5%; *** significant at 1%.
To better assess the situation, in the next two regressions three of previously used variables (exch, infa, and inff) are substituted by one variable – Real Effective Exchange Rate (reer). REER is a composite index that incorporates nominal exchange rate and prices levels of both Armenia and its trade partners.
The results are comparable with the previouse models. Again, the exchange rate appreciation has negative and highly significant effect for the degree of technical efficiency, and work experience has positive and significant coefficients.
It is important to note that while the sign of reer is opposite to the sign of exch, the effect it similar. It is explained by the methodology of reer calculation: the appreciation means an increase of value of reer but decrease of value of exch.

14. Estimating Loss in Export of IT and Food Processing Companies 1
ExportIT = – * te1*** ExportFOOD = – * te1*** 10% improvement in the degree of technical efficiency of an average IT company brings about 24.5 million dram or 73.9 thousand USD of export and 74 million (about USD 225 thousand) of additional exports of processed food.
To continue our analysis, we use Tobit model in order to estimate the possible effect of the change in the degree of technical efficiency (te1 and te2) on the export of IT and food companies. Unfortunately, the data on number of foreign customers obtained from tour operators and hotels is not consistent and reliable, and we cannot conduct a similar analysis for tourism industry.
If we had a reliable data on the number of foreign customers for each tour operator, we could use a similar model and regress the number of customers on the degree of technical efficiency to find out indirectly the total number of customers lost due to the exchange rate appreciation.
Both coefficients of TE are significant at 1% significance level.
The results suggest that on average, 10% improvement in the degree of technical efficiency of an average IT company brings about 24.5 million dram or 73.9 thousand USD and 74 million (about USD 225 thousand) of additional exports of processed food
At the rate of 331 AMD per 1 USD, Oct. 2007

15. Estimating Loss in Export of IT and Food Processing Companies 2
Loss in ExportIT = 66,034*Number of companies * ∆ exchange rate 13 IT companies Exchange Rate: , 2006 – 416 AMD/USD Export Loss: 140 million AMD, or 13% of their actual Exports Loss in ExportFOOD=12,196 *Number of companies * ∆ exchange rate 23 Food Processing companies Export Loss: 45 million AMD, or 3% of their actual Exports
Now we can use our estimates for calculating the effect of each point of dram appreciation for the export.
We use formula that we came up with which is
Loss in ExportIT = 66,034*Number of companies * ∆ exchange rate
It means that one point appreciation of the nominal exchange rate will cause the export of an average Armenian IT company to go down by * 244,478,000=66,034 drams which is equal about 200 USD (at the current rate of 1USD=331AMD).
In our survey, the total export of the surveyed IT companies was 1,095 million AMD during For the same period, the nominal exchange rate has appreciated from 579 AMD/USD in 2004 to 416 AMD/USD in According to (19), the total exports loss of our 13 IT companies amounts to 140 million AMD or about 13% of total exports.
And for food
Loss in ExportFOOD=12,196 *Number of companies * ∆ exchange rate
Similarly, a one point appreciation of the nominal exchange rate will cause the export of an average Armenian food processing company to go down by * 743,663,200=12,196 drams which is equal about 37 USD (at the current rate of 1USD=331AMD), and the food industry has lost about 45 million AMD of export opportunities or about 3% of actual exports.
The appreciation of the nominal exchange rate by 1 dram is causing the technical efficiency of an average Armenian company to decrease by On the other hand, from (13) we know that a decrease of TE by 10% will decrease the export of an average IT company by 24.5 million AMD.

16. Profit Loss of Tourism and Food Processing Companies
Each point of dram appreciation caused an average tour operator and hotel to lose about 112 thousand AMD (about 340 USD) of profit before tax.
Total loss million AMD or 15% of actual profit
and
Average food processing company lose just 14 USD of profit before tax which for our surveyed companies was slightly less than 1%.
Next, we estimate how the change in TE due to the dram appreciation has affected the profitability of the tourism and food processing industries. We use a random effect regression model. The results suggest that changes in TE has statistically highly significant effect for the profitability of tourism and companies. ENTER
On average, each point of dram appreciation causes an average tour operator and hotel to lose about 112 thousand AMD or about 340 USD of profit before tax . ENTER
During the period of , the lost profit of all surveyed tour operators and hotels amounted to 401 million AMD or 15% of actual profit. ENTER
For food companies, the average food processing company to lose just 14 USD of profit before tax and the profit loss was very modest at slightly less than 1% of actual profits.
Our analysis strongly suggests that the IT and tourism industries, and to lesser extent food processing industry, have been seriously affected by dram appreciation, and since the appreciation process continues, urgent measures should be undertaken by the government for helping companies to offset this negative pressure and stay competitive in domestic and international markets.

