metadata: authors: 0: author: Grover,Arti Goswami
1: author: Iacovone,Leonardo
2: author: Chakraborty,Pavel
count: Croatia
docna: 0: docna: Management Practices in Croatia : Drivers
            and Consequences for Firm Performance
docty: Policy Research Working Paper
subtopic: General Manufacturing,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Common Carriers Industry,Business Cycles and Stabilization Policies,Food & Beverage Industry,Construction Industry,Pulp & Paper Industry,Finance and Development,Financial Economics,Educational Sciences,Labor Markets,Business Environment
teratopic: Industry,Finance and Financial Sector Development,Macroeconomics and Economic Growth,Education,Social Protections and Labor,Private Sector Development
abstracts: cdata!: Embedding management and operational
            practices survey in a broader firm capabilities survey, this
            paper finds that an average firm in Croatia scores 0.532 on
            structured management practices, which is farther from the
            frontier (0.615 in the United States). This average,
            however, masks the wide heterogeneity in management
            practices among firms. Relative to advanced countries, a
            large share of firms in Croatia are badly managed.
            Management is particularly worse in services and more so in
            non-knowledge intensive services. Better managed firms show
            superior performance: improving the management score from
            the 10th decile to the 90th decile is expected to improve
            sales per employee by 36 percent, profits by 33 percent and
            the probability to innovate by 11 percent. Likewise, better
            managed firms more likely use sophisticated technologies and
            have a higher probability of accessing external finance.
            What drives firms to improve their management practices? As
            elsewhere in the world, global linkages of firms matter.
            However, unlike the evidence in advanced countries,
            management capabilities in Croatia is negatively associated
            with firm age, especially in services, indicating the
            possibility of allocative inefficiency, where learning and
            selection mechanism does not weed out the badly managed
            firms perhaps due to the lack of pro-competitive forces.
geo_reg_key: 517208,517191
repnme: repnme: Management Practices in Croatia : Drivers
            and Consequences for Firm Performance
display_title: Management Practices in Croatia :
            Drivers and Consequences for Firm Performance
listing_relative_url: /research/2019/11/31578829/management-practices-croatia-drivers-consequences-firm-performance
topicv3: Investment Climate m1326293 405,Manufacturing Sectors m1326521 1281,Education m1327048 470,Finance and Growth m1327404 1081,Labor Productivity m1504060 2756
publishtoextweb_dt: 2019-11-22T00:00:00Z
historic_topic: Industry,Finance and Financial Sector Development,Macroeconomics and Economic Growth,Education,Social Protections and Labor,Private Sector Development
origu: EFI-FCI-TIC-Firms,Entrep,&Innov. (ETIFE)
no_of_pages: 35
author_keys: 0: author_key: 1066720
1: author_key: 900307
2: author_key: 1214958
model_extractions: Policy Research Working Paper 9067 Management Practices in Croatia Drivers and Consequences for Firm Performance _Arti Grover Leonardo Iacovone Pavel Chakraborty_ Finance, Competitiveness and Innovation Global Practice November 2019
Policy Research Working Paper 9067

# **Abstract**

Embedding management and operational practices survey in a broader firm capabilities survey, this paper finds that an average firm in Croatia scores 0.532 on structured management practices, which is farther from the frontier (0.615 in the United States). This average, however, masks the wide heterogeneity in management practices among firms. Relative to advanced countries, a large share of firms in Croatia are badly managed. Management is particularly worse in services and more so in non-knowledge intensive services. Better managed firms show superior performance: improving the management score from the 10<sup>th</sup> decile to the 90<sup>th</sup> decile is expected to improve sales per employee by 36 percent, profits by 33 percent and the probability to innovate by 11 percent. Likewise, better managed firms more likely use sophisticated technologies and have a higher probability of accessing external finance. What drives firms to improve their management practices? As elsewhere in the world, global linkages of firms matter. However, unlike the evidence in advanced countries, management capabilities in Croatia is negatively associated with firm age, especially in services, indicating the possibility of allocative inefficiency, where learning and selection mechanism does not weed out the badly managed firms perhaps due to the lack of pro-competitive forces.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Management Practices in Croatia: Drivers and Consequences for Firm Performance**

Arti Grover, Leonardo Iacovone and Pavel Chakraborty<sup></sup>

**_JEL Classification_** : D22, D24, F14, L2, M2, O33,

**_Keywords_** : management, firm, productivity, competition, Croatia

**_Acknowledgements_** : The team is grateful to Todor Milchevski (GFCEE) for leading the survey implementation and Šime Smolić (Survey specialist, consultant) for his careful work on data collection. The team thanks Kristen Himelein (GPV02) for her invaluable advice on sampling strategy and Andrea August (Consultant) for excellent research assistance. The paper has benefited from overall guidance of Marialisa Motta (Practice Manager, GFCEE) and insightful suggestions from Austin Kilroy (GFCEE). The work on this paper is partially the result of the World Bank team activities for the Government of Croatia, under the ongoing Eastern Croatia Growth and Jobs Reimbursable Advisory Service, co-financed by the European Union from the European Regional Development Fund.

> The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. This paper does not necessarily represent the position of the European Commission and of the Croatian Government.

>  Finance competitiveness and innovation Global Practice, The World Bank, The authors can be reached at: <u>agrover1@worldbank.org, liacovone@worldbank.org; pavel.chakraborty@gmail.com.</u>
# **Section 1: Management in Croatia and Why it Matters?**

The persistence in productivity differences between firms, within the same industry, is striking and puzzling. For example, labor productivity for plants in the United States at the 90<sup>th</sup> percentile, within narrowly defined four-digit manufacturing industries, is four times as high as plants at the 10<sup>th</sup> percentile. Likewise, the difference in Total Factor Productivity (TFP) is twice as high, when controlling for other factors (Syverson, 2011). These differences persist over time and are robust to controlling for plantspecific prices in homogeneous goods industries. Such TFP heterogeneity is not an artifact of the United States establishment data but is evident in several countries. As an example, the average 90<sup>th</sup> to 10<sup>th</sup> percentile TFP ratios is over 5:1 in China and India (Hsieh and Klenow, 2009). Most often within industry productivity differentials are attributed to “hard” technological innovations, as embodied in patents or in adoption of advanced equipment. Recently, Bloom et al. (2016a) provide an alternative explanation, that is, productivity differences reflect variations in management practices, which is also the approach adopted in this paper to explain the heterogeneity in performance of firms in Croatia.<sup>1</sup> Persistent differences in productivity can be explained by several other factors, such as higher quality labor and capital, differential investment in information technology and research and development (R&D), learning by doing, firm structure, productivity spillovers, regulatory behavior, and differences in competitive regime. Bloom et al. (2016a) propose that some forms of management practices are like “technology”, where management is perceived as an intangible capital in which output is monotonically increasing. Differences in managerial quality are critical in explaining the cross-country differences in income levels, productivity, innovation and firm dynamism noted in Hall and Jones (1999). For example, Bloom and Van Reenen (2010) find that differences in productivity across firms in Asia, Europe, and North and South America can be attributed to the heterogeneity in management practices across firms in these regions.<sup>2</sup> Likewise, Bloom et al. (2016a) suggest that management practices account for nearly 30 percent of the differences in TFP across countries. An important finding here is that management quality also explains at least a large share of within country heterogeneity in firm performance as well. For instance, using evidence collected through the World management surveys, Bloom et al. (2016a) find that about one-fifth of the dispersion in TFP across firms within countries is explained by differences in management quality, thereby suggesting that firm- and sector-specific factors were at least as important as the general business environment in shaping managerial performance.

