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Managing Relational Contracts

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A wide range of important economic activities depend on self-enforcing informal “relational” contracts. For instance, a firm may buy a good knowing that it cannot sue the other firm if the quality is low – instead high quality is maintained through threat of the firm not making any future purchases. Relational contracts are typically modeled as being between a principal and an agent, such as a firm owner and a supplier. Yet in a variety of organizations, relationships are overseen by an intermediary such as a manager. Such arrangements open the door for collusion between the manager and the agent. We develop a theory of such managed relational contracts. We show that managed relational contracts can be both more and less efficient than the principal agent ones. In particular, kickbacks from the agent can help solve the manager’s commitment problem. When commitment is difficult, this can result in higher quality than the principal could incentivize directly. However, making relationships more valuable enables more collusion and hence can reduce quality.

Introduction

In 2006, the American retailer Aéropostale accused its chief merchandising manager Christopher Finazzo of receiving more than $25 million in kickbacks from a supplier, South Bay. Aéropostale argued that Finazzo had paid inflated prices to South Bay in exchange. Finazzo responded that he had favoured South Bay since they provided higher quality and a willingness to adapt to Aéropostale’s procurement needs. He argued that Aéropostale often remained “loyal” and “committed” to long-time “vendors even when those vendors charged higher prices” (Droney, 2017). In 2013, a jury found Finazzo and South Bay guilty of fraud. They appealed the restitution amount and in 2017 the Court of Appeals for the Second Circuit demanded a recalculation. Judge Droney argued that it was possible that Aéropostale did not lose money as a result of the kickback scheme. He argued that instead Finazzo’s “conduct may have reduced transactions costs for South Bay” and the relationship may have made it profitable for South Bay to pay kickbacks even at non-inflated prices (Droney, 2017).

Relational contracts between organizations are ubiquitous and are crucial for enforcing promises. Indeed, “lack of trust and commitment” is behind most supplier collaboration failures (Webb, 2017). The task of maintaining these relationships is often delegated to a manager like Finazzo. As illustrated by Aéropostale’s case, the firm can never guarantee that the manager will exclusively act in the firm’s best interest. Managers can exploit the (otherwise very valuable) trust relationship with their suppliers to collude with them. Does collusion between the manager and agent crowd out quality? Is collusion always detrimental for the principal?

In a new paper (Troya-Martinez and Wren-Lewis, 2018), we develop a theory of managed self-enforcing relational contracts.

Our model features a manager and an agent who have a bilateral relational contract over time (Levin, 2003). To model that the relationship is managed on behalf of a third party, we assume that profits are shared between the manager and a principal. Every period, the agent privately exerts costly effort to produce a quality which cannot be formally contracted on. To motivate effort, the manager promises to reward high quality with a price premium. This price is paid in part by the principal and in part by the manager. The manager and agent can also make side payments (which represent kickbacks, bribes or other favours) after the quality has been realized. The payment of both the price and side payments needs to be self-enforced.

Kickbacks as an enforcing mechanism

We find that collusion resulting from a managed relational contract can disincentivize quality if the manager pays a discretionary price premium regardless of quality. In particular, she may do so when she trusts that the agent will respond by making a side payment. More surprisingly, side payments can enhance a manager’s ability to commit, and hence allow higher quality. This is because the supplier will renege on paying side payments if the manager reneges on the promised price. This is consistent with evidence that side payments can help contract enforcement. Cole and Tran (2011) analyse informal payments in an Asian country and find that when contract payments are dependent on non-contractible quality, “the kickback is paid only after all contract payments have been made”. In a similar case, Paine (2004) describes how “a purchasing official called about an overdue payment for items already received, [explaining] ‘we can get you a check by next week if you can give us a discount — in cash so we can distribute it to employees’”.

Side payments are thus not necessarily detrimental for the firm when commitment is scarce. This theory thus provides an instance of the “reduced transaction costs” mentioned by Judge Droney.

More trust is not always better

Another interesting implication of a managed relational contract is the non-monotonicity of the relation between trust and efficiency. In the standard principal-agent model of relational contracts, more trustworthy relationships produce higher quality. In managed relational contacts, we show that the opposite may happen.

Figure 1 depicts the effort (and hence quality) exerted by the agent when the manager is in charge (purple) and when the principal is in charge (green). It depicts the effort as a function of the time discount factor delta, which is a measure of how valuable the relationship is (i.e. a larger delta implies a more valuable future). More valuable relationships produce higher effort, and hence higher quality, only up to a point. Once the relationship is sufficiently valuable, extra value facilitates collusion, which reduces effort. In particular, it allows the manager to pay the agent a high price in exchange for a side payment even when quality is low. This non-monotonicity result is consistent with evidence on firms’ use of guanxi, a system of trust-based “informal social relationship” in China which is often used to ensure “that a contract is honored” (Chow, 1997). Vanhonacker (2004) observes that “it would be naive to think—as many Western executives do—that the more guanxi you have on the front lines in China, the better”. Instead, he argues too much guanxi can “divide the loyalties of the sales and procurement people”.

Figure 1. Effort (or quality) with and without delegation to a manage

Source: Troya-Martinez and Wren-Lewis (2018). This figure plots the effort incentivized by the manager (in purple) and by the principal (in green) as a function of the discount factor (delta), which is a measure of how valuable the future is.

This result has important implications for policies designed to reduce fraud or corruption in contexts where relational contracts are valuable. Many such policies involve disrupting relational contracts in order to reduce manager-agent collusion, for instance by encouraging competition or increasing personnel rotation. The results of the analysis suggest that, in some circumstances, weakening manager-agent relations may simultaneously cut corruption and improve output. In other circumstances, however, there will be a trade-off, and reducing corruption may come at the cost of holding back potentially productive relationships.

Conclusion

The paper summarized by this brief is the first paper that studies the impact of collusion on relational contracts. The main take away messages are the following: First, when trust is a scarce resource, managed relational contracts are more credible and can incentivize more quality than direct relational contracts.

Second, collusion can crowd out productive effort when the relationship between manager and agent is too strong. In this case, trust is used to overpay the agent when quality is low.

Before the most recent Aéropostale judgment, it was common to use “the value of the kickbacks” as “a reasonable measure of the pecuniary loss suffered” by the third party (Droney, 2017). Judge Droney, however, argued that this “negative correlation” between kickbacks and loss should not be taken for granted. Indeed, our model has shown when this negative correlation may not exist. Hence, our conclusions may help explain why politicians and firm owners frequently turn a blind eye to employees accepting side payments (Banfield, 1975). On the other hand, our model also identifies when side payments undermine effort. In other words, it emphasizes the complex relationship between kickbacks and productive relational contracts. This complexity needs to be accounted for in policymaking.

References

  • Banfield, Edward C. 1975. “Corruption as a Feature of Governmental Organization.” The Journal of Law & Economics, 18(3): 587-605.
  • Chow, Gregory C. 1997. “Challenges of China’s economic system for economic theory.” The American Economic Review, 87(2): 321-327.
  • Cole, Shawn; and Anh Tran. 2011. “Evidence from the Firm: A New Approach to Understanding Corruption.” In International Handbook on the Economics of Corruption Vol. II. , ed. Susan Rose-Ackerman and Tina Soriede, 408-427. Edward Elgar Publishing.
  • Droney, J. 2017. “United States v. Finazzo.” 14-3213-cr, 14-3330-cr.
  • Levin, Jonathan. 2003. “Relational Incentive Contracts.” American Economic Review, 93(3): 835-857.
  • Paine, Lynn S. 2004. “Becton Dickinson: Ethics and Business Practices (A).” Harvard Business School Case 399-055.
  • Troya-Martinez, Marta; and Liam Wren-Lewis, 2018. “Managing Relational Contracts”, CEPR Discussion Paper Series DP12645 (v. 2).
  • Vanhonacker, Wilfried R. 2004. “When Good Guanxi Turns Bad.” Harvard Business Review, 82(4): 18.
  • Webb, Jonathan, 2017. “Why Do Supplier Collaborations Go Wrong? What Can Be Done About It?”, Forbes, 28 September 2017.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Towards a More Circular Economy: A Progress Assessment of Belarus

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This policy brief summarizes the results of our study, Shershunovich and Tochitskaya (2018),  on the circular economy development in Belarus. The aim of the work was to measure the circularity of the Belarusian economy using European Commission indicators. The analysis reveals that the circular economy in Belarus is still in the initial stage of its development. In 2016, the employment in circular economy sectors in Belarus accounted for 0.49% of total employment, and the investment amounted to only 0.27% of total gross investment. Belarus is also falling behind many European countries in waste recycling.

Introduction

The circular economy represents an economic system based on a business model of reduction, reuse, recirculation and extraction of materials in production, distribution and consumption of goods and services (Batova et al., 2018).

Transition to it offers great opportunities to transform the Belarusian economy and make it more sustainable and environmentally friendly, while preserving primary resources, creating new jobs and increasing competitiveness of enterprises.

In order to encourage the transition to a circular economy, it is important to have a proper monitoring system based on reliable and internationally comparable data. It helps to track progress towards a circular economy, conduct policy impact assessment, and analyze whether measures being taken are sufficient to promote an economy that reduces the generation of waste.

To assess the development of a circular economy in Belarus, a set of the European Commission (EC) indicators was used to capture the evolution of the main elements of closing the materials and products loop. The EC monitoring system comprises 10 indicators which are part of 4 pillars: production and consumption; waste management; secondary raw materials; competitiveness and innovation.

The reasons to use this system for Belarus are as follows: first, there is no set of indicators that provide a comprehensive overview of a circular economy in Belarus, while the EC monitoring framework allows us to capture its main elements, stages, and aspects; second, Eurostat calculates circular economy indicators for the European Union (EU) countries on a regular basis, which proves the high level of their practical application,     relevance and robustness; third, the EC is constantly working on their improvement. Thus, the EC set of indicators can be a tool to monitor trends in transition to a circular economy in Belarus.

Tight spots of waste statistics in Belarus

While calculating the circular economy indicators for Belarus the following problems with data affecting the quality of statistics have been identified:

  • methodological issues;
  • challenges with recording and coverage;
  • insufficient degree of international comparability of data, in particular woth the EU countries.

Such methodological problems as the blurred boundaries between the definitions of ‘waste’ and ‘raw materials’, and the lack of criteria for categorizing substances or objects as waste allow enterprises to classify certain substances or objects not as waste and therefore not to file information on them. As a result, less than half of the enterprises which might generate industrial waste, report it. Therefore, the question arises whether the statistical data reflect the real level of waste generation, recycling, and disposal in Belarus.

Data on municipal solid waste (MSW) have proved to be one of the areas of most serious concern. Absence of direct MSW weighing makes the data on it very sensitive to the conversion factor from volume to mass units. The differences between the Belarusian and European waste classifiers and definitions of key concepts (‘waste’, ‘recycling rate’) complicate the data analysis.

In addition, since Belarus is the 3rd world potash fertilizers producer, the share of potash waste in the total volume of waste generation is very high (63-68%). Only a small portion of this type of waste stream is recycled in Belarus (no more than 4%) due to lack of appropriate technologies of potash waste utilization used internationally.  As only Germany counting as one of the world’s largest producers of potash fertilizers within the EU, to increase the comparability of data between the EU countries and Belarus, potash waste hasn’t been considered when calculating the circular economy indicators. Given all the above mentioned problems, some of the EU indicators have been adapted to the existing Belarusian statistical data.

Illustration of waste statistics problems

Waste statistics problems result in overestimation or underestimation of some circular economy indicators. A good example is the recycling rate of all waste, excluding major mineral wastes. Belarus, which is a country without a proper legal framework for the circular economy or a well-established secondary raw materials market,  had one of the best performances in terms of the recycling rate (72-80%) among the EU countries in 2010-2016. This fact reflects the problems with waste statistics rather than success in waste recycling in Belarus.

Table 1. Recycling rate of all waste excluding major mineral wastes, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based on the data from the RUE “Bel RC «Ecology».

Actual picture of the circular economy development in Belarus

The indicators with minimum distortions in waste statistics show that some elements of the circular economy in Belarus are still in the initial stage of their development (tables 2, 3, 4, 5). Our study reveals that the recycling rate of MSW amounted to 15.4 % in 2014-2016, which is much lower than the EU average in 2014 and 2016. Thus, Belarus has a considerable potential to increase the recycling rate of MSW. The experience of Czechia and Lithuania shows that the MSW recycling rate can be increased relatively fast if efforts are made and resources permit.

Table 2. Recycling rate of MSW, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based on the data from the SE  “Operator of SMRs” and Belstat.

In 2016, the recovery rate of construction and demolition waste in Belarus reached 81%, though this indicator fluctuated between 59% and 79% in previous years. However, it can be further improved as in some European countries (Denmark, the Netherlands, Germany, Czechia, Poland and Lithuania) the recovery rate of this type of waste stream exceeds 90%.

Table 3. Recovery rate of construction and demolition waste, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based of the data from the RUE “Bel RC «Ecology».

Despite the fact that the decoupling of economic growth from an increase in waste volumes is an important issue on the international agenda, trends in waste generation in many countries follow a development of GDP. In 2010-2012, the generation of waste excluding major mineral wastes per GDP unit (42-46 kg/thsd of $, PPP) in Belarus (table 4) was comparable with countries such as Czechia, Lithuania, Germany, Denmark, Sweden. However, in 2014 due to waste generation growth, this indicator in Belarus exceeded above-mentioned EU countries and approached the level of Hungary and the Netherlands. It was far above Norway that was the best performer among the European countries and a good example of how a country could really decrease waste generation.

