The Choice of Women Between Employment and Homestay, and Their Happiness and Life Satisfaction: the Case of the South Caucasus

Home , Women in Armenia

The choice of women between employment and homestay, and their happiness and life satisfaction: the case of the South Caucasus

by Karine Torosyan1 Norberto Pignatti2 Nino Doghonadze3

This version: November 2016 (Preliminary and incomplete; please, do not cite)

Abstract: Modern time women are often facing an uneasy choice: to dedicate their time to reproductive household work, usually performed by females, or to join the labor force and spend time away from home and household duties. Both choices are associated with benefits, as well as non-trivial costs, and necessarily involve some trade-offs, influencing the general feeling of happiness and satisfaction women feel given their decision. The trade-offs are especially pronounced in traditional developing countries, where both the push for women to stay at home and the need to earn additional income are strong, making the choice even more controversial. To understand the implications of this choice on happiness and general life satisfaction of women in traditional developing countries we compare housewives and employed women of the South Caucasus region. The rich data collected annually by the Caucasus Research Resource Center for Armenia, Azerbaijan, and Georgia allow us estimating a probit model of choosing between work and home, based on which we match working women with their housewife counterparts and compare the level of happiness/satisfaction across the two groups. All three countries of interest are former republics of the USSR, are on the Europe-Asia divide, have strong traditional values, and are at comparable levels of development, which creates many commonalities between them. However, Azerbaijan is a Muslim country, Armenia follows Grigorian Apostolic Church, while Georgia belongs to Orthodox denomination, making the three countries diverge when it comes to cultural norms, especially those pertinent to the role of women in the society. These features make our comparison of the three countries even more interesting. An illuminating extension to this analysis is comparison of housewives with working women within Armenian and Azeri minorities living in Georgia, which will allow us separating the country effect from that of the ethnicity.

Key words: Female employment, reproductive housework, life satisfaction and happiness, propensity score matching.

1 International School of Economics in Tbilisi, e-mail: [email protected] (corresponding author) 2 International School of Economics in Tbilisi, e-mail: [email protected] 3 International School of Economics – Policy Institute, e-mail: [email protected] 1

Introduction and motivation

Happiness economics is a relatively new field, which aims to complement income-based measures of welfare, widespread in the economic literature, with - self-reported - levels of happiness, measured on the ordinal scale. While these measures are undoubtedly subjective, self-reported happiness has proved to be a good proxy for “objective” happiness. Psychologists have showed that this measure of happiness is associated with consistent patterns of pre-frontal cortex activity, heart rate, digestive disorders, headaches, etc. (Graham, 2005; Brodeur, Connolly, 2012).

Happiness is definitely an important measure of welfare and its pursuit is one of the strongest driving forces behind all human activities. Happiness is not only a measure of psychological well being. It is also strongly associated with physical health. In general, good health is associated with higher happiness but the causality between the two is not unidirectional, it runs to both directions. Anyway, it is usually the case that healthy communities are also happy communities (Ross, 2005).

The literature has identified a number of important determinants of happiness. Some of the determinants have lasting rather than transient effects4. One of the best known and the most interesting for economists is association between happiness and income. The so-called “Easterlin paradox” (Easterlin, 1974) implies that, while happiness increases with income within countries, richer countries are not necessarily happier. This means that a generalized increase in income within a country does not imply an overall increase in happiness, especially beyond a certain point, which might depend from a number of factors, including cultural traits (Graham, 2005).

Gender, education and age all seem to affect happiness. Most studies find that women tend to be happier than men (Subramanian et al, 2004; Gerdtha,, Johannesson, 1997). Education seems to positively influence the self-rated happiness, while urbanization adversely affects it (Gerdtha,, Johannesson, 1997; Subramanian et al, 2004). The relationship between age and happiness appears more complex (U-shaped), with the minimum varying by community (Gerdtha, Johannesson, 1997).

4 For example, people tend to get used to pecuniary benefits and losses relatively quickly, while it takes more time to get used to such non-pecuniary effects as marriage, employment, health, etc. (Easterlin, 1974) 2

Some share of self-rated happiness is driven by social connections, as showed by the studies emphasizing the “contagious” nature of happiness (Steptoe, Roux, 2009; Subramanian et al, 2004), while the rest is driven by the type of personality5. Additionally, religiousness was found to be positively related to happiness. Finally, different marital statuses affect happiness differently. A robust result in the literature is that married people tend to be both happier and healthier (Waite, Gallagher, 2000).

There are also a number of socio-economic variables that are of important interest for policy makers and economists. Among them, unemployment and inflation that are generally negatively associated with happiness, while trust and freedom have positive effects (Graham, 2005; Gerdtha and Johannesson, 1997).

In this paper we will explore the impact of a particular socio-economic variable, the employment status of women in the South Caucasus, on self-declared happiness. In particular, we will be comparing the happiness level of employed women with that of housewives. Why such a comparison, and why taking South Caucasian countries (Armenia, Azerbaijan and Georgia)?

The last three decades have been characterized by a notable increase in the labor force participation of women at the world level (World Bank, 2011). At first sight this constitutes undoubtedly good news, as higher female participation in the labor market implies an increased potential for growth and higher government revenues. Entering the labor market can also empower women and reduce the vulnerability of their households to negative shocks, thereby contributing also to the fight against poverty.