> 1 Bloom et al. (2016) suggest that management practices account for about one-fifth of the dispersion in TFP across firms within countries, and nearly 30 percent of the differences across countries. Thus, firm- and sector-specific factors are at least as important as the general business environment in shaping managerial performance.

> 2 Other performance measures, such as profitability and survival rates are also positively correlated with good management practices.
We focus on management for explaining the heterogeneity in performance of firms in Croatia for the following reasons: _First_ , management practices are strongly correlated with firm productivity in several countries (e.g. US, Russia, Mexico, Pakistan), and is robust to controlling for many other factors associated with productivity (e.g. exports, innovation etc.). _Second_ , while a frontier topic globally, there exists little evidence on managerial practices of firms in Croatia and the relationship between management and productivity. For instance, there is some evidence on management in countries from the Europe and Central Asia region, where Bloom et al (2011) find a strong association between managerial practices and firm performance but this study excludes Croatia.<sup>3</sup> _Three,_ given the strong association with firm performance, it remains a challenge to understand the drivers of management and why these practices do not diffuse more readily. We hope that our results will help shape a broader operational program in Croatia on firm-level interventions to improve management.

To quantify the importance of management for the performance of firms in Croatia, we conducted a survey on firm capabilities in Croatia, including a module for capturing management capabilities. Through this survey, we collected information on 727 small and medium enterprises (SMEs) in manufacturing and services sector. The survey was conducted between January and June 2019. Financial Agency (FINA) database provides the sample frame for this survey, where the sample was stratified by region and sector. Firms were randomly sampled and interviewed face to face. Survey data on managerial practices, technology adoption and innovation is merged with other firm performance data such as profits, sales per employee and TFP from FINA database.<sup>4</sup> This paper benchmarks management quality of firms in Croatia against other countries such as the United States and quantifies the link between management capabilities and firm performance (e.g. productivity, profits, adoption of technology).

How are structured management practices measured? The module on Business Capabilities and Entrepreneurship includes 15 core questions of the United States Census Management and Organizational <u>Practices Survey</u> (MOPS) main module. The survey was deliberately designed to replicate the United States Census MOPS to maximize comparability with the United States as well as other countries where this survey has been conducted (e.g. UK, Canada, Russia, Mexico and Punjab province in Pakistan). Following the scoring matrix of Bloom et al. (2019a), firm responses to questions on managerial practices is aggregated into a single management score that ranges from between 0 and 1 with 1.0 reflecting perfect

> 3 In 2008 and 2009, the European Bank for Reconstruction and Development (EBRD) in cooperation with the World Bank (WB) conducted a new survey, the EBRD-WB Management, Organization and Innovation (MOI) survey.

> 4 Financial Agency (Fina) is the leading Croatian provider of financial data services. The database comprises of all tax registered firms in Croatia.
(more formal, frequent and explicit) adoption of all structured management practice, and 0 score reflects the other extreme.

Analysis of this newly collected survey data in Croatia, suggests that, _first,_ an average manufacturing firm in Croatia scores 0.538 in adoption of advanced management practices, a value behind the one in frontier economies such as the United States (0.615, Bloom et al., 2019a). Comparing Croatia’s management practices with countries for which comparable data is available<sup>5</sup> , we find that an average manufacturing firm in Croatia is better managed than its counterparts in Russia (average score, 0.432; Grover and Torre, 2019) and Mexico (average score, 0.42; Bloom et al., 2019b) ( **Tables 1a and 1b** ), although we are concerned that the relatively higher score in Croatia vis-à-vis Mexico, for instance, could be the result of low response rate, leading to a possible selection bias and due to differences in coverage with respect to firm size in the sampling frame.<sup>6</sup> Moreover, the quality of management practices across firms in Croatia is highly heterogeneous, with a large share of poorly managed firms and a much smaller share of better managed firms, relative to advanced countries. For example, only 3.5 percent of the firms in Croatia have a management score of over 0.75 as compared to 18 percent in the US.

_Second,_ weaknesses in management practices, particularly relating to data driven performance monitoring, cripples the overall management score in Croatia. Monitoring includes specific practices that help managers define their key performance indicators (e.g. inventory, sales, absenteeism) and appropriately track them periodically over time. By comparison, Croatia’s average score on practices relating to target setting and incentives is relatively closer to that in the United States firms. Incentives practices relate to how managers and non-managers are awarded bonuses and promotion, for instance. The finding for Croatia regarding the weaknesses of these sub-indices is similar to that in Russian manufacturing, that is, monitoring aspect of structured management is weaker. By comparison, in the United States and Mexico

> 5 Apart from data availability, it makes sense to compare Croatia with Russia due to comparable per capita GDP in recent years and also the fact that both countries are transition economies in the same region. Comparison with Mexico is revealing in the sense that Mexico has a lower per capita GDP than Croatia, but has a strong manufacturing base, and thus offers another perspective for comparison (lower bound among emerging economies), aside from the US, which is the benchmark frontier economy (upper bound).

