International evidence from countries that previously attained a level of relative income similar to Latvia shows that subsequent growth of well over 4 percent per annum is feasible, but not guaranteed. Over one-third of countries reaching a similar stage of development as Latvia in 2014 outperformed the United States’ per capita real GDP growth by more than 2 percent. A decomposition of growth drivers shows that Latvia faces particular challenges from adverse demographic trends; to counter these, efforts will be needed to increase the employment ratio by reducing still high structural unemployment. On the other hand, Latvia has much scope for improving its convergence prospects by generating higher investment ratios, which are well below the levels achieved by good performers in the sample.


International evidence from countries that previously attained a level of relative income similar to Latvia shows that subsequent growth of well over 4 percent per annum is feasible, but not guaranteed. Over one-third of countries reaching a similar stage of development as Latvia in 2014 outperformed the United States’ per capita real GDP growth by more than 2 percent. A decomposition of growth drivers shows that Latvia faces particular challenges from adverse demographic trends; to counter these, efforts will be needed to increase the employment ratio by reducing still high structural unemployment. On the other hand, Latvia has much scope for improving its convergence prospects by generating higher investment ratios, which are well below the levels achieved by good performers in the sample.

Medium-Term Growth in Latvia1

A. Introduction

1. Latvia has rebounded from the economic crisis. Since 2010 real GDP growth has been among the fastest in Europe, despite a recent slowdown. Unemployment has come down from a peak of 20 percent in 2010 to around 11 percent in 2014, close to its historical average. The current account deficit, which had deteriorated to 21 percent of GDP in 2006, is now close to balance, and the real exchange rate is in line with fundamentals. The labor market is tightening, and the output gap is estimated to be close to zero.2

2. Latvia’s relatively low level of income per capita compared to euro area core economies presents an opportunity for rapid convergence. Per capita GDP in 2014 was only 33.1 percent and 33.5 percent of the average of Germany and France respectively (28 percent of that of the United States). In purchasing parity terms, the ratio was 53.1 percent and 59.5 percent (43.7 percent).

3. On the other hand, future economic growth is subject to significant risks. In particular, labor supply will be subject to demographic headwinds, arising from both net emigration and low fertility rates. According to the United Nation Population Statistics, the working age (15–64) of the population of Latvia is projected to shrink from 1.4 million to 1 million between 2015 and 2050. Over the same period, the old age-dependency ratio, defined as the number of people aged 65 and over as a ratio to those aged between 15 and 64, is projected to increase from 28.2 percent to 37.7 percent. The aging of the labor force could make it more difficult for Latvian labor force to upgrade skills. Moreover, investment growth had been close to zero in 2013–14, caused by uncertainty arising from geopolitical tensions, tightened lending standards, and weak growth in major trading partners. Should these factors persist, investment could be anemic in Latvia in the medium term.

Sources: United Nation Population Statistics

4. International experience suggests that countries that have reached Latvia’s relative income level have a mixed record of success in closing the income gap. There is now a substantial body of literature documenting that while some economies have continued to grow rapidly after attaining middle-income status, thereby attaining per capita income levels comparable to advanced Western countries, others have stagnated, falling into the so-called “middle-income trap” (Aiyar et al (2013); Eichengreen, Park and Shin (2013)).

5. This paper examines the prospects for Latvia continuing to rapidly reduce its distance from the productivity frontier. We look at the empirical record of countries that have in the past attained a similar relative level of income to that of Latvia at present, to gauge the plausibility of our forecast for Latvia’s medium term GDP growth of about 4 percent per annum. We find that more than a third of the countries reaching a similar stage of development managed to sustain higher subsequent growth. A decomposition of the factors underpinning this growth record reveals that while Latvia faces unusually strong demographic headwinds, it can still achieve rapid growth through a combination of investment and reductions in structural unemployment. We confirm the importance of investment and structural reforms for Latvia’s future convergence, using a sector-level analysis. We find that some sectors (e.g. the manufacturing sector) whose convergence is particularly sensitive to investment had less than robust investment in recent years. We also find that some sectors (e.g. the health sector) failed to converge despite intrinsically favorable conditions and robust investment, suggesting structural impediments. One challenge facing Latvia is that a large fraction of its work force3 is now hired by sectors that failed to converge over time and many of which experienced both low productivity levels and slow productivity growth. To foster inclusive and rapid growth, Latvia needs to unleash the growth potential of these sectors and facilitate the reallocation of labor across the economy. Again, investment and structural reforms to improve labor force skills and enable better access to finance are crucial.