This optimistic interpretation of the observed increase in female labor force participation, however, is not the only possible one. As a matter of fact, a woman can decide to enter the labor force for many different reasons. Sometimes, she does it in pursuit of her own interests and personal fulfillment. Other times, however, paid work is more a need rather than a choice. For instance, Pastore and Vereshchagina argue that – in the Belorussian case - female labor force participation is kept high, despite large and growing gender wage gaps, by the need of supporting household income. Along the same lines, Khitarishvili finds evidence of existence of an “added worker effect”6 in transition economies, following the 2008 economic

5 A study, carried out in Israel found that half of the variation in happiness was explained by personality (Cloninger, Zohar, 2011). 6 We speak of added worker effect when a negative shock hitting the household pushes a – previously non-working – household member (most likely women) in the labor market. 3 crisis, with a reduction in male employment, followed by an increase in female labor market participation, especially in weak labor market environments (Khitarishvili, 2013).

On top of that, increased female labor force participation comes, however, at a cost. When a woman enters the labor market, her household has to find an alternative way to ensure that all the household-related activities she was performing – housework, childcare and elderly care, to cite the most common ones – are taken care of. Typically, happiness of working women depends on whether housework is shared among family members or not. If these responsibilities cannot be outsourced or are not taken up by other members of the household, women end up working “the second shift”, as Arlie Hochschild defined it in 1989, and have to work longer hours with higher intensity to manage their work in and out of the house. The existence of many time and economic constraints of course can lead to physical and psychological stress, resulting potentially in high personal costs. This is why, women who carry the dual burden of breadwinning and full housework are usually less happy.

The increased burden on working women is likely to be especially heavy in the most traditional societies, where gender roles are still very strongly rooted. This is, for example, the case in the South Caucasus, where traditional values are still strong and the division of housework and care responsibilities among spouses is still quite unequal. Apparently, however, an unequal division of housework has a different impact on happiness in these countries. It has been suggested that women are less unhappy if unequal division of housework is a social norm in their country (Mencarini, Sironi, 2010). In some conditions, migration experiences – by exposing the partners to different cultural norm – can help exacerbate or alleviate the burden of women. According to the study of the Georgian case, for example, migration of men exacerbates gender differences in household task division, while female migration, on the contrary ameliorates the situation (Torosyan et al, 2015).

This said, it is not obvious, that entering the labor market should always lead to an increase in women’s happiness. This conclusion is confirmed by a number of cross-country studies, suggesting that happiness differences between housewives and working wives depend from the cultural and social context more than from their work status. For example, a study (Beja, 2012) shows that, while in Eastern Europe full-time work is superior to being housewife, in Asia self-employment brings more happiness to women. Another interesting cross-country study of homemakers and working women finds that homemakers are in fact a little happier than full-time working women. However, part-time workers were found to be the happiest (Treas et al, 2011).

Given the great variation among countries in terms of formal and informal institutions, cultural norms and population characteristics it is, however, complex to disentangle the contribution of the different factors to women’s happiness. This, in turn, makes extremely difficult designing policies that can successfully minimize women’s costs and ensure that the decision of entering the labor market is as “happiness-enhancing” as possible.

The comparison between the happiness of housewives vs. working wives in the South Caucasus offers us an excellent opportunity to disentangle these effects. All the three countries under analysis share a common past, having been part of the Soviet Union for many decades. Because of this, until the late 80’s, Armenia, Azerbaijan and Georgia shared the same type of formal institutions, which led them to become similar under many aspects (for example, female labor force participation and women’s educational levels). On the other hand, after the fall of the Soviet Union the differences in cultural and social norms which were not eliminated in the Soviet period (for example those associated with religion) gained progressively importance, leading – possibly – to a divergence in the impacts of these two different labor market statuses on the happiness level of women in the three countries.

Our study contributes to the existing literature in several directions. First, it studies the differences in happiness of working women and housewives in the South Caucasus region, not yet studied by other researchers. Second, we use a propensity score matching technique to assess who is showing the highest level of happiness between working women and housewives with similar characteristics. Finally, we exploit the substantial presence of Azeri and Armenian minorities in Georgia to explore to what extent the differences across countries can be attributed to different ethnic/cultural factors rather than to differences in the existing formal institutions.

After a brief discussion of the attitudes towards gender roles and the challenges faced by women attempting to reconcile work and family in the three countries under analysis, we describe the database and the methodology used in our analysis. We then discuss our results and, finally, draw our conclusions.

Countries’ background

Armenia, Azerbaijan and Georgia were part of the Soviet Union for more than 70 years. During these period they shared political, economic and social institutions. When the Soviet Union collapsed, however, each country followed its own path, re-asserting its own specificities.

Nowadays, the South Caucasian countries are still quite similar in many aspects, but are characterized also by a number of differences that are relevant to our study as they might affect the level of happiness of housewives and employed women in different ways.

The data collected by the World Values Survey7 for the years 2010-2014 (covering approximately the same time-span as the data we are analyzing in our work) generate strong impression that the South Caucasus countries are still quite traditional, with Georgia being relatively closer to less traditional European countries and Azerbaijan being the furthest. Family is extremely important in the South Caucasus: 94% of the population in Azerbaijan, 97% in Armenia and 98% in Georgia consider family to be very important in their lives.

Also, work is considered very important by the majority of the population, but the percentages go down to 67%, 71% and 73% of the population, respectively. Another interesting observation is that – while the share of men and women considering the family very important is nearly identical – a smaller share of South Caucasian women consider work very important (with respect to men), with only 55% of women in Azerbaijan, 67% in Armenia and 68% in Georgia stating that work is very important in their lives. This might be a reflection of the survival of the “male breadwinner model” in this area. In all three countries a substantial number of individuals agree with the statement that “When jobs are scarce, men should have more right to a job than women”, albeit to a different degree (78.6% in Azerbaijan, 56.2% in Armenia and 46% in Georgia)8.

