metadata: authors: 0: author: Petreski,Marjan
1: author: Oviedo Silva,Ana Maria
2: author: Cancho,Cesar A.
count: North Macedonia
docna: 0: docna: Expectations Set High : Understanding
            Reservation Wages in North Macedonia
docty: Policy Research Working Paper
abstracts: cdata!: The objectives of this study are to
            understand the determinants of reservation wages and measure
            the gap between reservation and market wages in North
            Macedonia. The study makes use of recently collected
            information on reservation wages in the Labor Force Survey
            2016 and 2017. The analysis relies on ordinary least
            squares, propensity score matching, Heckman-corrected
            estimates, and panel fixed effects. The results suggest that
            it is mainly supply factors that shape reservation wages in
            the country. Higher education achievement sizably increases
            future wage expectations, while age and longer spells of
            unemployment reduce them. Demand factors are found to be
            insignificant for reservation wage formation. Observed by
            skill level, the results suggest that low-skilled
            individuals consistently value their skills higher than what
            the market offers and set too high expectations. These
            circumstances are aggravated in cases where the household is
            well-off and/or receives remittances. By contrast, highly
            skilled individuals, despite maintaining a positive
            reservation wage gap, have a propensity to accept market
            wages even when they fall below their reservation wage,
            likely because these workers fear rapid depreciation of
            their skills.
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repnme: repnme: Expectations Set High : Understanding
            Reservation Wages in North Macedonia
display_title: Expectations Set High :
            Understanding Reservation Wages in North Macedonia
listing_relative_url: /research/2019/09/31432580/expectations-set-high-understanding-reservation-wages-north-macedonia
publishtoextweb_dt: 2019-09-25T00:00:00Z
no_of_pages: 29
author_keys: 0: author_key: 1622165
1: author_key: 674316
2: author_key: 1192826
model_extractions: Policy Research Working Paper 9023 Expectations Set High Understanding Reservation Wages in North Macedonia _Marjan Petreski Ana Maria Oviedo Cesar Cancho_ Poverty and Equity Global Practice September 2019
Policy Research Working Paper 9023

# **Abstract**

The objectives of this study are to understand the determinants of reservation wages and measure the gap between reservation and market wages in North Macedonia. The study makes use of recently collected information on reservation wages in the Labor Force Survey 2016 and 2017. The analysis relies on ordinary least squares, propensity score matching, Heckman-corrected estimates, and panel fixed effects. The results suggest that it is mainly supply factors that shape reservation wages in the country. Higher education achievement sizably increases future wage expectations, while age and longer spells of unemployment reduce them.

Demand factors are found to be insignificant for reservation wage formation. Observed by skill level, the results suggest that low-skilled individuals consistently value their skills higher than what the market offers and set too high expectations. These circumstances are aggravated in cases where the household is well-off and/or receives remittances. By contrast, highly skilled individuals, despite maintaining a positive reservation wage gap, have a propensity to accept market wages even when they fall below their reservation wage, likely because these workers fear rapid depreciation of their skills.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aoviedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Expectations Set High: Understanding Reservation Wages in North Macedonia**

**Marjan Petreski** University American College Skopje <u>marjan.petreski@uacs.edu.mk</u>

## **Ana Maria Oviedo**

The World Bank <u>aoviedo@worldbank.org</u>

**Cesar Cancho** The World Bank <u>ccancho@worldbank.org</u>

**Keywords:** reservation wage; market wage; determinants; North Macedonia **JEL classification:** J31; E24

This paper is a product of the Poverty and Equity GP. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aoviedo@worldbank.org. Corresponding author: marjan.petreski@uacs.edu.mk.
# **1. Introduction**

High and potentially downward rigid reservation wages – below which unemployed persons would not accept a job – may be a key reason for high and persistent unemployment rates. North Macedonia’s unemployment rate currently stands at 20.8%, though it has continued to decline since its peak of 36% in 2006. The decline – especially during the Global Financial Crisis - has been mainly driven by the government-supported expansion of foreign production capacities in the country and frequentlygenerous programs for subsidizing employment, though also potentially interfering with the absence of a census since 2002 and the accelerated emigration from the country since then. The level of the unemployment rate in North Macedonia also reflects structural characteristics of the labor force: a large share of low-skilled job seekers, accompanied by sizable skill mismatches, prevents fast and smooth transition to decent employment.

Still, the role of reservation wage for unemployment should be viewed in at least two important aspects. First, there is almost no evidence about the trend of reservation wages, nor about the way of their formation given personal and labor-market circumstances. The reservation wage in nationallyrepresentative surveys – like the Labor Force Survey – has been asked only in 2016, making the exploration of the job-search relationship between reservation wage, personal traits and institutional aspects (e.g. the generosity of the unemployment benefits) difficult so far. Second, despite the level and structural characteristics of unemployment in the country, still the decline in a decade has been sizable.

This paper has two objectives: first, to understand the determinants of reservation wages in North Macedonia; and second, to measure the gap between reservation and market wages. Both will enable understanding of the formation of reservation wages and its importance for tailoring of policies to alleviate its potentially adverse role for unemployment. We rely on two years of the Labor Force Survey – 2016 and 2017, despite maintaining only a pooled cross-section structure due to the short period on disposal. Given the constraints, the approach is only exploratory in nature, seeking to reveal facts about reservation wages in North Macedonia which were either unknown or assorted with anecdotal evidence only.

Results suggest that it is mainly supply factors which shape reservation wages in the country. Higher education attainment sizably increases future wage expectations, especially when persons are younger and with short unemployment spells. Then, reservation wage declines with age and with longer spells of unemployment. On the other hand, the insignificance of demand factors for reservation wage formation may suggest that job seekers either lack information about local conditions, or – more importantly – they are reluctant to fast accommodate, considering them substandard. The latter is especially the case for low-skilled individuals, who consistently value their skills higher than what the market offers and set too high expectations. On the other hand, highly-skilled individuals, despite 2
W = WR R (P1) (P2), (Z) 2(Z,wr) =f, 1Z,w): f(w)dw fw) fo w-ps (Z.w):f (w)dw Blwlw 2> We) —= ~~WRT~~ <u>Te ean fwndw</u> co ~~Wr~~ _ ~~(1+1r)'wr~~ t=0 (1+r)t ~ Tr coo <u>=P(Z,wr)-E(w|w2wp,)</u> t=1 ~~Gant~~ +u-c_
Whereby _u_ is the received unemployment benefits and _c_ is the fixed search costs born in the previous step. On (5), we add the present values of the returns to search, and obtain: Equating (6) and (4) and solving for w� gives: Equation (7) suggests that the reservation wage increases with the unemployment benefit and decreases with the search cost. It increases with the wage offer as well with the endowment of personal characteristics.

