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How Are Gender-role Attitudes and Attitudes Toward Work Formed? Lesson from the Rise and Fall of the Iron Curtain

Posted on January 21, 2019 | Policy Brief

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Gender differences in attitudes toward work and gender-role attitudes are important determinants of gender inequality in the labor market. In this brief we show that these attitudes vary considerably across countries and can also change within the same country over a relatively short time period. We then present evidence that politico-economic regimes that make substantial effort to bring women into the labor market can shape these attitudes: gender differences in attitudes toward work decrease, and gender-role attitudes become less traditional. Cultural norms with long historical roots are not necessarily invariant to large shocks, and policies aimed at raising women’s presence in the labor market can activate virtuous cycles of increasing female employment. 

Gender inequality and cultural attitudes

Levels of gender inequality in the labor market differ considerably worldwide, even among countries at similar levels of economic development. Policies, technology, and economic conditions have long been shown to play an important role in explaining cross-country and regional differences in gender inequality. More recently, researchers have emphasized the role of cultural attitudes, such as women’s attitudes toward work and gender role attitudes (i.e. the beliefs that individuals hold regarding the appropriate roles of men and women in societies). Fortin (2008), for instance, finds that gender differences in attitudes towards work account for part of the existing gender wage gap in the US.  Further, Fernández et al. (2004) show that differences in gender-role attitudes partly explain existing variation in female labor force participation. Given that gender differences in attitudes toward work and gender-role attitudes contribute to explain gender inequality in the labor market, economists have recently started studying the origins of these attitudes and their sources of variation over time.

In this policy brief we first document variation across space and over time in gender differences in attitudes toward work and gender-role attitudes; then, we present evidence that politico-economic regimes that put emphasis on women’s inclusion in the labor market can shape these attitudes.

Gender-role attitudes and attitudes toward work across space and over time

The World Values Survey (Inglehart et al., 2014) asks questions, among others, about the importance of work in one’s life, and about one’s beliefs on the appropriate roles for women and men in society.

Based on these questions, we measure gender differences in the importance given to work, and levels of agreement with statements regarding gender roles.  Below we show that such measures vary considerably among a sample of countries in Europe and Central-Asia, as well as within countries over time.

Figure 1 shows gender differences in the percentage of survey respondents who reported that work was very important or rather important to them in the survey wave of 1995-1998. There is substantial cross-country variation in whether men or women give more importance to work, and in the magnitude of the gender difference. Moreover, the underlying variation across women is larger than across men (data not shown): the minimum and maximum values among men are 84% (in Georgia) and 97.5% (in Bosnia), whereas the respective values for women are 77% (in Georgia) and 96.6% (in Macedonia).

Figure 1. Gender differences in attitudes toward work

Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: Please say, for each of the following, how important is work in your life, and the options given are Very important, Rather important, Not very important, Not at all important. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.

Figures 2 and 3 show variation across countries in gender role attitudes. The share of respondents who agree with the statement “A working mother can establish just as warm and secure a relationship with her children as a mother who does not work “varies from a minimum of 47% in Poland to a maximum of 93% in Finland. The share of respondents who agree with the statement “Both the husband and wife should contribute to household income” varies from a minimum of 78% in Armenia and Finland to a maximum of 98% in Albania.

Figure 2. Working mother: warm relationship with her children.

Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: People talk about the changing roles of men and women today. For each of the following statements I read out, can you tell me how much you agree with each?. Do you agree strongly, agree, disagree, or disagree strongly? A working mother can establish just as warm and secure a relationship with her children as a mother who does not work. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.

Figure 3. Husband and wife should both contribute to income.

Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: People talk about the changing roles of men and women today. For each of the following statements I read out, can you tell me how much you agree with each. Do you agree strongly, agree, disagree, or disagree strongly? Both the husband and wife should contribute to household income. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.

A recent strand of the economics literature analyzes the long-term determinants of attitudes and finds that they have very deep historical roots (see Giuliano, 2018). However, attitudes also evolve over time. Figures 4 and 5 show that while in some countries attitudes remain rather stable after 1998, in other countries they change substantially. In Russia, for instance, the gender difference in attitudes toward work has doubled over a period of ten years, with men becoming from 5 to 10 percentage points more likely than women to report that work is important to them. Turning to gender-role attitudes, the percent of respondents who think that a working mother can have a warm relationship with her children has increased the most in countries as different as Macedonia and Spain. The percent of individuals who think that both husband and wife should contribute to income has increased relatively sharply in Moldova, while declining rather substantially in Montenegro and especially in Serbia.

Figure 4. Gender differences in attitudes toward work over time.

Source: See Note to Figure 1.

Figure 5. Gender role attitudes over time.

Source: See Notes to Figures 2 and 3.

The graphs thus suggest that the attitudes considered here vary not only cross-sectionally but can also change over a relatively short time period. A natural question to ask is then: what type of shocks cause a change in gender differences in attitudes toward work and in gender role attitudes?

The role of politico-economic regimes in shaping attitudes

In recent work (Campa and Serafinelli, 2018), we show that politico-economic regimes that focus on women’s inclusion in the labor market can reduce gender differences in attitudes toward work and make gender-role attitudes less traditional. Studying the question of whether politico-economic regimes can change attitudes is difficult, because countries or regions exposed to different regimes are likely very different along many other dimensions, including their history, which is known to shape attitudes. To circumvent this problem, we exploit the imposition of state-socialist regimes across Central and Eastern Europe and their efforts to promote women’s economic inclusion (see Campa and Serafinelli, 2018). First we focus on the socialist regime that emerged in East-Germany in 1949. This regime favored women’s access to tertiary education and to qualified employment through massive childcare provision and other policies that were popular throughout the entire Central and Eastern European region. Conversely, in West-Germany, women were encouraged to either stay home after they had children or take part-time jobs after extended breaks (Trappe, 1996; Shaffer, 1961). Since East and West-Germany before 1949 were part of the same country and as such had a common history and shared institutions, we can compare attitudes in East- and West-Germany after the separation to isolate the impact of different politico-economic regimes on attitudes. In other words, the underlying hypothesis is that attitudes toward work and gender role attitudes in East- and West-Germany were the same before the separation. Such a hypothesis is arguably valid especially because we compare only individuals who, during the separated years, lived relatively close to the East-West border (e.g. within 50 km from the border), and are, thus, expected to have close enough (geography, culture and social norm-driven) preferences and attitudes before the separation.