How is the persistence of these traditional views affecting the perception of women towards work for pay and housework/caring activities? 67.6% of Azerbaijani women, 51.5% of Armenian women and 47.9% of Georgians women agree/strongly agree with the statement that “being a housewife is just as fulfilling as working for pay.”9 Finally, when it comes to the possibility to combine work and caring duties (with respect to children), women in the South Caucasian countries – with the notable exception of Georgian women - seem to be in line with other European countries. The share of women thinking that “When a mother works for pay, the children suffer” is 45.4% in Azerbaijan, 47.8% in Armenia and a surprising 68.4% in Georgia10.

7 For a description of the World Values Survey see: http://www.worldvaluessurvey.org/WVSContents.jsp 8 For further comparison, the share of individuals agreeing with this same statement was 26.1% in Poland and 12.0% in Spain. 9 The corresponding values for Poland and Spain are 37.4% and 45.8% 10 The corresponding values in Poland and Spain are, respectively, 55.4% and 29%. 6

The satisfaction women working for pay derive from their condition, relative to that of housewives, is not only affected by cultural norms and shared values, however. Another crucial aspect is the existence of formal institutions reducing the cost of participating actively to the labor market. Here below we show some selected indicators related to labor market outcomes for Armenia, Azerbaijan and Georgia.

Table 1. Female labor force participation (% of female population ages 15-64) and unemployment (% of female labor force) Labor force participation 2009 2010 2011 2012 2013 Armenia 53.5 55 58 57.9 58.4 Azerbaijan 67.1 67.3 67.5 67.8 68.1 Georgia 59 59.2 59.6 60.1 60.6 Unemployment Armenia 19.9 21.4 19.8 18.3 17.5 Azerbaijan 6.6 6.8 6.7 6.4 6.6 Georgia 15.6 15.1 14 13.9 13.2 Source: World Development Indicators

Female labor force participation in Armenia, Azerbaijan and Georgia was relatively similar in the period under consideration, with Armenia and Georgia converging towards higher participation rate of Azerbaijan. In all three countries, despite the adoption of Anti- discrimination laws, women are still mostly segregated in lower paid – lower responsibility jobs. Also, women’s wages remain substantially lower than men’s (the average wage for women amounts to about 60% of that for men).

Despite the comparable participation rates, women in Armenia and Georgia appear to have substantially more difficulties finding a job, with an unemployment rate that is between 2 and 3 times higher than that for Azeri women.

One can argue that happiness of women working outside the house is related to how supportive are formal institutions with regard to their efforts to balance work and private life. One would expect that women in countries providing more opportunities to get support services and/or to adopt a more flexible working schedule would have an easier time achieving a successful work-life balance and, therefore, be happier.

During our research were able to collect information about two such indicators. The first, reported in Table 2, is the Gross Enrolment Ratio in pre-primary education. Unfortunately, the available data for the three South Caucasian countries overlap only in two out of the 4 years. This is enough, however, to note how the enrollment of children in pre-primary education was substantially higher in Georgia, compared to Armenia and Azerbaijan, in 2008 and 2010, with Armenia progressively catching up, while Azerbaijani enrollment ratio 7 remained stationary over the observed period. Clearly this implies that, while Georgian working women with young children could count on a more developed formal support system in the period under analysis, women in the other countries had likely to rely on informal support or struggle more to take care of their childcare responsibilities.

Table 2. Gross Enrolment Ratio (GER) in pre-primary education (%) Country 2008 2010 2011 2012 Armenia 33 31 43 51 Azerbaijan 26 25 27 25 Georgia 63 58 - - Source: UNESCO, Education For All, years 2011, 2012, 2013/4 and 2015

Another, relevant, indicator of how difficult it can be for women to conjugate work and private life is the one in Table 3: share of women in part-time work. The possibility to engage in part-time work is one of the crucial elements for a strategy aiming at a better work-life balance. Part-time work allows women to free more time that can be used to cope with housework and caring responsibilities, reducing frictions, stress and overwork.

Table 3. Share of women in part-time work Country 2003 2004 2008 Armenia - - 30.1 Azerbaijan 24.3 - - Georgia 53.7 50.5 - Source: World Development Indicators

Data in this case are even more sparse than in the previous case. Also in this case, however, Georgian women seem to operate in a more favorable environment, with more than 50% of them engaged in part-time work, against less than one third in Armenia and Azerbaijan.

Data and Methodology

Empirical analysis in this paper is primarily based on the data from annual surveys conducted by the Caucasus Research Resource Center (CRRC) in all three countries of the South Caucasus region. This data collection initiative (currently referred to as “Caucasus Barometer”, or CB) started in 2004 with the goal of providing rich, nationally representative, comparable across countries and years database for researchers and policy makers interested in analyzing data on socio-economic, political, cultural, and other topical issues in the region.

CB surveys have been conducted annually in all three countries of the region following the same methodology. The target population is all adult residents (18 years and older) in each country, with the exception of residents in conflict affected regions and/or occupied territories (Abkhazia, South Ossetia, and Nagorno Karabagh). Sampling methodology is based on dividing the target population into three “macro-strata” by settlement type: capital, urban and rural. Within each stratum PSUs (primary sampling units, which are electoral precincts) are sampled with probability proportional to the number of registered voters in each stratum. To obtain a sampling frame within each PSU, a block listing procedure is used (which is listing/updating earlier created list of households actually living in each primary sampling unit). Finally, PSU lists are used to randomly sample households to be interviewed. The size of the sample in each country for each year is approximately 2000 respondents.

The timing of the fieldwork is November-December of each year, the data become publically available through the CRRC website11 shortly after being collected, entered and cleaned. Unfortunately, collection for the year 2014 was not implemented, and surveys for 2015 will be conducted in Georgia and Armenia, but not in Azerbaijan. This makes data from year 2013 the latest available for all three countries of the region. In our study we are making use of data starting from years 2010, as some of the variables of interest to us are not available in earlier surveys. Thus we make use of CB data for 2010-2013 time frame.