> 6 For comparator countries, the sample frame varies significantly. For example, MOPS in the United States used the sample frame of the Annual survey of manufacturing, comprising of around 50,000 plants in each wave for the years 2010 and 2015. Nearly 74,000 responses for the two years were received, that is, an average response rate of 74 percent. Likewise, in Mexico the survey was conducted for nearly 25,000 firms in manufacturing and services, with a response rate of 96 percent. The survey in Russia is perhaps closest to that in Croatia where 727 firms were interviewed face to face across five broad regions, with response rate similar to that in Russia (about 17 percent). Although the survey in Croatia covered both manufacturing and services, in this paper we present comparisons with manufacturing only. It is also worth reminding that although much of the lagging regions in Russia is not surveyed, the survey also skips bigger cities and agglomerations such as Moscow, Moscow Oblast, Saint Petersburg and Leningrad Oblast. Due to restricted coverage on location and size of the firms in Russia, management score could go in either direction. For example, we do not cover smaller firms (less than 25 employees) in Russia (which are covered in both Mexico and the US), and likewise, we do not cover much of the lagging regions in Russia as well. A broader coverage could therefore also pull down the current management score, depending on the share of firms that is added on either end of the location or size distribution. Furthermore, as we will learn later in the paper, a low response rate in Russia is likely leading to an overestimation of the observed management score.
||Manufa<br>Cro<br>|cturin<br>atia<br>|g<br>|||||
|---|---|---|---|---|---|---|---|
||Mean|sD|p(10)|p(25)|p(50)|p(75)|p(90)|
|Overall Management Score|0.538|0.149|0.340|0.431|0.542|0.652|0.718|
|Datadrivenperformancemonitoring||0.489|0.161|0.300|0.367|0.467|0.600|0.733|
|<br>TargetsandIncentives|<br>0.563|0.189|0.300|0.443|0.584|0.700|0.787|
||Rus<br>|sia<br>||||||
||Mean|sD|p(10)|=p(25)|p(50)|p75)|p(90)|
|Overall Management Score|0.4382|0.192|0.152|0.306|0.450|0.567|0.675|
|Datadrivenperformancemonitoring||0.323|0.227|0|0.133|0.300|0.500|0.633|
|<br>Targets and Incentives|<br>0.496|0.221|0.167|0.361|0.523|0.663|0.762|
|Mexic|o (Bloo|m et al.,|2019b)|||||
||Mean|sD|p(10)|p(25)|p(50)|p(75)|p(90)|
|Overall Management Score|0.420|0.201|0.125|0.275|O.440|0.576|0.673|
|Datadrivenperformancemonitoring||0.465|0.232|0.133|0.278|0.500|0.639|0.750|
|<br>TargetsandIncentives|<br>0.386|0.235|0|0.200|0.398|0.568|0.700|
|<br>United S|tates (B|loom et|al., 2013|)||||
||Mean|sD|p(10)|p(25)|p(50)|p75)|p90)|
|Overall Management Score|0.615|0.172|0.3879|0.521|0.648|0.742|0.806|
|Datadrivenperformancemonitoring||0.643|0.199|0.365|0.521|0.677|0.792|0.865|
|<br>TargetsandIncentives|<br>0.583|0.215|0.300|0.474|0.623|0.739|0.819|

Notes: Notes: ‘Management Score’ is calculated following MOPS (See Bloom et al., 2018). ‘Monitoring’ includes responses from Questions 21-25, whereas ‘Targets and Incentives’ includes responses from Questions 26-35.
<!-- Start of picture text --> Services<br>Croatia<br>Mean sD p(10) p(25) = p(50) p(75) p(90)<br>Overall Management Score 0.527 0.149 0.326 0444 0.538 0.639 0.702<br>Data driven performance monitoring | 0.460 0.160 0.283 0.350 0.450 0.567 0.667<br>Targets and Incentives 0.559 0.193 0.286 0.456 0.600 0.694 0.781<br>Mexico (Bloom et al., 2019b)<br>Mean SD p(10) p(25) p(50) p(75) p(90)<br>Overall Management Score 0.395 0.204 0.100 0.244 0404 0.554 0.661<br>Data driven performance monitoring | 0.409 0.226 0.067 0.222 O444 0.583 0.694<br>Targets and Incentives 0.382 0.243 0 0.188 0.396 0.573 0.717<br><!-- End of picture text -->

Notes: Notes: ‘Management Score’ is calculated following MOPS (See Bloom et al., 2018). ‘Monitoring’ includes responses from Questions 21-25, whereas ‘Targets and Incentives’ includes responses from Questions 26-35.
_Figure 1: Management matters for firm performance outcomes_ How do these results inform firm interventions designed for enhancing aggregate productivity? A simple answer to this question is that management can be improved with concerted effort. For example, an experiment with 17 textile firms in India provides a proof-of-concept that intensive individualized consulting can deliver lasting improvements in the practices of badly managed firms, where management practices improved, on an average by 38 percent in a 12 month period, resulting in productivity improvements by 17 percent (Bloom et al., 2013). Although this intervention improved management and firm performance, it came at a huge cost of approximately $75,000 per treated firm. This high cost is likely to be prohibitive for many small and medium enterprises (SMEs) to finance themselves, and for
governments seeking to scale this up for assisting a large number of firms. In another experiment, Bruhn et al. (2018) shows that using local consultants can also improve business practices and performance and is less costly (US$12,000 per firm) than hiring internationally renowned consulting firms as in Bloom et al. (2013). Iacovone et al. (2019) tested two alternative approaches to improving management among firms producing auto parts in Colombia. The first uses intensive and expensive one-on-one consulting, while the second draws on agricultural extension approaches to provide consulting to small groups of firms at approximately one-third of the cost of the individual consulting services. Their results show that both approaches lead to improvements in management practices of a similar magnitude (8-10 percentage points), so that the new group-based approach dominates on a cost-benefit basis.<sup>9</sup> This points to the potential of group-based approaches as a pathway to scaling up management improvements.

_Finally_ , given the evidence that management can be improved, our analysis of the survey data points to some of the shortcomings in the incentive system for firms that inhibit adoption of structured management practices. For example, management practices in Croatia are negatively correlated with firm age, and especially so in low-tech manufacturing and non-knowledge intensive services. This finding is surprising when compared with the United States or Mexico, where firms tend to improve their management practices as they age due to the “up-or-out” dynamics. The hypothesis is that as firms grow their production processes become more complex and in order to stay put in the market, they should have to upgrade their management capabilities. However, in Croatia, firms continue to survive without having the need to do so. This indicates the possibility that in Croatia learning and market selection is not operating perhaps due to the lack of pro-competitive forces.<sup>10</sup> Likewise, the finding that exporting firms are better managed confirms that selection and learning mechanism can work in Croatia if firms operate in competitive conditions of an international market. More precisely, management score of a manufacturing exporter in Croatia is likely to be higher by 7 percent than an average firm. The fact that this result is mainly driven by high-tech manufacturing exporters, where management score is likely to be 13 percent higher, points to the potential for improving management capabilities among other firms and sectors.