Table 4. Generation of waste excluding major mineral wastes per GDP unit (kg per thsd constant 2011 international $) in 2010-2016

Source: for the EU countries and Norway the data on generation of waste excl. major mineral wastes – Eurostat. For Belarus – own calculations based on the data from the RUE “Bel RC «Ecology». For the EU countries, Norway and Belarus the data on GDP, PPP in constant 2011 international $ – The World Bank.

In 2012, the share of gross investment in the circular economy sectors in Belarus (table 5) decreased in comparison with 2010, however, since 2014 it have shown an upward trend. For the EU countries and Norway this indicator also includes investment in the repair and reuse sector. For Belarus this sector has not been taken into account in calculation due to lack of data. In addition, the gross investment in tangible goods is a bit different from the gross investment in fixed assets used for Belarus as the latter doesn’t include non-produced tangible goods such as land.  Yet, even bearing in mind these differences in calculation, the circular economy appeared to be underinvested in Belarus compared to the EU countries and Norway.

Table 5. Gross investment in tangible goods (% of total gross investment) in circular economy sectors in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – Belstat.

The employment in the circular economy in Belarus accounted for only 0.49% of total employment in 2016, while in the EU countries and Norway this indicator was approaching 3%. This again proves the fact that Belarus has a long way to go towards the creation of a circular economy.

Conclusion

The analysis revealed contradictory results of the circular economy development in Belarus. While the country scores highly across some indicators compared to the EU countries and Norway, this to a large extent reflects the problems with waste statistics, rather than success in waste  management. The indicators with minimum distortions in waste statistics show that Belarus is falling behind leading countries in circular economy development. However, in the transition to a circular economy, the monitoring framework is an important component of this process, which permits to track a progress using the system of indicators. In order to ensure that these indicators accurately capture the key trends in the circular economy in Belarus it would seem useful to:

  • align the definition of ’waste’, ‘recycling rate’ with the international one, identify clear criteria for classifying substances or products as waste and secondary raw materials;
  • strengthen the accountability of entities for filing reports on waste;
  • improve the system of MSW and SMRs reporting and recording, and introduce MSW recording based on weighing wherever possible;
  • consider the option of improving the comparability of Belarus’ waste classifier with the European waste statistical nomenclature.¨

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

How Should Policymakers Use Gender Equality Indexes?

We look at the development of gender inequality in transition countries through the lens of the Gender Inequality Index (GII), which aims to capture overall gender inequality. Extending the measure back to 1990, we look at the development of the overall index as well as that of its components. We show that, even though gender inequality in transition countries for the most part has decreased since 1990, once overall development is taken in account these countries appear to fare better in 1990 than today. We also caution against relying exclusively on composite indexes to understand patterns of gender inequality. While the desire of policy makers to get one number that captures gender inequality development is understandable, weak correlations of the GII with other indexes (over years when multiple gender inequality indexes exist) as well as across sub-indexes suggests that such an approach has limitations. Finally, we emphasize the need to understand levels as well as trends and underlying mechanisms to better inform policy to improve gender equality.

On Measuring Progress

When studying economic development, or any issue really, one faces the challenge not only of finding the right way to identify and measure what are often complex changes, but also of communicating the bottom line efficiently. This naturally leads to the search for a single metric according to which we can rank progress and follow it over time. In the realm of economic development the standard measure is GDP growth. But, of course, focusing only on GDP leaves out many important dimensions of development, such as health and education.[1] In an attempt to capture these dimensions, while still arriving at a single number that measures development, the Human Development Index (HDI) was developed in the late 1980s. Since then, a number of alternative indexes capturing additional aspects of human wellbeing have been suggested; see the report by the “Commission on the Measurement of Economic Performance and Social Progress” (Stiglitz, Sen and Fitoussi, 2009).

Just as for overall development, there is great interest in single measures that capture the gender dimension of this development. Over the past decades a number of such “gender equality indexes” have been developed by international organizations such as the UNDP, the EIGE (European Institute for Gender Equality) and the WEF (World Economic Forum), to name a few.

These measures receive a lot of attention and in particular the reporting of country rankings tends to have an influence on political and policy discussions. The various indexes proposed differ in what dimensions they include (as will be explained below) and, much as a consequence of this, in the time periods they can cover. In some cases (as will also be shown below) it is possible to extend the time coverage of the indexes, but most of the times it is hard to recover the underlying data.

In this brief we summarize what the most popular indexes tell us about the development of gender equality in transition countries, contrasting these to Western European countries.[2] Whenever we have been able to find the underlying data, we also add to publicly available measures by extending indexes back to early 1990s. We then comment on the development of gender equality in transition countries and, perhaps most importantly, on why an indexes-based analysis should be interpreted with some care.

Gender Equality Before 1990

As has often been pointed out, the Soviet Union and many of the countries in Eastern and Central Europe were, at least in some dimensions, forerunners in terms of promoting gender equality (e.g., Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2018). This was mainly due to the high participation of women in the labor market as well as the (official) universal access to basic health care and education.

However, some scholars have suggested that not all aspects of gender equality were as advanced in the countries in the Soviet Union and in Central and Eastern Europe (Einhorn, 1993; Wolchik and Meyer, 1985). Even though women were highly integrated in the labor market, they were also still expected to take care of child rearing and house work (UNICEF, 1999). The gender pay gap and gender segregation in the labor market was also similar to levels found in OECD countries. In addition, despite the high number of women in representative positions in communist party politics, women were rarely found in positions of real power in the political sphere (Pollert, 2003).

Generally speaking, while the communist regimes succeeded in promoting women’s access to the labor market and tertiary education, they failed to eliminate patriarchy (LaFont, 2001). Such a dichotomy gives rise to a broad set of questions regarding gender equality in transition countries as well as the measurement of gender equality in this context. What happened to gender equality, in relation to economic growth, during the transition, when new governments often broke with the tradition of promoting women’s employment and education? Did gender equality enhanced by communism leave a legacy or did underlying patriarchic values characterizing many of the communist societies come to dominate? How should we regard developments of indexes that try to weight several components within a context, such as that of transition countries, where these components may move in different directions from each other, given the dichotomy characterizing gender relations?

The Different Indexes

There are several different indexes that are often quoted in policy discussions. Two important measures are the Gender Development Index (GDI) and the Gender Inequality Index (GII), both calculated by the UNDP and reported annually in the Human Development Report (HDR). A third, more recent index that has received increasing attention is the World Economic Forum’s global Gender Gap Index (GGI), which is published in the yearly Gender Gap Report. These three can serve as illustrations of what gender equality indexes typically try to capture.

The Gender Development Index (GDI) essentially measures gender differences in the Human Development Index (HDI). The HDI in turn aims to capture achievements in three basic dimensions of human development: health (measured by life expectancy), knowledge (measured by expected and mean years of schooling) and living standards (measured by GNI per capita). The GDI then basically tries to assess the relative performance in these three dimensions for men and women respectively. If health (or education, or income)  in the population on average goes up, this improves the HDI. But to the extent that the improvements are felt differently by men and women, this will show in the GDI. There are several potential problems with the measurement of this index, especially when it comes to dividing GNI per capita between men and women (see e.g. Dijkstra and Hanmer, 2000); on the other hand, the index offers a transparent way to connect gender inequality to the HDI measure.

The other UNDP measure, the Gender Inequality Index (GII), was reported for the first time in the 2010 Human Development Report. It was created to address some of the perceived shortcomings of its forerunner, the Gender Empowerment Index (GEM) which had been introduced together with the GDI in 1995 (see e.g., Klasen and Schuler, 2011 for problems with GDI as well as GEM). The GII measures gender inequalities in three dimensions of human development: 1) reproductive health, measured by maternal mortality and adolescent birth rates; 2) empowerment, measured by representation in parliament and secondary education among adults; and 3) economic status, measured by labor force participation. As with the GDI, the areas of health, education, and economic empowerment are present, but the index also considers some aspects of health that are more directly relevant for women, and includes a component trying to capture political participation. The economic measure of labor force participation is also somewhat easier to interpret (and measure) than GNI divided between men and women. As for the GDI, GII country-values from 1995 are available on the UNDP website.  Conveniently for our purpose, most of the underlying data that the index is based on are also made available from the UNDP for the years 1990, 1995, 2000, 2005, and every year between 2010 and 2015, with the only exception of female seat share in parliaments in 1990[3]. We downloaded the latter from the World Bank indicators database[4]. We also added information on the share of women in the 1990 Polish Parliament, from the Inter-Parliamentary Union[5], and on the share of women in the 1990 Georgian “Supreme Council,” from Beacháin Stefańczak and Connolly (2015).

A third more recently developed index is the Global Gender Gap Index. This covers areas of political empowerment, health and survival, economic participation and educational attainment, as measured using 14 different variables. An indicator is available for each of the sub areas covered, which are then weighted together in an overall indicator of the gender gap. The Global Gender Gap Index is clearly more detailed and provides a more nuanced picture of existing gender gaps compared to the GDI or the GII. But this amount of detail also comes with potential costs; it is more difficult to interpret the overall index as there are more underlying components that may change simultaneously, and it is also more difficult to reconstruct the index back in time.

What Does the GII Index Tell Us About Gender Equality in Transition Economies?

Among the above mentioned indexes, we focus on the GII here. Extending this measure when possible allows us to study gender inequality starting from 1990 for a limited set of countries (we expand the sample of countries when looking at different dimensions of the GII separately below)[6]. Figure 1 reports values for the index in box plots, which show the index median, maximum, minimum, 75th and 25th percentile for two groups of countries: transition countries and Western-European countries. When interpreting Figure 1, recall that higher GII values imply more inequality.

Figure 1. The Gender Inequality Index in transition countries and Western Europe, 1990-2015

Source: Own calculations based mainly on UNDP data.

Figure 1 shows that based on the GII, median gender inequality is larger in transition countries than in Western Europe and has been so throughout the entire period since 1990. In both regions the index shows a decreasing trend, after an initial increase in 1995 in the transition countries. Below we will show that this is mainly due to a drop in female representation in national parliaments. The variance of the index scores has declined over time in Western Europe, while it remained mostly unchanged in the transition countries[7].

This first piece of evidence from the data is somewhat at odds with the common notion that transition countries enjoy relatively low level of gender inequality. However, two qualifications are in order here. First, transition and Western European countries are generally at different levels of development. Figure 2 displays the country groups performance in relation to their level of human development. This is done by measuring the difference between their GII ranking and their HDI ranking among all the countries with non-missing GII values in the years considered. The larger the difference, the worse the group performance in terms of gender inequality in relation to its level of development.

Figure 2. Difference between Gender Inequality Index ranking and Human Development Index ranking in transition countries and Western Europe, 1990-2015

Source: Own calculations based mainly on UNDP data.

The trends of transition countries and Western Europe are now opposite. In the former group, in 1990 the median standing in terms of gender equality was better than that in human development; this difference appears to have narrowed over time, and it is close to zero in 2015. Western European countries have instead improved their gender equality in relation to their level of overall human development over the period studied. Put differently, the gains in human development made by former socialist countries since the transition have not translated into comparable gains in gender equality as measured by the GII index.

Second, it is also important to emphasize that, as noted above, according to several scholars the socialist push in favor of gender equality was directed only to certain spheres of women’s lives, namely their economic empowerment. This suggests that a composite index can mask important contrasting patterns among its components.

In Figures 3 to 5 we document that different variables indeed paint quite diverging pictures of gender inequality in transition countries.

Figure 3. Development of adolescent births and maternal mortality, 1990-2015

Figure 4. Development of secondary education and share of women in parliament, 1990-2015.

Figure 5. Labor force participation, 1990-2015

Source: Own calculations based mainly on UNDP data.

In each figure we display box-plots for the three areas covered by the GII: health (measured by teenage births and maternal mortality), empowerment (measured by secondary education and share of women in Parliament) and labor force participation. Looking at the different variables separately also allows us to increase the number of countries significantly, since for many countries only the seat share of women in parliaments is missing in 1990.

As the figures show transition countries in 1990 displayed relatively low levels of gender inequality in labor force participation and secondary education. Over the last 25 years, they have kept improving the latter, while the former has stalled, resulting in Western European countries displaying a higher median level of gender equality in labor force participation for the first time around 2010. Reproductive health, while improving since the transition, is still far from converging to Western European standards. Finally, political representation appears to be responsible for the increase in inequality immediately after the transition that we have noted in Figure 1. While it is hard to compare the meaning of representation in the context of 1990 totalitarianisms to that of the democratic regimes emerged later, during the regime change women de facto lost descriptive representation, which was sometime guaranteed in socialist times by gender quotas (Ostrovska, 1994).

In conclusion, breaking down the GII by its components shows that, while Western European countries have invariantly improved their levels of gender equality since 1990, the trend in transition countries depends on the measure one looks at: women maintained but did not improve their relative status in the labor force, they gained more equality in education and especially in terms of reproductive health, and lost descriptive political representation.

What Does the GII Index (And Other Indexes) Not Tell Us?

The conclusion in the previous paragraph raises the question of which other areas of progress, stagnation or deterioration in gender equality in transition countries that might be overlooked in the GII index. Above, we have summarized two more indexes, the GDI and the Gender Gap Index, which focus on additional dimensions of gender inequality but are more limited in terms of time availability. For the time over which there is overlap between the available indexes, the correlation between the GII index and the GDI and the Gender Gap Index respectively, is roughly 0.60. Interestingly, such correlation is higher in the sample of western European countries (0.64 and 0.68 respectively); when the sample is limited to transition countries, the correlations are down to 0.40 and 0.50 respectively.