B. An International Comparison of Income Convergence

6. Latvia experienced rapid income convergence between 2000 and 2014. Latvia’s per capita PPPGDP as a ratio to the United States’ per capita PPPGDP was 25 percent in 2000 and increased to 44 percent in 2014. To compare Latvia’s convergence with other countries’ experience, we define the distance to frontier (DTF) as one minus the per capita PPPGDP as a ratio to that of the United States and choose countries whose DTF crossed 75 percent (Latvia’s DTF in 2000). The dataset is the World Economic Outlook (WEO) Dataset and Table 1 shows countries’ growth performance in the next 15 years after crossing the 75 percent threshold.

Table 1.

Convergence After DTF Reached 75 Percent

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7. Latvia outperformed most countries at a similar stage of development. This is suggested by both the change in DTF over the 15-year period after the DTF crossed 75 percent, and the speed of convergence. We measure the speed of convergence using the per capita real GDP growth differential between a country and the United States. It is noteworthy that the speed of convergence in previous studies often means the half-life of convergence. Latvia is ranked among the top five out of 24 countries in Table 14.

8. How likely is per capita real GDP growth of 4 percent5 over the medium-term in Latvia? According to the WEO dataset, per capita real GDP will grow by around 2 percent in the United States, so to achieve growth of 4 percent or more, the speed of convergence needs to be at least 2 percent. To shed light on this question, we choose countries whose DTF crossed 56 percent, Latvia’s DTF in 2014, and examine their convergence experience over the 10 years after the 56 percent was crossed.6 Table 2 suggests that more than one third of countries outperformed the United States by 2 percent or more in per capita real GDP growth.7

Table 2.

Convergence After DTF Reached 56 Percent

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9. A “growth accounting” decomposition is useful in comparing the underlying growth drivers in Latvia against other countries’ experiences. We define the fast growth group to be countries whose per capita real GDP growth outstripped US growth by 2 percent or more in the 10 years after their DTF crossed 56 percent, and the slow group to be the rest. Table 3 lists the growth of real labor productivity, the level change in the employment ratio, where the employment ratio is defined as employment divided by population, population growth, and employment growth. All numbers are annual. We then create a spider chart based on the numbers in Table 3. The indices shown on the spider chart are created by dividing the deviation from the average of the whole sample by the standard deviation of the whole sample. From Table 3 and the spider chart, we can see that our projections for Latvia are underpinned by fairly conservative assumptions. Given that Latvia’s employment growth is predicted to be merely 0.1 percent a year, real GDP growth depends on the labor productivity growth, which is around 3.7 percent. This number sits between the averages of the fast and the slow growth groups.

Table 3.

Comparison between Latvia’s Projected Performance in 2015–20 and Historical Performances of Countries at a Similar Stage of Development

article image

10. The spider chart illustrates that Latvia faces much stronger demographic headwinds than other countries faced when they reached a similar stage of development, but also that these can be offset through measures to reduce high structural unemployment. For both fast and slow growth groups, the annual population growth is around 1 percent, while Latvia’s population will decline by 0.3 percent per annum. Moreover, much of the decline will be driven by net emigration which tends to occur among people of working age, thereby increasing the dependency ratio (which in turn is associated with lower per capita income growth: see Bloom and Canning (2004) and Aiyar and Mody (2012)). To counteract that, structural reforms will be needed to reduce Latvia’s still-high structural unemployment rate and improve employment as a share of the total population by 0.16 percent (the annual level change). A stronger increase in the employment ratio (from the current low level of below 50 percent) is projected than what had been experienced on average in other countries. But in this respect, Latvia starts from a much higher level of unemployment (10.3 percent)—seven out of eight countries had a lower rate of unemployment than Latvia when their DTF crossed 56 percent—and hence has much more room to improve the ratio. In fact, the reduction in the unemployment rate needed to achieve the projected increase in the employment ratio would still leave the level of unemployment in Latvia (9.3 percent in 2020 and around 8 percent in 2025 if the trend continues) substantially higher than the average level of unemployment of fast growth group (3.0 percent).

11. The spider chart also illustrates that Latvia has the opportunity to converge much faster than forecast if it is able to boost its investment. Latvia’s forecast investment ratio of about 25 percent is conservative, much closer to the average of the slow growth group than the fast growth group. If it is able to improve the business environment, upgrade infrastructure, and attract FDI, it should be able to sustain a higher rate of investment. The importance of investment is further demonstrated by running a regression of the speed of convergence on the investment to GDP ratio for countries in Table 2, with both referring to the ten-year period after a country’s DTF crossed 56 percent. The text chart illustrates the relationship between the two variables. We find that investment’s impact on the speed of convergence is both statistically and economically significant. Every 10 percent increase in the ratio of investment to GDP will raise the speed of convergence by 2.6 percent. To put this another way, if Latvia could increase its investment ratio from the projected 24.6 percent to the fast growth group level of 30.8 percent, this would result in raising the speed of convergence by 1.6 percent per annum.