The results of the comparison can be summarized as follows: (a) due to exposure to a different politico-economic regime, East-German women participated more in the labor market and became more educated than their West-German counterparts; (b) the importance given to work by East-German women increased, which led to a lower gender gap in attitudes toward work with respect to West-Germany; (c) both women and men in East-Germany developed less traditional attitudes than West Germans regarding the relationship of working mothers with their children and the gender division of roles in the household.

In the second part of the paper, we also extend the analysis to a number of transition countries in the Central and Eastern European region. We show that in Central and Eastern Europe between 1945 and 1990 gender-role attitudes became less traditional than in Western Europe.

Conclusion

In this brief we have documented that gender differences in attitudes toward work and gender role attitudes vary substantially across space and can change over a relatively short time period. Since these attitudes affect the level of gender inequality in the labor market, understanding their determinants is important and policy-relevant. In recent work (Campa and Serafinelli, 2018), we exploit the imposition of state-socialist regimes in Central and Eastern Europe and show that individuals exposed to different regimes develop different attitudes toward work and different gender-role attitudes.

Such a finding suggests that policies aimed at increasing women’s participation in the labor market can activate virtuous cycles; namely, such policies might improve the cultural acceptance of female work, thus potentially further raising women’s labor force participation. The evidence from the Central and Eastern European region also suggests that history is not necessarily an excuse for inaction regarding women’s participation in the labor market. While deeply rooted cultural norms can be an obstacle to women’s economic empowerment, these norms are not necessarily absolutely time-invariant, and can respond to important economic and policy shocks.

A caveat to such conclusions is that the evidence presented here is specific to women’s attitudes toward work and attitudes regarding the acceptability of female work. Other attitudes and norms are also important in defining the level of gender equality in a society, such as those involving the division of roles in a couple when both couple members work outside of the home, the acceptability of violence against women, the suitability of women and men to different fields of education. Little is known about these attitudes and more research is needed to understand which policies, if any, can change them.

References

  • Campa, P. and M. Serafinelli (2018), Politico-economic regimes and attitudes: Female workers under state-socialism, Review of Economics and Statistics, Forthcoming
  • Fernández, R., A. Fogli and C. Olivetti (2004), Mothers and sons: Preference formation and female labor force dynamics, Quarterly Journal of Economics 119(4): 1249–1299.
  • Giuliano (2018). Gender: A Historical Perspective, in Oxford Handbook on the Economics of Women, ed. Susan L. Averett, Laura M. Argys, and Saul D. Hoffman, New York: Oxford University Press, forthcoming.
  • Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014. World Values Survey: Round Three – Country Pooled Datafile Version: www.worldvaluessurvey.org/WVSDocumentationWV3.jsp.
  • Shaffer, H (1981), “Women in the two Germanies: A comparison of a socialist and a non-socialist society.”
  • Trappe, H (1996), “Work and family in women’s lives in the German Democratic Republic”, Work and Occupations 23(4): 354–377.

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 in Economics: From Survival to Career Opportunities

Posted on January 14, 2019 | FROGEE PROJECT, Policy Brief

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Gender inequality goes beyond discrimination and sexism. It is also a matter of efficiency and development, and therefore, the socioeconomic losses that result from such inequality must be acknowledged and tackled. This policy brief summarizes the presentations held during the 6th SITE Academic Conference at the Stockholm School of Economics on December 17-18 2018. The event brought together scholars from around the world to examine existing forms of gender inequality, its causes, consequences, and policy interventions through a series of keynote speeches, research presentations and panel discussions.

Gender and survival

The reality of gender inequality is diverse throughout the world. The extent to which women and men face different opportunities and reach different outcomes vary substantially across countries and regions, and the forms of inequality that women face also vary geographically.

While richer countries have mostly closed their gender gaps in health and education, in other parts of the globe women are still struggling to survive, to make their marriage and reproductive choices freely, and to achieve the same educational opportunities as men. This is exactly where modern economic research can facilitate the understanding of the roots of such inequalities in each society, as well as the most likely drivers of change.

Corno, Hildebrandt and Voena (2017) show that in Sub-Saharan Africa and India, the age of marriage is a result of short-term changes in economic conditions (such as a reduction in crop yields due to droughts). Therefore, through for instance insurance mechanisms and temporary transfers, economic policy can influence marriage markets and the age of marriage. Relatedly, according to Ashraf, Bau, Nunn and Voena (2018), a girl in Indonesia or Zambia has a higher probability of being educated if she belongs to a group practicing bride price, defined as the “price” paid by a groom or his family to the bride’s family. This means that marriage markets could be a driver of educational investment. Cousin marriage is another issue within this context. Edlund (2018) suggests that this system serves as a barrier for economic growth by favoring men over women, the old over the young, and the collective over the individual. In general, challenging these marriage systems and improving female economic opportunities require a deeper understanding of the economic role of traditional cultural norms and institutions.

Some groups of women struggle for survival even in the so called “developed world”, being victims of gender violence. Sex workers in the United States are a particularly vulnerable population in this matter. Cunningham, DeAngelo and Tripp (2017) point out that, given that prostitution in most cities of the US isn’t only illegal, but also very dangerous (recording the highest homicide rate of any female occupation), it is critical to improve sex workers’ safety. Craigslist Erotic Services (CES) seemed to have contributed to it, by reducing female homicide rates by 17.4%. Apparently, this was a result of street prostitutes moving indoors and being able to filter clients to be safer. It is, therefore, suggested that the closure of such a platform put sex workers in an even more vulnerable position. Similarly, when it comes to adult entertainment establishments and its relation to sex crimes, Ciacci and Sviatschi (2018) argue that this type of businesses helps decrease daily sex crimes between 7-13% in the precinct where they are located.