The paper beyond this point is organized as follows: Section 2 introduces the methodology of the management survey and presents some descriptive statistics for firms in Croatia. Section 3 presents the methodology for computing management scores, and the main descriptive results on the relationship

> 9 Their results suggest that group-based intervention led to increases in firm size over the next 1.5 years, including a statistically significant increase in employment, while the impacts on firm outcomes are smaller and statistically insignificant for the individual consulting. 10 In the United States it is well known that “up-or-out” dynamics in which unsuccessful firms exit or are bought out by more successful competitors who absorb the labor and capital released by exiting firms drives productivity and business dynamism (Haltiwanger et al., 2013). In the case of Croatia, such selection dynamics does not seem to work in a similar way, and that firms that are badly managed still continue to survive.
between various firm characteristics such as size, age, export status, ownership and so on with the observed management score. Section 4 presents a simple regression framework for assessing the link of management practices with firm performance. Section 5 explores the drivers of better management practices. The last section concludes with policy recommendations.

# **<u>Section 2: Critical Firm Capabilities: Management in Focus</u>**

# _2.1 Why do we need a management survey?_

Although managerial and organizational practices have been conceived to be critical in affecting firm outcome since the time of Adam Smith’s pin factory and Walker’s 1887 paper on business performance, these practices are not well measured in administrative data on firms or the commercial data on companies’ accounts. Some studies have used information on publicly traded firms to link managerial style of senior managers and company performance (e.g., Bertrand and Schoar, 2003), this data is rather limited to a country such as the United States and there is evidence that suggests a company is far more than simply the identity of its most senior employee (Bender et al., 2015). Furthermore, this type of data on its own does not tell much about how firms are managed or organized. Therefore, over the last decade Bloom and his co-authors have attempted to fill this gap in data by collecting comprehensive information on management practices (see Bloom et al., 2014).

In general, three broad management and organizational capabilities that are generally recognized to be important for firm productivity (Bloom et al., 2016b): (i) performance monitoring: collecting and analyzing information, on daily activities of the firm for continuous improvement, (ii) target setting: using and stretching short and long run targets, tracking outcomes, and taking appropriate action, and (iii) performance incentives: rewarding high-performing employees, and retraining or moving underperformers and careful hiring. To collect information on these practices, two alternative strategies could be deployed: (i) Open Ended questions (e.g. world management survey, WMS)<sup>11</sup> and (ii) Closed Ended questions (e.g. Management and Organizational Practices Surveys, MOPS).<sup>12</sup> **Table 2** summarizes the comparison of the two approaches on a number of dimensions.

> 11 Designing these surveys take some expertise in terms of selecting questions and response grids and hence intense training and monitoring of the interview team. The full questionnaire is available on <u>www.worldmanagementsurvey.com.</u> 12 The full questionnaire is available on http://bhs.econ.census.gov/bhs/mops/form.html
Aspect Open (e.g. WMS) Closed (e.g. MOPs) Accuracy of responses High: Interview format gives opportunity to Medium: Harder to elicit truthful answers if probe and ask for examples. Possible to respondents have preconceptions. Greater risk implement “double blind” method to reduce that respondents might misinterpret questions or preconception bias. tush through the survey. Cost per survey High: High quality trained interviewers needed Low: Initial design and execution costs, but this to run survey. Training includes one week initial fixed cost can be spread over a very large number training and ongoing debriefing and calibration. of respondents, so cost per survey is low. Costs Interviewers’ time primarily spent recruitmg can be higher in poorer countries where managers to take part in the survey (rather than enumerators administer surveys onsite because of just running interviews). unreliable mail and e-mail networks. Response rates Medium: Interview is interactive and managers High: Co-operation with a National Statistics more engaged. We obtainedan average response Agency can enable the survey to be mandatory. rate of 40%. Given this response rates of around 80%. Without such co-operation, response rates will be low. Replicability Medium: Training needed to ensure the surveyis High: Questionnaire essentially the same across delivered in same way. Useful to have some countries and already available pre-tested fromby individuals who have worked in previous survey US Census Bureau. waves as trainers for other surveys foster comparability. Training and survey material is available on-line. International High: Multiple countries can be interviewed Medium/High: Easier to implement but there is a comparability from same location. Using bilingual interviewers risk of differential interpretation if this is not means makes it is easier to cross-check carefully translated across languages. responses. Speed of delivery High: Can complete a full survey wave in about Medium: Involves cooperation with national 10 weeks. So including recruitment and set-up statistical agencies, so more planning work in tume possible to complete a survey wave from advance. The survey period typically is around 3 scratch in about 4 months. months plus 1 to 3 months of data cleaning.
|Zagreb|Slavonia|Dalmatia|
|---|---|---|
|Zagreb|Virovitica-Podravina|Zadar|
|CityofZagreb|Pozega-Slavonia|Sibenik-Knin|
|<br>Northern Croatia|Slavonski Brod-Posavina|Split-Dalmatia|
|Krapina-Zagorje|Osijek-Baranja|Dubrovnik-Neretva|
|Varazdin|Vukovar-Sirmium||
|Koprivnica-Krizevei|CroatiancoastlineandIstria||
|Bjelovar-Bilogora|<br>Lika-Senj||
|Medimurje|Primorje-Gorskikotar||
|Sisak-Moslavina|<br>Istria||
|Karlovac|||
<!-- Start of picture text --> (By Knowledge Intensity) (By Technology Intensity)<br>Region Tol<br>Croatian Coastline and Istria 37 38 17 37 129<br>Dalmatia 32 44 4 43 123<br>Northern Croatia 57 51 32 43 183<br>Slavonia 4] 38 17 49 145<br>ZagrebTotal 39 36 31 4] 1477m<br><!-- End of picture text -->

||(By Knowl|edge Intensity)|(ByTechnol|ogyIntensity)||
|---|---|---|---|---|---|
|Region|||||Tol|
|Croatian Coastline and Istria|37|38|17|37|129|
|Dalmatia|32|44|4|43|123|
|Northern Croatia|57|51|32|43|183|
|Slavonia|4]|38|17|49|145|
|Zagreb<br>|39|36|31|4]|147<br>|
|Total|||||7m|
||Mean|sD|p(l0)|= p(25) —|sp(50) ~|—sp(75)|p(90)|
|---|---|---|---|---|---|---|---|
|Sales per Employee<br>Profits<br>|4901.62<br>14305.47<br>|5000.41<br>90608.65<br>|1327.23<br>318.70<br>|2018.10<br>901.13<br>|3311.28<br>3245<br>|5927.47<br>8812.5<br>|9870.69<br>24225.45<br>|
|Wage per Employee|905.44|455.30|488.89|617.84|795.37|1077.14|1405.26|
|Exports|36761.50|147002.70|0|0|0|2400|82511|
|No. ofEmployees|24.43|30.35|7|9|14|25|53|
|Age|17.42|10.92|4|9|19|24|27|
|Registration for Innovation (dummy)<br>|0.084<br>|0.277<br>|0<br>|0<br>|0<br>|0<br>|0<br>|
|Use ofSophisticatedTechnology (dummy)<br>|0.695<br>|0.461<br>|0<br>|0<br>|1<br>|1<br>|1<br>|
|Access to External Finance (dummy)<br>|0.696<br><br>|0.460<br>|0<br>|0<br>|1<br>|1<br>|1<br>|
|Participation in Training Programs (dummy)||<br>0.704|0.457|0|0|1|1|1|
|Ownership: Family Owned (dummy)|0.750|0.434|0|0|1|1|1|
|Ownership: Professionally Run (dummy)<br>|0.431<br>|0.495<br>|0<br>|0<br>|0<br>|1<br>|1<br>|
|Exporter(dummy)|0.279|0.449|0|0|0|1|1|