Several factors might account for the differences across indexes. Unlike the GII, both the GDI and the Gender Gap Index, for instance, include measures of income inequality. On the other hand, the GDI, as pointed out above, does not account for issues related to reproductive health and political representation. The Gender Gap Index is the only one to include, among the health measures, sex-ratios (typically defined as the ratio of male live births for every 100 female births). This turns out to be especially important for some of the transition countries: in the most recent Gender Gap Report, Georgia, Armenia and Azerbaijan remain among the worst-performing countries globally on the Health and Survival sub-index, due to some of the highest male-to-female sex ratios at birth in the world, just below China’s. This goes hand in hand with very high scores in terms of gender equality in enrolment in tertiary education, for which each of these countries ranks first (at par with a few other countries), having completely closed the gender gap. In fact, women are more likely to be enrolled in tertiary education than men.

The relatively low correlation among the different indexes for the group of transition countries also deserves special attention, because it might be a direct consequence of the peculiar history of women’s rights and empowerment in the region. Since some dimensions of gender equality were fostered through a top-down approach, rather than as the result of demands and needs expressed by an organized society, it is more likely that over the last thirty years elements of modernization coexisted with more traditional forms of gender inequality.

Finally, it is worth pointing out that none of the above indexes accounts for important dimensions of gender inequality such as,: gender violence, division of chores in the household, political representation at the local level, and the presence of women in STEM’s professions (where the largest job creation might happen over the next couple of decades). Once more, some of these measures might be particularly relevant for transition countries. Just to mention one example, gender violence is an urgent issue in a few of the countries in the area[8]. A case in point in this respect is Moldova: in 2017, the country ranked 30th out of 144 countries in the Gender Gap Index. Its rank for the sub-index called “Economic Opportunity and Participation” was 11[9]. The country performs especially well in terms of economic opportunity and participation because women not only participate in the labor market in almost equal rates as men, but they are also relatively fairly represented in professions traditionally less feminized elsewhere, such as “professional and technical workers” and “legislators, senior officials and managers.” At the same time, gender violence appears quite prevailing in Moldova: according to the UN, in 2014 “lifetime prevalence of psychological violence” in Moldova was of 60%. Official country statistics also report that the percentage of ever-partnered women aged 15-65 years experiencing intimate partner physical or sexual violence at least once in their lifetime in 2011 was 46%[10].

While limited in scope, the example above illustrates how some of the available indexes might not capture some important drivers of gender inequality in the region.

Conclusion

In this policy brief, we have reviewed some of the available gender inequality indexes that are commonly used in policy discussion as well as in policy-making.

We have then discussed gender inequality in transition countries focusing on one of these indexes, the Gender Inequality Index, whose span we have extended to the beginning of the transition period. Our analysis has highlighted some points to be mindful of when using comprehensive indexes to discuss gender inequality, especially in transition countries:

  • It can be fruitful to analyze gender inequality indexes in relation to levels of development. Some issues related to gender inequality, such as maternal mortality, are potentially addressed with a comprehensive strategy aimed at overall development. Conversely, other drivers of gender inequality, such as women’s political empowerment or gender violence, might require more targeted policy interventions, since they do not necessary go hand in hand with overall development.
  • While comprehensive indexes can be useful in terms of effective communication, it is often difficult to compress all the potential forms that gender inequality can take into a single index, especially over time. This is due to both conceptual issues and data limitations. Moreover, even when this is done, a comprehensive index can overshadow important sources of gender inequality if it is composed of sub-indexes that move in opposite directions.
  • The previous point can be especially relevant in the context of transition countries, which historically experienced a top-down approach to gender equality, the results of which in the long-term appear to be major advancements in some dimensions of women’s empowerment and contemporary potential backlash in other dimensions. In the context of transition countries, for instance, it has been argued that low levels of female representation in political institutions can be the result of women’s large participation to the labor market while division of roles in the household remained traditional. In the words of anthropologist Suzanne LaFont, “Women have been and continue to be overworked, and their lives have been over-politicized, the combination of which has led to apathy and/or the unwillingness to enter the male dominated sphere of politics. Many post-communist women view participation in politics as just one more burden.”[11] In such a context, average values of an index on gender equality might mask high achievements in economic empowerment coexisting with lack of political representation.
  • Identifying policies to address gender inequality in transition countries might be especially difficult because, depending on the dimension that one focuses on, the challenge at hand is different: in terms of education and employment, the policy goal appears to be maintaining current levels of equality or increasing them from relatively high initial points; the type of policies to do so are likely different than those used in Western European countries in the last 30 years, where the challenge was rather how to increase equality from relatively much lower levels. Conversely, in other dimensions the challenge is how to make major leaps forward, which move transition countries closer to Western European standards: this is the case for sex-ratios, for instance, and reproductive health more in general. The importance of initial levels and trends for policy implications also showcases how crucial it is to acquire more historical knowledge of policies, institutions, and statistics.

Overall, policy discussions and policy-making should go beyond mere descriptions of what indexes and related international comparisons tell us about gender inequality. A better knowledge and understanding of all of the drivers of gender inequality, of their historical evolution, and of their connections both with overall development and among them, is crucial to give sound policy recommendations.

References

  • Beacháin Stefańczak, K.Ó. and Connolly, E.(2015),  ‘Gender and political representation in the de facto states of the Caucasus: women and parliamentary elections in Abkhazia’. Caucasus Survey, 3(3), pp.258-268.
  • Brainerd, E. (2000), ‘Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union’, Industrial and Labor Relations Review, 54 (1), pp. 138-162.
  • Campa, P. and Serafinelli, M. (2018), ’Politico-economic Regimes and Attitudes: Female Workers under State-socialism’, Review of Economics and Statistics, Forthcoming.
  • Dijkstra, A. and L. Hanmer (2000), ‘Measuring socio-economic gender inequality: towards an alternative for UNDP’s Gender-related Development Index’, Feminist Economics, Vol. 6, No. 2, pp. 41-75.
  • Einhorn, B. (1993), Cinderella goes to market: citizenship, gender, and women’s movements in East Central Europe, London: Verso.
  • Klasen, S. and Schuler, D. (2011) Reforming the Gender-Related Development Index and the Gender Empowerment Measure: Implementing Some Specific Proposals. Feminist Economics. (1) 1 – 30
  • LaFont, Suzanne (2001), ‘One step forward, two steps back: women in the post-communist states.’ Communist and post-communist studies 34(2), pp. 203-220.
  • Ostrovska, I. (1994). Women and politics in Latvia. Women’s Studies International Forum 2, 301–303.
  • Pollert, A. (2003), ‘Women, work and equal opportunities in post-Communist transition’, Work, Employment and Society, Volume 17(2), pp. 331-357.
  • Stiglitz, Joseph, Amartya Sen, and Jean-Paul Fitoussi (2009). `The measurement of economic performance and social progress revisited.’ Reflections and overview. Commission on the Measurement of Economic Performance and Social Progress, Paris.
  • Tur-Prats, Anna (2018). Unemployment and Intimate-Partner Violence:  Gender-Identity Approach. GSE Working Paper No. 1564
  • Unicef. Women in transition. 1999.
  • UN. The World’s Women 2015.
  • Wolchik, S. L. and Meyer, A.G. (1985), Women, State and Party in Eastern Europe, Durham, NC: Duke University Press.

Footnotes

  • [1] In contrast to a common perception, economists are generally well-aware of the limitations of GDP as a measure of welfare. In fact, the reference manual of national accounts, the SNA 2008, makes this explicit in stating that there is “no claim that GDP should be taken as a measure of welfare and indeed there are several conventions in the SNA that argue against the welfare interpretation of the accounts”.
  • [2] By “transition countries,” we refer to all countries that were part of the Soviet Union plus the Central and Eastern European countries that were heavily influenced by the Soviet Union before 1990 (not including Albania and former Yugoslavia). Starting from this, we – as will be made clear below – sometimes limit the set of countries further depending on data availability.
  • [3] http://hdr.undp.org/en/data
  • [4] https://data.worldbank.org/indicator/SG.GEN.PARL.ZS
  • [5] http://archive.ipu.org/parline-e/reports/2255_arc.ht
  • [6] For Western Europe these countries are: Austria, Belgium, Cyprus, Denmark, Finland, France, Greece, Iceland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland. The transition countries are: Armenia, Bulgaria, Georgia, Hungary, Poland, Romania, Russian Federation.
  • [7] The outlier among Western countries is Malta.
  • [8] While explaining the sources of gender violence in the region is beyond the scope of this report, incidentally we notice that, according to recent research, female economic empowerment in a context where patriarchal values are dominant might backfire against women in the form of increased gender violence. See Tur-Prats, 2018.
  • [9] http://reports.weforum.org/global-gender-gap-report-2017/dataexplorer/#economy=MDA
  • [10] UNFPA (2015). Combatting Violence against Women and Girls in Eastern Europe and Central Asia. https://eeca.unfpa.org/en/publications/combatting-violence-against-women-and-girls-eastern-europe-and-central-asia
  • [11] LaFont, Suzanne (2001). One Step Forward, Two Steps Back: Women in the Post-Communist States. Communist and Post-Communist Studies, Vol. 34, pp 208.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Gender Gaps in Transition – What do we learn (and what do we not learn) from gender inequality indexes?

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We look at the development of gender inequality in transition countries through the lens of the Gender Inequality Index (GII), which aims to capture overall gender inequality. By extending the measure back to 1990, we show that even though gender inequality in transition countries for the most part has decreased since the fall of the iron curtain, once overall development is taken into account, transition countries did better in relation to other countries in terms of rank differences before transition. We, however, caution against relying exclusively on composite indexes to understand patterns of gender inequality. While the desire of policy makers to get one number that captures gender inequality development is understandable, weak correlations across different overall indexes, as well as across different sub-indexes that make up each index, suggest that such an approach has limitations.

Indexes of gender inequality

In the public debate of socio-economic issues there is an understandable interest in single measures that summarize complex issues, describe historical developments and allow international comparisons. The use of GDP to measure economic development is the most immediate example of this way of proceeding. The same applies to gender inequality. Over the past decades a number of “gender equality indexes” have been developed by international organizations such as the UNDP, the EIGE (European Institute for Gender Equality) and the WEF (World Economic Forum), to name a few. These measures receive a lot of attention and in particular the reporting of country rankings tends to have an influence on political and policy discussions.

In this brief, we study the development of the Gender Inequality Index (GII) in transition countries, contrasting these to Western European countries.  By transition countries, we refer to all countries that were part of the Soviet Union plus the Central and Eastern European countries that were heavily influenced by the Soviet Union before 1990 (not including Albania and former Yugoslavia). Whenever we have been able to find the underlying data, we extend the GII measure back to the early 1990s. This extension allows us to measure the development of gender inequality through the lens of a single index since the beginning of the transition. We then discuss what the GII tells us about gender inequality in transition, but also – perhaps more importantly – what it does not tell us. Our analysis is discussed as well as shown in some more detail in our forthcoming companion FREE Policy Paper.

The Gender Inequality Index

The GII was reported for the first time in the 2010 Human Development Report. It measures gender inequalities in three dimensions of human development: 1) reproductive health, measured by maternal mortality and adolescent birth rates; 2) empowerment, measured by representation in parliament and secondary education among adults; and 3) economic status, measured by labor force participation.

GII country-values from 1995 are available on the UNDP website.  Conveniently for our purpose, most of the underlying data that the index is based on are also made available from the UNDP for the years 1990, 1995, 2000, 2005, and every year between 2010 and 2015, with the only exception of the female seat share in Parliament in 1990. Using the UNDP data, and data on the female seat share in Parliament in 1990 from additional sources (see the FREE Policy Paper for a list of sources), we obtain values for the GII from the beginning of the transition in 1990 until 2015.

What does the GII index tell us about gender equality in transition economies?

Figure 1 reports values for the GII index in box plots, which show the index 25th and 75th percentile (respectively bottom and top of the box), its median (horizontal line in the box), its maximum and minimum (whiskers), and outliers (dots) for two groups of countries: transition countries and Western-European countries. We have reconstructed the values of the GII index for a limited set of countries within these groups (see the note to Figure 1 for the list of countries). When interpreting Figure 1, recall that higher GII values imply more inequality.

Figure 1. The Gender Inequality Index in transition countries and Western Europe, 1990-2015

Nov122018_Figure1

Source: Own calculations based mainly on UNDP data. The transition countries are: Armenia, Bulgaria, Georgia, Hungary, Poland, Romania, and the Russian Federation. For Western Europe the countries are: Austria, Belgium, Cyprus, Denmark, Finland, France, Greece, Iceland, Italy, Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland.

Figure 1 shows that based on the GII, median gender inequality is larger in transition countries than in Western Europe and has been so throughout the entire period since 1990. In both regions, the index shows a decreasing trend, after an initial increase in 1995 in the transition countries. As we show in the Policy Paper, this decrease is mainly due to a drop in female representation in national parliaments. The variance of the index scores has declined over time in Western Europe, while it remained mostly unchanged in the transition countries.

The evidence from the GII is somewhat at odds with the common notion that transition countries enjoy relatively low level of gender inequality. However, it is important to notice that transition and Western European countries are generally at different levels of development. Figure 2 displays the country groups’ performance in relation to their level of human development. This is done by measuring the difference between their GII ranking and their Human Development Index ranking (HDI) among all the countries with non-missing GII values in the years considered. The HDI is an UNDP-developed measure of overall human development. See the policy paper for details about its measurement. The larger the difference between GII- and HDI-ranking, the worse the group performance in terms of gender inequality in relation to its level of development.