When discussing approaches to prostitution, the “Nordic Model” has been highly praised and adopted by several countries. The term refers to a reform initiated in Sweden that considers buying sex a criminal offense, while decriminalizing those who are prostituted. However, preliminary results from Perrotta Berlin, Spagnolo, Immordino and Russo (2018) suggest that intimate partner violence and violence against women might have increased because of its enactment in Sweden.

Gender violence, however, isn’t only domestic or affecting sex workers. Borker (2018) claims that, in India, female college students are willing to choose less prestigious universities, to make additional expenses and to spend more time on transportation than their male counterparts only to avoid harassment on the street or public transportation. Street harassment, therefore, perpetuates gender inequality in both education and potentially the labor market.

Challenging social norms

As already seen, even the most gender-equal countries still suffer from persistent forms of inequality that need to be acknowledged and tackled. Doing so will result both in fairer societies and in more efficient economies, because it will make full use of both halves of the world’s skills and knowledge.

Friebel, Auriol and Wilhelm (2018) state that, in Europe, it is harder for women to make a career in economics. The representation of women in academics is low, and the higher ranked the university, the lower is the representation. This could be a consequence of several issues, one of them being the “glass ceiling”.

The glass ceiling, according to Bertrand (2017), is the phenomenon by which women remain underrepresented in high-level occupations, and earn less. Even in countries such as Denmark and Sweden, women still receive less for the same jobs. There are many potential explanations for this. One of them refers to the gender differences in psychological attributes in work, such as the idea of women performing worse under pressure or being unwilling to compete. This interpretation ultimately falls under the nature vs nurture discussion and only accounts for up to 10% of the pay gap. Another reason states that women suffer the penalties associated with demanding more flexibility. Such demand comes from the need to perform non-market work, like domestic work and, especially, caring for children. This means that women, especially the more educated ones, are paying a disproportionate price in the labor market for raising a couple’s children. Giving women more flexibility won’t crack the glass ceiling, au contraire, it will backfire because flexibility is negatively priced in the market. Besides, it doesn’t address the earning gaps. A more compelling proposal is to shift the focus from increasing flexibility to changing social norms and gender role attitudes. Normalizing and encouraging paternal child care in workplaces, for example, could be a way to do so.

Social norms based on traditional gender stereotypes also seem to be the reason why in Sweden, promotions to top jobs dramatically increase women’s probability of divorce but do not affect men’s marriages, as reported by Folke and Rickne (2018). In this case, promoting norms and policies with a more gender-equal approach to couple formation could increase the share of women in top jobs.

Given the importance of social norms, understanding how they can change is crucial. In Saudi Arabia, two studies were conducted on the influence of misperceived social norms. Both showed that the low-cost intervention of simply providing information could make a big difference. In one case, Bursztyn, González and Yanagizawa-Drott (2018) have evidenced that most young married men privately support female labor force participation (FLFP) outside of home. Nevertheless, they tend to underestimate the level of support for FLFP by other men. When correcting those misperceptions, the men’s willingness to let their wives join the labor force increases. Comparably, Ganguli and Zafar (2018) have shown that there is an increased likelihood of working full-time for female students when they, along with their close circles, receive information about the labor market and the aspirations of other women peers.

Challenging social norms isn’t only beneficial when discussing the glass ceiling and FLFP, it also has the potential to improve public health. In fact, Milazzo (2018) argues that women’s increased mortality rate in India can be an unintended consequence of son preference. Son preference induces women with a first-born daughter to adopt behaviors that increase the risk of maternal morbidity and mortality. Therefore, interventions to change deeply rooted social norms such as the boy preference could significantly reduce maternal mortality risk.

Bridging research and policy

In Malawi, research by Perrotta Berlin, Bonnier and Olofsgård (2017) on aid project location suggests that proximity to aid has a positive effect on the lives of women and children. Likewise, Goldstein (2018) reports that the World Bank’s Empowerment and Livelihoods for Adolescents (ELA) program in Uganda has also led to positive reproductive outcomes and income effects. These results illustrate the importance of reducing the divide between research and policy. Research has the potential of serving as an instrument for informed policy-making and aid intervention.

The Organization for Economic Cooperation and Development (OECD), for instance, applies research to create tools that help improve economic and social well-being. Two of those tools are the Social Institutions and Gender Index (SIGI) and the Development Assistance Committee (DAC) Gender Equality Policy Markers. On one hand, Missika (2018) explains that the SIGI is a cross-country measure of discriminatory social institutions against women and girls. Though the progress is slow (it might take around 200 years to close the gender gaps), its use gradually promotes the creation of locally designed solutions that, combined with adequate legislation, could enhance gender equality. On the other hand, Williams (2018) states that the DAC Gender Equality Policy Markers are meant to ensure that women have access to and benefit from finance.

Consistently , for the Swedish International Development Agency (SIDA), which works on behalf of the Swedish government, gender equality is a priority that permeates its interventions. In this context, the Feminist Foreign Policy has strengthened Sweden’s commitment in the topic.

Prior to finalizing the conference, representatives of the FROGEE Network (Forum for Research on Gender Economics in Eastern Europe and Emerging Economies) made a short presentation about the key challenges for achieving gender equality in their countries and the research opportunities available.

Conference material, including presentations, can be found here.