Notes: ‘Sales per Employee’ is the real sales per employee of a firm. ‘Profits’ is the real profits of a firm. ‘Wage per Employee’ is the real wage per employee of a firm. ‘Exports’ is the total exports of a firm as calculated in Kuna. ‘No of Employees’ is the total number of employees of a firm. ‘Age’ is defined as 2017 - year of foundation of the firm. ‘Registration for Innovation’ is a dummy variable. It takes value 1, when a firm registers itself for trademarks, patents, utility models, and industrial designs. ‘Use of Sophisticated Technology’ is a dummy variable. It takes value 1, when a firm sophisticated technology for quality control. ‘Access to External Finance’ is a dummy variable. It takes value 1 if a firm has used the following types of finance for its business activities: (a) bank loans, overdrafts and other credit lines, (b) loans from HBOR or HAMAG, (c) Newly issued bonds, (d) Newly issued equity, (e) leasing, (f) factoring or invoice discounting, and (g) EU grants. ‘Participation in Training Programs’ is a dummy variable. It takes value 1 if a firm participated in a Govt. sponsored training program. ‘Ownership: Family Owned’ is a dummy variable. It takes value 1 when a firm is owned by the founder or family member of the founder. ‘Ownership: Professionally Run’ is a dummy variable. It takes value 1 if a firm is not run by either the founder or family member of the founder. ‘Exporter’ takes a value 1 if total exports of a firm is greater than zero.
<!-- Start of picture text --> N<br>32)2°<br>2<br>3<br>£28<br>$ *<br>=<br>ie)<br>a8<br>0 2 4 6 8 1 So<br><!-- End of picture text -->
# _3.2 On which management practices is Croatia doing better? Which practices are worse?_

Weaknesses in management practices particularly relating to data driven performance monitoring cripples the overall management score in Croatia. Data driven performance monitoring includes specific practices that help managers define their key performance indicators (e.g. inventory, sales, absenteeism) and appropriately track them periodically over time. Incentives management practices relate to, for example, processes by which managers and non-managers are awarded bonuses and promotion. The finding for Croatia regarding the weaknesses in practices relating to objective and frequent monitoring of production indicators is similar to that observed in Russian manufacturing, that is, monitoring aspect of structured management is weaker relative to incentives and targets.

_Figure 3: An average firm in Croatia is weaker at practices relating to monitoring performance indicators_ By comparison, this finding is in contrast with firms in the United States and Mexico which are stronger in monitoring vis-à-vis structured practices relating to workers’ incentives and target setting ( **Figure 3** , left panel; also see tables 1a and 1b). Underlying the lower average score on practices relating to data driven performance monitoring is the large heterogeneity across firms. Notably, the distribution of monitoring practices has a thicker left tail, thereby suggesting that a large share of firms in Croatia do not explicitly, or frequently track performance indicators. Likewise, the thin right tail of the distribution is indicative of the problem that a very small share of firms have specific and formal practices for monitoring performance indicators ( **Figure 3** , right panel).
||Mean|SD|p(10)|p(25)|p(50)|p(75)|p(90)|
|---|---|---|---|---|---|---|---|
|Manufacturing||0.538|0.149|0.340|0.431|0.542|0.652|0.718|
|<br>Services|<br>0.527|0.149|0.326|0444|0.538|0.639|0.702|
|Notes:‘Management|Score’is|calculat|edfollow|ingMOP|S(SeeB|loomet|al.,2018).|

|Employee Size|| Overall|Services|Manufacturing|
|---|---|---|---|
|6|0.505|0.505||
|7|0.532|0.532||
|8|0.507|0.507||
|9|0.502|0.502||
|10|0.490|0.527|0.449|
|11-15|0.509|0.519|0.500|
|16 — 20|0.519|0.558|0.489|
|20 — 30|0.556|0.547|0.561|
|30 —50|0.575|0.547|0.586|
|50+|0.599|0.580|0.605|

Notes: ‘Management Score’ is calculated following MOPS (See Bloom et al., 2018).

<!-- Start of picture text --> ca<br><!-- End of picture text -->
|Mean|Std. Dev|.<br>p(10)|p(25)|p(50)|p(75)|p(90)|
|---|---|---|---|---|---|---|
|Te<br><br>|chnology <br>|Intensive<br>|||||
|High Technology<br>0.554<br>LowTechnology<br>0.531|0.142<br>0.152|0.389<br>0.326|0.446<br>0.417|0.565<br>0.535|0.670<br>0.649|0.718<br>0.718|
|<br><br>Kn<br><br>|owledge <br>|Intensive<br>|||||
|Knowledge Intensive<br>0.546|0.141|0.364|0.462|0.569|0.651|0.713|
|Not-Knowledge Intensive | 0.507|0.154|0.269|0.424|0.515|0.621|0.694|
|Notes: ‘Management Score’ is calculated follow|ing MOPS|(See Bloom|et al.,|2018). ‘H|igh Techn|ology’ and ‘Low|
|Technology’ sectors are as defined by EUROST<br>*No-KnowledgeIntensive’servicesareas|AT indicat<br>definedby|ors based on <br>EUROSTAT|NACE<br>indica|Rev. 2. <br>torsbase|‘Knowled<br>don<br>NA|ge Intensive’ and<br>CERey.<br>2.|

||Mean|Std. Dev.|p(10)|p(25)|p(50)|p(75)|p(90)|
|---|---|---|---|---|---|---|---|
|||Manufac<br>|turing<br>|||||
|Family-Owned|0.531|0.153|0.319|O417|0.532|0.655|0.718|
|NonFamily-Owned|0.562|0.134|0.415|0.485|0.590|0.652|0.718|
|||Servi|ces|||||
|Family-Owned|0.529|0.151|0.326|0.439|0.536|0.647|0.704|
|NonFamily-Owned|0.521|0.142|0.344|0.450|0.538|0.631|0.695|
|||Manufac|turing|||||
|Family Run|0.530|0.151|0.326|0.422|0.530|0.646|0.717|
|Professionally Run ||0.550|0.147|0.354|0.456|0.576|0.660|0.721|
|||Servi<br>|ces<br>|||||
|Family Run|0.533|0.150|0.326|0.448|0.550|0.649|0.708|
|ProfessionallyRun||0.518|0.147|0.308|0.438|0.532|0.633|0.696|