In the empirical literature, the corpus of research of the reservation wage has been mainly determined by data availability. Studies on the theme exist since the 1980s, but mainly on developed economies and frequently lacking extensive temporal or even empirical dimension. For example, Bloemen and Stancanelli (2001) only identify this basic elements of the job search theory through theoretical and auxiliary restrictions. Some empirically-grounded studies for developed countries include: Kiefer and Neumann (1979); Franz (1982); Lancaster and Chesher (1983); Feldstein and Poterba (1984); Maani and Studenmund (1986); Jones (1988); Blau (1991); Hui (1991); Gorter and Gorter (1993); Hogan (1999); Christensen (2001); Bloemen and Stancanelli (2001); Prasad (2003); Addison et al. (2004; 2009); Shimer and Werning (2007); Krueger and Mueller (2016); Schmieder et al. (2016); Le Barbanchon et al. (2019). In general, the empirical literature is positioned in two strands: i) the determinants of reservation wages; and ii) the nexus between reservation wages, unemployment duration and unemployment insurance. The first strand documents the significance of a set of personal and institutional characteristics which are relevant for the reservation wages in various setups; while the second strand documents that both unemployment duration and insurance potentially affect the reservation wage, despite the sign and the direction of the causality in case of the unemployment duration has been frequently contested.

# **3. Data**

We make use of two yearly waves of the Labor Force Survey (LFS), 2016 and 2017. We restrict the sample to working-age population, i.e. 15-64. The reservation wage, as survey question, has been asked for the first time in 2016. This is advantageous in the sense that this study is the first to use this quite new information, though disadvantageous in the sense that panel structure of our data cannot be imposed, despite LFS – for the rest of the variables – is on disposal since 2006. However, each year is composed of four quarterly survey waves, providing a total of eight periods for analysis. Further however, households are interviewed at most four times, every second quarter, and then they drop out.

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This is also a constraint for a panel analysis with longer temporal structure. The key question pertinent to this analysis is: “What is the monthly wage or salary for which you would accept to work?” to which the respondent could provide an answer in seven intervals. For our analysis, the middle point of the interval is taken and hence the variable is treated as continuous. The answer has been provided only by unemployed persons, i.e. those who are without a job and actively searching for such. Annex 1 provides the definitions of all variables used throughout.

The following **Table 1** provides information for our sample. We have on disposal 10,787 observations of unemployed people distributed among 5,955 unique individuals. Of these, 4,172 observations or 1,364 unique individuals appear in at least two time periods and have both employment and unemployment experience, so that for them we observe both the reservation and the market wage over a fairly short period of time.

**Table 1 – Samples size**

||**Total sample**|**Individuals**<br>**appearing at**<br>**least two**<br>**periods**|**Unemployed**<br>**for at least**<br>**one quarter**<br>**over the**<br>**observed**<br>**period**|**Unemployed**<br>**with**<br>**employment**<br>**experience**<br>**over the**<br>**observed**<br>**period**|
|---|---|---|---|---|
|**Observations**|69,712|61,112|10,787|4,172|
|**Individuals**|31,588|22,988|5,955|1,364|
|_Source: Authors’_|_calculations base_|_d on LFS._|||

# **4. Stylized facts**

Reservation wages in North Macedonia follow a fairly typical normal-like distribution ( **Figure 1** ). However, compared to market wages, the reservation wage distribution is considerably narrower and bulged. Still, when both are compared in average and median terms, then no significant differences are evident ( **Table 2** ). This initial observation is against the widespread perception that reservation wages in North Macedonia are frequently set high so as being an obstacle to accepting a job.

5
**Figure 1 - Reservation and market wage distribution**

<!-- Start of picture text --> Employed (market wage)<br>Unemployed (reservation wage)<br>2 3 4 5 6 7<br>Log(wage per hour)<br>1.5<br>1<br>Density<br>.5<br>0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._

**Table 2 – Basic statistics of wages**

|||**Confiden**<br>|**ce interval**<br>|<br>||
|---|---|---|---|---|---|
||Average|Lower<br>bound|Upper<br>bound|Median|Observations|
|**Market wage per hour**|93.30|92.74|93.87|79.55|28,995|
|**Reservation wage per hour**|90.54|89.90|91.18|79.55|10,195|
|**Ratio of reservation wages**<br>**to market wages**|0.97|0.97|0.97|1.00||
|_Source: Authors’ calculation_|_s based on_|_LFS. Popu _|_lation weig_|_hts accordi_|_ngly used._|

The same conclusion could be reached if reservation wages are contrasted with market wages at the skill level. **Figure 2** reveals that reservation wages grow with education, similarly as market wages do. In addition, tertiary-educated job seekers may be even more rigid in setting their expectations than what the market rewards them. What the figures do not reveal, though, is the potential differences – in observables and/or unobservables – of employed and unemployed, which is frequently documented in various studies.

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**Figure 2 - Reservation and market wage distribution, by skill level**

<!-- Start of picture text --> a) Market wages b) Reservation wages<br><!-- End of picture text --> <!-- Start of picture text --> Low skilled Low skilled<br>High skilled High skilled<br>Medium skilled Medium skilled<br>2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7<br>Log(Wage) Log(Wage)<br>2 2<br>1.5 1.5<br>1 1<br>Density Density<br>.5 .5<br>0 0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._ Despite the general observation that reservation and market wages in North Macedonia do not significantly differ, still the process of reservation wage formation may be affected by individual characteristics whose role for already employed individuals is either attenuated or does not exist. **Figure 3** presents simple correlations of the reservation wages with the duration of the unemployment spell, the household income and the number of children living in the household. All three could capture, or be related with, the job searching costs. Apparently, the longer spells are related with declining reservation wages, while household income with rising reservation wage. The relation with the number of children is not clear-cut.