Speakers at the conference

Marianne Bertrand, University of Chicago

Alessandra Voena, University of Chicago

Alessandra González, University of Chicago

Anders Olofsgård, SITE

Annamaria Milazzo, World Bank

Bathylle Missika, OECD Development Centre

Eva Johansson, SIDA

Girija Borker, World Bank

Guido Friebel, Goethe University Frankfurt

Ina Ganguli, University of Massachusetts

Amherst Johanna Rickne, Stockholm University

Lena Edlund, Columbia University

Lisa Williams-Katz, OECD

Maria Perrotta Berlin, SITE

Markus Goldstein, World Bank

Michal Myck, CenEA

Riccardo Ciacci, The University Loyola Andalucía

Scott Cunningham, Baylor University

References

  • Ashraf, Nava; Natalie Bau, Nathan Nunn, and Alessandra Voena. 2018. “Bride Price and Female Education”. The National Bureau of Economic Research Working Paper No. 22417.
  • Bertrand, Marianne. 2017. “The Glass Ceiling”. Becker Friedman Institute for Research in Economics Working Paper No. 2018-38.
  • Borker, Girija. 2018. “Safety First: Perceived Risk of Street Harassment and Educational Choices of Women”. Job market paper.
  • Bursztyn, Leonardo; Alessandra González, and David Yanagizawa-Drott. 2018. “Misperceived Social Norms: Female Labor Force Participation in Saudi Arabia”.
  • Ciacci, Riccardo; and Maria Micaela Sviatschi. 2018. “The Effect of Adult Entertainment Establishments on Sex Crime: Evidence from New York City”.
  • Corno, Lucia; Nicole Hildebrandt, and Alessandra Voena. 2017. “Age of Marriage, Weather Shocks, and the Direction of Marriage Payments”. The National Bureau of Economic Research Working Paper No. 23604.
  • Cunningham, Scott; Gregory DeAngelo, and John Tripp. 2017. “Craigslist’s Effect on Violence Against Women”.
  • Edlund, Lena. 2018. “Cousin Marriage Is Not Choice: Muslim Marriage and Underdevelopment”. American Economic Association Papers and Proceedings, Volume 108, pages 353- 57.
  • Folke, Olle; and Johanna Rickne. 2018. “All the Single Ladies: Job Promotions and the Durability of Marriage”.
  • Friebel, Guido; Emmanuelle Auriol, and Sascha Wilhelm. 2018. “Women in Europen Economics”. [Mimeo]
  • Ganguli, Ina; and Basit Zafar. 2018. “Information and Social Norms: Experimental Evidence on Labor Market Aspirations of Saudi Women”. [Mimeo]
  • Goldstein, Markus. 2018. “Evidence on adolescent empowerment programs from four countries”. [Mimeo]
  • Milazzo, Annamaria. 2018. “Why are adult women missing? Son preference and maternal survival in India”. Journal of Development Economics, Volume 134, pages 467-484.
  • Missika, Bathylle. 2018. “Are laws and social norms still an obstacle to gender equality? Lessons from the SIGI 2019”. [Mimeo]
  • Perrotta Berlin, Maria; Evelina Bonnier, and Anders Olofsgård. 2017. “The donor footprint and gender gaps”. WIDER Working Paper 2017/130, United Nations University World Institute for Development Economics Research.
  • Perrotta Berlin, Maria; Giancarlo Spagnolo, Giovanni Immordino, and Francesco Flaviano Russo. 2018. “Prostitution and Violence: Empirical Evidence from Sweden”. [Mimeo]
  • Williams, Lisa E. 2018. “Financing for gender equality beyong ODA”. [Mimeo]

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.

US-China Trade War of 2018 and Its Consequences

Posted on December 17, 2018 | Policy Brief

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The trade war between the United States and China became one of the most significant events in the global economy in 2018. This policy brief explores the main drivers of the US-China Trade War, including trade imbalances and intellectual property concerns, and examines the potential consequences for both countries as well as the broader impact on other economies, such as Russia.

Chronology of the Trade War

Donald Trump started the war, raising import tariffs on solar panels in January 2018, of which the main supplier is China. In response, on April 2nd, China raised import duties on 128 commodities originating from the United States. On July 6th, the US increased tariffs on Chinese goods by 25 pp., imports worth $34 billion. China responded symmetrically. In August, the United States increased the tariffs on another $16 billion of imported goods from China, to which a symmetrical response again followed. In September, the United States again applied higher tariffs for $200 billion of Chinese exports, and China for $60 billion of US exports. At each stage of the conflict escalation, China appealed to the WTO with complaints about the actions of the United States, pointing to the inconsistency of their actions with the obligations and principles of the WTO. There were several meetings of official representatives from the United States and China – without any significant results.

What are the main reasons for this unprecedented escalation?

Imbalance and Intellectual Property

The economies of the US and China today are by far the largest in the world, and the trade turnover between the two countries is one of the most important. A remarkable feature of these trade flows over last decades is their imbalance. In 2017, the United States imported $526 billion worth of goods from China, while China’s imports from the United States amounted to $154 billion. Part of this imbalance is offset by trade in services, but it is not enough to even it out: in the same the year the United States delivered $57 billion worth of services to China while importing services of $17 billion from China.

Experts have different views on this imbalance. On the one hand, there is a perception that it is a source of world economy vulnerability, a source of potential crisis. Therefore, it is necessary to reduce the trade deficit. Another point of view is that this imbalance merely reflects the fact that the US economy and its assets are very attractive to investors from all over the world, including Chinese – and that, in turn, requires that the surplus of capital flows biased to US side, was compensated by the corresponding deficit of trade in goods and services. One such investor is the Chinese state itself, which for many years has been pursuing a policy of exchange rate undervaluation in order to promote foreign trade. It led to an enormous accumulation of foreign exchange reserves and as of January 2018, China held $1.17 trillion of US bonds and was the largest creditor of US government.

US President Donald Trump referred to this trade imbalance as one of the reasons for the outbreak of this trade war against China. Trump aims at reducing the deficit by $100 billion from the current $375 billion. The unilateral increase in import tariffs applied to Chinese goods was the first action of the US administration in this direction.

The second, no less important, formal reason for the trade war is the inadequate protection of intellectual property rights in China. China’s production of counterfeit products, the lack of adequate practices and laws to protect foreign technologies from illegal dissemination in the country, is not news to anyone. And although the almost two decades since China’s WTO accession have meant a largely modernized legal framework in this regard, a number of important provisions are still inconsistent with international practices, and the implementation of existing intellectual property rights leaves much to be desired. Established in 2012, The Commission on the Theft of American Intellectual Property identifies China as the most malicious violator of US rights. The exact damage is not known, but the commission assessment of the losses to the American economy due to the forced transfer of technology to Chinese partners – which is an unspoken condition of foreign manufacturers access to the Chinese market – industrial espionage, contradictions in legislation, requirements for the storage of sensitive data in China are in the range from $225 to $600 billion per year (Office of US Trade Representative, 2018).

While both the trade deficit and the intellectual property rights issue were recognized for many years, it was in 2018 that Trump started acting on them. Therefore, in order to discuss the potential impact of the conflict between the world’s largest economies on themselves and other economies, such as Russia, it is important to understand what drives the actions undertaken by Trump’s administration.