Figure 2. Difference between Gender Inequality Index ranking and Human Development Index ranking in transition countries and Western Europe, 1990-2015

Nov122018_Figure2

Source: Own calculations based mainly on UNDP data.

The trends between transition countries and Western Europe are now opposite. In 1990, the median standing in terms of gender inequality was better than that in human development for transition countries, and the relative level of gender inequality was lower than in Western Europe. The (negative) difference between GII and HDI ranking however appears to have narrowed over time, and it is close to zero in 2015. Western European countries have instead improved their gender equality ranking in relation to their ranking in terms of human development over the period studied. Put differently, the ranking improvement in terms of human development in former socialist countries since the transition have not translated into comparable gains in gender equality ranking as measured by the GII index.

It is also important to emphasize that, according to several scholars, a dichotomy in terms of gender relations existed in transition countries during the socialist period. This is because on one hand the socialists put substantial into effort to empower women economically (see e.g. Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2018), but on the other hand they failed to eliminate patriarchy (LaFont, 2001). This suggests that a composite index can mask important contrasting patterns among its components. In the Policy Paper we uncover such contrasting patterns. By looking separately at the different components of the GII index, we show that while Western European countries have invariantly improved their levels of gender equality since 1990, the trend in transition countries depends on the measure one looks at: Women maintained, but did not improve, their relative status in the labor force. They gained more equality in education and especially in terms of reproductive health, and lost descriptive political representation.

Conclusion

In this policy brief we have studied the development of gender inequality in transition countries through the lens of the Gender Inequality Index, whose span we have extended to the beginning of the transition period. We have shown that, based on this index, gender inequality has decreased since 1990 in transition countries, a trend which is common to that in Western Europe. However, once the changes in overall development during this period are taken into account, it appears that transition countries fared better in 1990 than today. Our analysis thus shows that analyzing gender inequality indexes in absolute terms and in relation to levels of development can deliver different conclusions. The factors that account for these differences should be kept in mind in policy discussions and policy-making. Some issues related to gender inequality, such as maternal mortality, are potentially addressed with a comprehensive strategy aimed at overall development. Conversely, other drivers of gender inequality, such as women’s political empowerment, do not necessary go hand in hand with overall development, and might therefore require more targeted policy interventions.

We have also cautioned the reader about the limitation of using comprehensive indexes to describe developments in gender inequality. A comprehensive index can overshadow important sources of gender inequality if it is composed of sub-indexes that move in opposite directions. This point can be especially relevant in the context of transition countries, which historically experienced a top-down approach to gender equality, the results of which in the long-term appear to be major advancements in some dimensions of women’s empowerment and contemporary potential backlash in other dimensions. It has been argued, for instance, that low levels of female representation in political institutions in transition countries can be the result of women’s large participation in the labor market while the division of roles in households remained traditional. In the words of anthropologist Suzanne LaFont (2001), “Women have been and continue to be overworked, and their lives have been over-politicized, the combination of which has led to apathy and/or the unwillingness to enter the male dominated sphere of politics. Many post-communist women view participation in politics as just one more burden”. In such a context, average values of an index of gender equality might mask high achievements in economic empowerment coexisting with lack of political representation.

References

  • Brainerd, E. (2000), ‘Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union’, Industrial and Labour Relations Review, 54 (1), pp. 138-162.
  • Campa, P. and Serafinelli, M. (2018), ’Politico-economic Regimes and Attitudes: Female Workers under State-socialism’, Review of Economics and Statistics, Forthcoming.
  • LaFont, Suzanne (2001), ‘One step forward, two steps back: women in the post-communist states.’ Communist and post-communist studies 34(2), pp. 203-220.
  • Pollert, A. (2003), ‘Women, work and equal opportunities in post-Communist transition’, Work, Employment and Society, Volume 17(2), pp. 331-357.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Resource Discoveries, FDI Bonanzas and Local Multipliers: Evidence from Mozambique

20181105 Resource Discoveries Image 01

Giant oil and gas discoveries in developing countries trigger FDI bonanzas. Across countries, it is shown that in the 2 years following a discovery, the creation of FDI jobs increases by 54% through the establishment of new projects in non-resource sectors such as manufacturing, retail, business services and construction. Using Mozambique’s gas driven FDI bonanza as a case study we show that the local job multiplier of FDI projects in Mozambique is large and results in 4.4 to 6.5 additional jobs, half of which are informal.

Natural Resources, FDI Job Multiplier and Economic Development

Large resource wealth has for several decades been associated with a curse, slowing economic growth in resource-rich developing countries (Venables, 2016). More recently, this wisdom has been questioned by several studies. Arezki et al. (2017) point out that giant discoveries trigger short-run economic booms before windfalls from resources start pouring in. And Smith (2017) provides evidence for a positive relationship between resource discoveries and GDP per capita across countries, which persists in the long term.

In a new paper (Toews and Vézina, 2018) we contribute to this research by showing that giant oil and gas discoveries in developing countries trigger foreign direct investment (FDI) bonanzas in non-extraction sectors. FDI has long been considered a key part of economic development since it is associated with transfers of technology, skills, higher wages, and with backward and forward linkages with local firms (Hirschman, 1957; Javorcik, 2015). Using Mozambique, where a giant offshore gas discovery has been made in 2009, as a case study,  we estimate the local multiplier of FDI projects. We find that the FDI job multiplier in Mozambique is large, highlighting the job creation potential of FDI in developing countries.

Resource Discoveries and FDI Bonanzas

In our study we focus on jobs created by FDI bonanzas triggered by resource discoveries. Multinationals might invest in countries being blessed by giant discoveries for a variety of reasons before production starts. First, they might expect to benefit from the decisions of oil and gas companies to increase investment in local infrastructure and to increase demand for local services provided by law firms and environmental consultancies. Second, multinationals may also expect governments and consumers to bring forward expenditure and investment by borrowing. Finally, multinationals might invest since particularly large discoveries have the potential to operate as a signal leading to a coordinated investment by a large number of multinationals from a variety of industries and countries.

Using data from fDi Markets we show that, indeed, FDI flows into non-extraction sectors following a discovery. FDI increases across sectors and by doing so creates jobs in industries such as manufacturing, retail, business services and construction. Using Mozambique as a case study we show that following the gas discovery, multinationals decided to invest in Mozambique triggering job creation in non-extraction FDI to skyrocket (see Figure 1).

Figure 1. FDI Bonanza in Mozambique

Source: Author’s calculations using fDiMarkets data.

FDI Job Multiplier

Using the FDI bonanza in Mozambique as a natural experiment, we proceed by estimating the FDI job multiplier for Mozambique. The concept of the local job multiplier boils down to the idea that every time a job is created by attracting a new business, additional jobs are created in the same locality. In our case, FDI jobs are expected to have a multiplier effect due to two distinct channels. Newly created and well paid FDI jobs are likely to increase local income and in turn the demand for local goods and services (Moretti, 2010). Additionally, backward and forward linkages between multinationals and local firms increase the demand for local goods and services (Javorcik, 2004).

Using concurrent waves of household surveys and firm censuses we estimate the local FDI multiplier for Mozambique to be large. In particular, we find that every additional FDI job results in 4.4 to 6.5 additional local jobs. Due to the combined use of household survey and the firm census we are also able to conclude that only half of these jobs are created in the formal sector, while the other half of the jobs are created informally.

Conclusion

Our results suggest that giant oil and gas discoveries in developing countries lead to simultaneous foreign direct investment in various sectors including manufacturing. Our results also highlight the job creation potential of FDI projects in developing countries. Jointly, our results imply that giant discoveries do have the potential to trigger extraordinary employment booms and, thus, provide a window of opportunity for a growth takeoff in developing countries.

References

  • Arezki, R., V. A. Ramey, and L. Sheng (2017): “News Shocks in Open Economies: Evidence from Giant Oil Discoveries,” The Quarterly Journal of Economics, 132, 103.
  • Hirschman, A. O. (1957): “Investment Policies and “Dualism” in Underdeveloped Countries,” The American Economic Review, 47, 550 – 570.
  • Javorcik, B. S. (2004): “Does Foreign Direct Investment Increase the Productivity of Domestic Firms? In Search of Spillovers Through Backward Linkages,” American Economic Review, 94, 605 – 627.
  • Javorcik, B. S. (2015): “Does FDI Bring Good Jobs to Host Countries?” World Bank Research Observer, 30, 74 – 94.
  • Moretti, E. (2010): “Local Multipliers,” American Economic Review, 100, 373 – 377.
  • Smith, Brock. “The resource curse exorcised: Evidence from a panel of countries.” Journal of Development Economics: 116 (2015): 57-73.
  • Toews and Vézina, (2018): “Resource discoveries, FDI bonanzas and local multipliers: An illustration from Mozambique” Working Paper.
  • Venables, A. J. (2016): “Using Natural Resources for Development: Why Has It Proven So Difficult?” Journal of Economic Perspectives, 30, 161 – 84.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Labor Market Adaptation of Internally Displaced People: The Ukrainian Experience

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This brief is based on research that investigates the probability of employment among displaced and non-displaced households in a region bordering territory with an ongoing military conflict in Eastern Ukraine.  According to the results, internally displaced persons (IDP) are more educated, younger and more active in their job search than locals. Nevertheless, displaced individuals, particularly males, have experienced heavy discrimination. After controlling for personal characteristics, the structure of the household, location, non-labour incomes and endogeneity of displacement, IDP males are 17% less likely to be formally employed two years after resettlement than locals.

Internally displaced persons in Ukraine

In 2014, 23 years after independence, Ukraine suddenly found itself among the top-ten of countries with the largest internally displaced population. During the period 2014–2016, 1.8 million persons registered as internally displaced. Potentially, about 1 million more reallocated to Russia and about 100,000 to other countries nearby, where they sought refugee or labour migrant status (Smal, 2016).

The Ministry of Social Policy of Ukraine (MSPU) has regularly published very general reports on displaced persons. According to these reports, at the end of February 2016, the internally displaced persons in Ukraine included 22,000 individuals from Crimea and over 1.7 million citizens from Eastern Ukraine. These are mostly individuals who registered as IDPs to qualify for financial assistance from the state and some non-monetary benefits. Among them, 60% are retired people, 23.1% are individuals of working age, 12.8% are children and 4.1% are people with disabilities (Smal and Poznyak, 2017). In fact, the MSPU registers not only displaced persons but also those who de facto live in the occupied territories and occasionally travel to territories controlled by the Ukrainian authorities to receive their pension or social benefits (so called ‘pension tourism’). On the other hand, some IDPs did not register either to avoid bureaucracy or because they were unable to prove their status due to lack of documents. Recent publications that are based on surveys portray a more balanced distribution: 15% are retired people, 58% are individuals of working age, 27% are children and 13% are people with disabilities (IOM and the Ukrainian Centre for Social Reforms, 2018).

Only limited information is available about IDPs’ labour market activity. According to the State Employment Service (SES), between March 2014 and January 2016, only 64,300 IDPs or 3.75% referred to the SES for assistance (Smal and Poznyak, 2017). On the one hand, this figure reflects the relatively low reliance of displaced Ukrainians on the SES services in their job search. On the other hand, the geographical variation in the share of SES applicants suggests that Ukraine’s IDPs who moved further from the war zone and their homes were more active in trying to find a job.

Data

Our primary data were collected in June–August 2016 by REACH and provided by the Ukraine Food Security Cluster (UFSC) as a part of the needs assessment in Luhansk and Donetsk oblasts of Ukraine – two regions that were directly affected by the conflict. These two regions have hosted roughly 53% of all IDPs in Ukraine (Smal and Poznyak, 2017). We argue that households that did not move far from the place of conflict are most likely to be driven by conflict only, while long-distance movers may combine economic and forced displacement motives.

The data set offers information on 2500 households interviewed in 233 locations and is statistically representative of the average household in each oblast. It includes respondents currently living in their pre-conflict settlements (non-displaced, NDs) and respondents who report a different place of residence before the conflict (IDPs). The IDP group comprises individuals with registered and unregistered status and from both sides of the current contact line. The non-IDP group includes only households living on the territory controlled by the Ukrainian Government that did not move after the conflict had started.

Our sample covers 1,135 displaced households that came from 131 settlements. Most of the reallocations took place in early summer 2014 with the military escalation of the conflict in Eastern Ukraine. Thus, the average duration of displacement up to the moment of the interview was 637 days (or 21 months). This is a sufficiently long period for adaptation and job search. However, there is enough variation in this indicator – some families left as early as March–April 2014, while others were displaced in June 2016, just a few days before the interviews started.

Results

Simple comparison shows that heads of displaced households are on average almost four years younger than those of non-displaced households (Table 1). In terms of education, displaced households are found to be more educated than non-displaced households, as there are significantly more IDP household heads with tertiary education and significantly fewer individuals with only primary, secondary or vocational degrees. In particular, 37% of IDP household heads hold a university degree compared with 22% of household heads among the local population. This seems to suggest positive displacement selection. IDPs are slightly more likely to be headed by females and unmarried persons, although these differences are statistically insignificant. Displaced households include more children aged under five (0.35 vs. 0.22 children per non-displaced household) and 6 to 17 years (0.42 vs. 0.34, respectively) and fewer members aged over 60 years (0.58 vs 0.66, respectively). There is no difference in the number of working-age adults or disabled individuals per household among IDPs and non-IDPs. The average household size is statistically similar for the groups (2.74 vs. 2.65 persons per IDP and non-IDP household, respectively).

Table 1. Selected descriptive statistics

Internally displaced households Non- displaced households
Household head employed 0.43*** 0.48***
Household head characteristics
Age (years) 48.10*** 52.85***
Male 0.49 0.52
Education
vocational 0.42*** 0.49***
university 0.37*** 0.22***
Household characteristics
Size (persons) 2.74 2.65
Number of children 0-5 0.35*** 0.21***
Number of children 6-17 0.42*** 0.34***
Number of members 60+ 0.58** 0.66**
IDP payments 0.50*** 0***
Humanitarian assistance 0.78*** 0.28***

There are further differences in the types of economic activity and occupations among IDPs and non-IDPs. Prior to the conflict, displaced respondents were more likely (than non-displaced persons) to be employed as managers or professionals and less likely to hold positions as factory or skilled agricultural workers. This result also speaks in favor of a positive displacement selection story.

As expected, the conflict has had a negative effect on human capital in the government controlled areas of Donetsk and Luhansk regions. We observe some deskilling at the time of the interviews, which is especially pronounced for IDPs. In particular, the share of managers among the IDPs had reduced from 12% to 5% and that of technicians from 15% to 12%, while the proportion of service and sales employees had increased from 10% to 13%, that of factory workers from 11% to 15% and that of skilled agricultural workers from 2% to 6%.

Considering the economic activity in the current location, we can note that on average the heads of displaced households are 5% less likely to be employed than those of non-displaced households (43% vs. 48%, respectively). In both groups, a large share of respondents report difficulties in their job search, but IDPs are 13% more likely to experience this problem. They report changing their pre-conflict occupation three times more often than non-IDPs (37% vs. 11%).

Government and non-government assistance may also drive the differences in employment. Economic theory states that individuals are less likely to work if they have some backup in the form of non-labour earnings. Financial support and humanitarian assistance are widely used to smooth a displacement shock. At the same time, improperly designed assistance schemes may reduce the stimulus to search for a job.

IDPs are 9% less likely to include earnings in their household’s top three main sources of income than the non-displaced population (46% vs. 55%, respectively), meaning that they rely more on various social payments and pensions. In addition, displaced households may be slightly more reluctant to search for a job due to displacement assistance from the government (received by 50% of IDPs compared with 0% for non-IDP households), although the amounts are quite modest. According to the existing legislation, IDPs can receive regular monthly state payments and one-time state payments. Regular monthly payments can be received by any IDP and cannot exceed UAH 3,000 (~$111) for an ordinary household, UAH 3,400 for a household with disabled people and UAH 5,000 (~$185) for a household with more than 2 children. Eligibility and the size of the one-time payment are determined by the local government. In the data set, 95% of IDPs receive less than UAH 3,000 while the 2016 average monthly wage was UAH 6,000 in Donetsk and UAH 4,600 in Luhansk regions.

In addition, IDPs are three times more likely to receive humanitarian assistance (78% vs. 28% among displaced and non-displaced persons, respectively). This support includes mostly food and winterisation items but also cash (26% among displaced vs. 12% among non-displaced assistance receivers). On the other hand, to cover reallocation and adaptation costs, some IDPs use their financial reserves, and as a result they are by 10 p.p. more likely to report no or already depleted savings. This may increase their stimulus to engage in a more active job search.

After taking into account the observed and unobserved differences between the groups as well as controlling for the location fixed effect, we find that the difference in the probability of employment between displaced and non-displaced persons increases from a casually observed slit of 5% to a chasm of 17.3%. This result suggests that IDPs are [negatively] discriminated despite being younger, more educated, skilled and more ‘able’ in the labour market. Specifically, 7 out of 17 p.p. (41% of the gap) are due to the variation in observed household head characteristics and family composition, while unobserved displacement-related features (such as attitude towards change, activism, mental and physical ability to reallocate) account for 5 p.p. (29%) of the gap. Controlling for particularities of a current location does not substantially affect the estimated differences.

Figure 1. Main results

We re-estimate these regressions using an employment indicator that includes both formal and informal employment (as defined by the respondents), accounting for occasional and irregular employment, including subsistence agricultural work. Since informal work is more common among IDPs, this definition of employment leads to a reduction in the average casually observed gap from 5% to 3%. However, after controlling for all the factors, we obtain the same result – a 17.8% difference between displaced and non-displaced households.

Conclusion

Policy makers and international donors should not be misled by the seemingly comparable probability of employment among IDPs and non-IDPs based on simple statistics. The average 0–5% difference in unconditional employment rates conceals the actual 17% gap in the likelihood of having a job. The contribution of unobserved displacement-related factors in hiding the true gap is large, especially for males seeking formal employment. Without adjusting for it, we would underestimate the real difference in employment probability by one-third to one-half.
Our study produces firm evidence that displaced individuals in Ukraine, particularly males, have been discriminated against in terms of employment. Our results further suggest that male heads of displaced households experience more discrimination in the formal labour market, while the situation is the opposite for females, who are more likely to face unequal treatment in the informal sector. Policy makers and volunteers should take this difference into account in the adaptation of male- and female-headed households.

Humanitarian assistance to displaced individuals was found to have no negative effect on their employment, which suggests that it is provided in an effective manner. Thus, this tool can be used to mitigate the discrimination.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Energy Demand Management: Insights from Behavioral Economics

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It has long been recognized that consumers fail to choose the cheapest and most efficient energy-consuming investments due to a range of market and non-market failures. This has become known as the ‘Energy Efficiency Gap’.  However, there is currently a growing interest in terms of understanding on how consumers make decisions that involve an energy consumption component, and whether the efficiency of their decisions can be improved by changing the market incentives and governmental regulation. Meeting this interest, the most recent SITE Energy Talk was devoted to Demand Side Management.  SITE invited Eleanor Denny, Associate Professor of Economics at Trinity College Dublin, and Natalya Volchkova, Assistant Professor at the New Economic School (NES) in Moscwo and Policy Director at the Center for Economic and Financial Research  (CEFIR) to discuss the Demand Side Management process. The aim of this brief is to present the principles of Demand Side Management and discuss a few implemented programs in Europe, based on the discussions  during this  SITE Energy Talk.

For the last two decades, climate change policies have mostly been focused on the energy supply side, constantly encouraging new investments in renewables. But reducing energy demand may be as effective. Indeed, Denny and O’Malley (2010) found that investing 100MW in wind power is equivalent, in terms of emissions, to a decrease in demand of 50MW. Hence, there is a clear benefit of promoting energy saving. This has been the central point of different Demand Side Management (DSM) programs that may diversely focus on building management systems, demand response programs, dynamic pricing, energy storage systems, interruptible load programs and temporary use of renewable energy. The goal of these programs is to lower energy demand or, at least, smoothen the electricity demand over the day (i.e. remove peak-hour segments of demand to off-peak hours) as illustrated in Figure 1.

Figure 1 – Smoothing electricity demand during the day

A behavioral framework

DSM encompasses initiatives, technologies and installations that encourage energy users to optimize their consumption. However, the task does not seem easy, given the well-documented energy efficiency gap problem (e.g. Allcott & Greenstone, 2012 or Frederiks et al., 2015): consumers do not always choose the most energy efficient investments, despite potential monetary saving. One reason why might be that energy savings per se are not enough to trigger investment in energy efficient solutions or products. As Denny mentioned in her presentation, consumers will invest when the total  private benefits are higher than the costs of investment. This trade-off can be summarized by the following equation:

This equation illustrates that any DSM design should take into account both non-monetary benefits and consumers’ time preferences. The non-monetary benefits, such as improved comfort, construction and installation time, but also warm glow (i.e. positive feeling of doing something good) or social comparison, may play a major role. Moreover, the consumers’ time preferences (reflected here by the discount rate ) are also crucial in the adoption of energy efficient products. In particular, if consumers have present biased preferences, they would rather choose a product with a lower cost today and greater future cost than the reverse (i.e. higher cost today with lower future cost). Since energy-efficient products often require higher upfront investment, consumers that are impatient for immediate gains, may never choose energy efficient products.

Ultimately, it is an empirical (and context specific) question when and why DSM programs can reduce the energy efficiency gap. We describe below some DSM programs that have been implemented and discuss their impact.

Smart meters, a powerful DSM tool

A common DSM program is the installation of smart meters, which measure consumption and can automatically regulate it. The adoption of smart meters allows real-time consumption measures, unlike traditional meters that only permitted load profiling (i.e. periodic information of the customer’s electricity use).

Figure 2 – Energy Intensity in Europe

As illustrated in Figure 2, many European countries have implemented smart meter deployment programs. Interestingly, most of those countries have a relatively high level of energy efficiency (proxied by the energy intensity indicator of final energy consumption). On the contrary, in the Balkans and non-EU Eastern Europe countries, which fare poorly on the energy intensity performance scale, no smart meter rollout programs seem to be implemented.

Following the European Commission (EC) directive of 2009 (Directive 2009/72/EC), twenty-two EU members will have smart meter deployment programs for electricity and gas by 2020 (see Figure 2).  These programs are targeting end-users of energy, e.g. households that represent 29% of the current EU-28’s energy consumption, industries (36.9%) and services (29.8%) (EEA). With this rollout plan, a reduction of 9% in households’ annual energy consumption is expected.

The situation across the member states is however very different. Spain was one of the first EU countries to implement meters in 1988 for industries with demand over 5MW. All the meters will be changed at the end of 2018. 27 million euros for a 30-year investment in smart meter installations is forecasted (EC, 2013). Sweden started to implement smart meter rollout in 2003 and 5.2 million monthly-reading meters were installed by 2009. Vattenfall, one of the major utilities in Sweden, assessed their savings up to 12 euros per installed smart meter (Söderbom, 2012). Similarly in the United Kingdom, the Smart Metering Implementation Programme (SMIP) is estimated to bring an overall £7.2 billion (8.2 billion euros) net benefit over 20 years, mainly from energy saving (OFGEM, 2010). In general, smart metering has been effective, but its effectiveness may diminish over time (Carroll et al, 2014).

From smart-meter to real-time pricing

The idea of real-time pricing for electricity consumers is not new. Borenstein and Holland (2005) and Joskow and Tirole (2006) argue that this price scheme would lead to a more efficient allocation, with lower deadweight loss than under invariant pricing.

By providing detailed information about real-time consumption, smart meters enable energy producers to adopt dynamic pricing strategies. The increasing adoption of smart meters across Europe will likely increase the share of real-time-pricing consumers, as well as the efficiency gains. With the digitalization of the economy, it is likely that smart metering will grow. Indeed, Erdinc (2014) calculates that the economic impact of smart homes on in-home appliances could result in a 33% energy-bill reduction, due to differences in shift potential of appliances.

In 2004, the UK adopted a time-of-use programme called Economy 10, which provides lower tariffs during 10 hours of off-peak periods – split between night, afternoon and evening – for electrically charged and thermal storage heaters. The smart time-of-use tariffs involving daily variation in prices were only introduced in 2017.

Likewise, France’s main electricity provider EDF, implemented Tempo tariff for 350,000 residential customers and more than 100,000 small business customers. Based on a colour system to indicate whether or not the hour is a peak period, customers can automatically or manually monitor their consumption by controlling connection and disconnection of separate water and space-heating circuits. With this program, users reduced their electricity bills by 10% on average.

In Russia, the “consumptions threshold” program discussed by Natalya Volchkova, gave different prices for different consumption thresholds. But it seems that the consumers’ behaviour did not change. This might be due to the thresholds being too low, and an adjusted program should be launched in 2019.

Joskow and Tirole (2007), argue that an optimal electricity demand response program should include some rationing of price-insensitive consumers. Indeed, voluntary interruptible load programs have been launched, mainly targeting energy intensive industries that are consuming energy on a 24/7 basis. These programs consist of rewarding users financially to voluntarily be on standby. For instance, interruptible programmes in Italy apply a lump-sum compensation of 150,000 euros/MWh/year for 10 interruptions and 3000 euros/MW for each additional interruption (Torriti et al., 2010).

Nudging with energy labelling

Energy labelling has been also part of DSM. Since the EC Directives on Ecodesign and Energy Labelling (Directives 2009/125/EC and 2010/30/EU), energy-consuming products should be labelled according to their level of energy efficiency. For Ireland, Eleanor Denny has tested how labelling electrical in-home appliances may affect consumers’ decisions, like purchasing electrical appliances or buying a house. First, Denny and co-authors have nudged buyers of appliances, providing different information regarding future energy bills saving. They find that highly educated people, middle income and landlords are more likely to be concerned with energy-efficiency rates, rather than high-income people.

In another randomized control trial, Denny and co-authors manipulate information on the energy efficiency label for a housing purchase. In Ireland, landlords are charged for energy bills even when they rent out their property. The preliminary findings are that landlords informed about the annual energy cost of their houses are willing to pay 2,608 euros for a one step improvement in the letter rating – the EU label rating for buildings ranges from A to G – compared to the landlords that do not receive the information (see CONSEED project).

Similar to the European Directive, the 2009 Russian Energy efficiency law includes compulsory energy efficiency labels for some goods and improvements of the building standards (EBRD, 2011). Volchkova and co-authors run a randomized controlled experiment on the monetary incentives to buy energy efficient products. In 2016, people in the Moscow region received a voucher with randomly assigned discounts (-30%, -50% or -70%- for the purchase of LED bulbs. Vouchers were used very little, irrespective of the income. It seems that consumption habits and not so much monetary rewards were the main driver of LED bulb purchase.

How can DSM be improved?

Any demand response program requires some demand elasticity. For example, smart meters and dynamic pricing only improve electricity consumption efficiency if demand is price elastic. As Jessoe and Rapson (2014) show, one should provide detailed information (e.g. insights on non-price attributes, real-time feedback on in-home displays) to try to increase demand elasticity. Hence it seems that  the low adoption of energy efficient goods is partly due to a lack of information or biased information received by the consumers. First, it is difficult for many to translate energy savings in kWh in monetary terms. Second, many consumers focus on the short-term purchase cost and discount heavily the long run energy saving. These information inefficiencies can, in principle, be diminished by private actors and/or governmental regulation. Denny mentioned the possibility of displaying monetary benefits on labels in consumers’ decision-making in order to improve energy cost salience. For instance, in the US or Japan, the usage cost information is also displayed in monetary terms. Moreover, lifetime usage cost (i.e. cost of ownership) should also be given to the customers since it has been shown that displaying lifetime energy consumption information has significantly higher effect than presenting annual information  (Hutton & Wilkie 1980; Kaenzig 2010).

Summing up, DSM programs, including those with a behavioral framework, are an important tool for regulators, households and industries helping to meet emissions reduction targets, significantly decrease demand for energy and use energy more efficiently.

References

  • Allcott, Hunt ; Greenstone, Michael. 2012. “Is There an Energy Efficiency Gap?”, Journal of Economic Perspectives, 26 (1): 3-28.
  • Borenstein, Severin; Holland, Stephen. 2005. “On The Efficiency Of Competitive Electricity Markets With Time-Invariant Retail Prices”, Rand Journal of Economics, 36(3), 469-493.
  • Carroll, James; Lyons, Seán; Denny, Eleanor. 2014. “Reducing household electricity demand through smart metering: The role of improved information about energy saving,” Energy Economics, 45(C), 234-243.
  • Denny, Eleanor; O’Malley, Mark. 2010. “Base-load cycling on a system with significant wind penetration”, IEEE Transactions on Power Systems 2.25, 1088-1097.
  • Erdinc, Ozan. 2014. “Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households”, Applied Energy126(C), 142-150.
  • European Bank for Reconstruction and Development. “The low carbon transition”. Chapter 3 Effective policies to induce mitigation (2011).
  • European Commission. Electricity Directive 2009/92. Annex I.
  • European Commission. Ecodesign and Energy Labelling Framework directives 2009/125/EC and 2010/30/EU.
  • European Commission. “From Smart Meters to Smart Consumers”, Promoting best practices in innovative smart metering services to the European regions (2013).
  • European Commission. “Benchmarking smart metering deployment in the EU-27 with a focus on electricity” (2014).
  • European Environment Agency. Data on Final energy consumption of electricity by sector and Energy intensity.
  • Frederiks, Elisha R.; Stenner, Karen; Hobman, Elizabeth V. 2015. “Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour”, Renewable and Sustainable Energy Reviews, 41(C), 1385-1394.
  • Hutton, Bruce R.; Wilkie, William L. 1980. “Life Cycle Cost: A New Form of Consumer Information.” Journal of Consumer Research, 6(4), 349-60.
  • Jessoe, Katrina; Rapson, David. 2014. “Knowledge is (less) power: experimental evidence from residential energy use”, American Economic Review, 104(4), 1417-1438.
  • Joskow, Paul; Tirole, Jean. 2006. “Retail Electricity Competition, Rand Journal of Economics, 37(4), 799-815.
  • Joskow, Paul; Tirole, Jean. 2007. “Reliability and Competitive Electricity Markets”, Rand Journal of Economics, 38(1), 60-84.
  • Kaenzig, Josef; Wüstenhagen, Rolf. 2010. “The Effect of Life Cycle Cost Information on Consumer Investment Decisions Regarding Eco‐Innovation”, Journal of Industrial Ecology, 14(1), 121-136.
  • OFGEM. “Smart Metering Implementation Programme” (2010).
  • Söderbom, J. “Smart Meter roll out experiences”, Vattenfall (2012).
  • Torriti, Jacopo; Hassan, Mohamed G.; Leach, Matthew. 2010. “Demand response experience in Europe: Policies, programmes and implementation”, Energy, 35(4), 1575-1583.

Project links

Eleanor Denny and co-authors’ European research projects:

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Revisiting Growth Patterns in Emerging Markets

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Recent studies document that emerging markets are rather similar in their growth patterns despite profound differences in starting conditions and productivity fundamentals. This challenges the common view on productivity as the main growth engine. The crucial role of the external environment for emerging markets emphasized by numerous studies adds to this doubt. I argue that productivity fundamentals still matter and remain the core driver of sustainable growth. However, external factors are crucial for understanding deviations from the trajectory of sustainable growth, i.e. episodes of growth accelerations/decelerations.

Challenges for Understanding Growth in Emerging Markets

As we enter the 4th decade of economic transition in Central and Eastern Europe (CEE), the causes and directions of causality of long-term growth in emerging markets might need to be reconsidered. Some recent studies emphasize that growth trajectories in emerging markets are pretty similar, i.e. average growth rates do not differ too much, while jumps and drops in growth rates are synchronous for the bulk of emerging economies (e.g. Fayad and Perelli, 2014). For instance, a decade ago the level of GDP per capita (in 2011 international $) in Macedonia was roughly 45% of that in the Slovak Republic, which likely reflected the productivity (measured through the Global Competitiveness Index) gap  between them. During the last decade, Macedonia has roughly closed this productivity gap. Growth theory would postulate that this should have transformed into faster output growth in Macedonia vs. Slovak Republic closing well-being gap. However, the two countries’ had throughout the decade roughly equal average output growth and the well-being gap today is still the same as it was ten years ago.

Such observations seem to conflict with existing theoretical views. First, this is a challenge to the well-being convergence concept that results from growth theory. Moreover, if we measure growth in terms of the speed of closing the well-being gap with respect to the frontier (the US economy), one may argue even for divergence. For instance, Figure 1 presents a scatter-plot for a sample of emerging markets relating the initial conditions – well-being level in 1995 (GDP per capita  relative to one of the US economy) – and the average speed of well-being gap (vs. the US economy) closing throughout 1996-2017  (measured in p.p. of corresponding gap ).

Second, the evidence that productivity gains do not automatically trigger output growth challenges a common view that productivity is the major driver for sustainable growth.

Figure 1.Starting Conditions and Well-Being Gains

Source: Own computations based on data from World Development Indicators database (World Bank).

What are possible explanations for the observed similarity in growth rates of emerging markets?

A study by the IMF (2017) suggests a response: growth in emerging markets is similar and synchronous due to the external environment. This study emphasizes the crucial dependence of medium-term growth in developing countries on the following factors: growth of external demand in trade partners, financial conditions, and trade conditions. Moreover, it states that these factors are dominant in explaining the episodes of growth strengthening/weakening.

Does this explanation change the growth nexus for emerging markets? Can one state, that while external factors are crucial for growth and growth in developing countries is rather homogenous, the productivity gains are not so important anymore?

I would say no. First, for better understanding of growth patterns we must clearly compare the relative importance of productivity gains vs. external factors in affecting the growth schedule. Second, we must separate relatively short-term fluctuations in GDP growth from sustainable growth.

Detecting Relative Importance of Growth Drivers

To answer the question about the relative importance of productivity fundamentals and growth factors, I study a panel of 34 emerging market economies (EBRD sample netted from 3 countries for which the data is not available) for 11 years (2007-2017).

To evaluate the relative importance of productivity and external factors, I use a standard approach of running panel growth regressions with fixed effects. At the same time, I make a number of novelties in the research design.

First, for measures of productivity, I engage a unique database – Global Competitiveness Indicators by World Economic Forum (WEF). Although this database provides an insightful perspective on productivity fundamentals at the country level, it is rather seldom a ‘guest’ in economic research. From this database, I extract a number of individual indicators in order to detect which ones among them that have the strongest growth-enhancing effect. For an alternative specification, I use principal components of 9 individual indicators from this database as proxies for productivity gains.

Second, for external factors, I use an approach similar to the IMF (2017) and calculate variables representing external demand growth, trade conditions, and financial conditions (such as a measure of capital inflows) for each country. Moreover, in respect to external demand growth, I use different competing measures (based on either imports of GDP growth of trade partners) and choose the best one in each individual equation. By doing so, I allow this dimension of the external environment to be represented in each model to the largest possible extent.

Third, I depart from using output growth as the only measure of economic growth and response variable in growth regressions. I argue that for international comparison purposes it is worthwhile to consider also the speed of closing the gap towards the frontier (the US economy). On the one hand, this measure is strongly correlated with the traditional output growth rate. On the other hand, this measure, in a sense, nets out the growth rate of a country from global growth, thus capturing something more unique and peculiar just to individual countries’ gains in well-being. Furthermore, I argue that in the discussion about the factors behind growth, one should distinguish between relatively short and long term growth. Annual growth rates, especially at relatively short time horizon, are too dependent on fluctuations, which may be interpreted in terms of growth rate strengthening/weakening. However, to emphasize the property of growth sustainability, we should get rid of ‘unnecessary noise’. For this purpose, I also introduce a trend growth rate measured in a most simple way as the 5 year moving average (following the discussion in Coibion et al. (2017), show that the bulk of measures of ‘potential’ growth are not good enough to get rid of demand shocks and these measures are pretty close to simple moving average measures).

I apply this definition of trend growth both to ‘standard’ GDP growth rate and to the speed of closing the gap towards frontier. So, finally I have 4 response variables: ‘standard’ growth rate, the speed of closing the gap to frontier, and two corresponding measures of trend growth.

Sustainable Growth Mainly Depends on Productivity

Having short-term (annual) growth rate as response variable (either ‘standard’ or the one in terms of closing the gap) provides results close to those in IMF (2017). It may be interpreted in a way that the external environment is more important than productivity factors. If dividing all regressors into two broad groups of factors – external and productivity – the former is responsible for up to 70% of the growth effect, while the latter for about 30%. Among external environment factors, the most important one is financial conditions. Its relative importance is roughly 50% of the group of external factors’ total.

Among productivity fundamentals, an important contributor to short-term growth is the quality of the macroeconomic environment. According to the methodology of WEF (2017), this indicator encompasses the fiscal stance, savings-investment balance, the external position, inflation path, debt issues, etc.

When refocusing from short-term growth to the growth trend as a response variable, the relative importance of the factors behind growth changes. Productivity fundamentals in this case drive up to 80% of growth effect, while external factors are responsible for the remaining 20%. It is worth noting here that the proportion in favor of productivity factors is higher for the concept of closing the gap to frontier rather than for ‘standard’ trend growth rate. This evidence may be interpreted as additional justification for treating this measure of growth as ‘good’ at reflecting individual properties of a country in a global landscape.

Furthermore, the role of individual variables also changes. Among external factors, the most important role in driving sustainable growth belongs to trade conditions and external demand growth, while the role of financial conditions is either miserable or insignificant at most. Among productivity factors as drivers of trend growth, the quality of the macroeconomic environment seems to play a special role, as well as the efficiency of the goods market and the financial system.

Conclusions

The evidence showing rather similar and synchronous growth in emerging markets and recent evidence on the crucial importance of external factors for emerging markets should not lead us to incorrectly believe that productivity fundamentals do not matter anymore. Productivity fundamentals are still the core driver of sustainable growth. At the same time, we should keep in mind the important role of the external environment for emerging markets. However, changes in the external environment are more likely to generate relatively short-term growth rate fluctuations, while having a modest impact on the sustainable growth trajectory. Hence, a country aiming to secure sustainable growth should still first of all think about productivity fundamentals.

References

  • Coibion, O., Gorodnichenko, Y, Ulate, M. (2017). The Cyclical Sensitivity in Estimates of Potential Output, National Bureau of Economic Research, Working Paper No. 23580.
  • EBRD (2017). Transition Report 2017-2018, European Bank for Reconstruction and Development, London, UK.
  • Fayad, G., and Perelli, R. (2014). Growth Surprises and Synchronized Slowdown in Emerging Markets—An Empirical Investigation, IMF Working Paper, WP/14/173.
  • IMF (2017). Roads Less Traveled: Growth in Emerging Markets and Developing Economies in a Complicated External Environment, in IMF World Economic Outlook, April, 2017, pp. 65-120.
  • World Economic Forum (2017). The Global Competitiveness Report 2017-2018, Geneva: World Economic Forum.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Losers and Winners of Russian Countersanctions: A welfare analysis

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In this brief we provide a quantitative assessment of the consequences of countersanctions introduced by the Russian government in 2014 in response to sectoral restrictive measures initiated by a number of developed countries. Commodity groups that fell under countersanctions included meat, fish, dairy products, fruit and vegetables.  By applying a basic partial equilibrium analysis to data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc., we obtain that total consumers’ loss due to countersanctions amounts to 288 bn Rub or 2000 rubles per year for each Russian citizen. Producers capture 63% of this amount, importers 26%, while deadweight loss amounts to 10%. 30% of the transfer from Russian consumers toward importers was acquired by Belarus. The gain of Belarusian importers of cheese is especially impressive – 83% of total importer’s gains on the cheese market.

In August 2014, in response to sectoral sanctions initiated by some countries against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.).

Russian countersanctions were, in particular, imposed on meat, fish, dairy products, fruit and vegetables. Later the list of counter sanctioned goods was edited: inputs for the production of baby food and medicines have been deleted from the ban list, while new items were added. Salt was added to the list in November 2016 and animal fats in October 2017.

The popular idea behind the countersanctions was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the countersanctions was to support domestic producers via trade restrictions, or by other words – import substitution.

We apply a basic partial equilibrium analysis in order to evaluate the effect of countersanctions on the welfare of main stakeholders – consumers, producers and importers. The overall results are in line with general microeconomic consequences of trade restrictions in a small open economy, that is, we observe a decline in consumer surplus, increase in producer surplus and redistribution across importers. Perhaps, even more interestingly, we are able to provide a numerical assessment of redistribution effects between Russian consumers and producers, on the one hand, and among importers from different countries, on the other.

Partial equilibrium welfare analysis

We apply a framework of the classical analysis of import tariff increases to Russian countersanctions. Countersanctions resulted in increased domestic prices, declining consumption and increased domestic production. Given the increase in prices and declined volumes of consumption, we evaluate the losses by consumers as a decline in consumer surplus. Respectively, given the increase in prices and increase in domestic output we identify the producers gains as an increase in producer surplus. The only difference with a classical analysis is the lack of increase in government revenues. In this case increases in domestic prices were driven by restrictions on trade with historical partners which were substituted by more costly producers. Given the changes in the composition of importers after sanctions, we identify countries which lost and gained access to the Russian market. We use changes in volumes of trade as a measure of respective gains and losses. Figure 1 presents all relevant concepts.

In order to measure all relevant welfare changes, we rely on consumption, production and price data from Rosstat and Euromonitor, trade data from the UN Comtrade database. We use data for 2013 as a benchmark before countersanctions and compare it to 2016. The measures of own price elasticities of Russian demand and supply were taken from the literature. We use real price (in terms of 2013 prices) and volume information for consumption and supply in 2016 as the resulting points on the supply (point C) and demand (point A) curves as shown on Figure 1. Then we restore the consumption and production points on these curves (points F and B) as they would have been in 2013 given the own price elasticities of demand and supply and price level as of 2013.

Figure 1. Visualization of deadweight losses, consumer and producer surplus changes

Welfare analysis

Data

We consider 12 commodity groups that were included in 2014 in the countersanctions list: pork, cheese, poultry, apples, beef, tomatoes, processed meat, fromage frais, butter, oranges, condensed milk, grapes, cream, sour milk products, milk, and bananas.

Prices and volumes information are taken from Rosstat official statistics, which in a few cases were adjusted by data from Euromonitor. Import values were obtained from the UN Comtrade database. The summary of the original data and results of welfare analyses are reported in table 1. Below we discuss in details the situation in three markets – beef, apples and cheese.

Table 1. Summary table of the welfare effects of countersanctions

Group Price (RUR per kg, 2013) Production (thous. tons) Consumption (thous. tons) Elasticity Consumer losses, RUR mn Producer surplus, RUR mn Deadweight loss, RUR mn Importer gains, RUR mn
2016 2013 2016 2013 2016 2013 demand supply
Beef 376 357 238 240 600 897 -0.78 0.1 11311 4388 234 6690
Poultry 109 108 4468 3610 4577 4084 -0.78 0.45 3263 3173 13 77
Pork 286 289 2042 1299 2282 1919 -0.78 0.2 -7167 -6447 38 -757
Milk 55 47 5540 5386 5704 5595 -0.93 0.3 48234 42507 4443 1284
Butter 343 271 251 225 340 340 -0.93 0.18 27468 17680 3370 6419
Cheese 358 283 605 435 748 764 -0.93 0.28 63493 44259 8437 10797
Fromage frais 233 190 407 371 456 457 -0.93 0.3 21803 17104 2600 2099
Apples 84 70 324 313 986 1665 -0.85 0.1 15225 4562 1238 9425
Bananas 61 47 0 0 1141 1165 -0.9 0.1 18967 0 2315 16652
Oranges 65 59 0 0 932 1059 -0.9 0.1 6054 0 272 5782
Grapes 175 131 174 101 366 459 -0.85 0.1 18312 7527 2351 8435
Tomatoes 82 65 1130 863 1583 1718 -0.97 0.1 28824 18177 3290 7357

Data sources: Rosstat, Euromonitor, UN COMTRADE

Bold figures were used to mark the commodity groups with a noticeable consumption growth in 2013-2016, italic figures – for those with consumption decrease, and underlined – for groups where consumption changed insignificantly during the period.

Beef

The Russian beef market experienced a drastic decrease in consumption during two years under countersanctions.  In 2013 constant prices, the average real of 1 kg of beef increased by 5.3% from 357 Rub/kg in 2013 up to 376 Rub/kg in 2016. Domestic output decreased by 0.8% and to 238 thousand tons in 2016 from 240 in 2013. Domestic consumption decreased by 33.1% to 600 thousand tons in 2016 from 897 in 2013. Our estimations indicate that  consumer losses amount to  11.3 bn Rub or 3.5% of beef consumption in 2013; producers’ gains are 4.4 bn Rub or 1.4%; deadweight losses are estimated at 0.2 bn Rub or 0.07%; and importers’ gains equal 6.7 bn Rub or 2.1%.

Out of total 6.7 bn Rub of importers’ gains, importers from Belarus acquire the major share (88%) – 5.9 bn Rub. Importers of beef from India and Colombia gained 0.4 bn Rub (6% of total) and 0.3 bn Rub (5%) respectively. Beef importers from Mongolia gained 0.03 bn Rub, from Kazakhstan – 0.01 bn Rub. Importers of beef from Brazil, Paraguay, Australia, Uruguay, Ukraine, Lithuania, Poland, and Argentina lost market shares in over the period 2013-2016.

Cheese

Average real price for 1 kg of cheese increased by 26.5% up to 358 Rub/kg in 2016 from 283 Rub/kg in 2013, both in constant 2013 prices. Domestic output increased by 39.1%  to 605 thousand tons in 2016 from 435 thous. tons in 2013. Domestic consumption decreased by 2.1% to 748 thous. tons in 2016 from 764 thous. tons in 2013. Our results indicate the following effects of countersanctions on cheese market: consumers’ losses amounted to 63.5 bn Rub or 29.4% of cheese consumption in 2013; producer’s gain is 44.3 bn Rub or 20.5%; deadweight loss is estimated at 8.4 bn Rub or 3.9%; importers’ gains equal 10.8 bn Rub or 5.0%.

Out of a total 10.8 bn Rub of importer’s gains on the cheese market, importers of cheese from Belarus acquired the major share (82.9%) – 9.0 bln Rub, importers of cheese from Argentina gained 0.5 bn Rub (4.8% of total importers’ gain), importers from Uruguay gained 0.4 bn Rub (3.9%), Swiss cheese importers gained 0.2 bn Rub, importers from Armenia – 0.2 bn Rub (1.8%). While importers of cheese from Ukraine, the Netherlands, Germany, Finland, Poland, Lithuania, France, Denmark, Italy, and Estonia lost market access over 2013-2016.

Apples

In 2013 constant prices, average real price for 1 kg of apples increased by 20.0%  up to  84 Rub/kg in 2016 from 70 Rub/kg in 2013. Domestic output increased by 3.5% to 324 thous. tons in 2016 from 313 thous. tons in 2013. Domestic consumption decreased by 40.8% to 986 thous. tons in 2016 from 1665 thous. tons in 2013. According to our analysis, the  effects of countersanctions on the apple market are the following: consumers’ losses amounted to 15.2 bn Rub or 13.1 of apple consumption in 2013; producer’s gain is 4.6 bn Rub or 3.0%; deadweight loss is estimated at  1.2  bn Rub or 1.1%; importers’ gains equal 9.4  bln Rub or 8.1%.

Out of a total 9.4 bn Rub of importer’s gains, importers from Serbia acquired the major share (49.7%) – 4,7 bn Rub, importers of apples from China gained 1.6 bn Rub (16.7% of total importers’ gains), those importing from Macedonia gained 0.8 bn Rub (8.4%), from Azerbaijan 0.6 bn Rub (6.0%), and from South Africa 0.4 bn Rub (4.5% of total importers’ gains). While importers of apples from Poland, Italy, Belgium, and France lost market access.

Overall effects for 12 commodity groups

We calculated the welfare effects for 12 commodity groups: beef, poultry, milk, cheese, cottage cheese, ton butter, dairy products, apples, bananas, oranges, grapes and tomatoes.

Total consumers’ loss due to countersanctions amounts to 288 bn Rub, producers gain 63% out of this amount (182 bn Rub), 26% of total consumers’ loss is redistributed to importers (75 bn Rub), deadweight losses amount to 10% (31 bn Rub).

Distribution of importers’ gains

Belarus is the major beneficiary of Russians countersanctions: its exporters gain 29.4 bn Rub (38%), Ecuador’s exporters are in the second place with 16.4 bln Rub (21). Exporters from Serbia gained 5.1 bn Rub (7%).

Conclusion

There is no doubt that countersanctions were paid out of the pockets of Russian consumers: our estimation of total consumer losses amounts to 288 billion rubles, i.e. each Russian citizen paid 2000 rubles per year.  Out of this sum, Russian producers received 144 billion rubles, i.e. transfer from Russian consumers to producers equals 1260 rubles per person per year. Among Russian sectors, major gains and associated increases in production happened in pork industries (50%), poultry (20%), dairy products (10-30%), fruit and vegetables (10-50%).

The transfer from Russian consumers toward importers from non-sanctioned countries equals 75 billion rubles a year (520 rubles per person per year), out of which 30% was acquired by Belarusian importers. Countersanctions lead to deadweight losses in the efficiency of Russian economy equal to 31 billion rubles or 215 rubles per person per year.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Whistleblower Protections but no Rewards: EU Commission Proposes a New Directive

20180626 Whistleblower Protections Image

On the 17th of April 2018 the European Commission adopted a package of measures to increase protections for whistleblowers (European Commission Newsroom, 2018). This is good news, as whistleblower protection in Europe has been uneven and in some member states non-existent. Transparency International (2013) rated a disappointing four European countries as having adequate or extensive protection. In a report by Wolfe et al (2014), several European countries, including Germany, France and Italy, were judged to have inadequate laws with respect to several aspects of whistleblower protection, although France and Italy recently improved them considerably. Corruption, fraud of various types, and related forms of economic crime are widespread almost everywhere in the world (See e.g. Dyck et al 2014, and Global Crime Survey 2016). Criminal organizations such as drug cartels, have become increasingly sophisticated and their ability to use the international financial markets has made it ever more difficult for law enforcement agencies to discover them with more traditional law enforcement tools (see e.g. Radu 2016 for an overview). Incentivizing whistleblowers through protection and rewards can prove effective at getting information on these hard-to-detect crimes. Whistleblower protection is central for ensuring democratic values such as freedom of speech and fair elections, and recent cases also suggest that it may be central for protecting investigative journalists and their sources.

The Need for Protection and Possibly Rewards

On February 26th 2018 Ján Kuciak, a Slovakian journalist, was murdered in his home for investigating political connections to organized crime in the heart of Europe (Washington Post, 2018); Daphne Caruana Galizia was killed on 16th of October 2017 by a car bomb while she had been writing about corruption in Malta in connection with the Panama papers (Financial Times, 2018a); Maria Efimova, an employee at a private bank that claimed that her employer had illegally moved funds for Maltese politicians, is under an arrest warrant from Malta and Cyprus on seemingly unrelated charges (The Guardian, 2018); and Hervé Falciani, who blew the whistle on the bank he was working for in Switzerland that helped clients evade billions of dollars in taxes, was arrested in April in Spain after an arrest warrant issued by Switzerland on March 19th, though he has now been released on bail (Financial Times, 2018b).

While some EU countries recently improved whistleblower protection, some seem to be heading in the opposite direction. An extreme example is Germany. A provision packed into the German Data Retention Framework of 2015 allows for prison sentences of up to 3 years for the handling of “stolen data”, and journalists are no longer protected against search and seizure (European Digital Rights, 2017). This provision was included despite Germany’s problems with underreporting of corporate crime.  On the need of whistleblowing in the country, consider Volkswagen’s emissions scandal in 2015 when the public learned that the company had installed defeat devices in millions of diesel cars to ‘cheat’ on environmental emissions standards and increase pollution all over the world. The response of management was to blame a set of “rouge engineers” (Congressional Hearing, 2015), while we now know that power points on how to circumvent U.S. emissions tests by a top technology executive circulated within the company as early as 2006 (New York Times, 2016). Excess diesel emissions were associated 38 000 premature deaths in 2015 (Anenberg et al, 2017), implying that whistleblowing could have saved thousands of lives, yet the wrongdoing went on for close to a decade without anyone blowing the whistle. Cheating on emissions tests also turned out to be an industry wide phenomenon.

Germany also has a history of treating whistleblowers poorly. Consider for example the case where a German nurse brought a complaint to her employer in December of 2004 over poor treatment of patients, and she was fired in January 2005. Her employer cited repeated illness as the reason for being fired, the nurse claimed that it was retaliation for speaking out about poor conditions. The nurse then filed a complaint in German Labor Court which was dismissed in August 2005. She then brought the claim to the European Convention of Human Rights, alleging that her right to expression under article 10 of the European Convention of Human Rights had been violated by her employer. She won that case in 2011, and Germany was ordered to pay the nurse 10 000 Euro in non-pecuniary damages, and 5000 for costs and expenses (Heinish V Germany 2011).