**Figure 3 - Reservation wage and some personal characteristics of the unemployed**

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<!-- Start of picture text --> a) Unemloyment spell<br><!-- End of picture text --> <!-- Start of picture text --> 8000 10000 14000 18000 25000 35000 40000<br>Researvation wage (MKD)<br>10<br>8<br>6<br>4<br>Average unemployment duration 2<br>0<br><!-- End of picture text --> <!-- Start of picture text --> b) Household income<br>8000 10000 14000 18000 25000 35000 40000<br>Researvation wage (MKD)<br>300000<br>200000<br>100000<br>Average household income (MKD)<br>0<br><!-- End of picture text --> 8
<!-- Start of picture text --> c) Number of children<br>8000 10000 14000 18000 25000 35000 40000<br>Researvation wage (MKD)<br>1.5<br>1<br>.5<br>Average number of children in the household<br>0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._ Similarly, reservation wage formation could be affected by demand-side characteristics, prevalently the labor-market conditions. **Figure 4** presents correlations of the reservation wages with the average skilland field-specific unemployment rates, per period. Higher reservation wages exist in areas where these rates are lower, potentially suggesting that the overall pressure existent on the labor market has a role to play for the reservation wage formation in North Macedonia. Despite, this finding may highly interfere with the personal skill level and field of education.

**Figure 4 - Reservation wage and some labor-market characteristics**

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<!-- Start of picture text --> a) Skill-specific unemployment rate<br>8000 10000 14000 18000 25000 35000 40000<br>Researvation wage (MKD)<br>b) Field-specific unemployment rate<br>8000 10000 14000 18000 25000 35000 40000<br>Researvation wage (MKD)<br>30<br>20<br>10<br>Average skill-specific unemployment rate<br>0<br>30<br>20<br>10<br>Average field-specific unemployment rate<br>0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._ In conclusion, reservation wages in North Macedonia follow similar, though more compressed, distribution as market wages. However, their formation may be interfering with factors which are potentially more relevant for unemployed persons, like the searching costs and local employment conditions.

# **5. Methodology**

This paper has dual objectives: i) to analyze reservation wage determinants; and ii) to calculate the reservation wage premium or discount. The economic model to analyze reservation wage determinants 10
is based on the job search theory exposed in Section 2. In particular, we convert equation (7), into the following estimable form: Whereby w�,� is the reservation wage of person _i,_ u� is the unemployment benefit, which given data constraints explained below, takes the form of a dummy variable of value 1 if the person is a recipient of unemployment benefit and 0 otherwise; c� is the search cost, which is approximated by the duration of the unemployment condition; and X��� is a vector of observable characteristics. We divide the observable characteristics on supply and demand factors. The supply factors are divided in two groups: personal (gender, age, level of education, marital status, field of education and sources of living income: wages and pension of other household members, remittance receipt, social assistance receipt, income from capital) and household (number of children in the household, the total income in the household). Marriage and the number of children in the household are also used as proxies to the search costs. The demand factors are captured by defining the skill- and field-related unemployment rate. Namely, as we observe skills (approximated by 11-level scale of education) and field of education (approximated by 11-type scale of fields) for both employed and unemployed persons in our sample, we could calculate the skill- and field-specific unemployment rate (per period, where applicable). Both will measure the labor-market conditions for the skill- and/or field-niche of person _i,_ hence enabling understanding of how demand may have shaped the reservation wage. Note that, to avoid collinearity of the skill- and field-specific unemployment rates with the personal level and field of education, we define them through the most disaggregated scale, while the personal variables are represented with some reasonable aggregation. Moreover, at the personal level, both level and field of skills are more time invariant (as majority of people in the sample would have the schooling already finished at the time of interview). ε� is the idiosyncratic error which is assumed to be well-behaved.

The inclusion of the unemployment spell as approximation of the search costs may be imprecise, for various reasons. For instance, search costs may increase as the time goes by if the persons is pressed by shortage of income for livelihood; however, they may also be unrelated to the unemployment duration in case the persons lives in a household well-endowed with source of income or factors of production. The inclusion of the variables on the sources of livelihood and/or number of children should capture this variation. However, still the unemployment spell may well capture other variations other than the search costs, like the depletion of human capital, or the learning of the labor market conditions which may lead to changing of the initially set reservation wage.

To estimate equation (8), we rely on a simple OLS estimation. We pool all unemployed persons over the eight quarters we have on disposal, rather than setting a panel. We do this because 45% of the unemployed persons appear in our pooled sample only once, so that imposing a panel structure would not make much sense (see also the discussion in Section 3). Therefore, any unobserved factor relevant 11
for the reservation wage (like ability) would remain in the error term only. Such factors may interfere with the observed factors and caution in the interpretation, from this point of view, may be beneficial. On the other hand, however, having a panel structure would wipe out time-invariant indicators; a major part of our skill- and field-related education variables, are time invariant, especially given we have a span of only two years. We do include period-specific dummies, to capture general demand trends. The second drawback is that through this simple approach we may only understand correlations rather than causations. Some variables undoubtedly may suffer endogeneity bias. Take the unemployment spell: not only longer spell may press the reservation wage downward, but also high reservation wage may keep the unemployed persons out of a job longer. Hence, our estimates will be interpreted only in terms of relationships, rather than effects, though still concomitant with the objectives of the study.

In the second part of the analysis, we would like to provide estimates of the reservation wage gaps (premiums or discounts) existing in the Macedonian labor market. Namely, reservation wage may be indeed a stumbling block to (first) employment, if set very high, especially because the person does not have a full grasp of the potential of the local labor market. Or, the opposite, people may be guided by the market wages of people in their surroundings, discounting the importance of their specific circumstances determined by their observed and unobserved traits. There is a third case in North Macedonia, whereby public service jobs have a wage premium of at least 10%, which together with the perception that such job is easier to perform, created a widespread wish for employment in the public administration. Hence, the reservation wage is being formed also considering this into account.