Populism

Trump won the elections in 2016 with a minimum margin against the Democratic rival. To provide support for his decisions and to increase the chances of being reelected for the next term in 2020, it is crucial to maximize the pool of his supporters. Trade policy measures aimed at import substitution are very effective populist policies in any country. One of the first steps made by the US toward trade war was the increase in import tariffs on steel and aluminum – for all countries. Metallurgy and coal industries are among the most organized and strong lobbyists in any country. The European Union as an economic organization started with the European Coal and Steel Association. By aligning interests with these sectors much can be achieved in relation to trade liberalization, and vice versa – by increasing the level of protectionism, a significant popularity increase can be among voters whose incomes depend on the success of companies in these industries.

Deterrence

China works hard raising the technological level of its economy. In recent years the Chinese government and Communist party launched a number of ambitious programs aimed at achieving a technological breakthrough, lessening the dependence on imported technologies by substituting them with ones produced by domestic innovation centers. These programs specify the priority sectors, in which state subsidies are provided for the acquisition of foreign technologies by Chinese companies and their adaptation. One of the common arguments was that the United States believes that powerful state support for technology sectors in China, along with the existing problems in protecting intellectual property rights, increases the risks and potential losses of American companies.

However, while these concerns seem reasonable at first, they should not be taken at the face value.

China’s ability to push out American companies in the high-tech sector on the world market seems rather limited. So far, China has only succeeded in increasing its share in the middle and low technology segments. Instead, in recent years, China is rapidly increasing its defense spending, which in 2017, for the first time, reached a level of 1 trillion yuan (about $150 billion). China’s defense spending is the second highest in the world after the United States. Moreover, it’s growing very fast. While in 2005 the Chinese nominal defense expenses were only 10% of American expenses, in 2018 they are already around 40%. The dominance of state enterprises in the defense industry in China implies that the real purchasing value of these expenditures is quite comparable. New and existing Chinese industrial policy programs target military and dual-use industries among others. Therefore whilst addressing the intellectual property rights problem in China now, Trump’s administration also aims at preserving US leadership position in the military sector, which finds widespread support in Trump’s main voter groups among Republicans.

Obsolete Weapon

Historically, trade wars implied tariff escalations to protect domestic industries from foreign competition. Today, the Trump administration behaves in a similar manner. However, the circumstances now are fundamentally different from those in the first half of 20th century and earlier. Firms not only trade in final goods, but more and more they trade in intermediate products and within firms themselves (Baldwin, 2012). The distribution of the production process to many companies across different countries of the world leads to two important effects, which were not observed in previous trade wars.

First, it is the effect of the escalation of tariff protection in the framework of the value chains. The import tariff is applied to the gross value of the product crossing the customs border. However, the exporting firm’s contribution to the gross value might be quite small. So the effective level of the tariff will be higher than the nominal level of the tariff, known as a so called amplification effect  (World Bank, 2017, page 98). It means that the effective growth of the tariff by 25 percentage points in relation to Chinese imports will significantly exceed 25 % and in some cases can even become prohibitive. So, the tariff warfare will result in significantly greater losses for the sectors involved in the value chains, compared to the sectors less exposed to them. It means that foreign investors and multinational companies in China will suffer bigger losses compared to purely domestic Chinese companies. The Peterson Institute for International Economics made an assessment and confirmed these observations (Lovely and Yang, 2018).

Second, China’s participation in international multinational companies most often occurs in the assembly segments, while developed countries’ companies contribute at other stages, such as with innovation, design, financial and consulting services, marketing, and after-sales services. Then, the protectionist measures against goods produced in China by multinational companies will hit an American economy, generating losses in the service segments. A similar episode happened, for example, in 2006, when the European Union introduced anti-dumping duties on imported footwear from China and Vietnam, which in turn lead to a decline in the services sector in Europe – imported footwear contained a significant share of the value added created by European designers and distributors (World Bank, 2017). Obviously, we will observe the same consequences in the United States now, since the role of the American services sector in creating and promoting Chinese goods on the American market is significant and according to World Bank estimates in 2011, the contribution of value added generated by foreign services in China’s gross exports amounted to about 15% (World Bank, 2017).

Thus, not only the economy of China, but also the US economy itself will suffer from the growth of import tariffs in the USA. The USA is not an exception here – the governments of most countries continue to live in the paradigm of trade policy, which suits the structure of the world trade as at the beginning of the 20th century, while trade has gone far ahead and requires much more elaborate effective regulatory tools than tariffs on imported goods.

Consequences for Russia

The consequences of the US trade war with China for the Russian economy depend on what the main goals of the war are. If the motive is primarily electoral – to secure enough support in 2020, one can expect that the protective measures will be short-lived, and the geographical distribution of investment flows will remain almost intact and that China will remain an important location for global value chains transactions.  The trade war will in this case lead to some economic slowdown in the short term. The main effects will be related to the redistribution of income within economies, where protected sectors will benefit on the expense of all other sectors. In these circumstances, Russia would suffer direct losses from the growth of tariffs on their exports to US (now it is predominantly steel and aluminum), but for the economy as a whole, the losses will not be significant, especially relative to the losses Russia bears because of sanctions.

However, if the main reason for the trade war has a long-term perspective, the investors will be forced to adjust the geography of their investment plans and China will face a significant outflow of foreign investments, which will significantly affect Chinese – and global – economic growth. In this case, both for Russia and for the whole world, the indirect effect of the US-Chinese trade conflict will be quite noticeable and it will take years to create new trade links and restore world trade and global value chains.

References

  • Baldwin, Richard, 2012. “Global supply chains: why they emerged, why they matter, and where they are going”, CTEI Working papers 2012-13, The Graduate Institute, Geneve
  • Lovely, Mary E., and Liang Yang, 2018. “Revised Tariffs Against China Hit Chinese Non-Supply Chains Even Harder.” PIIE Policy brief, Peterson Institute
  • Office of the US Trade Representative. March 22, 2018. “Executive office of the President findings of the investigation into China’s acts, policies, and practices related to technology transfer, intellectual property, and innovation under section 301 of the trade act of 1974.”  https://ustr.gov/sites/default/files/Section%20301%20FINAL.PDF
  • World Bank, 2017. “Measuring and analyzing the impact of GVCs on economic development”. World Bank, Washington DC.

