rafmacalaba/datause-encoder-data huggingface dataset:
WPS3189

# **Do Market Pressures Induce Economic Efficiency?**

# **The Case of Slovenian Manufacturing, 1994-2001**

# Peter F. Orazem<sup>a</sup> and Milan Vodopivec<sup>b</sup>

# **Abstract**

The Slovenian transition represents a slow but steady liberalization of constraints on competition. Using a unique longitudinal data set on all manufacturing firms in Slovenia over the period 1994-2001, this study analyzes how firm efficiency changed in response to changing competitive pressures, holding constant firm attributes. Results show that the period was one of atypically rapid growth of total factor productivity (TFP) relative to levels in OECD countries, and that the rise in firm efficiency occurs across almost all industries and firm types: large or small; state or private; domestic or foreign-owned. Changes in firm ownership type have no impact on firm efficiency. Rather, competitive pressures that sort out inefficient firms of all types and retain the most efficient, coupled with the entry of new private firms that are at least as efficient as surviving firms, prove to be the major source of TFP gains. Market competition from new entrants, foreign-owned firms, and international trade also raise firm efficiency in the industry. Results strongly confirm that market competition fosters efficiency.

# World Bank Policy Research Working Paper 3189, January 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_ ______________________________

> a Iowa State University

b World Bank Partial support form the World Bank research project RF-P064129-RESE-BBRSB, as well as the William Davidson Institute at the University of Michigan is gratefully acknowledged. The paper would not have been possible without tremendous help in obtaining the data from The Statistical Office of Slovenia and the Agency of Slovenia for Public Statistics and Services. Helpful comments and advice were provided by Mićo Mrkaić, Matija Rojec, Marko Simoneti, and Andreja Peterca. Tomaž Rejec and Jakob Tomše provided excellent assistance in setting up the data sets, and Donna Otto in preparing the manuscript.
2 is its traditional equilibrium structure which assumes competitive behavior, incorporates no explicit treatment of time and in its present form, contains no monetary features and has balanced government and external sector accounts.<sup>2</sup> It uses data from a 1986 Social Accounting Matrix (SAM) constructed by a modelling group in CMte d'Ivoire; part of the newly created macro-economic unit at the Ecole Nationale de Statistique et d'Economie Appliquee (ENSEA).3 In the text, we describe the model structure,<sup>4</sup> and illustrate its application to social policy design in Cote d'Ivoire by presenting model-based analyses of tax incidence in CMte d'Ivoire and the design of anti-poverty programs. For policy changes in each of these categories, we solve the model for a range of possible policy changes, and compare model results with base-case data. These results show both how key features of the Ivorian economy (such as large interhousehold transfers) need to be taken into account in analyzing the impacts of possible new social policies in C6te d'Ivoire, and how important general equilibrium considerations can be in such analyses.

The paper is divided into _5_ sections. Section 2 describes the model in detail; Sections 3 and 4 present examples of its application, first with analysis of tax incidence in C6te d'Ivoire, and second with evaluation of anti-poverty programs. A concluding section summarizes work thus far with the model, and plots a path for the future.

2 Hence the impacts of international debt renegotiations for C6te d'Ivoire could not be adequately addressed through the present modelling framework.

3 For more details on the institutional setting within which the model has been developed and is planned to operate in the future, see Wahba and Whalley (1992).

4 Appendices A and B present a formal description of the model and the input data used (including elasticity and other parameter values).
||%|45.6|11.6|19.6|3.6|13.6|6|100|
|---|---|---|---|---|---|---|---|---|
|Turkey|n|121|17|23|22|36|31|250|
||%|48.4|6.8|9.2|8.8|14.4|12.4|100|
|**Total**|**n**|**1,253**|**134**|**237**|**246**|**279**|**275**|**2,424**|
||**%**|**51.69**|**5.53**|**9.78**|**10.15**|**11.51**|**11.34**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

_Age:_ Respondents are generally fairly established in their respective markets. The average respondent has over 17 years of experience operating in its respective host market (Table 6). On average, respondents have over 10 years of experience across all 10 countries.