To estimate the reservation wage premium, we apply three procedures. In the first, we compare unemployed individuals with their employed peers. We do this by applying propensity score matching, i.e. by comparing the average reservation wage of unemployed persons with the average market wage of employed persons similar in a few observable characteristics: gender, age, education and field of the studies. The predictions obtained in a probit model are used as scores to find as close as possible peers and then to compare them. In a similar fashion, in the second procedure, we predict the possible market wages of unemployed, based on their observable characteristics and using information from a Heckmanstyle coefficients obtained on employed individuals. The predicted market wage is compared with the observed reservation wage and the gap is calculated. In the third procedure, we compare unemployed individuals with themselves had they been employed in the past or in the future compared to the date when they reported a reservation wage. The disadvantage of doing this is that from the analysis we drop all unemployed persons who did not have any market wage during the time horizon of our analysis (which is anyway short). If those dropped are systematically different from those who stayed in the sample, then the obtained results would be biased. The advantage, however, is that the ‘matching’ is perfect, i.e. we compare each person with themselves, which would imply matching on both observables and unobservables; in contrast, the propensity score matching does it on observables only. In the third procedure, the reservation wage gap is calculated both backward (i.e. when the person was unemployed 12
and then got employed) and forward (i.e. when the person was employed and then got jobless). By so doing, we would be able to qualitatively judge if having employment acted as a learning mechanism of the market conditions.

# **6. Results and discussion**

# **6.1. What determines reservation wages in North Macedonia?**

The first contingent of results we analyze is the determinants of reservation wages in North Macedonia. They are presented in **Table 3** . The table is organized so that it adds the independent variables group by group. The complete economic model is estimated in column (5). Then, columns (6)-(8) divide by education level. The adding of independent factors group by group is important because some of them may refer to similar personal or household facets, as well because of measuring the same characteristic from the supply and demand side.

Females have lower reservation wages than men, by 7-8%, on average. The gender reservation wage gap is most pronounced among the low-skilled and then declines with education. Reservation wage reduces with age, by 0.2% per annum, reflecting a combination of depreciated human capital, lost motivation and hopes, learning about market conditions and so on. Most notably, hope and selfappreciation is most rapidly lost among tertiary educated. Marriage implies higher reservation wage by about 2.5%, though by more and only among low-skilled. The number of children matters as well: additional children in the household increase reservation wage by about 1.2%. As both marriage and number of children may be used as a proxy for the searching costs, they imply that the increase of search cost works positively on the reservation wage.

The return to education is found significant, both statistically and economically, for the reservation wage. The completion of secondary education is related to about 7% higher reservation wage, compared to complete or incomplete primary education. On the other hand, the role of tertiary education is overwhelming and persistent: individuals with completed tertiary education have 32% higher expected wage compared to primary-educated ones, being a 25% premium over those with secondary education. The inclusion of other explanatory variables apparently attenuates the role of education. The result may be driven by the more favorable employment opportunities for tertiary educated, but also due to the long-lasting policies of the 2006-2017 government based on proliferation of the access to (not necessarily the quality of) the higher education and the awareness-rising campaigns “Knowledge is power” which resulted in massive enrollment at universities.

The unemployment benefits work disparagingly for reservation wage: persons who receive them have a higher reservation wage, on average, than people who do not receive it, by about 2%. This is most prevalent among low-skilled workers, while it does not exist for the high-skilled ones. The longer a 13
person is without job, the lower the reservation wage, again concomitant to the searching costs: a year longer in unemployment, reduces the reservation wage by 0.2%. The effect vanishes for the highlyskilled. However, one should note that due to potential reverse causality, this coefficient may be attenuated. As searching costs are captured in the model imprecisely, through both marriage/children variables and the unemployment spell variable, it is important to note that the inclusion of the latter in the model does not change previous conclusions, pointing to the model’s robustness. Finally, households’ welfare works positively for the reservation wage formation: when household income increases by 10%, the reservation wage increases by 0.4-0.5%, on average. The result vanishes for lowskilled individuals, likely reflecting the correlation between the level of education and the household income.

Sources of own livelihood generally matter for the reservation wage creation. Particularly, persons living in a household who are recipients of remittances from a relative who is not a member of the household, have higher reservation wage by 4-5% compared to a person relying on the wages and pensions of other household member. Apparently, remittances expand the comfort zones for recipients, which has been also found in a recent study of Petreski (2019). However, remittances’ role for the reservation wage is important only for the mid-skilled individuals. Interestingly, social assistance is negatively related to reservation wage. It could be that social-assistance recipients, who are already on the lowest part of the income spectrum have low expectation about wages, which may also be a reflection of their specific circumstances and endowment with some characteristics which have not been already captured by the other variables. The result appears for low-skilled individuals only, reflecting their higher probability to be low-educated. Likewise, with opposite sign, capital income works positively: these are usually households well-endowed with various assets, positioning them on the high part of the income spectrum. The addition of the sources of livelihood for the unemployed persons does not alter any previous result, especially given that sources may capture related variation with household welfare.

Education fields are generally insignificant for the reference-wage differentiation, though with some exceptions. The reference category is ‘general education and services.’ Social sciences impose a reservation wage premium by 4.2%, yet the result vanishes when demand factors enter the regression. On the other hand, health care professionals expect a wage that is 11.1% higher than the reference category. The other profiles do not have a distinct wage expectation. However, a more robust picture is obtained when looking by level of education, especially given primary school completion is not associated with different fields (i.e. all persons with at most primary education are classified under ‘general education’). A degree in health inflates the reservation wage, at the tertiary level. However, the latter should be observed against the international competitiveness of such profiles and their propensity to emigrate in Western Europe.

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Finally, the two variables capturing demand – the skill- and field-specific employment rate – suggest that the role of demand is subdued if at all present.

In conclusion, there are few factors which potentially work for inflating reservation wages in North Macedonia. Most notably, higher education attainment sizably increases future wage expectations, especially when persons are younger and with short unemployment spells, hence with lower probability of having past work experience. While demand is found not to matter for reservation wage formation, it could only mean that demand conditions are not taken into account in the process, leading to a dangerous gap between expectations and what the market could really offer. The condition is aggravated in cases where the household is well-off and/or receives remittances. On the other hand, however, longer spells and low household welfare – including the cases of social assistance receipt – depress the reservation wage, suggesting a potentially precarious vicious cycle of skills – household welfare – reservation wage.