Note

A longer version of this brief has been published in Russian by Republic: https://republic.ru/posts/92217

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.

Money Laundering: Regulatory or Political Capture?

Posted on December 10, 2018 | Policy Brief

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Danske Bank has recently been accused of having laundered more than 200 billion Euros through its Estonian branch. The size of the scandal has reinvigorated the discussion over lax enforcement by regulators and poor bank compliance with anti-money laundering laws. In this brief, we concisely review some recent cases of poor regulatory and political behaviour with respect to these matters, focusing in particular on the UK, whose financial system seems to have become a main hub for this type of financial misconduct.

A widespread phenomenon

The size of the recent money laundering scandal at Danske Bank, involving more than 200 billion Euros, has surprised many. Money laundering is a widespread issue in an increasingly complex world where financial transactions are many and instantaneous, while oversight slow and limited (Radu 2016). According to the United Nations Office on Drugs and Crime, an estimated $800 – $2 trillion is laundered every year (United Nations Office on Drugs and Crime). The source of laundered money is often from corruption, crime and drug cartels (as with the HSBC scandal, see below). Attempts to blow the whistle on these illegal transactions have gotten several people killed, especially in Russia (The Daily Beast, October 2018).

Malta’s Pilatus bank recently had its license revoked by the European Central Bank after its chairman was charged with money laundering (Reuters, October 2018). The investigative reporter Daphne Caruana Galizia was killed in a car bomb in October of 2017 in Malta (The Guardian, October 2017). She was leading the Panama Papers investigation into corruption in the country and had accused Pilatus bank of processing corrupt payments (The Guardian, November 2018). In Sweden, some banks have recently been criticized for insufficient actions against money laundering. Experts at the regulator recommended extensive sanctions, but upper management stopped them (Svenska Dagbladet, December 2018). In November, Deutsche Bank’s headquarters in Frankfurt were raided by prosecutors in a money laundering investigation (BBC, November 2018).

Back to Danske Bank. Its Estonian branch was recently accused of having laundered money, amounting to over 200 billion Euros of suspicious transfers (Financial Times, November 2018). In 2011 the Estonian branch accounted for 0.5% of Danske Bank’s assets, while generating 12% of its total profits before taxes. In 2013, 99% of the profits in the branch came from non-residents. Many of the non-resident customers are believed to be from Russia and other ex-soviet states (Forbes, September 2018). The alleged money laundering came to light due to the whistleblower Howard Wilkinson, who headed Danske Bank’s market trading unit in the Baltics from 2007 to 2014. Surprisingly, his anger over these transactions was not primarily aimed at top management in Copenhagen, or failure of rank and file employees to follow protocol in customer acquisition, but against the UK, who he claimed is “the worst of all” when it comes to combating money laundering (Financial Times, November 2018). In fact, the UK institutions seem to have been at the very heart of the scandal (ibid):

“Mr Wilkinson’s emails to Danske executives in 2013 and 2014 highlighted how UK entities were “the preferred vehicle for non-resident clients” at the heart of the scandal.”

In an address to European Union Lawmakers, he said (Reuters, November 2018):

“The role of the United Kingdom is an absolute disgrace. Limited liability partnerships and Scottish liability partnerships have been abused for absolutely years”.

Regulatory or political capture?

The increasingly central role that the UK appears to be playing as a hub for financial crime is perhaps not new or surprising. The UK has indeed come to be widely recognized as one – though certainly not the only – main hub for these illegal transactions (see e.g. Radu 2016, p.15). The UK’s National Crime Agency estimates 93 billion GBP of tainted money is flowing into Britain annually (Financial Times, September 2018).

And according to the classic theory of regulatory capture (Stigler, 1970), it is to be expected that a large, wealthy and highly concentrated sector such as the UK financial industry, will be able to capture regulatory institutions and lead them to act more in its favour than in that of the (national or international) community. However, besides being a concentrated source of special interests, the financial sector also represents a large share of the UK economy. It could be the case, therefore, that the capture goes all the way up to the political system and the government (as in Becker 1983, and Laffont, 1996). So, is it the alleged crime-friendly environment in the UK financial system linked more to problems of regulatory capture, or to deeper political capture?

Already in 2004 there were worrying signs of possibly deep political capture.  At the time, Paul Moore, a senior risk manager at Halifax Bank of Scotland (HBOS), raised concerns about the bank’s risk taking and was subsequently fired by the executive James Crosby. Crosby then proceeded to become Deputy Chairman at the Financial Services Authority (FSA). HBOS then collapsed during the financial crisis of 2008 and merged with Lloyds bank, leading to one of the most concentrated banking systems in the world (the top 5 banks have 85% of the UK banking market). Many took this to substantiate Moore’s claim that the bank had been taking excessive risks. During Prime Minister’s question time in the House of Commons, David Cameron commented on then Prime Minister Gordon Brown’s decision to appoint Crosby to the FSA:

“Sir James Crosby, the man who ran HBOS and whom the Prime Minister singled out to regulate our banks and to advise our Government, has resigned over allegations that he sacked the whistleblower who knew that his bank was taking unacceptable risks.” (cited in Dewing and Russell 2016, p.165)

A suggestive episode directly involving politicians and money laundering is the case of HSBC, with headquarters in London. HSBC avoided criminal prosecution in the US and entered into a deferred prosecution agreement with the DOJ in 2012 (Department of Justice, December 2012). HSBC was found to have violated U.S. Anti-Money Laundering and Sanctions Laws by laundering billions of dollars linked to Mexican drug cartels, groups in Iran and Syria, and groups linked to terrorism. While HSBC apparently had systems to flag suspicious transactions, employees were told to disregard red flags (Garrett 2014, p.201). The case led to a 2016 House Committee report entitled “too big to jail” that was extensively used against the Democrats by the Trump presidential campaign (Committee on Financial Services, 2016).