Notes: ‘Management Score’ is calculated following MOPS (See Bloom et al., 2018). ‘Family-Owned’ is a firm, where the majority of the shares are held by the founder and/or family member of the founder. ‘Non Famlily-Owned’ is a firm where it is owned by anybody else (where the majority of the shares are held by private individuals, managers, dispersed shareholders, private capital or risk capital, etc. and run by managers). ‘Family Run’ is a firm, where it is run by the founder or a family member of the founder. ‘Professionally Run’ is a firm where it is run by other than the founder and/or family member of the founder.
- Region Overall

- Croatian Coastline and Istria 0.530 Dalmatia 0.519

- Northern Croatia 0.514 Slavonia 0.526 Zagreb 0.571

Notes: ‘Management Score’ is calculated following MOPS (See Bloom et al., 2018).

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<!-- Start of picture text --> mEmanagementsore (median) ©——-managementsore (SD)<br><!-- End of picture text -->

||Mean|Std.Dev.|p(10)|p(25)|p(50)|p(75)|p(90)|
|---|---|---|---|---|---|---|---|
|||<br>Manuf<br>|acturin<br>|g<br>|||<br>|
|Exporter|0.567|0.125|0.403|0.485|0.576|0.673|0.712|
|Non-Exporter|| 0.522|0.159|0.313|0.413|0.521|0.646|0.718|
|||Ser|vices|||||
|Exporter|0.534|0.158|0.318|0.431|0.550|0.647|0.717|
|Non-Exporter|| 0.525|0.146|0.326|0.450|0.536|0.638|0.694|
|Notes:‘Managem|entScore’|iscalculated|followi|ngMOPS|(SeeBl|oometal|.,2018).|
= A; f By 6Mji, UX; 0q + U; log ~~(+)~~ i = alog ~~(+)~~ i + vlog ~~(+)~~ i + (@ +B + — Dla + 5M: + UX + f+ d
|||Sales Per|Employee|||Prof|its||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MScoreoverall|0.935***<br>(0.205)|0.234***<br>(0.090)|||0.845**<br>(0.379)|0.844**<br>(0.386)|||
|Log(Employees)||0.077***<br>(0.026)|0.079***<br>(0.027)|0.081***<br>(0.025)||—0.085<br>(0.083)|-—0.072<br>(0.082)|—0.077<br>(0.083)|
|Log(Cap/Employees)||0.043***<br>(0.011)|0.042***<br>(0.011)|0.043***<br>(0.011)||0.092**<br>(0.042)|0.093**<br>(0.041)|0.093**<br>(0.042)|
|Log( Mat/Employees)||0.563°**<br>(0.038)|0.565***<br>(0.038)|0.564***<br>(0.038)|||||
|DuighEdu||0.128°**<br>(0.034)|0.127°**<br>(0.035)|0.133***<br>(0.034)||—0.048<br>(0.148)|-—0.048<br>(0.148)|—0.031<br>(0.147)|
|MScore yonitoring|||0.182*<br>(0.095)||||0.453,<br>(0.374)||
|MScorer¢.7||||0.129*<br>(0.071)||||0.589*<br>(0.306)|
|R-Square|0.03|0.86|0.86|0.86|0.20|0.21|0.21|0.21|
|N|727|724|724|724|513|513|513|513|
|Region Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IndustryDummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Columns (1) — (4) use sales per employee of a firm as the dependent variable; columns (5) - (8) use profits of a firm as the dependent variable. “MScoreOyerall’ is the overall management score of a firm as calculated following Bloom et al. (2018). ‘MScoreyfonitoring’ is the data driven performance monitoring management score. ‘MScorer¢.7’ is the targets and incentives management score. ‘Employees’ is the total employees of a firm. ‘Cap/Employees’ is the capital per employee of a firm. We use fixed assets of a firm as an indicator for capital. ‘Mat/Employees’ is the per employee materials consumed by a firm. ‘DuHighEdu’ takes a value 1 if more than 20% workers of a firm have a higher education degree. Robust standard errors are in the parenthesis. Marginal effects are reported in case of columns (5) - (8). Intercepts included but not reported. *,**,*** denotes 10%, 5% and 1% level of significance, respectively.
|||||||Technology|Adoption||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MScoreoverall<br>Log(Employees)|0.286°**<br>(0.075)|0.175***<br>(0.087)<br>0.045**<br>(0.018)|0.051***<br>(0.018)|0.044**<br>(0.018)|0.509"**<br>(0.106)|0445***<br>(0.111)<br>0.043<br>(0.029)|0.049*<br>(0.028)|0.048*<br>(0.029)|
|Log(Cap/Employees)||0.015*<br>(0.009)|0.014"<br>(0.009)|0.015*<br>(0.009)||0.007<br>(0.011)|0.006<br>(0.011)|0.008<br>(0.011)|
|Log(Mat/Employees)||0.013<br>(0.014)|0.015<br>(0.015)|0.012<br>(0.014)||0.027<br>(0.017)|0.030°<br>(0.017)|0.028"<br>(0.017)|
|DuighEdu||0.076**<br>(0.030)|0.079***<br>(0.030)|0.078**<br>(0.031)||—0.033<br>(0.043)|—0.036<br>(0.043)|—0.025<br>(0.043)|
|MScoreyponitoring|||0.002<br>(0.081)||||0.350°**<br>(0.103)||
|MScorerez||||0.194°*<br>(0.078)||||0.313°**<br>(0.086)|
|R-Square|0.05|0.19|0.18|0.20|0.02|0.10|0.10|0.10|
|N|727|554|554|554|727|693|693|693|
|Region Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Notes: Columns (1) — (4) use innovation|dummy as|the depend|ent variable.|It takes a|value 1<br>if a|firm register|s itself for|a trademark, patent, utility|
|models, and industrial designs. Columns (|5) - (8) use|a a techno|logy adoption|dummy|(it indicates|whether a f|irm has ado|pted sophisticted technology|
|for quality control) as the dependent vari|able. ‘MSco|reQyeraii’|is the overall|managem|ent score of|a firm as ca|lculated fol|lowing Bloom et al. (2018).|
|‘MScoreMonitoring’ 1s the data driven|performanc|e monitorin|g managemen|t score. ‘|MScorey¢.7’|is the target|s and incen|tives management score.|
|‘Employees’ is the total employees ofa fi<br>capital.‘Mat/Employees’istheperempl|rm. ‘Cap/E<br>oyeemater|mployees’ <br>ialsconsu|is the capital <br>medbyafirm.|per emplo<br>‘DyighE|yee of a fir<br>du’takesa|m. We use fi<br>value1ifmo|xed assets o<br>rethan20|f a firm as an indicator for<br>%workersofafirmhavea|