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# **Table 3 – Determinants of reservation wages in North Macedonia**

||||_Dependen_|_t variable: Lo_|_g of the reserv_|_ation wage_<br>|By education<br>|<br>|
|---|---|---|---|---|---|---|---|---|
|||||||Primary|Secondary|Tertiary|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Female**|<br>-0.071***|<br>-0.074***|<br>-0.074***|<br>-0.082***|<br>-0.080***|<br>-0.106***|<br>-0.082***|<br>-0.048***|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.013)|(0.008)|(0.015)|
|**Age**|-0.002***|-0.002***|-0.002***|-0.002***|-0.001***|-0.001*|-0.001***|-0.005***|
||0.000|0.000|0.000|0.000|0.000|(0.001)|0.000|(0.001)|
|**Married**|0.024***|0.027***|0.025***|0.024***|0.022***|<br>0.036**|0.015|<br>0.031|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.017)|(0.011)|(0.020)|
|**Number of children**|0.001|0.011***|0.012***|0.013***|0.013***|0.007|0.016***|0.011|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.007)|(0.004)|(0.013)|
|**Secondary education**|0.116***|0.100***|0.097***|0.086***|0.069**||||
||(0.007)|(0.008)|(0.008)|(0.009)|(0.033)||||
|**Tertiary education**|<br>0.414***|<br>0.384***|<br>0.380***|<br>0.352***|<br>0.327***||||
||(0.010)|(0.011)|(0.011)|(0.013)|(0.052)||||
|**Recipient of**||<br>0.022**|<br>0.023***|<br>0.022**|<br>0.021**|0.034**|0.026**|0.024|
|**unemployment benefits**||(0.009)|(0.009)|(0.009)|(0.009)|(0.016)|(0.012)|(0.022)|
|**Unemployment spell**||<br>-0.002***|<br>-0.002***|<br>-0.002***|<br>-0.002***|<br>-0.002***|<br>-0.002***|<br>0.000|
|<br>**duration**||0.000|0.000|0.000|0.000|(0.001)|(0.001)|(0.002)|
|**Log of household’s welfare**||0.048***|0.047***|0.045***|0.046***|0.005|0.054***|0.068***|
|||(0.008)|(0.009)|(0.009)|(0.009)|(0.020)|(0.011)|(0.021)|
|**f**<br>Remittances|||<br>0.047***|<br>0.046***|<br>0.044***|<br>0.022|<br>0.056***|<br>0.021|
|**f:**<br>**s o**|||(0.015)|(0.015)|(0.014)|(0.028)|(0.018)|(0.032)|
|**of**<br>**(Re**<br>**ion**<br>**)**<br>Social|||<br>-0.034***|<br>-0.034***|<br>-0.033***|<br>-0.060***|<br>-0.021|<br>0.032|
|**es**<br>**d**<br>**ns**<br>**ers**<br>assistance|||(0.011)|(0.011)|(0.011)|(0.016)|(0.015)|(0.048)|
|**urc**<br>**hoo**<br>**/Pe**<br>**th**<br>Capital|||<br>0.075***|<br>0.068***|<br>0.067***|<br>0.143***|<br>0.014|<br>-0.013|
|**So**<br>**eli**<br>**ges**<br>**o**<br> <br>income|||(0.020)|(0.020)|(0.020)|(0.031)|(0.022)|(0.051)|
|**liv**<br>**Wa**<br>Other|||<br>-0.035*|<br>-0.035*|<br>-0.032|<br>-0.123***|<br>0.031|<br>0.005|
|<br>income|||(0.020)|(0.020)|(0.020)|(0.030)|(0.022)|(0.076)|
|<br> <br>Humanities||||<br>0.008|<br>-0.011||<br>-0.014|<br>0.018|
|**of**<br>**es**<br>**ted**<br>**:**<br>**ral**<br>and arts||||(0.017)|(0.068)||(0.088)|(0.126)|
|**eld**<br>**udi**<br>**ple**<br>**Ref**<br>**ne**<br>Social||||<br>0.042***|<br>0.027||<br>0.016|<br>0.061|
|**Fi**<br>**st**<br>**om**<br>**(**<br>**Ge**<br>sciences||||(0.010)|(0.052)||(0.063)|(0.099)|
|**c**<br>STEM||||-0.01|-0.03||-0.075|0.075|

16
|||||(0.008)|(0.068)||(0.084)|(0.130)|
|---|---|---|---|---|---|---|---|---|
|Agriculture||||-0.008|-0.028||-0.06|-0.001|
|||||(0.013)|(0.073)||(0.090)|(0.143)|
|Health care||||0.125***|0.111**||0.061|0.187**|
|||||(0.014)|(0.047)||(0.057)|(0.089)|
|**Skill-specific employment**|||||0.001||||
|**rate**|||||(0.001)||||
|**Field-specific employment**|||||0.001|||0.002|
|**rate**|||||(0.002)|||(0.003)|
|**Urban**|||||-0.022***|-0.092***|-0.004|0.016|
||||||(0.006)|(0.012)|(0.007)|(0.016)|
|**Observations**|10,195|10,194|10,194|10,114|10,114|2,447|5,755|1,912|
|**R-square**|23.7%|24.5%|24.9%|25.5%|25.6%|13.1%|5.9%|7.1%|

_Source: Authors’ calculations._ _Note: *,** and *** denote statistical significance at the 10, 5 and 1% level, respectively. Standard errors provided in parentheses. Standard_ _<u>errors robust to heteroskedasticity. Period fixed effects included, but not shown due to space. Population weights accordingly used.</u>_ 17
# **6.2. Reservation wage gap**

Section 6.1 provided the grounds for understanding what shapes reservation wages in North Macedonia. It still does not provide a meaningful guidance on how large the problem is, i.e. how distant are expectations from reality. To pursue the second objective of this paper, we compare reservation wages with market ones. To do this in a methodologically-grounded manner, we conduct three types of comparisons.

In the first comparison, we match unemployed individuals with likewise employed ones, based on a score similarity.<sup>1</sup> The score is obtained from a probit function whereby age, gender, education and field of the educational profile are used. The probit results are presented in Annex 2. The reservation wage gap, after matching between individuals, is presented in **Table 4** , for a few matching alternatives. Results robustly suggest that the reservation wage gap in North Macedonia is positive and hovers around 13-15% of the market wage. This finding is in stark contrast with our raw observations in Section 3, however it suggests that consideration of personal characteristics – which lead to estimating an ‘adjusted’ reservation wage gap – plays an important role for reservation wage creation and potentially more than or exclusively important compared to labor-market conditions (as found in **Table 3** also).

