rafmacalaba/datause-encoder-data huggingface dataset:
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.
To measure the history of militarized disputes, we use the Correlates of War (COW) dataset (Militarized Interstate Disputes (v3.1)). We created a dyadic count of the river riparians involved in militarized disputes (disputes could be bilateral or multilateral). The total number of recorded disputes is used for each river dyad.

Based on the claim that increased interdependence in the form of trade decreases the likelihood of militarized conflict among countries and enhances cooperative political relations (Mansfield and Pollins 2003), studies have shown that heightened trade facilitates environmental treaty formation and acts as a contract enforcing mechanism (Neumayer 2002b; Stein 2003). Studies examining international water treaty formation have likewise found a significant relationship between cooperation and trade (Espey and Towfique 2004; Brochmann and Hensel 2009, Tir and Ackermann 2009; Dinar et al. 2011). We follow the same rational expecting high trade relations to increase the likelihood of cooperative behavior.

To measure trade relations, we obtained two separate trade datasets. In this framework, we adopt the significant finding by Arora and Vamvakidis (2005) that relatively important trading partners tend not to change much over time, further justifying the use of long-term values. Using average annual country-level data from the Gleditsch (2004) dataset, we constructed an annual trade variable for each dyad that expresses total trade (imports + exports) between Country 1 and Country 2 as a fraction of their combined GDP, describing the economic importance of trade to the riparians (Sigman 2004).<sup>6</sup>

# <u>4.1.3.5.</u> _<u>Power and wealth</u>_

The literature has provided a variety of views relating to the role of power in international hydropolitics. Some authors have claimed that an asymmetric inter-riparian power relationship impedes international cooperation over shared rivers (Hijri and Gray 1998; Just and Netanyahu 1998:9). Other scholars (particularly those echoing neorealist assumptions) have argued that hegemony, or power asymmetry, actually facilitates cooperation especially when the stronger party is downstream and initiates and imposes a cooperatve regime (Lowi 1993:10). Empirical

> 6 Given that exports from Country 1 to Country 2 were not always equal to imports by Country 2 from Country 1, due to discrepencies in national accounting, we took the average of the two for each dyad.

15
# **Table 4. Cyclicality: Averages of Country-Level Correlation Coefficients between Various Inflows and GDP, 1980-2007**

# **a. Simple average**

||REM|FDI|ODA|REM+FDI+<br>ODA|
|---|---|---|---|---|
|ALL DEVELOPING COUNTRIES|0.50|0.52|-0.02|0.52|
|East Asia and Pacific|0.52|0.50|-0.20|0.46|
|Europe and Central Asia|0.67|0.64|0.14|0.70|
|Latin America and the Caribbean|0.68|0.60|-0.11|0.68|
|Middle East and North Africa|0.32|0.51|-0.17|0.32|
|South Asia|0.59|0.52|-0.28|0.65|
|Sub-Saharan Africa|0.30|0.42|0.13|0.35|
|Low income|0.33|0.40|0.19|0.39|
|Lower middle income|0.58|0.57|-0.18|0.54|
|Upper middle income|0.57|0.60|-0.03|0.62|

# **b. GDP-weighted average**

||REM|FDI|ODA|REM+FDI+<br>ODA|
|---|---|---|---|---|
|ALL DEVELOPING COUNTRIES|0.66|0.71|-0.20|0.73|
|East Asia and Pacific|0.82|0.82|-0.30|0.86|
|Europe and Central Asia|0.12|0.71|-0.23|0.67|
|Latin America and the Caribbean|0.83|0.68|-0.12|0.75|
|Middle East and North Africa|0.12|0.45|-0.20|0.21|
|South Asia|0.83|0.85|-0.67|0.86|
|Sub-Saharan Africa|0.72|0.46|0.56|0.51|
|Low income|0.43|0.62|0.10|0.56|
|Lower middle income|0.77|0.78|-0.36|0.80|
|Upper middle income|0.60|0.67|-0.11|0.69|