|<br>higher education degree. Robust standard|<br> errors are|<br>in the paren<br>s|<br>thesis. Interc<br>ignificance,re|<br>epts incl<br>spectively|<br>uded but not <br>.|<br> reported. *,|<br>~*,*** deno|<br>tes 10%, 5% and 1% level of|
<!-- Start of picture text --> Access to External Finance Participation in Training Programs<br>(1) (2) (3) (4) (5) (6) (7) (8)<br>MScoreoveralt 0.363°** 0.331°** 0.625*** 0.391***<br>(0.112) (0.110) (0.105) (0.113)<br>Log(Employees) (0.030)0.026 (0.030)0.035 (0.030)0.028 0.122***(0.027) 0.126***(0.027) 0.129***(0.027)<br>Log(Cap/Employees) 0.053°** 0.053*** 0.053*** 0.0003 0.0002 0.0004<br>(0.012) (0.012) (0.012) (0.012) (0.012) (0.012)<br>Log(Mat/Employees) (0.018)0.017 (0.018)0.020 (0.018)0.017 0.059°**(0.019) 0.060***(0.019) 0.061***(0.019)<br>DuighEdu —0.076* -—0.072* —0.071* 0.072* 0.069* 0.081**<br>(0.042) (0.043) (0.041) (0.038) (0.038) (0.038)<br>MScore yonitoring 0.164 0.350°**<br>(0.110) (0.114)<br>MScorers:7 0.272*** 0.250°**<br>(0.083) (0.085)<br>R-Square 0.01 0.12 0.11 0.12 0.04 0.19 0.19 0.18<br>N 727 700 700 700 727 690 690 690<br>Region Dummies Yes Yes Yes Yes Yes Yes Yes Yes<br>Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes<br>Notes: Columns (1) — (4) use a dummy indicating whether a firm was able to finance through different external sources (such as bank loans, newly<br>issued bonds, newly issued equity, etc.) as the dependent variable. Columns (5) - (8) use a dummy indicating whether a firm has participated in govt.<br>training program or not as the dependent variable. ‘MScoregyerai’ is the overall management score of a firm as calculated following Bloom et al.<br>(2018). ‘MScoreygonitoring’ 1s the data driven performance monitoring management score. ‘MScore74.7’ is the targets and incentives management<br>score. ‘Employees’ is the total employees of a firm. ‘Cap/Employees’ is the capital per employee of a firm. We use fixed assets of a firm as an indicator<br>for capital. ‘Mat/Employees’ is the per employee materials consumed bya firm. ‘DuighEdu’ takes a value 1 if more than 20% workers of a firm have a<br>higher education degree. Robust standard errors are in the parenthesis. Intercepts included but not reported. *,**,*** denotes 10%, 5% and 1% level of<br>significance, respectively.<br><!-- End of picture text --> <!-- Start of picture text --> 14<br>12<br>1<br>08<br>0.6<br>an<br>°<br>0.2<br>Overall Monitoring —_Incentivesandtargets Overall Monitoring —_Incentivesandtargets<br>Sdes per employee Profits<br><!-- End of picture text --> <!-- Start of picture text --> 0.7<br>0.6<br>05<br>04<br>03<br>02 !<br>0.1<br>-0.1<br>0.2<br>Overall Monitoring Incentivestargets and Overall Monitoring Incentivestargetsand<br>Innovation Adoption of sophisticated technology<br><!-- End of picture text --> <!-- Start of picture text --> 0.7<br>0.6<br>05<br>04<br>0.3<br>0.2<br>0.1<br>0<br>01<br>02 Overall Monitoring _incentivesand Overall Monitoring _Incentivesand<br>targets targets<br>Accessto external finance Training programs<br><!-- End of picture text -->
<!-- Start of picture text --> Management Score<br>Sectors facturing Tech Tech Intensive Intensive<br>Q) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)<br>Log(Employees) 0.047*** 0.055*** 0.064*** 0.042*** 0.053°** 0.073°** 0.037°* 0.044***<br>(0.007) (0.008) (0.011) (0.010) (0.019) (0.014) (0.015) (0.015)<br>Log(Age) —0.017** —0.030*** | —0.019* -0.034*** -0.019 —0.021 —0.020 —0.044***<br>(0.007) (0.007) (0.011) (0.009) (0.017) (0.014) (0.013) (0.013)<br>DeEzporter 0.025** 0.024° 0.039** 0.004 0.075** 0.034* —0.015 0.024<br>(0.013) (0.013) (0.017) (0.018) (0.030) (0.020) (0.028) (0.025)<br>DuighEdu 0.036"*(0.015) 0.048"**(0.014) 0.041°*(0.021) 0.056°°*(0.020) —0.017(0.031) 0.066°°(0.028) (0.040)0.046 0.057**(0.024)<br>DFamity—owned —0.003(0.013) (0.013)0.015 0.0003(0.020) (0.017)0.025 (0.040)0.016 —0.0002(0.022) (0.021)0.029 (0.025)0.020<br>R-Square 0.12 0.09 0.08 0.09 0.08 0.17 0.18 0.18 0.27 0.18 0.13 0.22<br>N 727 724 727 727 727 724 313 411 101 212 204 207<br>Region Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes<br>Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes<br>Notes: Columns (1) — (12) use management score of a firm as the dependent variable. ‘Log(Employees)’ is the total number of employees ofa firm.<br>‘Log(Age)’ is the age of a firm. ‘Dgzzporter’ takes value 1 when a firm’s export values are greater than zero. ‘Dyighzdu’ takes value 1 if more than 20%<br>workers of a firm have a higher education degree. ‘DFamily—owned’ takes a value 1 if a firm is family-owned and run by the owner or a family member<br>of the owner. Robust standard errors are in the parenthesis. Intercepts included but not reported. *,~*.*** denotes 10%, 5% and 1% level of<br>significance, respectively.<br><!-- End of picture text -->
correlations (column 6) – doubling firm size is associated with approximately 0.047 point increase in management score. Going across columns, this relationship appears to be stronger for manufacturing and particularly low-tech. Services, and especially knowledge intensive services, have a weaker relationship of management capabilities with firm size. This result is analogous to the finding in Mexico where the relationship between firm size and management is weaker in services relative to that in manufacturing (Bloom et al., 2019b).