C. Sectoral Convergence

12. In this section, we conduct a convergence analysis at a more granular level than the previous analysis. We assess the performance of different sectors of the Latvian economy in terms of ability to reduce distance to the technology frontier, and then compare these results against the sectoral convergence record of a large sample of countries.

13. At a sectoral level Latvia’s convergence has been extremely heterogeneous, with a significant number of sectors exhibiting absolute divergence. To see this, we define the DTF of a sector as one minus the labor productivity of the sector as a ratio to the average labor productivity of the same sector in France, Germany, and the United Kingdom. We plot the change in DTF from 2002 to 2012, normalized by DTF in 20028, for 14 2-digit sectors. The text chart suggests that some sectors experienced absolute divergence between 2002 and 2012, having larger DTF in 2012 compared with in 2002. These include the education and health sectors.

14. A challenge for Latvia’s convergence is that a significant fraction of the labor force was “stuck” in sectors that have failed to converge. Table 4 lists sectors’ DTF, share of total employment, and labor productivity in 2002, 2007, and 2012. Sectors exhibiting absolute divergence over the full sample period hired 35 percent of the total employment in 2002, and the number increased to 42.3 percent in 2012. This pattern also holds at the individual sector level—except for the public administration sector, all other sectors that failed to converge had their share of total employment increase between 2002 and 2012. Moreover, the workers joining these sectors were not attracted by higher wage levels or better wage prospects (in which case the pattern may be actually good for the Latvian economy). Indeed, the data suggest the opposite. We plot sectors’ labor productivity in 2002 and the change in their share of total employment between 2002 and 2012. We can clearly see that first, except for one sector (finance), other sectors failing to converge had low productivity levels relative to other sectors. However, the fitted lines suggest that at the same level of productivity, sectors failing to converge tended to see a stronger increase in share of total employment9. Moreover, sectors failing to converge had slower productivity growth10 relative to sectors successful in convergence. The average productivity growth between 2002 and 2012 was merely 23 percent in the former sectors, in contrast with 96 percent in the latter sectors.

Table 4.

Distance to Frontier, Share in Total Employment and Productivity

(2002, 2007, and 2012)

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15. Why did workers join or remain in sectors that failed to converge, as they would expect to have a lower life time income as a result? First, it is possible that many workers had low skills and could not work in sectors with higher productivity: this is related to the problem of lack of skills and skill mismatch (see the Baltic Cluster Report, 2014). Second, sectors that converged successfully may depend more on external financing, which was constrained during the recession and thus might have acted as a brake on further employment expansion in those sectors.11

16. The inefficient sectoral allocation of labor hurts aggregate labor productivity growth. We decompose aggregate productivity growth into three components: individual sectors’ productivity growth, reallocation of labor from less productive sectors to more productive sectors, and an interaction between the two. Box I explains the detailed methodology for the decomposition. We plot the three components’ contribution to aggregate labor productivity growth between 2001 and 2012. In a country with some sectors showing absolute convergence and some showing absolute divergence, we would expect a strong reallocation from sectors with lower productivity levels and slower productivity growth to those with higher productivity levels and faster productivity growth. The chart suggests that re-allocation contributed to one to two percent growth in aggregate labor productivity in 2003–08 and little afterwards.


(In Percent)

Citation: IMF Staff Country Reports 2015, 111; 10.5089/9781475535525.002.A001

Sources: Eurostats, WEO and the author’s calculations

17. Another important question is: why did sectors fail to converge? One explanation is the lack of convergence may reflect of a lack of skills. Similar to a mechanism shown by Young (2014), if low-productivity sectors attract relatively lower skill workers, a failure to converge may simply reflect the deterioration of the average worker’s skill in these sectors. A second possibility is lack of financing, which may hamper the convergence of sectors heavily depending on external financing (Rajan and Zingales (1996)). A third explanation is that convergence may be intrinsically more difficult in some sectors than others. In their pioneering work on sectoral convergence, Bernard and Jones (1996) find that “manufacturing shows little evidence of either labor productivity or multifactor productivity convergence, while other sectors, especially services, are driving the aggregate convergence result”. Their explanation for the finding is that for the tradable goods sector, comparative advantage make countries specialized in producing different goods. In contrast, technologies in non-tradable goods sectors are similar across countries, and hence, technology diffusion will be slower in tradable sectors compared to non-tradable sectors. Note, however, that Bernard and Jones (1996) study 1970–85, a period with different characteristics from the 2000–14 period on which we focus. For example, the 2000–14 period witnessed a faster growth in the global supply chain, which may help convergence in the manufacturing sector.