_Trading behavior:_ Respondents are active in global trade, both in terms of importing into and exporting from host countries. On average, respondents import 46 percent of their inputs, and exports account for 34 percent of their sales (Table 6). There is wide variation in average trading behavior across host countries, with average export share of sales ranging from 15 percent (Brazil) to 53 percent (Vietnam) and average import share of inputs ranging from 33 percent (China) to 58 percent (Mexico and Vietnam).

_Ownership._ On average, foreign investors hold large shares in affiliates in our sample. The average stake held by foreign investors is 87 percent across the 10 MICs, and within-country averages are above 80 percent in every country except for Nigeria (Table 6). Roughly two-thirds of respondents are 100 percent owned by foreign investors.

**Table 6. Descriptive statistics for years in country, foreign ownership, and trading behavior**

||**Years**<br>|**in coun**<br>|**try**<br>|**Foreign **<br>|**owners**<br>|**hip (%)**|**Expor**<br>|**t % of s**<br>|**ales**|**Import**<br>|**% of i**<br>|**nputs**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**Mean**|**SD**|**N**|**Mean**|**SD**|**n**|**Mean**|**SD**|**n**|**Mean**|**SD**|**n**|
|Brazil|20.9|18.5|239|92.0|21.4|250|14.8|24.6|222|42.0|33.1|221|
|China|14.6|6.5|234|96.9|12.0|250|36.0|36.2|234|33.3|33.4|212|
|India|14.9|10.2|248|90.1|23.2|250|32.7|34.9|237|35.7|32.3|221|
|Indonesia|17.5|10.5|252|84.3|25.1|258|31.5|39.1|241|43.7|35.0|241|
|Malaysia|22.0|15.2|232|84.4|29.7|250|47.1|39.6|238|47.8|31.2|224|
|Mexico|17.2|14.0|247|89.8|24.6|249|35.7|36.6|244|58.1|31.9|242|
|Nigeria|20.3|21.7|160|51.8|36.0|164|16.8|23.3|153|44.4|32.4|154|
|Vietnam|10.5|6.1|249|94.8|16.8|253|52.7|42.1|214|58.1|34.6|205|
|Thailand|20.5|12.6|234|83.6|29.4|250|37.8|36.5|226|45.5|32.3|217|
|Turkey|15.8|12.2|244|88.5|21.6|250|30.8|34.9|236|51.4|35.0|223|
|**Total**|**17.3**|**13.6**|**2,339**|**86.8**|**26.4**|**2,424**|**34.1**|**37.1**|**2,245**|**46.1**|**34.0**|**2,160**|

_Source_ : Computation based on the 2019 GIC Survey.

# **5. METHODOLOGY**

We employ simple summary statistics to assess different factors’ relative importance to foreign investment decisions. We examine and compare the distribution of responses across questions in a given thematic area (location decision-making criteria, legal and regulatory obstacles, investment protection, or investment promotion). Within each thematic area, questions are phrased in the exact same way, with 9
*

- other Eastern European countries have also introduced incomes policies. These policies usually variants of tax-based incomes policie - have typically been in place for short durations and later renegotiated, often in somewhat different form. Collective bargaining has also emerged, with similar confrontation among unions for worker representation, as in Slovenia. Moreover, minimum wage scales for different categories of labor, such as those in Slovenia, have been agreed upon in Bulgaria, Poland, and Romania.

In many respects, other economies have chosen less generous and less protective policies dtan Slovenia. One example is job security. Czech and Polish workers are given advance notice of up to - 3 tee months, and Romanian workers one month compared to Slovenia's six months." Similarly, Slovenian workers are entitled to severance pay of one month's wage for each two years of job tenure, while in most Central and Eastern European economies severance pay amounts to up to two mounth's pay. The maximum duration of this unemployment benefit in all other transitional economies is 12 months (only six in the Czech republic and former Soviet Union economies), compared to the maximum duration of 24 months in Slovenia.

The pension replacement rate in Slovenia has also been the largest among transitional economies. In 1992, average pension amounted to 76 percent of average wage, compared to 64 percent in Poland and _50_ percent in Bulgaria and Hungary. Slovenia's public expenditures on pensions as a percent of GDP have been among the highest in transitional economies. Only Poland at 14.7 percent of GDP has a higher pension burden. Government sponsored early retirement programs, similar to Slovenia's, have been introduced in Poland, Romania, and, to a lesser degree, in Hungary.