The reservation wage gap declines with education, though persisting at 7-9% at the tertiary-education level and being as high as 22% at the primary-education level. The declining reservation wage gap with education may be also related to the increased capacity for learning and access to market information with education. Hence, although we concluded that the reservation wage increases with education, the declining gap suggests that wage expectations rise with education in a similar manner as market wage. However, such increase is faster for expectations than for actual market wages, resulting in a persistent reservation wage gap even at the tertiary level.

The analysis by field of education suggests persistent reservation wage gaps across all fields. In particular, the reservation wage gap is high for general programs and agriculture, as a major part of primary-educated individuals belong to these fields. The rest of the fields face 8-10% gap, without any statistically significant difference among them.

> 1 The comparison of unemployed individuals for whom the market wage is unobserved and employed individuals whose market wage is known, on observable characteristics, suggests that both groups are different in terms of the latter. The Hotelling test that the vectors of means are equal for the two groups, results in an F- value of 260 and p-value of 0.000, providing evidence to reject the null hypothesis.

18
**Table 4 – Cross-section (between-person) reservation wage gap (%)**

|||**Ma**<br>|**tching opti**<br>|**ons**<br>||
|---|---|---|---|---|---|
||Default|Caliper<br>(0.0001)|Nearest<br>neighbor<br>(10)|Nearest<br>neighbor<br>(50)|Caliper<br>(0.0001),<br>Neighbor<br>(10)|
|**Entire sample**|15.66***|15.64***|13.58***|12.76***|13.41***|
|||**By educatio**|**n**|||
|**Primary**|22.52***|22.53***|20.61***|18.51***|20.95***|
|**Secondary**|14.74***|14.84***|11.70***|9.60***|11.87***|
|**Tertiary**|9.00***|9.09***|7.73***|6.15***|7.83***|
|||**By field**||||
|**General programs**|20.48***|20.38***|17.18***|14.75***|17.17***|
|**Humanities and arts**|9.19*|9.27*|7.81***|5.45***|11.80***|
|**Social sciences**|14.10***|13.93***|10.68***|8.56***|9.00***|
|**STEM**|8.35*|8.25*|9.34***|8.21***|8.65***|
|**Agriculture**|21.81***|22.34***|17.10***|12.50***|17.57***|
|**Health care**|11.46*|10.52*|12.20***|11.37***|10.94***|

_Source: Authors’ calculations. Note: *,** and *** denote statistical significance at the 10, 5 and 1% level,_ _<u>respectively. Population weights accordingly used.</u>_ In the second comparison, we compare the reported reservation wage of unemployed individuals with their potential market wage. The latter is not observed, as they are unemployed. We rely on the approach of Prasad (2003) and use predictions for the market wages, based on observed ones of employed individuals. We first run a Heckman-type (1976; 1979) two-stage Mincerian function to be able to predict market wages based on observable human-capital characteristics, accounting for the selectivity bias. The Mincer-equation results, corrected for selectivity, are provided in Annex 3.

The findings about the declining reservation wage gap are largely corroborated. **Figure 5** plots the reservation wage gap against the potential market wage, which could be considered a conceptual measure of skills endowment. There is a clear negative relationship between the gap and the skill level. For the highly-skilled individuals, the reservation wage gap is prevalently negative. For these individuals, the value of employment may be high enough so that their propensity to accept jobs even below their reservation wage is high. This could be related to the value they assign to their investment in higher education and the (fear of) accelerated depreciation of their human capital, but also to the greater understanding of market potential. By contrast, for large fraction of low-skilled individuals, reservation wages are significantly over the potential market wage, presenting an obstacle for lowskilled individuals into finding and accepting a job.

19
**Figure 5 - Reservation wage and predicted market wage**

<!-- Start of picture text --> Median predicted market wage<br>Low-skilled >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> High-skilled<br>200<br>100<br>0<br>Reservation wage gap (% of predicted market wage)<br>-100<br><!-- End of picture text --> _Source: Authors’ calculations._ In the third comparison, we compare individuals who, over the observed period, have had both employment and unemployment experiences, so that for them both market and reservation wage are available over a fairly short period of time. In such case, panel structure could be imposed; the utilization of fixed effects wipes out all unobservable factors which are potentially correlated with the reservation wage. The disadvantage is that we drop all unemployed individuals without employment experience over the observed period and those who appear only one in our sample, which reduces our sample to a quarter (see **Table 1** ), which may unquestionably inflict selection bias. From this viewpoint, results should be approached with caution. However, this is the only point where we could make use of the anyway scant temporal information. We assume asymmetry in the reservation wage gap, i.e. in the reservation wage formation depending on whether it follows or precedes the earning of the market wage.

Results are provided in **Table 5** . The first thing to note is that we obtain higher reservation wage gap compared to the between-case in **Table 4** . This may actually suggest that i) persons who have had any work experience may be systematically different than the entire cohort of unemployed (e.g. live in considerable shortage of living funds); and/or ii) unobservable factors (e.g. higher motivation to search intensively) – which are not accounted for in the between-variation calculation – may have played an important role. This point remains challenge for future work. Our focus, however, in **Table 5** is on another important aspect: individuals generally reduce their wage expectations only by about 2 20
percentage points, after an experience of employment. Nevertheless, the reservation wage gap remains high and persistent even after the person learned market conditions through its own experience. It may suggest that many job seekers persistently misjudge their prospects or anchor their reservation wage on their beliefs rather than on the previous wage. The finding may not be surprising for North Macedonia, given the prevalence of underemployment: for instance, Petreski and Davalos (2019) find that 57.1% of youth (15-29) in North Macedonia reported that at least two of the following underemployment conditions on the workplace prevailed: the person worked less than 35 hours per week though wanted to work more, the person had been overqualified, the person expressed insecurity on the job; persons’ salary had been below the reservation wage; and the person had temporary or no written contract. Hence, persons who faced such conditions of employment in the past, likely retain their high reservation wage, despite the sluggish downward adjustment.

Observed by education, the high reservation wage is persisting at all levels, though the downward adjustment is faster at lower levels of education. This likely reflects the learning benefit for this group, corroborating the notions of Burdett and Vishwanath (1988) who argue that the selection process informs job seekers about market conditions over time. While, there is no adjustment at the other two levels, corroborating our previous discussion on underemployment, which is more important at higher level of skills. This is further confirmed when results are observed by field: large downward adjustment is observed in general programs and agriculture, as they largely correlate with low skills. Interestingly, downward adjustment is present in science, technology and engineering, while upward adjustment in humanities.