23
Policy Research Working Paper 11081

# **Abstract**

Are firms adapting to climate change? This paper studies this question by combining geocoded World Bank Enterprise Survey data with spatially granular weather data to estimate temperature response functions for nearly 160,000 firms in 134 countries over a 15-year period. Our results show that market imperfections in low- and middle-income countries constrain firms’ ability to adapt. Small and medium-size firms in low- and low-middle income countries are most vulnerable, with revenues declining by 12 percent in years with temperatures 0.5°C above historical averages. The impact is equally strong for manufacturing and services firms and result from declines in labor productivity and wages. Heat-sensitive sectors and less resilient firms are more severely affected, reinforcing the causal interpretation. Unique firm-level information on policy constraints including limited financing, burdensome regulations, and unsafe conditions suggest that such factors raise adaptation costs, undermining economic resilience to climate change.

This paper is a product of the Development Research Group, Development Economics, the Finance, Competitiveness and Innovation Global Department, and the International Finance Corporation. 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 mlang@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
- _Which reform efforts are most likely to succeed?_

- _Will the same intervention be equally effective across the whole public sector or even within the same sorts of organizations?_

To illustrate by returning to the analysis of the Bolivian public sector, in that context 11 components of the institutional environment were identified: six components captured rule credibility; four referred to policy credibility; and one referred to resource adequacy and predictability.

Rule credibility in the Bolivian public sector's institutional environment comprises: existence of rules in recruitment, existence of rules in evaluation, existence of rules in training, existence of rules in recording, fair treatrnent, and predictable career path. Policy credibility comprises four specific aspects of the institutional environment, i.e., policy consistency, policies communicated clearly to employees, policies supported by officials, political interference/micro-management. Just one question examined the predictability of resources.52 Accordingly, for the Bolivian public sector, a third model was written where performance is a function of eleven different components of the institutional environment.

# Model III

Performance = h (Existence rules in recruitment, Existence of rules in evaluation, Existence of rules in training, Existence of rules in recording, Fair treatment, Predictable career path, Policy consistency, Policy communication, Policy support, Political interference/micromanagement, Resource predictability) To unpack the relationship between institutional environment and performance, or in other words to quantify the relationship between performance and different potential reform efforts, an econometric model was utilized to estimate model I _.3_ Its estimated coefficients were used to derive appropriate indicators. For example, to find how much perfornance changes upon changing the institutional environment via making policies consistent, the following formula was utilized:

# _Rate of performance_ **_improvement_**

A _performancelApolicy consistency polkyComillmy performance,_ 52 In each country where BNPP surveys were administered, survey managers customized the survey instrumen **t** o country-specifi **c** oncerns, stressing some areas over others. As a result, not all surveys provided questions to capture all these aspects of the institutional environment or of performance. _53_ In a linear model, the estimated coefficient for a specific aspect of the institutional environment variable is the partial derivative of the performance with respect to that aspect of the institutional environment.

28
Berk, C. 2016. “Indexing Oil from a Financial Point of View: A Comparison between Brent and West Texas Intermediate.” _International Journal of Energy Economics and Policy_ 6 (2): 152-158.

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Buncic, D., and C. Moretto. 2015. “Forecasting Copper Prices with Dynamic Averaging and Selection Models.” _The North American Journal of Economics and Finance_ 33 (2015): 1-38.

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62
accounting framework. One approach is to account for the contribution of measured inputs, and label the “residual” – how much output is produced taking into account the volume of inputs – as an estimate of TFP. Another approach is to estimate the production function to try to measure the additional factors that affect productivity and output: when a characteristic of firm such as private ownership is associated with higher output, we say that after accounting for the contribution of the factors of production (capital and labor) to output, private ownership has a positive impact on TFP.

To obtain estimates of the average TFP level by country, the production function equation is first estimated on _country-averages_ : averages were calculated for firm capital, labor and output were calculated for each of the 55 country surveys, and a regression estimated using these 55 observations<sup>4</sup> . This approach is known in econometrics as the “between estimator” because the variation between countries is the source of the information for estimating the effects on productivity; compare to the “within” approach presented below.