CB surveys ask a wide variety of questions, aiming at collecting detailed data about the respondent. Questions range from those on personal characteristics (gender, age, education, ethnicity, religion, etc.) to various opinion and perception questions (including questions about perceived economic rank, trust level, happiness and life satisfaction). In addition a subset of questions collect data on all household members, from which household composition/structure can be constructed. Finally there are questions on economic wellbeing of the household, its expenditures, and other household descriptors. Table A1 in the Appendix offers definitions and measurement details for variables used in our analysis.

In our study we are primarily interested in comparing the reported happiness and life satisfaction levels of adult women in the target region across their employment status, with a focus on how employed women feel compared to their housewife counterparts. For this purpose, we narrow our sample down to these two groups: employed women and housewives, and fit a probit regression that helps to explain how probability of being in either group relates to various personal and household characteristics. Based on probit

11 For more information about Caucasus Barometer data initiative see http://www.crrccenters.org/20119/Project-Overview 9 estimation results we find women from both groups that are close matches for each other (based on their predicted probability of being in each group) and then compare the average level of happiness and life satisfaction across the matched respondents. The first section of the Appendix offers some technical details behind this methodology and its implementation. In our analysis section below we report results that were obtained closely following the propensity score estimation procedure described in the Appendix.

Analysis and Results

We begin our analysis from comparing the reported levels of happiness and life satisfaction (our “outcome” variables) for employed women and housewives in all three countries of the region. Table 4 provides a tabulation of the number of women by their employment status in each country of interest12. As we can see, the two categories we focus our attention on are very large and together cover between 57-70% of adult women in the region.

Table 4: Distribution of women by employment status and by country Armenia Azerbaijan Georgia Economic Status Num % Num % Num % Housewives 1366 41.4% 1246 43.5% 1138 30.6% Employed 968 29.4% 736 25.7% 1001 26.9% Unemployed 730 22.1% 775 27.1% 1131 30.4% Self-employed 234 7.1% 107 3.7% 452 12.1% Total 3298 2864 3722

Table 5 lists average levels of happiness and life satisfaction for housewives and employed women in each country. We observe an interesting range of cases when we compare happiness and life satisfaction of employed women and housewives in the three countries of interest. In Armenia women who are in employment are significantly less happy and satisfied on average than housewives. The situation in Azerbaijan is similar, but the differences in life satisfaction and happiness levels across the two groups are smaller. In Georgia, however, women in employment appear to be more satisfied with their life and slightly happier (but insignificantly so) compared to housewives in this country. It is interesting to note that women in Georgia appear to be happier and more satisfied with their life compared to women in the other two countries of the region.

12 Note: we do not report data on several categories of employment status, such as “Retired and not working”, “Student and not working”, “Disabled”, and “Other”. Among these groups “Retired and not working” is the largest (comparable in size to the group of unemployed in Table 1), but it primarily consists of older age women, and we drop that group from our analysis. 10

This reversal in the way working women feel compared to housewives in the countries of the South Caucasus region across countries is a very interesting observation. Could it be that the women who end up in employment in each country are different from the housewives, and this is what drives the difference in life satisfaction and happiness? Below we explore how various factors and characteristics of women contribute to the probability of being employed versus being housewives in each country, and then we use this information to check if there is any difference in the outcome variables for comparable women in each given country.

Table 5: Average level of life satisfaction and happiness, by country and employment status (based on data for years 2010-2013). Employed Housewives Diff t-stat Country N Mean St.Dev. N Mean St.Dev.

Armenia 848 6.47 0.08 1133 7.02 0.08 -0.55 -4.95 Azerbaijan 657 5.97 0.08 1060 6.35 0.07 -0.38 -3.62 Georgia, all 834 7.13 0.07 821 7.05 0.08 0.08 0.79

Geo, only 788 7.15 0.08 724 7.24 0.08 -0.09 -0.84 Happiness Geo, minor. 62 6.73 0.29 119 5.99 0.19 0.73 2.19 -0.21 Armenia 843 5.35 0.08 1131 5.56 0.08 -1.90 Azerbaijan 661 5.23 0.08 1064 5.41 0.07 -0.18 -1.71 Georgia, all 839 5.90 0.08 822 5.45 0.08 0.45 4.02

Geo, only 793 5.94 0.08 721 5.58 0.09 0.37 3.16 Life Life satisf. Geo, minor. 62 5.06 0.28 118 4.74 0.21 0.33 0.92 Note: t-stats are for test of mean difference in average life satisfaction and happiness levels between employed women and housewives, within each country. Differences that are significant at least at 10% level are shown in bold.

Before proceeding, we want to offer an addition insight into the data. Georgia is an ethnically diverse country13: it is home to several ethnic minorities, Armenian and Azeri being the largest (based on CB samples we estimate that 5.4% of population in Georgia are ethnic Armenians, and 6.2% are ethnic Georgians). This diversity of Georgia allows us splitting the sample for the country into ethnically Georgian and non-Georgian women. This split is interesting because we can compare how ethnic minorities living in Georgia fare against their fellow nationals living in the mother-land, and how they compare to Georgians living in Georgia. This way we can trace “nationality effects” (being more similar to people of the same ethnicity) versus “country effect” (being more similar to other ethnicities living in the same country).

13 The situation in Armenia and Azerbaijan is very different – both countries are predominantly populated by their own nationals, and are home to tiny ethnic minorities of Russians, Kurds and other Caucasians. 11

Table 5 provides the summary statistics for these subsamples in Georgia. The level of happiness and life satisfaction for women of ethnic minorities living in Georgia is below that for Georgian nationals (and is the lowest among all groups). Of course, the small size of this sub-sample (and as a result large variation associated with reported averages) should be taken into consideration. However within the group of ethnic minorities, we do not observe lower happiness and life satisfaction for employed women versus housewives, like we do in Armenia and Azerbaijan. So, at least at this stage, this seems to be more of a country effect, rather than ethnicity/culture.