**Table 5 – Panel (within-person) reservation wage gap**

||**Reservation**<br>**wage gap**<br>**before**<br>**employment**|**Reservation**<br>**wage gap**<br>**after**<br>**employment**|
|---|---|---|
|**Entire sample**|24.9***|22.6***|
|**By**|**education**||
|**Primary**|33.1***|21.0***|
|**Secondary**|19.9***|21.1***|
|**Tertiary**|27.3***|27.2***|
||**By field**||
|**General education**|29.1***|23.8***|
|**Humanities and arts**|17.2**|55.0***|
|**Social sciences**|26.4***|25.5***|
|**STEM**|19.4***|15.8***|
|**Agriculture**|30.5**|9.95|
|**Health care**|37.0***|10.2|

_Source: Authors’ calculations. Note: *,** and *** denote statistical significance at the 10, 5 and 1% level, respectively. Fixed effects accordingly used. Population weights accordingly_ _<u>used.</u>_ 21
In conclusion, the reservation wage gap in North Macedonia is persistent and statistically significant. In cross-individual context, it is about 13% of the market wage, corroborating the notion that it represents a binding constraint for people to search for and accept a job. However, the gap – as opposed to the level – of the reservation wage, declines with education, suggesting that reservation wage rises with education, but learning and information-spreading mechanisms are more effective for low-skilled individuals to better align their expectations with reality. Still, persons who experienced periods of both unemployment and employment generally maintain a high reservation wage, with somewhat downward adjustment reflecting the learning experience. This is especially true for lower-skilled individuals.

# **7. Conclusion**

This study explores, for the first time, the nature of reservation wages in North Macedonia, by taking advantage of newly collected information from the Labor Force Survey 2016 and 2017. Given the fairly scant information on reservation wages, we mainly use it in a pooled cross-section context, while being aware of the limitations it imposes onto our conclusions. However, a number of interesting observations emerge, that suggest avenues for future research.

Results suggest that it is mainly supply factors that shape reservation wages in the country. That is, higher education sizably increases future wage expectations, especially when persons are younger and with short unemployment spells, hence with lower probability of having past work experience. Then, the reservation wage declines with age and with longer spells of unemployment. On the other hand, the insignificance of demand factors for reservation wage formation may suggest that job seekers either lack information about local conditions, or – more importantly – they are reluctant to fast accommodate, considering them substandard. The latter is especially the case for low-skilled individuals, who consistently value their skills higher than what the market offers and set too high expectations. These circumstances are aggravated in cases where the household is well-off and/or receives remittances. On the other hand, highly skilled individuals, despite maintaining a positive reservation wage gap, have a propensity to accept market wages even when they fall below their reservation wage. It is likely that hope and self-appreciation are most rapidly lost among the tertiary educated.

The findings of this study, combined with expanded future research, may be useful for policy makers in several ways. For example, the study disputes the widespread perception that reservation wages are only binding for high-skilled individuals and confirms this is also true for low-skilled individuals. The former are more likely to fear the accelerated depletion of their human capital and agree to work for a lower wage than their reservation wage. The latter are likely to persistently misjudge their prospects or anchor their reservation wage on their beliefs rather than on the observed wage. Therefore, increasing labor market information is more relevant for lower-skilled individuals. Still, the mechanism for the 22
highly-skilled should be further explored, especially in the context of the skill mismatches, accelerated emigration and the constant complaint – by employers – that it is hard to find workers in the country.

# **8. References**

- Addison J. T., Centeno M. and Portugal P. (2004). Reservation Wages, Search Duration, and Accepted Wages in Europe. IZA Discussion Paper 1252.

- Addison, J., M. Centeno, and P. Portugal, (2009). Do reservation wages really decline? Some international evidence on the determinants of reservation wages. _Journal of Labor Research_ , 30(1), pp. 1–8.

- Blau, David M. (1991). Search for Nonwage Job Characteristics: A Test of the Reservation Wage Hypothesis. _Journal of Labor Economics_ , 9, pp. 186-205.

- Bloemen, Hans G. and Elena G.F. Stancanelli, (2001). Individual Wealth, Reservation Wages, and Transitions into Employment. _Journal of Labor Economics_ , 19(2), pp. 400-439.

- Burdett , K. and Tara Vishwanath (1988). Declining Reservation Wages and Learning. _The Review of Economic Studies_ , 55(4), pp. 655–665.

- Christensen, Björn (2001). The Determinants of Reservation Wages in Germany. Does a Motivation Gap Exist? Kiel Working Paper, No. 1024.

- Feldstein, M., and J. Poterba (1984). Unemployment Insurance and Reservation Wages. _Journal of Public Economies_ , 23, pp. 141–167.

- Franz, Wolfgang (1982). The Reservation Wage of Unemployed Persons in the Federal Republic of Germany: Theory and Empirical Tests. _Zeitschrift f ̧r Wirtschafts-und Sozialwissenschaften_ , 102, pp. 29-51.

- Gorter, Dirk and Cees Gorter (1993). The Relation Between Unemployment Benefits, the Reservation Wage and Search Duration. _Oxford Bulletin of Economics and Statistics_ , 55, pp. 199-214.

- Heckman, J. (1976). The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models. _Annals of Economic Social Measurement_ , 5(4), pp. 475-492.

Heckman, J. (1979). Sample Selection Bias as a Specification Error. _Econometrica_ , 47, pp.153- 163.

- Hogan, V. (1999). The Determinants of the Reservation Wage. Working Paper WP99/16. University College Dublin, Department of Economics, Dublin.

- Hui, Weng-Tat (1991). Reservation Wage Analysis of Unemployed Youths in Australia. _Applied Economics_ , 23, pp. 1341-50.

- Jones, Stephen R.G. (1988). The Relationship between Unemployment Spells and Reservation Wages as a Test of Search Theory. _Quarterly Journal of Economics_ , 103, pp. 741-65.

- Kiefer, Nicholas M. and George R. Neumann (1979). An Empirical Job-Search Model, with a Test of the Constant Reservation-Wage Hypothesis. _Journal of Political Economy_ , 87(1), pp. 89-107.