The report states that on the 10th of September 2012 UK Chancellor George Osborne (the UK’s chief financial minister) wrote a letter to Federal Reserve Chairman Ben Bernanke (with a copy transmitted to then Treasury Secretary Timothy Geithner). In the letter, Chancellor Osborne insinuated that the U.S. was unfairly targeting UK banks by seeking settlements that were higher than comparable settlements with U.S. banks. He also worried about what criminal sanctions against HSBC would imply for financial stability. Criminal charges could also lead to a revoked license, making the bank unable to do business in the US (Financial Times, July 2016). HSBC was eventually ordered to pay a 1.9 billion dollar fine, while another whistleblower claims that the money laundering still went on (Huffington Post, August 2013).

The FSA also appeared much more concerned about criminal sanctions against HSBC than with money laundering for the bloodiest drug cartel in history (estimated to be responsible for several tenths of thousands of murders). In fact, the house committee report states that “The FSA’s Involvement in the U.S. Government’s HSBC Investigations and Enforcement Actions Appears to Have Hampered the U.S. Government’s Investigations and Influenced DOJ’s Decision Not to Prosecute HSBC” (p.24).

Things have not improved more recently. In 2013 the FSA was split up into the Financial Conduct Authority and the Prudential Regulation Authority (FCA & PRA). In 2014 the FCA & PRA came out with a note requested by the British parliament on whether financial incentives for whistleblowers should be introduced in the UK. These financial incentives, or reward programs, are used extensively in the US in tax, procurement, and securities. The FCA & PRA came out strongly against rewards in their seven-page note, yet do not cite a single piece of evidence (PRA and FCA, 2014). Most importantly, the note contains important factual misstatements about available evidence on their effectiveness that were easy to check at the time of the report (Nyreröd & Spagnolo 2017, National Whistleblower Center 2018). Nor was the note amended when one of us repeatedly communicated the mistakes to the agencies. This suggests persistent and deep regulatory capture. Consistent with this interpretation is the sanctioning behavior of UK regulators.

A blatant recent example is the ridiculous fine against CEO of Barclays Bank Jes Staley. 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 FCA & PRA decided to just fine him £642 000 – a small fraction of his pay package that year (Reuters, May 2018). When Moore was asked about the fine he replied that “it is a very clear sign to whistleblowers not to bother” (Reuters, April 2018).

Conclusion

Is this regulatory capture, or political capture? The impressive list of consistent cases of regulatory slack and of political complacency suggests both, at least in the case of the UK. But the problem of regulatory capture in the case of financial crimes goes way beyond the somewhat extreme case of the UK. In all jurisdictions financial misbehavior has recently only led to settlements between regulators and the infringing financial institution, with settlement payments way too low to generate (financial stability concerns, and) deterrence effects. Banking regulators appear mainly concerned about banks’ health and profitability, so that large financial institutions have not only become too big to fail, but also too big to jail, and now even too big to fine, at least to the appropriate extent (Spagnolo 2015). All this even though the financial crime has been that actively supporting through money laundering criminal organizations that killed tenths of thousands of innocent people.

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.

Unemployment in Transition and Its Long-Term Consequences

Posted on December 3, 2018 | Policy Brief

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We examine the relationship between the experience of unemployment in the early years of the socio-economic transition in Poland and a number of wellbeing measures about two decades later. The analysis takes advantage of the rich content of data from the Survey of Health, Ageing and Retirement in Europe (SHARE) by matching retrospective information on labour market experiences with outcomes observed in the survey after year 2006. While there is a strong correlation between unemployment and general wellbeing measures such as life satisfaction, depression and subjective assessment of material conditions, the relationship cannot be interpreted as causal. On the other hand, we find that unemployment in the early years of the transition has strong, negative, long-term consequences for income and house ownership. The analysis sheds light on the implications of unemployment and on the nature of job losses in the follow-up of the Polish ‘shock-therapy’.

Introduction

Next year, the countries of Central and Eastern Europe will celebrate the 30th anniversary of the political breakthrough and the beginning of a major socio-economic transformation which followed. In the Polish case, the ‘shock therapy’ approach to the reform process implemented by the Mazowiecki government, though not without faults, has generally been viewed as the origin of the country’s economic success story. Afterwards, Poland experienced nearly three decades of uninterrupted economic growth and the Polish GDP returned to its pre-reform level already in 1995.

However, discussions of negative implications of the reform package still fuel the academic discourse as well as the political debate. While the majority of the population managed to avoid significant economic difficulties, many families experienced the painful hardship of the transition period in the form of job losses, poverty and exclusion. Given the scale of the socio-economic change, surprisingly little is known about the long-term consequences of individual experiences at that time. In particular, it is unclear if the negative outcomes observed many years after the reforms started can be causally linked to individual experiences in the early 1990s.

This lack of evidence is not unique for Poland and is largely due to unavailability of good individual-level data spanning the time before and after the collapse of communism. Since the transition cannot be lived through again, we shall never know how socio-economic conditions would have looked like under numerous alternative reform scenarios. However, as we show in a recent paper (Myck & Oczkowska, 2018), much can be learnt from the combination of contemporary and retrospective information on the nature of labour market histories during the transition and their relationship to outcomes recorded many years later.

The analysis presented in Myck and Oczkowska (2018) relies on the treatment of the systemic changes in the early 1990s as a major exogenous shock and on differentiating between reasons behind individual experiences of unemployment. We demonstrate that the observed strong correlation between unemployment in the initial years of the transition and a number of subjective wellbeing measures in later life is endogenous, and may reflect unobservable individual characteristics. It seems plausible to argue that these characteristics were the reasons behind the recorded job losses once the economy was liberalised and firms could fire their least productive employees.

Work histories in the SHARE dataset

The analysis is based on individual-level data from the Polish part of the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a multidisciplinary biennial panel survey focusing on individuals aged 50 years and over. Since the start of the project in year 2004 seven waves of data have been collected, and the survey was conducted in Poland in waves 2, 3, 4, 6 and 7. While the standard waves of the survey focus on contemporary conditions of respondents such as health, economic conditions, labour market activity and social networks, in wave 3 (the so-called SHARE-Life), participants were asked about their life histories including their family history, mobility and labour market experiences. The detailed labour market histories recorded in SHARE-Life allow us to identify transition-related job losses, which can be matched with current information on several measures of material conditions and wellbeing for the same individuals.

In Figure 1 we present labour market profiles since 1988 of those in the sample who were working prior to the start of the reform process.

Figure 1. Labour market status 1988 – 2008 conditional on working in 1988 in Poland

Source: Myck and Oczkowska, 2018.

The figure shows that along with rapidly increasing unemployment rates, the degree of inactivity of the Polish population grew substantially in the two decades following the transition. This data confirms that in the follow-up of the ‘shock-therapy’ reforms many individuals faced unemployment, while others, especially among older groups of employees, used several other labour market exit options, such as retirement or disability.

Analysing long-term consequences of economic shocks

To examine the role of unemployment experiences in the initial years of the transition for outcomes observed a few decades later, we use data from waves 2, 3 and 4 of the SHARE study. The analysis focuses on two groups of later-life outcomes – objective measures of material conditions such as household income, real assets and house ownership, and subjective indicators of wellbeing such as life satisfaction, depression or reporting difficulties in making ends meet.

We are able to control for an extensive set of individual characteristics which are usually unobservable to the researcher, through a complex set of background variables available in SHARE. These include respondents’ childhood conditions, parental background as well as health and labour market experience prior to 1988. With regard to the experience of unemployment we differentiate the instances of unemployment between the initial (1989-1991) and later (1992-1995) period of the transition to examine the potential differential implications of the rapid pace of the reforms in the early 1990s. Most importantly though, the data allows us to distinguish between different reasons behind job losses and we can separately examine the relationship with plant/office closures and other reasons for unemployment. Following other examples in the literature (Farber, 2011; Jacobson et al., 1993), we argue that plant closures can be treated as reasons for exogenous job separations. This in turn allows us on the one hand, to give a causal interpretation to the estimated coefficients, and on the other, to interpret those on other reasons for unemployment in the light of the causal relations.

Effects of unemployment experience on later-life outcomes

We find that experiencing unemployment due to plant/office closure between 1989 and 1991 is associated with almost a 30 percent lower level of household income and a lower probability of house ownership of about 10 percentage points (pp) some two decades afterwards. There is also a strong relationship between unemployment in the early years of the transition and wellbeing measures two decades later – individuals who experienced unemployment in the first three years of the transition have a 14 pp. higher likelihood of reporting great difficulties in making ends meet, a 10 pp. lower probability of  high life satisfaction and a 11 pp. higher likelihood of depression. However, since these relations do not hold for unemployment due to plant closures, they cannot be treated as causal. The results are therefore most likely driven by unobserved factors which simultaneously determine the lower level of outcomes two decades after the ‘shock-therapy’ reforms, and the likelihood of experiencing unemployment in the early 1990s.

Conclusion

In this policy brief we outline recent results on long-term implications of labour market developments in the early years of the economic transition in Poland. The analysis is based on a combination of contemporary and retrospective data from the SHARE survey, and focuses on the associations between the experience of unemployment in the initial years of the transition in Poland and a number of outcomes measured about two decades later. Using plant/office closures as exogenous sources of job separations during the early 1990s, we find a strong and statistically significant, negative, long-term effect on income and home ownership, which can be treated as causal.

We also find strong negative associations between unemployment for other reasons than plant / office closures and a number of subjective measures of wellbeing. This relationship however, does not hold for the exogenous reasons for job losses, which suggests an important role of unobservable factors that lead to unemployment and at the same time are responsible for the lower level of outcomes in later life. This is consistent with the labour market reality of central planning characterised by labour hoarding and maintaining employment regardless of workers’ productivity. When the economic reality changed in 1989, the least productive individuals were the first to be fired, and as our analysis shows, these are also the individuals with lower subjective levels of wellbeing two decades later. We confirm thus that those who lost their jobs in the early 1990s have lower measures of the subjective wellbeing outcomes, although the latter cannot be identified as specific consequences of unemployment in the first years of transition.

References

Acknowledgement

The authors gratefully acknowledge the support of the Polish National Science Centre through project no. 2015/17/B/HS4/01018. For the full list of acknowledgements see Myck and Oczkowska (2018).

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.

Managing Relational Contracts

Posted on November 26, 2018 | Policy Brief

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

Introduction

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

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

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

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

Kickbacks as an enforcing mechanism

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

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

More trust is not always better

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

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

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

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

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

Conclusion

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

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

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

References

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

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

Towards a More Circular Economy: A Progress Assessment of Belarus

Posted on November 19, 2018 | Policy Brief

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

Introduction

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

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

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

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

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

Tight spots of waste statistics in Belarus

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

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

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

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

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

Illustration of waste statistics problems

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

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

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

Actual picture of the circular economy development in Belarus

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion

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

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

References

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

How Should Policymakers Use Gender Equality Indexes?

Posted on November 13, 2018 | Policy Brief, Policy Papers

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

On Measuring Progress

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

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

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

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

Gender Equality Before 1990

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

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

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

The Different Indexes

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

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

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

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

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

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

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

Source: Own calculations based mainly on UNDP data.

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

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

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

Source: Own calculations based mainly on UNDP data.

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

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

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

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

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

Figure 5. Labor force participation, 1990-2015

Source: Own calculations based mainly on UNDP data.

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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

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

References

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

Footnotes

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

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

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

Posted on November 12, 2018 | Policy Brief

20181112 Gender Gaps in Transition Image 01

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

Indexes of gender inequality

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

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

The Gender Inequality Index

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

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

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

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

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

Nov122018_Figure1

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

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

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

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

Nov122018_Figure2

Source: Own calculations based mainly on UNDP data.

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

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

Conclusion

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

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

References

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

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

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

Posted on November 5, 2018 | Policy Brief

20181105 Resource Discoveries Image 01

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

Natural Resources, FDI Job Multiplier and Economic Development

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

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

Resource Discoveries and FDI Bonanzas

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

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

Figure 1. FDI Bonanza in Mozambique

Source: Author’s calculations using fDiMarkets data.

FDI Job Multiplier

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

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

Conclusion

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

References

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

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