Next we move to the matching exercise. To estimate the probability of being employed we apply a probit model estimation where the dependent variable is a binary variable =1 if a woman is employed and =0 if she is a housewife. As regressors we use several variables available in the dataset. Table A2 in the Appendix provides summary statistics for the variables used in probit model, limited to the sub-sample of complete observations (some observations are lost due to missing values in model variables: 20% of observations are incomplete in Georgia, 14% in Armenia, and 11% in Azerbaijan) for women who reported to be employed or housewives.

Table A3 provides probit estimation results from each country. Selection of variables for these models is based on several criteria, including: theoretical considerations offered by literature on this topic, data availability, satisfaction of the balancing property required for propensity score matching. In general, we have pseudo-R2 ranging from 0.26 to 0.50, so the models achieve reasonable fit. The directions of impacts implied by coefficient estimates are mostly in line with what literature reports for similar variables. We control for geographic areas by allowing for locality fixed effects. In the CB each country is divided into 9 regions/areas, with one being the capital and the other 8 corresponding to rural and urban areas (each split into 4 quadrants: NE, NW, SE, SW).

Based on probit results we then split our sample into blocks within which the balancing property is tested for. All our specifications pass this test. The number of blocks in different cases varies between 8 and 11. As the final step we match treatment and control observations similarity in their predicted probability of being employed. For robustness we employ three methods of choosing similar observations: nearest-neighbor, radius and Kernel matching. Differences in the average outcomes for matched treatment and control observations are reported in Table 6.

The first observation based on Table 6 is that most of the unconditional differences in happiness and life satisfactions that we found before (see Table 5) are explained by differences in the characteristics of women who chose employment over being a housewife. Looking at comparable women in both categories, we observe that some of differences in their happiness and satisfaction go away. A strong exception is the case of Armenia, where happiness of women in employment remains significantly below that of housewives and similar is magnitude to what we estimated before matching. We also observe lower life satisfaction for working Armenian women, but the effect only comes out significant with radius matching. There is some evidence of lower happiness for employed women in Azerbaijan, but once again, the estimate is significant only with radius matching.

In Georgia we capture a higher average level of life satisfaction for working women compared to their non-working matches. Also, there is hint of a positive effect on women of ethnic minorities living in Georgia: if they are employed they are more satisfied with their life, compared to housewives with similar characteristics. This effect seems to be quite sizable (but once again, it is significant only with one of our matching methods – we suspect that this is due to small sample size that we have for minorities).

Table 6: Average Effect of Treatment on the Treated (ATT), by country Nearest neighbor Radius Kernel Country ATT t-stat ATT t-stat ATT t-stat

Armenia -0.57 -2.47 -0.56 -4.71 -0.55 -2.44

Azerbaijan -0.12 -0.39 -0.32 -2.94 -0.13 -0.69 Georgia, all 0.10 0.42 -0.07 -0.57 -0.09 -0.40

Geo, only -0.07 -0.29 -0.17 -1.38 -0.14 -0.56 Happiness Geo, minor. 0.42 0.82 0.62 1.36 0.58 0.66

Armenia -0.08 -0.33 -0.28 -2.35 -0.10 -0.52

Azerbaijan 0.13 0.50 -0.15 -1.35 0.18 1.13 Georgia, all 0.26 1.04 0.28 2.24 0.09 0.37

Geo, only 0.32 1.05 0.20 1.54 0.05 0.2 Life Life satisf. Geo, minor. 1.41 2.00 0.57 1.16 0.60 1.07 Note: We test for difference in mean(1=employed)-mean(0=housewife)

One problem that we have when dealing with self-rated measures of happiness and life satisfaction on the scale from 1 to 10 is tendency of people who feel at the levels corresponding to mid-ranges of values to choose central option(s) as their response. That leads to clustering of answers at central values of the variable range, which results in altering the distribution of true feelings and can lead to biases in statistics based on reported values.

Table 7 confirms our concern about clustering of values in the middle of the variable range both for life satisfaction and happiness measures. High frequency of these “middle” 13 responses makes average values of happiness and life satisfaction to be closer to those values both for employed women and housewives, making it harder to detect differences in averages across the two groups.

Table 7: Frequencies of happiness and life satisfaction responses, by country Happiness Life Satisfaction % of sample -> Arm Az Geo % of sample -> Arm Az Geo Extremely unhappy, 1 4.3 1.4 1.2 Not satisfied at all, 1 8.9 4.9 7.1 2 2.1 2.2 0.7 2 5.1 5.0 4.8 3 2.9 6.5 3.3 3 7.8 9.4 9.9 4 3.1 7.7 5.6 4 5.5 12.5 7.4 5 22.1 21.2 22.2 5 31.0 22.7 26.3 6 6.5 14.5 9.0 6 7.2 15.2 10.6 7 13.0 15.3 13.9 7 9.6 11.3 13.6 8 13.2 15.6 15.5 8 9.9 12.2 10.7 9 7.0 6.9 6.8 9 4.6 3.2 3.3 Extremely happy, 10 25.8 8.8 21.9 Completely satisfied, 10 10.5 3.7 6.5

% of sample -> Arm Az Geo % of sample -> Arm Az Geo Very Happy 32.7 15.5 27.9 Very satisfied 14.9 6.8 9.6 Very unhappy 6.4 3.5 1.9 Very unsatisfied 13.9 9.7 11.5

As an alternative measure of happiness and life satisfaction we propose to look at extreme values of the two variables. We generate 4 new variables: “Very happy” and “Very satisfied” that combine values 9 and 10 for the corresponding variables, and “Very unhappy” and “Very unsatisfied” combining values 1 and 2 for the two variables (frequencies for these new variables are shown in the lower panel of Table 7). These new variables can now be compared across the treatment and matched control groups, see Table 8.