17. Policy Recommendations
Effect of Work Experience:
one year increase of average work experience of the company’s staff offsets about 2 points of dram appreciation.
Allow companies to spend more than 1% of revenue for training purposes
Creating a link between educational institutions and employers in the area of curriculum development
Exemption or delayed payments of VAT on Investments and/or Import of capital assets
According to the model, one of the company level determinants of technical efficiency is work experience, one year increase of average work experience of the company’s staff offsetting about 2 points of dram appreciation. If we consider trainings as a means of improving skills and adding experience, they can become an important tool for improving efficiency and productivity of the company. However, the companies are limited in spending amount for the training purposes. According to the current legislation, amount of training expense exceeding 1% of the total revenue cannot be reported as a cost of the company . This restriction should be removed which will create incentives for the companies to spend more money on staff training. Also, more free training should be organized through the state business assistance programs.
Improving the knowledge and making education better targeted is another challenge especially in IT sector. Many managers complained that the new graduates have very poor skills and knowledge, and they have to spend a lot of resources to train and educate them. Currently, there are already joint programs between private sector and educational institutions, and this cooperation in the area of curriculum development should be developed further. Before confirming a certain course, the curriculum should be reviewed and discussed with potential employers, and only after their approval the course should be taught in the college, and vise versa, the companies should offer to the schools classes that they think must be taught or improved.
Also, as mentioned by almost every company, 20% VAT tax and 10% customs duties on imports of capital assets for investment purposes (such as equipment, electronics, technology, and even software, etc) creates additional tax burden and affects the investment decision of the companies. Of course, it would be ideal if import of capital assets is exempt from VAT tax and customs duties. However, if it not possible at all, the government could consider allowing ICT and food processing industries to pay the VAT by installment, according to a personal schedule based on the accepted depreciation scheme. For example, if a depreciation period of imported server is 3 years, the company will pay the calculated VAT tax during 3 year period, at 3 equal installments.
I also want to highlight some shortcomings of our study the major being data quality. In our survey, we collected the official data which is submitted by companies to tax authorities and statistical service, and we understand that the data is not perfect and is biased most probably downward. However, we believe that it is still reflects the real picture and is more or less consistent. We run test for constant return for scale and the hypothesis of CRS is confirmed.
Another limitation, is small number of companies that agreed to provide data.

18. A. How much would be the difference (in percentage terms) of Company’s 2006 revenue, if the exchange rate remained at the level of 2003, i.e. 580 drams per 1 USD?
In order to supplement the findings of the empirical analysis and get better insight into the situation, we conducted a series of brief interviews with CEO’s of a number of companies from both industries.
38 IT, 21 Hotel, 33 Tour Operators, 30 Food processing companies.
We tried to understand how the company managers assess the situation with exchange rates. %

19. B. What AMD/USD exchange rate would be the most favorable for Your Company and would make it competitive?
Almost all respondents mentioned that they are concerned not only with the real appreciation of the currency but also with unpredictability of the exchange rates, and sometimes the uncertainty creates even more problems when they need to make price calculation, distribute booklets, sign agreements, etc.
AMD/USD

20. C and D. What is the percentage change of Company’s Domestic prices (in AMD) and Export prices (in USD) compared to 2003?
Both export and domestic prices have increased during last 4 years. However, while domestic prices increased at average 11 percent (which is consistent with the inflation in the country for the same period), the export prices have grown at about 35 percent, which in its turn is in line with the appreciation rate for the same period.
This is an evidence that the companies raise their foreign prices in order to offset the exchange rate effect, which of course makes them less competitive in the international market.

21. E. What percentage of your Company’s capital assets and human recourses is being used (rate of utilization), on average, during year?
The answers to this question reveal serious efficiency problem which is even more striking for hotels with their average occupancy rate of about 34 percent. While the tourism industry has a seasonal nature and 76 percent of work load can be somehow justified, for IT industry the rate of 77 percent should be an issue of concern. %

22. F. Please, evaluate State – Your Company interrelations according to 0-10 point system (0 - extremely unfavorable, 10 - the most favorable).
The average ranking of this answer was 4 points. The main reasons of such a low evaluation of the relationship with the State are tax and customs administration problems, corruption, lack of business assistance programs, State’s inability to quickly respond to the issues raised by the companies, etc.

23. THANK YOU! s: