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Labor Market Adaptation of Internally Displaced People: The Ukrainian Experience

Posted on October 29, 2018 | Policy Brief

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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

Posted on October 22, 2018 | Policy Brief

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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 Energy, 126(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

Posted on October 15, 2018 | Policy Brief

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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

Posted on October 1, 2018 | Policy Brief

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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

Posted on June 26, 2018 | Policy Brief, Policy Papers

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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.

Conflict, Minorities and Well-Being

Posted on June 17, 2018 | Policy Brief

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We assess the effect of the Russo-Georgian conflict of 2008 and the Ukrainian-Russian conflict of 2014 on the well-being of minorities in Russia. Using the Russian Longitudinal Monitoring Survey (RLMS), we find that the well-being of Georgians in Russia suffered negatively from the 2008 Russo-Georgian conflict. In comparison, we find no general effect of the Ukrainian-Russian conflict of 2014 on the Ukrainian nationals’ happiness. However, the life satisfaction of Ukrainians who reside in the southern regions of Russia in close proximity to Ukraine is negatively affected. We also show that the negative effect of conflict is short-lived with no long-term legacy. Additionally, we analyze the spillover effect of conflict on other minorities in Russia. We find that while the well-being of non-Slavic and migrant minorities who have recently moved to Russia is negatively affected, there is no effect on local minorities who have been living in Russia for at least ten years.

Militarized conflict affects a myriad of socioeconomic outcomes, such as the level of GDP (Bove et al. 2016), household welfare (Justino 2011), generalized trust and trust in central institutions (Grosjean 2014), social capital (Guriev and Melnikov 2016), and election turnout (Coupe and Obrizan 2016). Importantly, conflict has also been found to directly affect individual well-being (Frey 2012, Welsch 2008).

However, previous research studying individual well-being in transition countries largely abstracts from heightened political instability and conflict proneness, while this has been particularly pertinent in transition countries. Examples of transition countries facing various types of conflicts are abound, such as Yugoslavia, Ukraine, Tajikistan, Russia, Armenia, Azerbaijan, Moldova, and so on. Therefore, it is imperative to explore how conflict shapes well-being in transition countries.

In a new paper (Gokmen and Yakovlev, forthcoming), we add to our understanding of well-being in transition in relation to conflict. We focus on the effect of Russo-Georgian conflict of 2008 and the Ukrainian-Russian conflict of 2014 on the well-being of minorities in Russia. The results suggest that the well-being of Georgians in Russia suffered negatively from the 2008 Russo-Georgian conflict. However, we find no general effect of the Ukrainian-Russian conflict of 2014 on the Ukrainian nationals’ happiness, while the life satisfaction of Ukrainians who reside in the southern regions of Russia in close proximity to Ukraine is negatively affected. Additionally, we analyze the spillover effect of conflict on other minorities in Russia. We find that while the well-being of non-slavic and migrant minorities who have recently moved to Russia is negatively affected, there is no effect on local minorities who have been living in Russia for at least ten years.

Data and Results

We employ the Russian Longitudinal Monitoring Survey (RLMS) which contains data on small neighborhoods where respondents live. Starting from 1992, the RLMS provides nationally-representative annual surveys that cover more than 4000 households with 10000 to 22000 individual respondents. The RLMS surveys comprise a broad set of questions, including a variety of individual demographic characteristics, health status, and well-being. Our study utilizes rounds 9 through 24 of the RLMS from 2000 to 2015.

In this survey, we identify minorities with the question of “What nationality do you consider yourself?” Accordingly, anybody who answers this question with a non-Russian nationality is assigned to that minority group.

We employ three measures of well-being. Our main outcome variable is “life satisfaction.” The life satisfaction question is as follows: “To what extent are you satisfied with your life in general at the present time?”, and evaluated on a 1-5 scale from not at all satisfied to fully satisfied. Additionally, we use “job satisfaction” and “health evaluation” as outcomes of well-being.

Our results suggest that our primary indicator of well-being, life satisfaction, for Georgian nationals has gone down in the Russo-Georgian conflict year of 2008 compared to the Russian majority (see Figure 1). The magnitude of the drop in life satisfaction is about 39 percent of the mean life satisfaction. Our estimates for the other two well-being indicators, job satisfaction and health evaluation, also indicate a dip in the conflict year of 2008. Lastly, our estimates show that the negative impact of the conflict does not last long. Although there is a reduction in the well-being of Georgians both on impact in 2008 and in the immediate aftermath in 2009, the rest of the period until 2015 is no different from the pre-2008 period.

Figure 1. Life Satisfaction of Georgian Nationals in Russia


Source: Authors’ own construction based on RLMS data and diff-in-diff estimates.

Furthermore, when we investigate the effect of the Ukrainian-Russian conflict of 2014, we find no negative effect on the life satisfaction of Ukrainians. One explanation for why the happiness of Ukrainians in Russia does not seem to be negatively affected in 2014 is that the degree of integration of Ukrainians into the Russian society is much stronger than the degree of integration of Georgians. On the other hand, our heterogeneity analysis reveals that in the southern parts of Russia closer to the Ukrainian border, where there are more Ukrainians who have ties to Ukraine, Ukrainian nationals are differentially more negatively affected by the 2014 conflict. The differential reduction in the happiness of Ukrainians is about 19 percent of the mean life satisfaction.

Moreover, we also look into whether there is any spillover effects of the Russo-Georgian and the Ukrainian-Russian conflicts on the well-being of other minorities. We first carry out a simple exercise on non-Slavic minorities of Russia. We pick the sample of non-Slavic ex-USSR nationals that are similar to Georgians in their somatic characteristics, such as hair color and complexion. This group of people include the nationals of Azerbaijan, Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan and Tajikistan. We treat this group as “the countries with predominantly non-Slavic population” as their predominant populations are somatically different from the majority Russians, and thus, might either have been subject to discrimination or might have feared a minority backlash to themselves during the times of conflict. This conjecture finds some support below in Figure 2 in terms of violence against minorities. We observe in Figure 2 that hate crimes and murders based on nationality and race peak in 2008.

Our estimates also support the above hypothesis and propose that there is some negative effect of the 2008 conflict on non-slavic minorities’ happiness as well as their job satisfaction, whereas 2014 conflict has no effect.

Figure 2. Hate Murders in Russia over Time

Source: Sova Center

Next, we investigate the spillover effects of conflict on Migrant Minorities. Migrant minorities are minorities who have been living in their residents in Russia for less than 10 years. We conjecture that these minorities, as opposed to the minorities who have been in place for a long time, could be more susceptible to any internal or external conflict between Russia and some other minority group for fear that they themselves could also be affected. Whereas other types of longer-term resident minorities, which we call Local Minorities, are probably less vulnerable since they have had more time to establish their networks, job security, and most likely also have Russian citizenship. Our estimates back up the above conjecture and demonstrate that migrant minorities suffer negatively from the spillover effects of the 2008 conflict onto their well-being captured by any of the three measures, and not from the 2014 conflict, whereas there is no negative impact on local minorities.

Conclusion

In this paper, instead of focusing on the direct impact of conflict on happiness in war-torn areas, we contribute to the discussion on conflict and well-being by scrutinizing the well-being of people whose country of origin experiences conflict, but they themselves are not in the war zone. Additionally, we show that some other minority groups also suffer from such negative spillovers of conflict. Being aware of such negative indirect effects of conflict on well-being is essential for policy makers, politicians and researchers. Most policy analyses ignore such indirect costs of conflict, and this study highlights the bleak fact that the cost of conflict on well-being is probably larger than it has been previously estimated.

References

  • Bove, V.; L. Elia; and R. P. Smith, 2016. “On the heterogeneous consequences of civil war,” Oxford Economic Papers.
  • Coupe, T.; and M. Obrizan, 2016. “Violence and political outcomes in Ukraine: Evidence from Sloviansk and Kramatorsk”, Journal of Comparative Economics, 44, 201-212.
  • Frey, B. S., 2012. “Well-being and war”, International Review of Economics, 59, 363-375.
  • Gokmen, Gunes; and Evgeny Yakovlev, forthcoming. “War and Well-Being in Transition: Evidence from Two Natural Experiments”, Journal of Comparative Economics.
  • Grosjean, P., 2014. “Conflict and social and political preferences: Evidence from World War II and civil conflict in 35 European countries” Comparative Economic Studies, 56, 424-451.
  • Guriev, S.; and N. Melnikov, 2016. “War, inflation, and social capital,” American Economic Review: Papers & Proceedings, 106, 230-35.
  • Justino, P., 2011. “The impact of armed civil conflict on household welfare and policy,” IDS Working Papers.
  • Welsch, H., 2008. “The social costs of civil conflict: Evidence from surveys of happiness” Kyklos, 61, 320-340.

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 Equality and Economic Development: From Research to Action

Posted on June 8, 2018 | Policy Brief

20180608 Gender Equality and Economic Development Poster 01

It’s increasingly being acknowledged that gender inequality is not just a human rights issue, but of first order importance for economic development. It is also an issue of high priority for the Swedish government, with the feminist foreign policy gaining a lot of attention worldwide. This policy brief shortly summarizes presentations held during a full day conference at the Stockholm School of Economics on June 1, 2018. The event focused on how gender discrimination negatively impacts the productivity of low and middle income economies, but also how reforms and specific initiatives can better the situation. The perspective was both long term, how norms and laws governing women’s rights have evolved over time, and short term, illustrating the current challenges women and societies face, with a particular emphasis on the situation in Eastern Europe. This was the 7th installment of SITE Development Day – a yearly development policy conference organized with support from the Swedish Ministry for Foreign Affairs.

From Research: Causes, Costs and Remedies

Cross-country differences in gender equality are often explained by variation in formal institutions such as laws and policies, and informal institutions such as social norms, religion and culture. A recent literature has focused on understanding the underlying drivers behind the variation in gender norms, arguing that these norms themselves may be functions of predetermined fundamentals such as geography, language and external shocks such as wars, revolutions or the slave trade. An influential line of research has emphasized that certain agricultural conditions have given prominence to technologies that require more muscular strength (the plow), whereas in shifting agriculture, hand-held tools like the hoe and the digging stick, require less upper body strength, are more labor intensive and easier to combine with child care. The former conditions are therefore associated with a stricter gender division of labor that generated a norm that the natural place for women is in the home. That these differences still linger have been empirically shown looking at cross-country variation in outcomes such as female labor force participation, political representation, inheritance rules, polygamy, parental authority and women’s freedom of movement. The variation is also found among second generation immigrants, where the attitudes from the parents’ ancestry are reflected also among those born and raised in western societies with more equal gender norms.

There has been an increasing emphasis on trying to estimate how gender inequality inhibits economic development, and to put numbers on the foregone economic development and growth from continuing inequality. A key indicator of inequality in this respect is the gender gap in labor force participation. There has been progress globally in this respect, but we are still far from equality and outcomes vary dramatically across regions and countries. Traditional approaches to estimate the benefits of increased female labor force participation (flfp) has assumed perfect substitutability between men and women. New evidence suggests that this may not be true, that men and women are complementary, which implies that increased flfp increases production beyond just the fact that more people are put to work. This also means that more women in work increases the productivity of men, in other words a win-win situation. This complementarity effect can take place at the workplace (think of diversified company boards), but recent research suggests that this is particularly true at the macro level. This is likely because men and women tend to work in different sectors and occupations that are themselves complementary, yielding the additional benefit at the macro level. Estimates of welfare gains of eliminating barriers to female labor force participation to levels seen in the US, suggest improvements of on average 22 % in South Asia and 18 % in the Middle East and North Africa region.

One important policy tool to influence gender outcomes, and sometimes also gender norms, is tax and benefits policy. These sets of policies are almost never explicitly gender biased, but the impact of details of policies in areas such as inheritance law, parental leave, pensions and taxes all affect the incentives that men, women and couples face. It is also important to understand that these policies often operate in an environment that is far from being without a gender bias, suggesting that there may be motivation for government intervention to correct outcomes and also lead the way to slowly change norms. As models of household decision-making suggest that partners may not operate as a unitary actor maximizing joint welfare, and women typically have lower bargaining power within the household, policies that leave discretionary power to the couple may lead to highly unequal outcomes. Instead policies may need to be individualized, such as tax policy and parental leave policy.

The conference also contained a panel specifically focusing on Eastern Europe. The communist legacy meant that these countries, in some dimensions such as flfp, started from much more equal levels than other countries at comparable levels of income in the 1990s. The most immediate gender crisis in some ways was on behalf of men, whose life expectancy dropped dramatically. This crisis for men also created externalities in the form of domestic violence and orphaned children. Since 1990, there has therefore been some reversals in gender outcomes, and in some areas, such as political representation, the region on average performs quite poorly. Individual countries also face very different challenges. In Georgia the sex ratio at birth increased dramatically in the 1990’s as economic hardship and conflict coincided with the introduction of new technology to determine the sex of a child in utero. In Belarus inequality strikes both ways, with men having more than 10 years lower life expectancy, have higher retirement age and are drafted to military service. On the other hand women are under-represented in politics and largely responsible for unpaid homework, partly due to a very generous 3 year-long paid maternity leave policy. The tradition of bride kidnapping in parts of Central Asia (as high as 10-25 % of women in parts of rural Kyrgyzstan) was brought up, and research showing birthweight losses of children to kidnapped mothers equivalent to those measured elsewhere in conflict zones (100-200 g) suggest that this is indeed a real violation of these women.

To Action: Policies for gender equality

The SDG 2030 agenda and the concurrent finance for development process both emphasize the importance of having all sectors of society onboard in the quest of achieving the new development goals. The event therefore included representatives of both the private, public and civil societies, and featured a range of different initiatives across these sectors. A sector in which many women work for foreign companies in developing countries is textile. Here foreign companies can lead the way through initiatives beyond direct wage and employment policies that improve women’s welfare, such as information campaigns devoted to personal hygiene or policies that transfer salaries directly to the personal account of the employees (an approach that matters when there is unequal bargaining power within the household, as shown through research). Also initiatives to reduce harassment and support female careers can make a difference. A sector on the other side of the spectrum is the telecommunications sector, which is very male dominated. This bias typically start from an early age, and is reinforced by gender stereotypes. Active work in the community to early on reaching out with tech programs explicitly targeting girls can make a difference, and so can making people aware of unconscious biases.

Aid agencies and NGOs also play an important role in promoting gender equality in partner countries. Research shows that women in relative terms tend to spend resources in ways that benefit the family more, and discrimination can be counteracted through policies specifically targeting women and trying to strengthening their situation both outside and inside the household. Initiatives that give women access to credits, and foster collective action and political engagement have been tested on large scale in for instance India. Aid financed investment funds target female entrepreneurs, and engage in programs to integrate women into the investment process. Investors also have the leverage to stress the importance of partner companies investing in their female employees, for instance though education, safe transportation and separate changing rooms. A major player like Sida can engage in a dialogue also with partner governments to incentivize them to live up to commitments made in conventions and treaties, but also empower change agents that can put pressure on patriarchic structures. In the health sector, priority is given to sexual and reproductive rights, but beyond targeted interventions it is also important to mainstream a gender perspective into all types of projects and programs. It’s acknowledged that measuring impact is a challenge, and some partners are perceived as more receptive than others, but the perception is that attitudes are changing.

A Government Perspective

From the Swedish government’s side it was emphasized that gender equality is a goal in itself, as well as a prerequisite for economic development. The by now well-known feminist foreign policy is based on three R’s: that all women and girls should have access to rights, representation and resources. The policy is backed up by an action plan with clearly expressed goals in areas of peace and violence, political representation, economic empowerment and sexual and reproductive health rights. These goals will be evaluated for results (a fourth “R”) and, due to international demand, the foreign ministry is currently preparing a handbook for feminist foreign policy to document the process and the lessons learned. In the collaboration with Eastern Partnership countries, gender equality became part of the summit declaration in 2015. There’s an increasing willingness to talk about gender in the partnership countries, but many challenges remain, as also exemplified by recent experience from working in the government of Ukraine. Swedish initiatives are often a catalyst for change, though, with EU politicians and administrators slowly following pace. It was emphasized that to argue for the case of women and girls, data and research is crucial, so the FREE initiative to create a center of excellence in gender economics (FROGEE) was received with much appreciation.

To get more information about the presentations during the day and references to the data and literature discussed above, please visit this page.

Participants at the conference

  • Ann Bernes, Ambassador for Gender Equality and Coordinator of Sweden’s Feminist Foreign Policy, Ministry for Foreign Affairs.
  • Raphael Espinoza, Senior Economist, IMF.
  • Paola Giuliano, Associate Professor of Economics, UCLA, Anderson School of Management.
  • Michal Myck, Director at CenEa, Poland.
  • Anna-Karin Dahlberg, Corporate Sustainability Manager at Lindex.
  • Richard Nordström, General Director at Hand in Hand.
  • Karin Kronhöffer, Director Strategy and Communication at Swedfund.
  • Anne Larilahti, VP Head of Sustainability Strategy at Telia.
  • Jesper Roine, Deputy Director, SITE.
  • Charles Becker, Research Professor of Economics, Duke University.
  • Tamta Maridashvili, Researcher, ISET-PI, Georgia.
  • Lev Lvovskiy, Research Fellow, BEROC.
  • Elsa Håstad, Director at the Department for Europe and Latin America at Sida.
  • Inna Sovsun, Vice President at Kyiv School of Economics (KSE), Ukraine.
  • Anna Westerholm, Sweden’s Ambassador for the EU Eastern Partnership.
  • Carin Jämtin, Director General at Sida.
  • Torbjörn Becker, Director at SITE.

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.

Understanding Currents in the Contesting Information Spheres

Posted on May 17, 2018 | Policy Brief

Computers and internet merely added new forms to age-old forms of propaganda. Its general purpose is, as it always has been, dualistic: to shape citizens’ image of their own country, and to streamline their views of foreign partners, competitors or enemies. Studies on information wars are often one-dimensional, i.e. presenting only actions directed against one’s own state. New Russian textbooks on information wars have a more complex approach and present long historical retrospective overviews.

Reports on disinformation campaigns are nowadays regular in the information sphere in Sweden, as in the West in general. The changes of today’s propaganda compared to classic stereotypes of the Cold War confrontations seem obvious. However, many debates on how to counter a feared information war or fake news campaigns apparently lack a long-term historical perspective. Therefore, they appear unnecessarily alarmist and might even miss their claimed purpose – to promote a sound political debate on domestic and international affairs.

Trends in Swedish information spheres – a retrospective overview

From time to time, a dominant political climate and consensus is challenged. During the prosperous 1950s, Sweden formed a self-image of the “golden middle way” between capitalism and socialism. Many aspects of this self-image were indeed partly myths. A Swedish author, Göran Palm, happened to be one of the succinct observers to challenge our prejudiced visions. His books “An unjust reflection” and “Indoctrination in Sweden” reached a wide audience and forced many to reconsider our achievements as a welfare state. Gunnar Fredriksson, editor of a Social-Democratic newspaper, alerted readers to the intricacies of “the politicians’ language” as a means to distort realities or evoke positive or negative emotions.

These books from the late 1960s were milestones for heightening the public awareness of mass media manipulation. A similar trend and radical change of Sweden’s self-image is taking place today. Until recently, the predominant view has been that Sweden represents a successful experience in forming a multicultural society, despite a few obvious crisis phenomena.

However, an awareness concerning the stress on the social fabric has spread from outsiders in the political scene towards mainstream parties. One example can highlight how changes have occurred. In January 2017, the Swedish journalist Katerina Janouch was scolded for an interview on Czech television, in which she inter alia stated her own personal view of the many problems that Sweden definitely is confronted with. After a vivid debate with harsh arguments involving even high-ranking politicians over her apparently controversial statements, she wrote a diary-like book “The Image of Sweden”. On a micro level, this fascinating personal experience succinctly shows how the image of Sweden changed over the last year, what has been accepted and what is still hotly debated concerning economics, migration and social problems.

Picture 1. “Bilden av Sverige” Book Cover

Over a short period, new political trends appeared. The political agenda has changed; serious debates treat formerly taboo topics. This is essentially because objective challenges to the economic stability, social fabric and cohesion cannot be ignored.

Even more noteworthy is, that given the outcome of the US presidential election campaign and the Brexit plebiscite of 2016, in particular the alleged role of outsiders’, supposedly decisive, involvement in these political events, Sweden has revitalized its organs on countering foreign political propaganda, which had been inactive after the Cold War era. Leading newspapers jointly with radio and TV intend to cooperate in order to thwart any attempts in 2018 to covertly interfere or overtly influence the upcoming parliamentary elections in September. Alerts against supposed disinformation campaigns by Russian mass media were at the center-stage of an annual defense policy conference in Sälen. The previous attempts to describe and analyze the supposed Russian information war efforts towards Sweden as presented hitherto seem, in my view, to lack in source collection from Russian mass media and blogospheres. They merely illustrate rather than form a structured picture of the Russian information spheres as a multiform complex.

Contests between the information spheres in Russia and the West

Therefore, as the Swedish proverb goes, “let’s turn the keg” and try to see things in a new perspective, by turning our usual modes of thought and preconceptions upside-down. A broad awareness on state propaganda in Russia, in the past as well as at present, can deepen our understanding of ongoing information wars. How does a Russian student in political sciences become aware of the formations of their nation’s self-image, as well as of foreign propaganda against their country? How do Russian scholars analyze their recent conflicts with neighboring states? What can they tell us of the general awareness concerning information warfare in the Russian public?

Three Russian historians, Viktor Barabash, Gennadii Bordiugov and Elena Kotelenets, all active in AIRO-XXI about which you can read more of here, give a broader perspective on how state propaganda has changed since the early 20th century till our times. They illustrate how countries at war, starting during World War I, directed propaganda to mass armies with, in general, literate soldiers and by that tried to influence the enemy’s morale. They evaluate how effective various forms of propaganda were, given the new technologies radio and TV during the Second World War and the Cold War eras.

After several in-depth chapters on the technological changes in the information era, on the cyber technological advances that have radically transformed traditional espionage, they finally describe how the information wars were carried out in Russia’s conflicts since 2000 (South Ossetia in 2008, Ukraine during the “Orange Revolution” and “Euro-Maidan”). Particular emphasis is devoted to how the conflicting parties formed their propaganda to their own population, on the one hand, and versus the opposing state, on the other hand.

Picture 2. ”Gosudarstvennaia propaganda i informatsionnye voiny” Book Cover

It is striking that in contrast to the Russian textbook by Barabash, Bordiugov and Kotelenets, very few analysts in Sweden have managed to present the contemporary information wars as a two-sided conflict; with two sides mutually intertwined in their mass media and social media strivings. Instead, information warfare is described as originating solely from more or less sophisticated “troll factories” in various locations in Russia. A couple of obviously forged “documents” ascribed to Swedish political leaders are sometimes referred to, although their actual effects have been nil.

In Sweden, as well as in the West in general, much has been stated on the real or imagined disinformation campaigns launched by Russia. Sometimes, they are said to direct public opinion in other states or even to influence the electorate (USA, United Kingdom). The role of relatively peripheral news agencies like RT (Russia Today) or Sputnik have seen their role amplified beyond reasonable belief. A further simplification is to reduce any Russian interpretation of events as a piece of falsification (fake news). Warnings of “Putin’s narrative” or “Russian Television fake stories” are common in mass media. In comparison, students of the Barabash textbook must undertake textual analyses of conflicting Russian and foreign opinions.

If one does not know history, you are likely to repeat its mistakes – so goes the proverb. Just as likely is the case where one repeat past generations’ mistakes because you are leaning on the mythology surrounding many events in your country’s past.

Minister of Culture Vladimir Medinskii has carried out a broad research project on the shifting images of Russia in the West, from eldest time when written sources by travelers are available. Although other historians criticized his original thesis on this subject for certain methodological flaws, there is no doubt that Medinskii accomplished a great feat as a popularizer of intricate phases in Russia’s history.

One book concerns the new historiography of the 1939–45 war on the Eastern Front. Since the late 1980s, many formerly taboo topics concerning the war were studied based on formerly secret archives as well as on interviews with veterans. In his book on the Great Patriotic War, Medinskii carefully unravels old myths and rejects new simplifications or distortions of battle histories.

Picture 3. “Mify o Rossii” Book Cover

Every historical nation tends to develop its own historiographical paradigm, which might be more or less objective and in conformity with general interpretations in other nations. However, just as often one nation’s image of their neighbors, former enemies or partners may differ substantially; thus are created the stereotypes of “the others”. In his grand comparative survey of Russia from the 12th century to the present, Medinskii provides the engaged reader with a plethora of examples of distortions of Russia’s history, created not only by foreign observers but also by ideologically motivated compatriots. Many legends on “eternal traits” in Russia are challenged. A Western reader of Medinskii’s book is bound to reflect on the various measures by which his or her country is evaluated in comparison with Russia.

In conclusion, the information contests or wars are only one element in the wider concept of cyber and hybrid wars. Observing our Swedish debate on the nefarious effects of alleged Russian disinformation, the absence of self-awareness is remarkable on how our own image of Russia (in our mass media and in the public opinion) is in itself the unconscious product of a pre-war attitude (sometimes alluded to as our age-long Russia-fear /Rysskräck/).

On the contrary, the legacy of the Soviet epoch has apparently raised the cultural curiosity among the Russian public. Mass media and publishing companies created a multidimensional panorama of their country’s past. The concerned Russian readers seem fairly well aware of politicization of historical issues and international affairs. Not for nothing do they often get substantial “food for thought” from the foreign news media translations, provided online by the InoSmi.ru site; a translation bureau, which took over the task of the Soviet-era magazine “Za Rubezhom”, and which lends its commentary fields open for anyone to comment. Even a cursory survey of commentary fields reveals their spontaneous character, rather than something created by Kremlin’s purported “troll armies”.

It goes without saying that a general and highly sophisticated awareness of overt or covert forms of meddling by a foreign state in the political process of any country must be welcomed and promoted. However, it is an open question how successful certain organized counter-disinformation strategies will be, e.g. EU’s site EUvsDisinfo.eu, NATO’s East StratCom Task Force or the Swedish joint public radio and TV with leading newspapers to “combat fake news”. Leaving much broader fields in the information sphere for freer opinion making in mainstream media as well as in the blog sphere might prove to be a sounder path towards dialogues, debates and mutual understanding.

References

  • Barabash, V. & G. Bordiugov & E, Kotelenets, Gosudarstvennaia propaganda i informatsionnye voiny (2015),  AIRO-XXI
  • Fredriksson, G., Det politiska språket (1966 and later editions), Tiden.
  • Janouch, K., Bilden av Sverige (2017), Palm Publishing.
  • Palm, G., En orättvis betraktelse, (1966) and Indoktrineringen i Sverige (1968), PAN/Norstedts
  • Medinskii, V., Voina: Mify SSSR, 1939 – 1945 (2011) and Mify o Rossii (2015), Abris/OLMA

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.

Women Entrepreneurs in Belarus: Characteristics, Barriers and Drivers

Posted on May 13, 2018 | Policy Brief

20180513 Women Entrepreneurs Image 01

This policy brief summarizes the results of the research on aspects of female entrepreneurship in Belarus. The aim of this work was to shed a light on what the features of female-owned business in Belarus are and whether there are any differences in the motives and barriers it faces compared with male-owned companies. Results show that female-owned companies are smaller in size, less likely to grow fast and less effective in the monetization and promotion of their innovative products and ideas. This is partly due to differences in social roles, motives, decision-making process and macroeconomic factors.

Women’s entrepreneurship is not just a question of gender equality but one of the sources for the sustainable economic development of the country. The presence of women among decision makers is beneficial for companies’ performance, effectiveness and innovativeness, and impacts the growth of profitability of the company (Akulava, 2016; Noland et al., 2016).

Little is known about the state of women’s engagement in economic governance in Belarus. According to the 5th wave of the BEEPS survey conducted by the World Bank, female top managers operate in around 32.7% of Belarus’ firms and 43.6% of firms have women among their owners (The World Bank, 2013). At the same time EBRD research shows that, on average, for every 10 men taking loans for the development of their own enterprise, only one woman did. Furthermore, the probability of loan rejection is 55% higher for women than for men in Belarus (these average numbers were presented by EBRD representatives during the conference “Business Territory: Women’s View”, Minsk, 2017). Unfortunately there is no information on the size and purpose of the loans, but potentially this may be a sign of discrimination and constraints on women’s economic activity.

We tried to expand the understanding of the role of women in Belarus’ private sector and to uncover individual, social, economic and cultural barriers that affect economic behavior and career choices of women, as well as introduce new drivers for female entrepreneurship in Belarus.

For this purpose we conducted interviews in 3 focus groups with the involvement of women entrepreneurs and also ran a survey that covered 407 owners and top decision-makers in the small and medium enterprises (SMEs).

The data analysis showed that around 30% of businesses belong to women (Table 1). Women tend to choose to operate in wholesale/retail trade, manufacturing, and medical/social services. Trade is the most popular with 28.9% of female-owned companies being part of this industry, while manufacturing stays second (10.1%). Trade also attracts the largest share of the male-owned companies (29.6%), next go manufacturing (23.9%) and construction (18.9%).

Table 1. Sectoral distribution by gender of the owner

 

Female-owned Male-owned
Share in total sample (%) 30.3 69.7
Sectoral distribution
Trade 29.0 29.6
Manufacturing 10.1 23.9
Construction 7.3 18.9
Medical and social services 8.7 1.3
Hotel and catering 8.7 2.5
Transport 7.3 10.1
Other 29.0 13.8

Innovative behavior changes slightly depending on the gender of the owner (33.3% of female- and 38.9% of male-owned companies have implemented innovations during the last 3 years). The measure of implemented innovative activities includes information on whether the company introduced any radical or incremental innovation (product/service/novelty in business processes/new strategy) during the last three years.An average female-owned firm grows much slower than male-owned business (Table 2). The annual sales gain and the sales gain over the last 3 years are 4 times and 2 times smaller respectively. The average number of employees is also smaller among female-owned companies (10 vs. 17 employees). On average, the owner of the male-owned firm has almost 15 years of relevant working and 13 years of managing experience. Similar characteristics for female owners are 12.8 and 9.7 respectively.

However, the realization of the implemented innovations as well as their relevance look more successful among the male-owned businesses. According to the answers in the survey, the profit share due to implemented innovations equals 28.8% among male-owned businesses and just 16.4% among female-owned. Thus, the major part of return is generated by the established business model and not the novelty.

Table 2. Business characteristics by gender of the owner

Female-owned Male-owned
Sales growth 1yr (%) 7.6 27.1
Sales growth 3yr  (%) 18.4 36.1
Size of the company (employees) 10.6 17.3
Age of the company (years) 8.8 10.2
Relevant experience of the owner (years) 13 14.7
Managing experience of the owner  (years) 9.7 12.8
Owners with a higher education (%) 91.3 86.2
Implemented innovation  (%) 33.3 38.9
Profit share of implemented innovations  (%) 16.4 28.8

 

One of the potential reasons for differences in characteristics and performance indicators between genders is self-selection, meaning that women are choosing less productive sectors in order to have more flexibility in balancing various social roles they play. In order to check for this, we compare the characteristics mentioned above in three different sectors (manufacturing, wholesale/retail trade and medical/social services) (Table 2a). The male-owned companies form the majority in the manufacturing sector, while medical/social services industry is mostly presented by female-owned business. Finally, the wholesale/retail trade sector is located somewhere in between and is well presented by both female- and male-companies.

Table 2a. Business characteristics by gender of the owner in manufacturing, wholesale/retail  trade and medical/social services

Wholesale/Retail Trade Manufacturing Medical and social services
Female-owned Male-owned Female-owned Male-owned Female-owned Male-owned
Sales growth 1yr (%) 9.8 31 2 26.2 10 n/a
Sales growth 3yr  (%) 16.4 37.9 5.6 42.3 17.5 n/a
Size of the company (employees) 5.9 14 23.7 19.8 13 8.5
Age of the company (years) 8.8 7.8 16.1 9.2 12.6 16
Relevant experience of the owner (years) 13 13.8 15.3 14.8 15.2 16
Managing experience of the owner  (years) 9.8 11.2 12.3 13.3 10.3 22
Owners with a higher education (%) 85 83 100 89.5 100 50
Implemented innovation  (%) 35 34.1 57.1 57.9 16.7 50
Profit share of implemented innovations  (%) 2.5 25 30 34.1 n/a n/a

There are differences in size and age of the businesses subject to the industry of the businesses. However, controlling for industry does not reveal any significant changes in the picture in terms of companies’ performance and effectiveness. Male-owned firms are still growing faster and are more successful in promoting implemented innovations Thus, this is likely not an issue of self-selection but of the way male and female owners operate their businesses.

The analysis revealed a number of internal and external barriers creating obstacles for doing business that breaks down into the following categories: social roles, educational patterns, decision-making process and general macroeconomic factors.

Women’s social roles in Belarus

Women in Belarus are mainly at the wheel of domestic responsibilities, which are rarely shared with male partners. According to the survey results, 40% of female and just 9% of male entrepreneurs are responsible for at least 75% of family duties (Table 3). 37% of female and only 0.74% of male owners said that they are in charge for taking care of kids. The same is true for the responsibility to stay at home when kids are sick (32.6% vs. 1.28).

Table 3. Distribution of domestic responsibilities by gender of the owner

Women Men
Family duties
less than 25% 10.91 37.5
around 50% 49.10 53.5
more than 75% 40.00 9.00
Kids
taking care of kids 36.96 0.74
staying at home, when kids are sick 32.61 1.48

At the same time, participants of the focus groups admitted that particularly childbirth motivated them to start their own business with flexible working hours and the possibility to work from home, which is generally not possible in corporate business in Belarus. Thus balancing between family and business becomes challenging, impacting career decisions. That motive also appeared in the survey where on average 13% of female and 2.5% of male owners started businesses in order to combine work with parenting. This trend does not change much if we control for industry.

Education

There is no significant gender difference in the educational level of business owners. According to the survey data, 91.3% of female and 86.2% of male owners have a university degree or higher. However, the established social role models of Belarusian women influence both their career and educational choices. Usually girls tend to choose education in arts and humanities, law or economics, rarely going to technical universities. Lack of technical background further prevents their access into hi-tech profitable industries.

Business and economic environment

During the interviews, women stated that “Both men and women businesses face generally the same obstacles in starting up, operational management and strategic development. But in an unfriendly environment – mostly men survive”. Similar messages were obtained from the survey, with almost no significant difference in the estimation of barriers was revealed. The main external barriers mentioned were government control (32.2% of female and 29.3% of male owners), administrative burden (44.1% vs. 41.1%) and tax system (33.5% and 30.5%) (Table 4). Almost all barriers were equally mentioned by the respondents except for corruption. Corruption is the only obstacle that differs between men and women, pointed out by 50% of women, while just 12% of men considered it a problem. We interpret it as women being more risk-averse and less likely do bold and dangerous actions in business like bribing. That corresponds to the literature, which finds women more risk-averse than men (Castillo and Freer, 2018; Croson and Gneezy, 2009).

Table 4. Main obstacles and motives for doing business by gender of the owner

Women Men
Main barriers
Government control 32.2 29.3
Administrative burden and legal system 44.1 41.1
Tax system 33.5 30.5
Corruption 49.7 11.8
Human capital 16.1 17.1
Unfair competition 28.5 26.9
Motivation to start-up business
Sudden business opportunity 47.8 42.8
Willingness to earn more 29 34.6
No chance to continue the previous activity 14.5 13.2
Improvement of state’s attitude to entrepreneurs 13 13.2
Possibility to combine work and parenting 13 2.5

Conclusion

The statistical evidence showed that female-owned businesses are smaller in size and grow more slowly compared with male-owned competitors. There are no signs of gender differences in entrepreneurial innovativeness. However, the monetization of implemented innovations is more successful among male-owned companies.

Altogether, the barriers of female entrepreneurship in Belarus are associated with the huge burden of household duties and childcare; hindered access to technical and business education; lack of managerial experience and industry knowledge. The existing exogenous barriers, excessive control, contradictory regulations and unfriendly entrepreneurial ecosystems are seen as additional constraints and contribute to the quality and dynamics of female business.

The obtained results confirm the necessity for adding a gender perspective to SME’s policy support in Belarus as well as for taking it into account when estimating the potential effects of business support programs and policies.

Further research of women entrepreneurship, collection of reliable statistics, comparison of the results with other transition countries are vital. These will give an encouragement to new gender specific initiatives and will contribute to economic growth and innovative perspectives of Belarus.

References

  • Akulava, M. (2016a). Gender and Innovativeness of the Enterprise: the Case of Transition Countries. Working Paper No. 31.
  • Castillo, M. and M. Freer. (2018). Revealed differences. Journal of Economic Behavior & Organization, 145: 202-217.
  • Croson, R. and U. Gneezy. (2009). Gender Differences in Preferences. Journal of Economic Literature, 47(2): 448-474.
  • Noland, M., Moran, T. and B. R. Kotschwar. (2016). Is gender diversity profitable? Evidence from a global survey. Peterson Institute for International Economics. Working Paper No. 16-3.

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.

Career Women and the Family – A New Perspective on the Role of Minimum Wage

Posted on April 29, 2018 | Policy Brief

20180430 Career Women and the Family Image 02

This brief finds that whereas in the 1980s richer women had fewer children than women near the middle of income distribution in the US, it is no longer true today. It argues that the rise in inequality is the main driver for this change. Greater income inequality enables high-income families to outsource household production to lower-income people. Changes to minimum wage laws are thus likely to affect the fertility and career decisions of the rich.

“I have frequently been questioned, especially by women, of how I could reconcile family life with a scientific career. Well, it has not been easy.”

 – Marie Curie, 1867-1934

Much has been made of women “leaning in” at work at a cost to their families. Indeed, this discussion has become more prevalent as women have surpassed men in higher education in most developed countries, and have entered prestigious careers en masse, a fact reinforced by public policy. For example, in 2012 the European Commission published a special report on women in decision-making positions, suggesting legislation to achieve balanced representation of women and men on company boards. One natural question to ask is, how high is the cost of a woman’s career to her family? This is a difficult, multifaceted, and even sexist question to ask.

High-income women have historically had fewer kids (Figure 1 for the year 1980). Social scientists’ leading explanations rely on the difficulty of combining children and a career. Under this view of the world, as more women focus on their careers, they have fewer children. On the other hand, the evidence shows that more educated (or wealthier) women produce more educated children. Given these two regularities, the majority of children are born to poorer mothers, and thus receive an inferior education. Moreover, this creates a feedback loop that depresses the average education through time making us question our ability to sustain a satisfactory average level of education.

Figure 1. Fertility rates by income deciles, 1980 and 2010

 

Notes: Calculated using Census and American Community Survey Data. The sample is restricted to white, non-Hispanic married women. Fertility rates are hybrid fertility rates, constructed by age-specific deciles. Deciles are constructed using total household income.

However, the negative relationship between family income and fertility ceases to hold after the 2000s. Figure 1 shows that for the year 2010, the cross-sectional relationship between income and fertility has flattened or even become a U-shape. Today, high-income women have higher fertility rates than those of women near the middle of income distribution. This is a result of a substantial increase in fertility among women in the 9th and 10th decile of family income: they increased their fertility by 0.66 & 0.84 children, respectively. The rise in fertility of high-skilled females was first documented in Hazan and Zoabi (2015), discussed in a previous FREE Policy Brief. The implications are profound; children are more likely to be born to wealthier or more educated mothers than in the past. This has a far-reaching impact on the future composition of the population.

How can we understand the change in fertility patterns over time? We argue that rising wage inequality played an important role. Data for the years 1980 and 2010 show that average real hourly wages, quoted in 2010 $ grew from $28 ($51) to $50 ($64) for women (men) in the 10th decile of the income distribution. This increase was accompanied by stagnant wages for women (men) in the 1st decile, precisely the people who are most likely to provide services that substitute for household chores (Figure 2).  Thus, growing wage inequality over the past three decades created both a group of women who can afford to buy services that help them raise their children, and a group who is willing to supply these services cheaply. In a recent paper, we found that the increase in wage inequality from 1980 and 2010 can actually explain the rise in high income fertility (Bar et al. 2017). Moreover, this rise in inequality has resulted in a large increase in college attendance through the changing patterns of fertility. This is because more children are now born to highly educated mothers.

Figure 2. Wives’ Wage by Income Decile 1980 & 2010

 

Notes: Calculated using Census and American Community Survey Data. The sample is restricted to white, non-Hispanic married men. Deciles are constructed age-by-age, using total household income. Representative wages for each decile is the average of these decile-specific wages from ages 25 to 50.

Our new understanding of the interrelation between income inequality, the relative cost of home production substitutes, fertility pattern and educational choice induces us to rethink some typical economic debates. For instance, consider the minimum wage. The typical debate about the minimum wage is focused on how it affects lower wage individuals in terms of income and their ability to find work. However, if people who earn the minimum wage are disproportionately also those who help raise wealthier families’ children, or simply make running a household easier, then a higher minimum wage can make home production substitutes more expensive for high wage women, making it harder for them to afford both a family and a career. While indirect, this effect can be significant. Figure 3 shows the distribution of the real wage, relative to the minimum wage, both for the industries of the economy associated with home production substitutes and other sectors of the economy. The figure clearly shows that workers in industries associated with home production substitutes are concentrated around the minimum wage and thus are much more likely to earn wages that are close to the minimum wage.

Figure 3. The distribution of real wages, relative to the effective real minimum wage in each state and year, by sector of the economy

Notes: Data from Current Population Survey, 1980–2010, using all workers.

Interestingly, we calculate a change in the cost of home production substitutes following an increase of the Federal minimum wage from $7.25 to $15/hour, as suggested by Bernie Sanders during the 2016 presidential election. It turns out that this increase in the minimum wage would increase the cost of market services that substitute for household chores by about 21.1%. Indeed, the minimum wage has a strong impact on the average wages of workers producing home production substitutes. However, how does this increase affect the economy?

According to our theory, higher costs of home production substitutes would affect women’s choice of how to allocate their time between labor force participation and home production, including raising children. The higher cost of these substitutes induces women to buy less of them and spend more of their time producing home production goods. Indeed, we find that the increase in the minimum wage decreases fertility and increases mothers’ time at home, and more so for higher income households. The magnitudes are large. A 10th (5th) decile household decreases fertility by 12.8% (9.4%), while the mother spends 9.7% (2.5%) more time at home. Notice that these numbers are calculated under the assumption that women can adjust fertility. What about those who are “locked in” their fertility choice? We recalculate changes in mother’s time at home for these mothers using the model’s fertility in 2010 with the increased cost of market services that substitute for household chores. A 10th decile mother increases time at home by 25.9%, while a 5th decile mother increases it by 13.1%. These numbers are larger as the family has not had a chance to scale back fertility. The short run effect on labor supply is also very large. The average reduction in labor supply by women in the 9th and 10th deciles is 3.5%.

Whether an increase in the minimum wage is good or bad for the society is a big question.  Not only does it lie beyond the scope of our theory, but also beyond the scope of social sciences.  However, the one modest contribution we try to make is in observing that an increase in the minimum wage heightens the rivalry between a woman’s career and family. As such, it forces women to forgo one in order to opt for the other.

The sexist nature of our question lay in the implicit assumption that it is the mother’s responsibility to look after the children or home production in general, rather than the father’s. While once this was a nearly universal attitude, it is now increasingly common for fathers to take a more central role in childcare rather than leave everything to the mother. How does this change in gender roles affect our analysis? In modern times, both spouses’ careers are potentially affected by children, as both parents take a role in child care. Fathers are now facing the same tradeoffs as mothers did in the traditional gender role story: children vs. careers. As a result, marketization is more important than ever for career oriented parents.

Talk to a high wage family and no doubt that they’ll readily tell you how important their ability to purchase daycare, prepared food, or other help at home is to their success as parents. Perhaps parents don’t realize that the price of these goods are so intricately linked to inequality or the minimum wage, but the policy maker should bear in mind that these are key factors for career women and the family.

References

  • Hazan and Zoabi (2015), “Do Highly Educated Women Have Smaller Families” The Economic Journal
  • Bar, Hazan, Leukhina, Weiss, and Zoabi (In progress) “Is the Market Pronatalist? Inequality, Differential Fertility, and Growth Revisited”