As before, Armenia stands out as the country where we find a strong negative effect on happiness and life satisfaction associated with employment. The magnitude of the effect is rather sizeable: the frequency of “very happy” and “very satisfied” responses among employed women is roughly half of that for housewives. There is no difference in the lower tail of distributions for our outcome variables: working women and housewives in Armenia are equally likely to be very unhappy or very unsatisfied with their life.

In Azerbaijan there is some evidence that women in employment are both less likely to report that they are very happy and are more likely to feel very unhappy, compared to housewives. This evidence is weak, however, given that we observe significant difference only with some of matching methods.

Table 8: Average Effect of Treatment on the Treated (ATT), by country Nearest neighbor Radius Kernel ATT t-stat ATT t-stat ATT t-stat Armenia Very Happy -0.19 -4.48 -0.13 -6.15 -0.16 -4.83 Very Satisfied -0.07 -2.21 -0.06 -4.01 -0.05 -2.07 Very Unhappy 0.00 0.06 0.01 0.72 0.01 0.41 Very Unsatisfied -0.01 -0.34 0 0.1 -0.01 -0.30 Azerbaijan Very Happy -0.03 -0.68 -0.05 -2.89 -0.02 -0.92 Very Satisfied -0.02 -0.43 -0.01 -0.64 0.01 0.73 Very Unhappy 0.02 0.72 0.02 1.54 0.03 2.14 Very Unsatisfied 0.00 0.03 0.00 0.48 0.00 0.31 Georgia Very Happy -0.01 -0.16 -0.03 -1.18 -0.05 -1.05 Very Satisfied 0.02 0.41 0.01 0.65 0.00 0.12 Very Unhappy 0.01 0.3 0.01 1.05 0.01 1.40 Very Unsatisfied -0.09 -2.74 -0.04 -2.56 -0.04 -1.53 Note: We test for difference in mean(1=employed)-mean(0=housewife)

In the case of Georgia while there are no significant differences in the top part of the distribution of both happiness and life satisfaction, we see an interesting pattern at the bottom of the distribution. Georgian women who are employed are less likely to feel very unsatisfied with their lives than housewives. This finding might explain why we observed a positive ATT estimate when looking at the overall average life satisfaction – there are fewer “bottom” case among working Georgians, and this tends to shift their average life satisfaction level up compared to that of Georgian housewives.

Conclusion

We conduct a comparative analysis of happiness and life satisfaction of employed women and housewives in the three Republics of the South Caucasus using matching technique. Our findings indicate the there are some (not very sizeable) differences in the way women feel given their employment status. In particular, we find a strong and negative effect on employed women in Armenia, a milder but still negative effect on working women in Azerbaijan, and a positive effect on working women in Georgia.

When checking for differences in extreme responses among employed women and housewives, we find a very large negative impact in Armenia both on happiness and life satisfaction from being employed. We also find much lower incidence of extreme

15 dissatisfaction with life among working Georgian women, which might explain the positive difference in average life satisfaction we observed earlier.

Separate analysis of women of Armenian and Azeri ethnicity living in Georgia shows that there is no negative effect from being employed among this group. This makes us believe that the negative impact we find in Armenia and Azerbaijan is more of a country-specific effect, rather than cultural/ethnic factor. Our discussion in the background section contains candidates for factors behind these country effects. Both higher pre-school enrollment rate and higher incidence of part-time work in Georgia might help women in this country to minimize stress of combining household duties with work and not sacrifice their happiness and life satisfaction. More research in this direction can help to determine the true nature of relationship between availability of childcare and part-time work on well-being of women and their labor force participation.

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Appendix

Propensity Score Estimation Details In this section we briefly discuss the technical details behind the propensity score matching technique and its application in our paper.

Let em={0,1} be our “treatment” variable, where em=1 if a woman is employed, em=0 otherwise (she is a housewife). Then the conditional probability of being employed, given pre-treatment characteristics of the woman (X) is: P(X) = Pr(em =1| X) = E(em | X)

Rosenbaum and Bubin (1983) show that if the treatment (em=1 in our case) is random within blocks defined by X (and as a result, by P(X)), then the Average effect of Treatment on the Treated (ATT) can be expressed as follows:

ATT = E(Y1i -Y0i | emi =1) =

= E((E{Y1i | emi =1, P(Xi )}- E{Y0i | emi = 0, P(Xi )}) | emi =1) where the outer expectation is with respect to the distribution of {P(Xi)|emi=1} and Y1i and

Y0i are outcomes of variable of interest in two counterfactual scenarios (if woman i is employed versus if she was a housewife).

This results hinges on the following two conditions: Condition 1: Balancing of pre-treatment variables given the propensity score P(X), or em  X | P(X) Basically, this condition requires that observations with the same propensity score have the same distribution of observable (and unobservable) characteristics independently of treatment status.

Condition 2: Unconfoundedness given the propensity score. If assignment to treatment is unconfounded given pre-treatment information:

Y1, Y0  em | X then it will be also unconfounded given the propensity score:

Y1, Y0  em | P(X)

The procedure for matching works in the following way:  We use probit model to estimate P(X) = Pr(em =1| X), and based on results we

predict the propensity score for each observation: 푃̂(푿).

 Based on values of 푃̂(푿). we split the sample into equally spaced blocks, such that within each block the average of the propensity score is the same (statistically) for treated and control units  Within each block we test that the means of each and every characteristic in X do not differ (statistically) between treatment and control units (this is necessary for the Balancing condition to be in place).  We match our treated (employed) and control (housewives). As a matching method we use three alternatives: nearest neighbor (where the control observation with the closest value of predicted probability is selected as a match), radius (where all control units with predicted probability of being employed falling within given radius from a given treatment observation) and Kernel matching based on Epanechnikov kernel and default bandwidth of 0.06 (in this method all control observations are used as match, but are weighted according to the distance in their predicted probabilities from the predicted probability of a given treatment observation) .  Given the matching mechanism, we compute the differences in outcomes between treatment and matched control observations, and take the average of these differences (ATT). In the case of nearest neighbor and radius matching, analytical standard errors are computed. In the case of Kernel matching standard errors are bootstrapped (using 100 repetitions).

Data Notes and Additional Results In this section we offer definitions of variable used in our analysis and report additional empirical results behind findings discussed in the paper.

Table A1: Definitions of variables used in the analysis Variable Definition/range Overall life satisfaction (ranges form 1=not satisfied at all to Life satisfaction 10=completely satisfied) Reported level of happiness (ranges from 1=extremely unhappy to Happiness 10=extremely happy) Binary variable: =1 if a woman is in employment (working either part- time or full time, even if the respondent is retired / is a student, Empl./ h.wife including seasonal work), =0 if she is a housewife (based on self- reported status) Ethnic minority Binary variable: =1 if woman belongs to an ethnic minority, =0 otherwise Self-reported health rating (ranges from 1=extremely poor to 5=very Health rating good) Age Respondent's age Years of education Years of formal education the woman has completed Knowledge of English language (ranges form 1=no basic knowledge to Knowledge of English 4=advanced knowledge) Knowledge of Russian language (ranges form 1=no basic knowledge to Knowledge of Russian 4=advanced knowledge) Attendance of relig. Frequency of attending religious services (ranges form 1=every day to serv. 7=never) Reported level of religiosity (ranges form 1=not at all religious to 10=very Level of religiosity religious) Frequency of fasting when required by religious tradition (ranges form Frequency of fasting 1=always to 5=never) Importance of religion in woman's daily life (ranges form 1=not at all Importance of religion important to 4=very important Binary variable: =1 if a woman has family member or close relative Has relatives abroad currently living abroad, =0 otherwise Desire to migrate Binary variable: =1 if a woman wants to migrate, =0 otherwise HH expenditure last mo. HH spending last month (ranges from 1=more than 1200 USD to 8=none) Current economic rung Current perceived economic rung (ranges from 1=lowest to 10=highest) Perceived relative economic condition (ranges from 1=very poor to Relative ec. Condition 5=very good) HH monetary income last month (ranges from 1=more than 1200 USD to HH income last month 8=none) HH economic situation (ranges from 1=not enough money for food to HH economic situation 5=enough money for all needs) Num. kids [0;2] y.o. Number of own kids [0;2] years old who live in the HH Num. kids [3;5] y.o. Number of own kids [3;5] years old who live in the HH Num. kids [6;10] y.o. Number of own kids [6;10] years old who live in the HH Num. kids [11;15] y.o. Number of own kids [11;15] years old who live in the HH Being single Binary variable: =1 if a woman is single (never married), =0 otherwise Num. empl. adults in Number of additional employed adults in the HH (not counting the HH woman herself) Binary variable: =1 if mother(-in-law) of the woman lives in the HH, =0 Grandmother in HH otherwise 21

Binary variable: =1 if father(-in-law) of the woman lives in the HH, =0 Grandfather in HH otherwise

Capital Binary Variable: =1 if a woman is from the capital city, =0 otherwise Binary Variable: =1 if a woman is from the rural areas of the North- NE rural Eastern part of the country, =0 otherwise Binary Variable: =1 if a woman is from the rural areas of the North- NW rural Western part of the country, =0 otherwise Binary Variable: =1 if a woman is from the rural areas of the South- SE rural Eastern part of the country, =0 otherwise Binary Variable: =1 if a woman is from the rural areas of the South- SW rural Western part of the country, =0 otherwise Binary Variable: =1 if a woman is from the urban areas of the North- NE urban Eastern part of the country, =0 otherwise Binary Variable: =1 if a woman is from the urban areas of the North- NW urban Western part of the country, =0 otherwise Binary Variable: =1 if a woman is from the urban areas of the South- SE urban Eastern part of the country, =0 otherwise Binary Variable: =1 if a woman is from the urban areas of the South- SW urban Western part of the country, =0 otherwise