The basic estimation results are reported in column 1 of Table 1; the coefficients in the parentheses show the productivity gaps between Serbia and selected countries. Croatia, Hungary, Poland and Slovenia all had higher TFP than Serbia in 2002 or 2003.<sup>5</sup> The TFP “residuals” as defined above are plotted in Figure 2 versus GDP per capita. The figure shows the strong positive correlation between country-average TFP and GDP per capita: richer countries have more productive firms.<sup>6</sup>

**Table 1: Basic TFP regressions using country-survey-averages and firm-level data** **<u>(manufacturing only)</u>**

||Regression on country-<br>survey-averages|Regression on firms with<br>country-survey dummies|
|---|---|---|
|Fixed capital|0.574**|0.387**|
|Labor|0.445**|0.663**|
|Country survey dummy variables included|No|Yes|
|Selected estimates of relative TFP<br>Benchmark: Serbia 2002=0|||
|Croatia 2002|[0.612]|0.798**|
|Hungary 2002|[0.817]|0.922**|
|Poland 2002|[0.543]|0.717**|
|Poland 2003|[0.938]|1.087**|
|Slovenia 2002|[1.146]|1.347**|
|Number of observations|55 country-survey-averages<br>based on 14,687 firms|14,687 firms|

** significant at the 1 percent ; heteroskedastic-robust standard errors; [ ] indicates estimate based on a residual

The figure and the table also show that Serbia is well below its East European peers in productivity. Croatia is a natural comparator country. The country-survey-averages TFP regression implies that the productivity of Serbian manufacturing firms in the BEEPS 2002 survey is 61 percent<sup>7</sup> below that of Croatian firms; the gap based on the PICS 2003 5
schooling for the first measure of exposure to school policies. The years of schooling variable is also statistically significant for the second measure of exposure (Table 1.10, Column 10), but estimates are not significant for the other indicators.<sup>28</sup> We also examine the 2014 VHLSS, which is the most recent survey round that collects data on individuals’ birth place. Clearly, given the (much) longer time interval, sample attrition issues (for example, because of death) are more severe with this survey. Nevertheless, we experiment with estimating Equation (1) for those who were born in 1946-1949 as the (contaminated) treatment group, and those who were born in 1950-1961 as the control groups. While we expect any estimated treatment impact to be severely downward biased, the treatment impact (Table 1.10, Panel B, Appendix 1,) is still positive and statistically significant at the 5 percent level for completing at least primary education for both the first and second measures of exposure to school access. The other education outcome variables are statistically insignificant but have positive coefficients.

Finally, we provide in Table 1.11 in Appendix 1 estimates using the hybrid approach where an individual is assigned the province-level, instead of the individual-level, number of years of exposure to the ME program (which is determined according to the province-level years of the

> 28 A number of individuals report different years of birth and years of education in the panel data of the 1992-1993 and 1997-1998 VLSSs, which raises concerns about measurement errors. To reduce the measurement errors, we restrict our estimation sample of the 1992-1993 VLSS to the panel individuals whose differences in their reported birth years and years of education between the two rounds are less than 2 years. To further investigate these measurement issues, we divided the panel sample in 1992-1993 into two groups: group 1 consists of individuals who reported the same birth years in both survey rounds, and group 2 the remaining individuals. We subsequently check the age and education profiles of these two groups. We find that group 1 is significantly younger and more educated than group 2 (e.g., achieving an average of 7.2 years of school versus 5.7 years of school for group 2). This supports our hypothesis that individuals in group 2 might have provided incorrect birth years. This result is consistent with previous studies on survey recall bias. For example, recall bias was observed to decrease for more educated survey respondents in various countries including India (Das, Hammer, and Sánchez-Paramo, 2012), Malaysia (Beckett et al., 2001), Sweden (Kjellsson, Clarke, and Gerdtham, 2014), and the US (Kennickell and Starr-McCluer, 1997).