3Luformation on ocher economies is taken from the following sources: Burda (1993); Scarpetta, Boeri and Reutersward (1993); Earle and Oprescu (1993); and World Bank PRDTE's data bank on taitional economies.
WPS3784 about 50 per cent higher on poor quality roads compared to good quality roads.<sup>6</sup> For many farmers roads are not accessible or totally inadequate and head carrying is the only available mode of transport. Given the uncertainty of the magnitude of such a reduction in transport costs on producer prices, we perform the simulations using two pass-through assumptions that is the extent to which the fall in transport costs is reflected in higher producer prices for farmers: a 100 percent pass-through and a 50 percent pass-through.

We follow Deaton (1989, 1997) whereby the short-run effects of price changes are directly proportional to income shares. In particular, holding all the other components of household income constant and assuming that coffee produced in Rwanda is entirely exported (there is almost no domestic consumption of coffee), the direct short-term welfare impact of a price shock on household _i_ is given by: Where _Y_ 0 is initial income of household _i_ , Δ _Y_ is the change in income following a change in the price of coffee _,_<sup>φ</sup> _icoffee_ is the share of coffee income (sales) in total household income as provided by the EICV survey and _P_ 0 _coffee_ the initial average producer price of coffee _._ Table 3 shows the different sources of income for Rwandan households, including income derived from coffee sales. At the national level, close to 57.6 percent of households’ income comes from labor market activities and almost 26 percent of income is derived from the sale of agricultural products. In rural areas, agricultural income accounts for close to 42 percent of total income, most of which is derived from the sale of food crops (33 percent) while a smaller portion comes from the sale of commercial crops,

> 6 A World Bank study found that lack of maintenance on the road between Gitarama and Kibuye raised vehicle operating costs from a 1989 level of $1.00 per kilometer to almost $3.40 per kilometer in 1996. The recent rehabilitation of the road has resulted in a fall of more than 50 percent in vehicle operating costs and to a reduction of overall transport costs of about 40 percent. As a result, agricultural surpluses in the area can now be sold in markets throughout the country, and a general shift is taking place from subsistence agricultural to production for the market (World Bank (2005)).

7
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PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES 19 Gopinath, G., Itskhoki, O., and Rigobon R., 2010. Currency Choice and Exchange Rate Pass-through. American Economic Review 100(2), 304-336 Gopinath, G., Itskhoki, O., 2010. Frequency of Price Adjustment and Pass-through. The Quarterly Journal of Economics 125 (2), 675-727

- Gopinath, G., Itskhoki, O., 2011. In Search of Real Rigidities. NBER Macroeconomics Annual 2010 (25),

- 261-309

- Kimball, M., 1995. The Quantitative Analytics of the Basic Neomonetarist Model. Journal of Money

- Credit and Banking 27, 1241-77

- Melitz, M., Ottaviano G., 2008. Market Size, Trade and Productivity. The Review of Economic Studies

- 75(1), 295-316

- Nakamura E., Steinsson J., 2012. Lost in Transit: Product Replacement Bias and Pricing to Market,

- American Economic Review 102(7), 3277-3316

Obstfeld, M., Rogoff, K., 2000. Six Puzzles in International Macroeconomics. NBER Macroeconomics Annual (15), 339-390
change implemented between 2004 and 2006 that may have had a discontinuous effect on pollution.

Another concern could be that construction activity undertaken to build the new rail lines may have added to localized pollution in the period preceding the metro extension and this would then over-estimate the DM effect. On speaking to officials from the DMRC we were told that such construction activity is typically completed fifteen to thirty days prior to the opening of a new line so as to conduct trial runs to ensure safety of passengers. Therefore, at least for the shorter window lengths, we do not expect this issue to be a problem. Another worry could be that metro officials choose the extension dates in a systematic manner to coincide with either high- or low-pollution days. We think that this is highly unlikely. Given the public enthusiasm for the metro and the recognition of economies of scale in its operation, the DMRC has always been eager to open a new line once it had met all safety requirements.