Table A2: Summary statistics for variables used for matching, by country Armenia Azerbaijan Georgia Variable Mean St. D. Mean St. D. Mean St. D. Employed/ housewife 0.43 0.49 0.38 0.49 0.50 0.50 Age 41.49 12.42 39.49 11.26 42.47 12.72 Years of education 12.38 2.53 11.57 2.31 13.53 2.71 Knowledge of English 1.77 0.94 1.34 0.67 1.71 0.95 Knowledge of Russian 3.11 0.67 2.12 0.99 2.96 0.89 Health rating 3.13 0.82 3.51 0.85 3.24 0.81 Ethnic minority 0.01 0.09 0.05 0.22 0.12 0.32 Level of religiosity 7.02 2.63 4.83 2.31 6.82 2.18 Attendance of relig. serv. 5.04 1.30 5.51 1.19 4.59 1.32 Frequency of fasting 4.72 0.77 3.61 1.37 3.80 1.39 Importance of religion 3.36 0.86 3.15 0.79 3.41 0.63 Relative ec. condition 2.98 0.65 2.82 0.75 2.82 0.63 HH income last month 4.67 1.26 3.72 1.17 4.75 1.37 HH economic situation 2.14 0.94 2.41 0.93 2.34 0.93 HH expenditure last mo. 4.36 1.15 3.69 1.05 4.68 1.32 Current economic rung 4.32 1.60 4.27 1.65 4.44 1.67 Has relatives abroad 0.78 0.41 0.46 0.50 0.54 0.50 Desire to migrate 0.25 0.43 0.13 0.34 0.06 0.23 Num. kids [0;2] y.o. 0.08 0.28 0.10 0.32 0.09 0.30 Num. kids [3;5] y.o. 0.14 0.39 0.18 0.43 0.14 0.39 Num. kids [6;10] y.o. 0.23 0.52 0.28 0.58 0.20 0.49 Num. kids [11;15] y.o. 0.24 0.55 0.29 0.58 0.21 0.50 Being single 0.09 0.29 0.06 0.25 0.11 0.31 Num. empl. adults in HH 0.85 0.92 0.99 0.79 0.75 0.81 Grandmother in HH 0.28 0.45 0.25 0.43 0.28 0.45 Grandfather in HH 0.16 0.36 0.17 0.38 0.16 0.37 Capital 0.31 0.46 0.32 0.47 0.27 0.45 NE rural 0.10 0.30 0.07 0.26 0.11 0.31 NW rural 0.10 0.30 0.06 0.24 0.11 0.32 SE rural 0.08 0.28 0.14 0.35 0.13 0.33 SW rural 0.09 0.29 0.05 0.23 0.08 0.28 NE urban 0.08 0.27 0.11 0.32 0.07 0.26 NW urban 0.10 0.30 0.13 0.34 0.11 0.31 SE urban 0.05 0.21 0.09 0.29 0.07 0.25 SW urban 0.09 0.28 0.01 0.10 0.05 0.22 Observations 1987 1740 1673

Table A3: Probit regression results Georgia, Georgia, Armenia Azerbaijan Georgia, all only minor Variable Coef. St.Er. Coef. St.Er. Coef. St.Er. Coef. St.Er. Coef. St.Er. Age 0.02 0.00 0.03 0.00 0.03 0.00 0.03 0.00 0.05 0.02 Being single 1.00 0.14 1.48 0.19 1.44 0.18 1.47 0.19 1.19 0.70 Years of education 0.16 0.02 0.28 0.02 0.13 0.02 0.15 0.02 0.09 0.07 Knowledge of English 0.03 0.04 0.15 0.07 0.13 0.05 0.13 0.05 0.43 0.21 Knowledge of Russian 0.03 0.06 0.10 0.05 0.10 0.05 0.21 0.22 Health rating 0.12 0.05 0.12 0.05 0.14 0.05 -0.02 0.21 Ethnic minority -0.09 0.40 -0.26 0.13 Attendance of rel. serv -0.06 0.03 -0.07 0.02 0.01 0.03 0.01 0.03 0.18 0.16 Level of religiosity -0.02 0.01 0.01 0.02 0.15 0.08 Frequency of fasting 0.02 0.03 -0.04 0.16 Importance of religion 0.11 0.06 0.15 0.06 -0.05 0.26 HH exp. last mo. -0.12 0.06 -0.08 0.07 -0.10 0.25 Current ec. rung -0.03 0.03 -0.03 0.12 Relative ec. condition -0.12 0.06 0.12 0.08 0.09 0.07 0.17 0.29 HH income last mo. -0.28 0.03 -0.09 0.04 -0.14 0.06 -0.15 0.07 -0.48 0.25 HH ec. situation -0.05 0.04 0.01 0.05 0.01 0.05 -0.09 0.23 Desire to migrate 0.07 0.08 -0.10 0.11 -0.16 0.16 0.05 0.19 -0.21 0.35 Has relatives abroad -0.16 0.08 -0.07 0.08 -0.12 0.08 -0.09 0.08 0.53 0.41 Num. kids [0;2] y.o. -0.34 0.13 -0.37 0.14 -0.56 0.14 -0.62 0.14 -0.32 0.75 Num. kids [3;5] y.o. -0.20 0.10 -0.04 0.10 -0.23 0.10 -0.24 0.11 -0.29 0.56 Num. kids [6;10] y.o. -0.23 0.07 -0.19 0.07 -0.11 0.08 -0.09 0.08 -0.21 0.31 Num. kids [11;15] y.o. -0.11 0.06 -0.15 0.06 0.09 0.07 0.08 0.07 -0.47 0.43 N empl. adults in HH -0.11 0.04 -0.37 0.05 -0.24 0.05 -0.19 0.05 -0.71 0.23 Grandmother in HH 0.18 0.09 0.29 0.12 0.13 0.10 1.39 0.43 Grandfather in HH -0.04 0.12 -0.23 0.13 0.11 0.12 0.17 0.12 -0.41 0.50 Capital -0.17 0.12 0.09 0.13 0.08 0.13 NE rural -0.21 0.12 0.16 0.14 0.20 0.14 -1.57 0.98 NW rural -0.51 0.13 0.16 0.15 0.19 0.15 SE rural -0.04 0.12 -0.19 0.14 0.51 0.14 0.53 0.15 SW rural -0.06 0.12 -0.40 0.19 NE urban 0.04 0.12 -0.18 0.14 0.33 0.17 0.36 0.17 -0.52 0.54 NW urban 0.03 0.12 -0.12 0.14 0.38 0.15 0.42 0.15 SE urban 0.20 0.15 -0.11 0.15 0.43 0.17 0.33 0.17 1.23 0.54 _cons -1.30 0.47 -3.92 0.39 -3.68 0.59 -3.78 0.59 -3.91 2.28 Pseudo R2 0.26 0.33 0.29 0.27 0.50 Observations 1987 1740 1673 1529 183 Log-Likelihood -1010.48 -777.13 -828.15 -773.37 -59.02 Note: the table reports McFadden’s pseudo R2; estimates that are significant at least at 10% level are in bold.