28
Table A2 : Analysis of subsistence basket of infrastructure services

||Quintil|es||||Total|
|---|---|---|---|---|---|---|
||1st|2nd|3rd|4th|5th||
|Cost of subsistence basket||||||58|
|Water and sewerage (minimum unmeasured charge)||||||13|
|Electricity (120kWh/month)||||||13|
|Natural gas (42m<sup>3</sup>/month)||||||20|
|Telephone (100 mins/month)||||||12|
|Public transport (one return commuting journey per day)||||||44|
|Average expenditure share for subsistence basket (%, OPSM)|12.5|9.1|8.2|7.2|5.0|7.6|
|Water and sewerage|2.8|2.0|1.8|1.6|1.1|1.7|
|Electricity|3.8|2.8|2.5|2.2|1.5|2.3|
|Natural gas|2.8|2.0|1.8|1.6|1.1|1.7|
|Telephony|3.1|2.2|2.0|1.8|1.2|1.9|
|Public transport (Metropolitan Area head of household commuting)|12.1|9.4|7.4|5.8|4.2|6.8|
|Average income share for subsistence basket (%, OPSM)|17.1|9.3|7.0|5.3|3.0|6.5|
|Water and sewerage|3.8|2.1|1.6|1.2|0.7|1.5|
|Electricity|5.2|2.8|2.1|1.6|0.9|2.0|
|Natural gas|3.8|2.1|1.6|1.2|0.7|1.5|
|Telephony|4.2|2.3|1.7|1.3|0.7|1.6|
|Public transport (Metropolitan Area head of household commuting)|15.3|8.3|6.3|4.8|2.7|5.8|

_Source_ : OPSM Survey, November 2002 54
other interim recommendations, one could read that nations should move ahead on unilateral and regional climate agreements before a post-Kyoto deal and calls for a South Pacific regional emissions trading scheme that helps prevent deforestation in Indonesia and Papua New Guinea.

As with the US programs, the analytic literature is not well represented in peer-reviewed journals. With Australia’s recent ratification of the Protocol, this is likely to change as current programs are accommodated within a national and international system.

# **6 Carbon markets**<sup>63</sup>

As discussed, the flexibility mechanisms of the Kyoto Protocol are designed to bring about the same type of market efficiencies that are generally associated with trade. Moreover, they are expected to mobilize private capital on a scale that public transfers cannot. Through price discovery, private markets associated with the flexibility mechanisms are expected to reveal whether current regulations place a value on greenhouse gases emissions that is consistent with what modeling efforts suggest are need to curb global warming.

In this section, we discuss what can be broadly termed carbon financial markets. These are the markets that are motivated directly by greenhouse gas policies. As the discussion of national policies in the preceding section suggests, the markets operate across a heterogeneous set of public and private institutions. Regulatory uncertainty associated with some markets is high and basic information can be scarce. Even so, the scale, sophistication and pace of growth in these markets and derived risk markets are remarkable. Project investment levels supported by these markets are likely to be larger than anticipated by model studies.

## **Model studies of potential size of the market for the flexibility mechanisms**

Several estimates of the potential demand for emissions reductions as well as the size of the CDM market are listed in Table 1. The estimates of the potential demand for GHG offsets ranges from 600 to 1713 MtCO2e (million tons of CO2 equivalent) per year over the first commitment period, 200812. The estimates differ substantially mainly due to uncertainties involved with the projection of emissions growth in Annex I countries and alternative macroeconomic model specifications (Springer, 2003). The estimates of the size of the CDM market ranges from 0 to 520 MtCO2e per year.

The wide range is due primarily to alternative assumptions about the supply of Kyoto allowances (AAUs) from Russia and Ukraine, which can serve as an alternative for project credits. In many of the modeling exercises, the combination of US withdrawal from Kyoto markets and the

> 63 This and the following two sections draw heavily on Capoor and Abrosi (2007).

40