Large firms do not appear to be doing better. Even after the Siemens scandal in 2008, when the company was discovered pursuing a long-term, extensive and systematic strategy of bribing foreign governments and purchasing agencies, and promises about a drastic change in corporate governance. Recent cases suggest that the corporate culture at Siemens has not improved. Meng-Lin Liu, a compliance officer at Siemens China, brought attention to alleged kickbacks paid in connection with equipment sales to army hospitals in China to the chief financial officer for healthcare in China. He was fired after reporting internally and filed a claim alleging violations of the Foreign Corrupt Practices Act. Siemens lawyers argued that since he was no longer an employee, he was not entitled to protection under Dodd-Franks definition of “whistleblower” (Forbes, 2014).

The situation in such an important European country like Germany suggests that protection applying across all member states is needed, and the experience of other countries further suggest that protection may not be enough. In the UK, the country recognized to have some of the best protections in the EU (Wolfe 2014, Transparency International 2013), whistleblowers are still experiencing pushback. The recent case of Jes Staley, Barclays Bank’s CEO is enlightening. He ordered his security team to unveil the identity of an uncomfortable whistleblower, going so far as to request video footage of the person who bought the postage for the letter. Yet, the Financial Conduct Authority and the Prudential Regulation Authority (FiCA & PRA) decided to just fine him £642 000 – a small fraction of his pay package that year (Reuters, 2018). Cases like this suggest that the US Congress was right in pushing for rewards. The mild sanctions established by the UK regulators sent a loud and clear message to prospective whistleblowers: even in the UK, where protection was judged as high in the above-mentioned reports, a CEO that violates the law trying to uncover someone reporting his potential mismanagement (probably not to give him a premium), will just have to pay a mild fine, if he is caught of course!

In the following we review the new proposal for whistleblower protections and argue that evidence from the US suggests that financial incentives for whistleblowers may still be needed to ensure an adequate level of reporting. We then consider objections to monetary rewards which are praised by regulators in the US, while EU agencies remains hesitant. Finally, we conclude with suggestions on how to improve the European legislation.

The EU Proposed Directive Versus US Developments

The new Directive includes mandatory establishment of internal reporting channels for firms with more than 50 employees that should allow for anonymous claims (Article 5). It includes prohibition against a wide range of retaliation (Article 14); and the burden of proof is reversed in case of alleged retaliation (Article 15). Who counts as a whistleblower under the Directive is defined widely to encompass subcontractors, trainees, and people associated with a wrongdoing firm in a “work-related context” (Article 2).

The Directive is bound to improve the situation for whistleblowers given the current uneven protection. It bears similarities with the US Sarbanes-Oxley act of 2002 (SOX), but it goes beyond SOX in that it applies more broadly. Since SOX, the legal debate in the US has increasingly focused on rewards to whistleblowers as protections alone are often insufficient to ensure an adequate level of reporting.

After the financial crisis, the US concluded in the Dodd-Frank Act of 2008 that protections were insufficient, and that above and beyond protections, Dodd-Frank allows for rewards to whistleblowers who report wrongdoings in securities trading where the sanction against the wrongdoing party exceeds 1$ million.

The use of rewards was not unfamiliar to the US before Dodd-Frank. They had formerly concluded that in the tax area, whistleblowers who report tax evasion should be eligible for rewards through the Tax Relief and Health Care Act of 2006 which established the Internal Revenue Service “Office of the Whistleblower”. Although previously to 2006 whistleblowers could receive rewards at the IRS, this was entirely up to the agency’s discretion.

In the procurement area, whistleblowers are also eligible for rewards in the US under the False Claims Act (FCA) enacted in 1863. The commitment to rewards was reaffirmed in 1986 when revisions to the act reinvigorated the whistleblower or “qui tam” provisions of act (for an overview of reward programs, see Nyreröd & Spagnolo 2017).

Despite being regarded as having some of the best whistleblower protections in the world (see e.g. Wolfe et al 2014), the US did not settle for protections alone in key regulatory areas. The new EU directive does not address rewards at all which is unfortunate given their law enforcement potential if they are coupled with independent and competent judicial institutions.

Although the US experiment with whistleblower rewards is working, the only EU institution to evaluate reward policies to our knowledge is the UK’s PRA & FiCA on the request of the UK parliament. Their assessment concludes strongly against rewards, yet they do not provide any evidence to back up their negative assessment and make claims that later evidence has refuted. In the following, we review the concerns raised by critics of reward programs, primarily the PRA & FiCA.

Evidence on the Effectiveness of Rewards

Under reward programs in the U.S whistleblowers can receive a percentage of the fine imposed on the wrongdoing firm or person. The range is usually between 15-30% of the sanctions against the firm, and of the money recovered. The exact reward percentage within the range is determined by how central the whistleblowers information was to unearth and sanction the wrongdoing.

One fundamental concern with rewards is their cost effectiveness. Some argue that they can come with a costly government structure and that they attract a lot of meritless claims by opportunist employees, which increase the administrative costs (PRA & FiCA 2014, Ebersole 2011).

On the other hand, many argue that they can be a cost-effective tool in an age when governments are looking for austere economic policies (Engstrom 2014). Some argue that they are at least as efficient as classical “command and control” methods of enforcement, such as selecting random persons or firms for audit. We evaluate cost-effectiveness with respect to three important effects: deterrence, increased quality of claims, and increase quantity of claims.

A significant part of determining cost-effectiveness is the extent to which whistleblowing has any significant deterrence effects on future misbehavior. Johannesen & Stolper (2017) found that whistleblowing had deterrence effects in the off-shore banking sector. They studied the stock market reaction before and after the whistleblower Heinrich Kieber leaked important tax document from the Liechtenstein based LGT Bank. They found abnormal stock returns in the period after the leak, and the market value of banks known to derive some of their revenues from offshore activities decreased.

Wilde (2017) also provide evidence that whistleblowing deters financial misreporting and tax aggressiveness. Using a dataset of retaliation complaints filed with OSHA between 2003 through 2010 on violations of paragraph 806 which outlaw’s retaliation against employees who provide evidence of fraud, he found that firms subject to whistleblower allegations exhibited decreases in financial misreporting and tax aggressiveness.

As for experimental evidence, Abbink and Wu (2017) conducted laboratory experiments studying collusive bribery, corruption, and the effects of whistleblower rewards on deterrence. They find that amnesty for whistleblowers and rewards strongly deter illegal transactions in a one-shot setting, but in repeated interaction the deterrence effect is limited. Their results support a reward mechanism, especially for petty forms of bribery (which are more like one-shot games).

Bigoni et al (2012) conducted laboratory experiments on leniency policies and rewards as tools to fight cartel formation. They find that rewards financed by the fines imposed on the other cartel participants had a strong effect on average price (returning it to a competitive level). In the model setting, this implies that rewards have a deterring and desisting effect on cartel formation.

Another central question is whether rewards increase the quality and quantity of claims. PRA & FiCA (2014) writes that “There is as yet no empirical evidence of incentives leading to an increase in the number or quality of disclosures received by regulators” (PRA & FiCA 2014, p.2).

As for increased quality, there is evidence suggesting that this claim is untrue. Dyck et al (2010) compared whistleblowing in the health care sector where rewards are available through the FCA with non-healthcare sectors where they are not. They found that 41% of fraud cases are detected by employees in the healthcare sector. That number was only 14% for other sectors, a difference highly statistically significant (at the 1% level) despite a small sample size (Dyck et al 2010, p. 2247).

More recently, Call et al (2017) examined empirically the link between whistleblowing and (i) penalties, (ii) prison sentences, and (iii) duration of regulatory enforcement actions for financial misrepresentation. They found that whistleblowers’ involvement in financial misrepresentation enforcement actions was correlated with higher monetary sanctions for the wrongdoing firm and increased jail time for culpable executives. They also found that enforcement proceedings began quicker, and further that whistleblower involvement increased the likelihood that criminal sanctions were imposed by 8.58%, and that criminal sanctions were imposed against the targeted wrongdoer increased by 6.64%.

Another highly contested point is the relation between the quantity and quality of claims and regulatory effectiveness. Some argue that rewards may attract a lot of meritless claims by employees who are either malicious or hope to reap some reward (PRA & FiCA 2014, Ebersole 2011). This does seem to have been the case with some reward programs, but not to the extent many opponents of rewards argue, and this effect does not render rewards a futile or ineffective policy approach, see Nyreröd & Spagnolo (2017) for a thorough discussion.

There are, however, valuable lessons to be learned from the quantity of claims received and the percentage of claims determined to have merit from, for example, the IRS Whistleblower Office. At the IRS there has been a significant backlog of claims, and an exceedingly small number of claimants receive rewards. The IRS program, under 7623(a), does not have a threshold for claims to be considered, and the vast majority of claims fall under 7623(a). These are lessons for optimal design, but not an insurmountable obstacle for effective reward programs. One way around this problem is to have a threshold for claims to be considered. Another is the FCA model, where persons pursue litigation on their own if the Department of Justice declines to join, thereby taking on the risks and costs of losing in court.

Concerns over administrative burden and costly government structures are not salient enough to warrant a rejection of reward policies, as benefit in deterrence and quality outweigh the administrative costs of reviewing even large quantities of incorrect claims.

Entrapment and Malicious Claims

Another central concern has been that “Some market participants might seek to ‘entrap’ others into, for example, an insider dealing conspiracy, to blow the whistle and benefit financially”, FiCA & PRA (2014).

There are presently good ways of preventing this issue, which does not seem to have been salient in the U.S. experience with these policies. Regarding the FCA, for example, when the relator (whistleblower) initiated or planned the wrongdoing, courts can reduce the reward below 15% as they see fit (False Claims Act, 31 U.S.C. §3730 (d) (3)). The IRS has similar restrictions that in cases where the whistleblower planned and initiated the tax evasion, they may considerably reduce or deny any reward. If the whistleblower is convicted of criminal conduct related to the suit, then they should deny her any reward (Internal Revenue Code, 26 U.S.C §7623 (b) (3)).

These restrictions on reward payouts is probably the reason why, judging from the reports by the U.S agencies, entrapment has not emerged as a salient issue in the US experience with the various programs. As for evidence, the National Whistleblower Center (2014) claims they did not find a single case of entrapment in over 10 000 cases in which the planner and initiator of the wrongdoing received an award. Of course this does not exclude the possibility that a poorly run European agency/regulator might mismanage the whistleblower program to the point where this indeed becomes an issue; a sufficiently incompetent administration can generate problems even with the most robust and effective tools.

A related concern is that financial incentives could encourage employees to submit fraudulent claims, e.g. to “fabricate claims of wrongdoing for personal profit” (Howse & Daniels 1995, p.540, see also Rose 2014, p.1283). A similar concern is that: “Financial incentives might lead to more approaches from opportunists and uninformed parties passing on speculative rumors or public information. The reputation of innocent parties could be unfairly damaged as a result” (PRA & FiCA 2014, see also Vega 2012, p.510). There is also the fear that opportunistic whistleblowers will force “corporations into financial settlements in order to avoid the adverse reputational and related effects caused by highly public, albeit ill-founded, accusations” (Howse & Daniels 1995, p.526/27).

Although evidence on this is hard to find, judging from the reports of agencies, fraudulent and malicious has not been a significant issue. This is probably because fraudulent reporting is a crime, and a whistleblower who report fraudulent information exposes him or herself to a legal fight with the falsely accused employer and to sanctions against perjury and defamation. Indeed, in the case of the IRS, the information is submitted under penalty of perjury (Internal Revenue Code, 26 U.S.C §7623 (b)(6)(C)), which is also the case of the SEC if the whistleblower is represented by an attorney (Exchange Act, U.S.C 78u-6(h)). In the case of the FCA, should the whistleblower lie to the court, he risks felony charges punishable by up to five years in jail for perjury, and the possibly of being convicted of other crimes related to lying under oath. Further, the FCA has a reverse fee-shift for obviously frivolous claims (Engstrom 2016, p.10).

Whether fraudulent claims are a concern for the efficacy of a whistleblower reward program depends to a large extent on the precision of the court system. Buccirossi et al. (2017) analyze this concern within a formal economic model. They show that fraudulent reports are entirely irrelevant for countries with sufficiently precise/competent court systems, provided strong sanctions against perjury, defamation and lying under oath are there to balance the incentives generated by large bounties. Where the judicial system makes a lot of mistakes, instead, this may not be sufficient for the scheme to have crime deterrence effects, which may make it preferable not to introduce large rewards for whistleblowers.

Conclusions

Some suggest that the European hesitation over improving whistleblower protection and considering rewards may have partially historical roots, as both Nazi Germany and Soviet Russia relied heavily on citizens reporting on one another (Givati 2016, p.26.). But the lack of voices speaking out against what the Nazi’s were doing should suggest the opposite, and it is not clear how these parallels should be drawn when we are talking about rewarding whistleblowers in the financial offices of private corporations.

It is also the case that most valuable information to law enforcement is often in the hands of higher-ups in the organization, those who have more to lose in the case of whistleblowing (Engstrom 2016), and for whom protections would be an insufficient compensation relative to their current position and salary. The blunt tool that is horizontal protection for whistleblowers who report violations of EU law could be coupled with precise tools such as rewards for violations of specific EU laws whose undermining can be particularly detrimental to financial stability or the environment.

If European countries and their regulatory and law enforcement institutions are not capable of having an open and honest debate, competently based on the available evidence from rigorous research and from previous experiences in other countries, then they would hardly be able to competently design and properly administer a system of rewards for whistleblowers. As argued in Buccirossi et al. (2017), in countries with weak institutions high powered tools like whistleblower rewards should better be avoided, as in the hand of incompetent law makers and corrupt regulators they would likely produce more damage than good.

References

Disclaimer: Opinions expressed in policy papers and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.