_Second,_ as noted in the descriptive in Section 3, regressions confirm that firm age in Croatia is negatively correlated with management score, that is, adoption of structured management practices is lower among older firms (column 2). Following similar analysis in Bloom et al. (2019b) on Mexico and the work of Hsieh and Klenow (2014) regarding the life cycle of firms, this finding appears to be indicative of poor allocative efficiency among firms in Croatia where clearly, market selection is not operating perhaps due to the lack of pro-competitive conditions, especially in services. Nevertheless, the finding in Croatia is somewhat similar to that observed in a survey of manufacturing firms in Russia such that firms also do not improve with age (Grover and Torre, 2019), however, in Croatia this negative association is primarily driven by the services sector.

_Third,_ demonstrate that management score is positively and significantly correlated with the likelihood being an exporter, and having higher a share of tertiary educated workers (columns 3-4). Exporters are more likely to be better managed, and significantly so in manufacturing. More precisely, the management score of a manufacturing exporter in Croatia is higher by 7 percent than a non-exporter. This result is mainly driven by high-tech manufacturing firms, where the same difference is likely to around 14 percent, thereby suggesting that management capabilities can be improved among other firms in all sectors. Column (5) uses an indicator for ownership of firms. It takes value 1 if a firm is family-owned and run by a family member of the founder and 0 otherwise. We do not find any relation between ownership and management practices of a firm.

Column (6) puts all these together and the results remains the same, qualitatively, that is, – firm size, export status and education of employees are significantly correlated with management score. The overall explanatory power of firm characteristics in explaining management score is still quite limited – in the most complete specification (column 6) explains only 20 percent of the overall variation in the management score, implying that nearly 80 percent of the variation in management practices is explained by idiosyncratic, firm-specific factors.
<!-- Start of picture text --> 0.09<br>0.07<br>0.05 !<br>0.03<br>0.01<br>-0.01<br>-0.03 . :<br>-0.05<br>ps F 8 8 FF $$ BP F €\' BRB BF Fg<br>$ 3 = 2 5 = ES 5 = 2 5 =<br>° g 3 ° F 9 ° F g ° g 9<br>i Ss = Ss<br>5 io] 5 5<br>E E E E<br>Sze Age Exporter Higher education among<br>workers<br><!-- End of picture text -->
_Five,_ our work confirms that better management practices is associated with superior firm performance. Firms with more structured management practices have higher sales per employee, profitability, are more innovative and more likely to adopt sophisticated technology. This result is highly robust to a numerous checks and controls as well as varying measures of firm performance.

_Finally_ , while explaining the differences in management practice across firms, it becomes clear that among the firm characteristic that explain at least a portion of the variation in management practices, firm size, foreign linkages and the education level of non-managers are the most relevant. Surprisingly firm age is negatively associated with better management practices, especially in services. This implies that unlike in the United States and Mexico, firms do not learn to be better managed as they age. Thus, it is possible that a lack of pro-competitive forces in Croatia and more so in the services sectors do not promote learning and possibly hinder market selection.

If management matters for firm performance, then why firms do not adopt better management practices? Our survey also includes a question relating to the firm’s own perception of its management practices, which was originally a part of WMS survey questionnaire. More specifically, we ask the following question: “ _Excluding yourself, how well managed is your firm on a scale of 1 to 10, where 1 is worst practice, 10 is best practice and 5 is average_ ”. Prior studies using WMS data that compared self-perception of management capabilities with the objective management score found a huge divergence between observed management capabilities and perceived one. In line with these findings, we uncover that firms in Croatia also have a higher perception on their management practices ( **Figure 9** ). Given that firms do not realize that there is a problem with respect to their management practices, it does not get the required attention. Our survey objectively measures management capabilities among firms, and also provides an understanding of the capabilities that these firms are weak at.

_Figure 9: Firms perceive their management to be better than it actually is_ <!-- Start of picture text --> Difference vs. Real Management Score<br>Croatian Firms (Manufacturing and Services)<br>0 .2 .4 .6 .8 1<br>Real Management Score<br>Diff = Perceived - Real Fitted values<br><!-- End of picture text -->
Following the ABC framework for firm growth proposed in Grover, Medvedev and Olafsen (2019)<sup>21</sup> , we make the following recommendations for enhancing productivity and business dynamics in Croatia:

1. **A** llocative efficiency: The fact that management capabilities are negatively associated with firm age is an indication of a lack of learning and selection mechanism operating in Croatia, especially in the services sector. Comparison with peer countries reveals slow pace of business environment reforms in Croatia. The 2018 Global Competitiveness Report of the World Economic Forum ranks Croatia at 68th place, the lowest among EU Member States. Croatia also continues to lag Central, Eastern and Southeastern Europe (CESEE) countries in key Doing Business indicators, including those relating to starting a business, dealing with construction permits, access to credit and resolving insolvency. Croatia’s overall ranking on Doing Business 2019 is 58th, falling 7 places compared to last year. Firm entry is poorly ranked, at 123rd place, with 8 procedures that typically last 22.5 days and costs of 6.6 percent of income per capita. This is more burdensome than what is required in the ECA region and double the average of OECD countries. Relative to its CESEE peers, Croatia is not doing better on resolving insolvency matters either. The inefficient insolvency framework obstructs exit and re-entry of businesses into markets, which could potentially contribute to significant misallocation in Croatia. The Doing Business Report 2019 ranks Croatia at 59th place out of 190 economies on the ease of resolving insolvency. Policies to support healthy business dynamics need to establish the basic enabling conditions and facilitate reallocation of resources (e.g., labor, capital, land) across firms, for example, by easing the business environment for entry and exit of firms.  Such policies will increase competition and market will ensure that firms are motivated to upgrade and invest in their capabilities.

2. **B** usiness to Business linkages: Our paper finds that firms with foreign linkages, that is, exporters are more likely to be better managed. In this context, policies to connect firms to export markets can encourage learning and hence better management practices. Such firms are more likely to engage in quality upgrading, leading to higher productivity and firm growth.

**3. C** apabilities of firms: More critically, our work confirms the tight association of managerial capabilities with firm performance for firms in Croatia. Management matters not only for productivity but also for profits and innovation. Thus, firms in Croatia are more likely to benefit from training on structured management relating to these practices.

> 21 This framework proposes that policies for improving firm dynamism and supporting job creation should steer away from picking potential winners and focus on improving **A** llocative efficiency, strengthening **B** usiness-to-business spillovers, and building firm **C** apabilities. These policies rely on encouraging innovation, network and agglomeration economies, global linkages, worker skills and managerial capabilities, and financial development to rebalance growth/productivity policies.