Finally, Delhi is characterized by a multitude of pollution sources. According to Guttikunda and Calorie (2012), domestic sources, such as burning of biofuel for cooking and heating, use of diesel generator sets, waste burning, and construction, together account for 20, 19, and 26 percent of _NOx_ , _CO_ , and _PM_ 2.5<sup>,</sup> respectively. These sources tend to be sporadic and sometimes mobile, and it is possible that we have not accounted for all of them. In the results section we talk about what definitive conclusions may be drawn in spite of this threat to identification.

# III DATA SOURCES

All the data used in this study are from secondary sources. Data on pollutants were obtained from the Central Pollution Control Board (CPCB), which within Delhi is dampened.

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|Control of Corruption (lagged)|Control<br>of<br>Corruption<br>indicator<br>from<br>Worldwide Governance Indicators. Higher<br>values of the variable imply lower corruption.<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of firms covered in the calendar<br>year.<br>Source: Worldwide Governance Indicators,<br>World Bank.|
|---|---|
|Profit Tax Rate|The amount of taxes on profits paid by the<br>business expressed as a percentage of total<br>commercial profits of the business as per the<br>Doing Business methodology, World Bank. To<br>make the data comparable across countries,<br>several<br>assumptions<br>are<br>made<br>about<br>businesses. The main assumptions are that they<br>are limited liability companies, they operate in<br>the country's most populous city, they are<br>domestically owned, they perform general<br>industrial or commercial activities, and they<br>have certain levels of start-up capital,<br>employees, and turnover.<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of firms covered in the calendar<br>year.|
||Source: Doing Business, World Bank.|
|Cheating on Taxes is Justified (dummy)|<br>In the World Value Surveys, respondents were<br>asked the following question: “Do you think<br>cheating on taxes is justified?” Answers were<br>recorded on 1 (Never justified) to 10 (Always<br>justified) scale with higher values implying a<br>higher justification for cheating on taxes. We<br>first compute country-level average of the<br>responses for each year for which data are|

42
Table 1: Share of Import Used for Exports: Baseline Pool Regressions

|Industries|(1)<br>All|(2)<br>Ordinary|(3)<br>Processing|(4)<br>Ordinary|(5)<br>Processing|
|---|---|---|---|---|---|
|Dependent Variable|EXP|EXP|EXP|EXP|EXP|
|IMP|0.756***|0.352***|1.112***|0.327***|1.014***|
||(0.030)|(0.023)|(0.031)|(0.031)|(0.039)|
|_φ_(I, E, K)|No|No|No|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Observations|419,030|185,493|226,042|68,250|36,691|
|Adjusted R<sup>2</sup>|0.726|0.720|0.779|0.737|0.801|

Notes: Robust standard errors clustered at the industry-year level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level.

Table 2 presents the regression results at the industry level, with the polynomial control function only added in the even numbered columns. Ordinary and processing firms are pooled together, and an interaction term between the processing firm dummy and imports allows us to estimate separate import shares for each group. The results indicate that processing firms across all industries have significantly higher import shares, consistently close to one. In contrast, the import shares for non-processing firms range from 0.114 to 0.673, with a simple average of around 0.321. Notably, we cannot reject the that use all their materials for whereas hypothesis processing firms imported exports, ordinary exporters only use part of their imports for export production. Based on these findings, we set the import shares of processing firms to one when constructing their DVAR. For ordinary firms, DVAR will be constructed using the industry-specific import shares estimated in this table.

Figures 1 and 2 present the DVARs at aggregate country- and industry-levels, for the processing and non-processing exports from 2000 to 2016. These aggregate DVARs are constructed as the export share weighted average of firm-level DVAR, according to Kee and Tang (2016). The confidence intervals of these aggregate DVARs, based on the associated bootstrap standard errors, are also shown in the figures. Over the sample period, China’s DVAR in exports has steadily increased, from 0.382 in 2000 to 0.746 in 2016. This is driven largely by the rise in the DVAR for processing exports, from 0.341 to 0.641, while the DVAR of ordinary exports has remained stable at around 0.853 over the sample period. Similar pattern is also observed at the industry level with processing exports display increasing DVARs in nearly all industries.

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