- Krueger, Alan B., and Andreas I. Mueller. (2016). A Contribution to the Empirics of Reservation Wages. _American Economic Journal: Economic Policy_ , 8(1), pp. 142-79.

23
- Lancaster, Tony and Andrew Chesher (1983). An Econometric Analysis of Reservation Wages. _Econometrica_ , 51, pp. 1661-76.

- Le Barbanchon, Thomas, Roland Rathelot, and Alexandra Roulet. (2019). Unemployment Insurance and Reservation Wages: Evidence from Administrative Data. _Journal of Public Economics_ , DOI: 10.1016/j.jpubeco.2017.05.002

- Maani, S.A., and A.H. Studemund (1986). The Critical Wage, Unemployment Duration, and Wage Expectations: The Case of Chile. _Industrial and Labor Relations Review,_ 39(2), pp. 264–276.

- Petreski, B. and Davalos, J. (2019) Analysis of youth underemployment in North Macedonia, Montenegro and Serbia. PEP Working Paper. Forthcoming.

- Petreski, M. (2019) Remittances and labour supply revisited: New evidence from the Macedonian behavioural tax and benefit microsimulation model. _Migration Letters_ . Forthcoming.

- Prasad, Eswar E. (2003). What Determines the Reservation Wages of Unemployed Workers? New Evidence from German Micro Data. IZA DP No. 694.

- Schmieder, Johannes F, Till von Wachter, and Stefan Bender, (2016). The Effect of Unemployment Benefits and Nonemployment Durations on Wages. _The American Economic Review_ , 106(3), pp. 739-77.

- Shimer, Robert and Iván Werning, (2007). Reservation Wages and Unemployment Insurance. _The Quarterly Journal of Economics_ , 122(3), pp. 1145-1185.

24
# **Annex 1 – Variables’ definitions**

|**Variable**|**Description**|
|---|---|
|**Log reservation wage**|The logarithm of the wage self-reported as the level below which the<br>respondent would not accept a job offer, per assumed work hour of a<br>standard working week. The variable is observed only for unemployed<br>persons.|
|**Log market wage**|The logarithm of the wage the respondent received at the time of the<br>interview, per actual work hour. The variable is observed only for<br>unemployed persons.|
|**Female**|1 if persons is women, 0 otherwise|
|**Age**|Age expressed in years|
|**Married**|1 if persons is married, 0 otherwise|
|**Number of children**|Number of children living in the household|
|**Education**|Three dummy variables for primary, secondary and tertiary education.<br>Primary is the reference category.|
|**Recipient of unemployment**<br>**benefits**|1 if the person received state unemployment compensation in the 4 weeks<br>preceding the interview, 0 otherwise|
|**Unemployment spell duration**|The period, expressed in years, that elapsed since the last time the person<br>was employed.|
|**Log of household’s welfare**|Calculated variable, based on coefficients obtained from SILC. Note that<br>LFS does not contain data on income except for wages. The following<br>equation was used: Log of household’s welfare = 0.151*Log of the wage<br>per hour + 0.237*Average education level in the household +<br>0.022*Average age in the household -0.057*Number of children in the<br>household+6.81|
|**Sources of livelihood**|Five dummy variables created for the following sources: wages/pensions<br>of others, remittances, social assistance, capital income and other income.<br>Wages/pensions of others are the reference category.|
|**Field of studies completed**|Six dummy variables created for the following fields: general programs,<br>humanities and arts, social sciences, STEM, agriculture and health care.<br>General programs used as the reference category.|
|**Skill-specific employment rate**|The employment rate by skill by period.|
|**Field-specific employment rate**|The employment rate by field by period.|
|**Urban**|1 if the person is residing in urban area, 0 otherwise.|
|_Source: Labor Force Survey._||

25
# **Annex 2 – Propensity Score Matching**

|_Dependent variable: Probability of being_<br>_unemployed_|
|---|
|**Female**<br>0.002|
|(0.005)|
|**Age**<br>-0.009***|
|0.000|
|**Secondary education**<br>-0.109***|
|(0.007)|
|**Tertiary education**<br>-0.175***|
|(0.008)|
|**Humanities and arts**<br>-0.013|
|(0.012)|
|**Social sciences**<br>0.004|
|(0.008)|
|**STEM**<br>-0.037***|
|(0.007)|
|**Agriculture**<br>-0.009|
|(0.011)|
|**Health care**<br>-0.079***|
|(0.010)|
|**Observations**<br>40,095|
|_Source: Authors’ calculations._|
|_Note: *,** and *** denote statistical_|
|_significance at the 10, 5 and 1% level,_<br>_respectively. Marginal effects reported._|
|_Standard errors provided in parentheses._|
|_Standard errors robust to heteroskedasticity._|
|_Population weights accordingly used._|

26
**Annex 3 – Mincerian earnings function estimated with the Heckman method**

||**Outcome**<br>**equation**|**Selection**<br>**equation**|
|---|---|---|
||Dependent:<br>Log<br>observed<br>market<br>wage|Dependent:<br>probability<br>of<br>employment|
|**Female**|-0.106***|-0.114***|
||(0.006)|(0.017)|
|**Age**|-0.000*|0.019***|
||0.000|(0.001)|
|**Secondary education**|0.162***|0.327***|
||(0.009)|(0.025)|
|**Tertiary education**|0.537***|0.550***|
||(0.012)|(0.033)|
|**Humanities and arts**|0.032**|-0.057|
||(0.013)|(0.041)|
|**Social sciences**|0.057***|-0.006|
||(0.009)|(0.028)|
|**STEM**|0.021***|0.123***|
||(0.008)|(0.023)|
|**Agriculture**|-0.060***|0.102***|
||(0.012)|(0.039)|
|**Health care**|0.107***|0.180***|
||(0.012)|(0.039)|
|**Married**||0.355***|
|||(0.021)|
|**Number of children**||0.016*|
|||(0.009)|
|**Constant**|4.356***|-0.733***|
||(0.018)|(0.038)|
|**Observations**|40,095|40,095|

_Source: Authors’ calculations. Note: *,** and *** denote statistical significance at the 10, 5 and 1% level, respectively. Standard errors robust to heteroskedasticity. Population weights_ _<u>accordingly used.</u>_ 27