18. We extend Bernard and Jones (1996) along two dimensions in our examination of sectoral convergence in Latvia. First, we study 33 European countries12 between 1977 and 2013, whereas Bernard and Jones (1996) look at 17 OECD countries between 1970 and 1987. Second, we consider distance to frontier (DTF) and the ratio of investment to gross value added,13 whereas Bernard and Jones (1996) considers only the DTF.14 We define sectors at a two-digit level and consolidate some sectors15 such that we can merge the period where information is classified using the NACE Rev. 1 with that using the NACE Rev. 2. Table 4 lists the names of these sectors.

19. The regression function is specified as follows.


where i indicates the country and j the industry. We first estimate this regression for the whole sample, with industry fixed effects, and then estimate this regression industry by industry. In the regression for the whole sample, the estimated co-efficients (and standard deviations) for the DTF and the investment to gross-value added ratio are are 7.56 (1.57), and 1.21 (0.58) respectively. Both are statistically significant. Latvia’s average DTF in 2014, weighted by employment, was 37.8 percent16 The result suggests that Latvia will grow by 2.85 percent faster than countries that are already at the productivity frontier if all sectors have the same speed of convergence as suggested by the coefficient of the DTF. Again, this provides support for the possibility of rapid convergence in Latvia.

20. Table 5 shows the estimation results of the industry by industry regressions. We can see that intrinsic convergence is absent in several sectors, many of which however are sensitive to investment. Similar to Bernard and Jones (1996), we find that the manufacturing sector lacks intrinsic convergence. Intrinsic convergence is also absent in construction, hotel, finance and public administration sectors.17 Among these sectors, manufacturing, construction, and finance are quite sensitive to investment.

Table 5.

Estimation Results of the Sector-Level Convergence Regression

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21. The results in Table 5 provide two main messages. The first is that an investment recovery is crucial for Latvia’s convergence. Take the manufacturing sector as an example: the results suggest that, given the lack of intrinsic convergence in this sector, generating sufficiently high investment is very important. The coefficient in Table 5 suggests that a 10 percent higher investment to gross value added ratio in the manufacturing sector can lead to 1.7 percent higher labor productivity growth.18

22. However, we find that Latvia had a shortfall in investment in this sector. In the sample, the average productivity growth rate in the manufacturing sector was around 5.2 percent. We plot labor productivity against the investment to gross value added ratio (we do not bother with controlling for DTF since it is not significant as suggested by Table 5). We can see that to achieve 5.2 percent growth, the investment to gross value added ratio should reach around 30 percent. Latvia’s current investment level was significantly lower than that, and the average investment to gross value added ratio was only 26 percent between 2008 and 2012 (the same ratio was 41 percent between 2003 and 2007). So there is room for Latvia to promote the manufacturing sector’s convergence through investment. Among other factors, reviving credit supply—which has been shrinking for many years—will be essential to underpin investment growth.

23. The second message is that the absolute divergence in some sectors cannot be simply blamed on lack of intrinsic convergence or lack of investment. A case in point is the health sector. Table 4 shows that the health sector has intrinsic convergence and is sensitive to investment. Moreover, this sector seems to have an investment level comparable to other countries’ experiences: investment to gross value added ratio was 18 and 20 percent in 2002–07 and 2008–12 respectively, while the average investment to gross value added ratio in our sample was only 13 percent. This suggests that there are other structural impediments which keep the sector from performing in line with its potential. While it is beyond the scope of this paper to analyze the individual factors responsible for growth performance in each sector, it does allow us to say that DTF and investment are not responsible for underperformance in sector like health, so that further investigations should focus on areas such as governance, incentives and skills mismatches.

Aggregate Productivity Growth Decomposition, Data and Methodology of Convergence Regression, and Others

I. Aggregate labor productivity growth decomposition:

Define aggregate labor productivity Xt as YtNt where Yt is aggregate gross value added divided by GDP deflators, and Nt is total number of employees. Similarly, sector level productivity Xit can be defined as YitNit, where Yit is sector level gross value added divided by GDP deflators and N it is the number of employees in sector i.

Aggregate labor productivity is then the weighted average of sector-level productivity:




The growth rate of labor productivity can be decomposed as:




The three terms in equation (1) reveal different drivers for aggregate productivity growth: (i)

iωi,t1gi,tθ=iθitXit1iθit1Xit11 measures the re-allocation of labor across sectors; (ii)

iωi,t1gi,tX=iθitXitiθit1Xit11 measures the individual sectors’ productivity growth; (iii)

iωi.t1gi.tθgi.tX measures the interaction between (i) and (ii).

D. Concluding Remarks

24. International evidence from countries that previously attained a level of relative income similar to Latvia shows that subsequent growth of well over 4 percent per annum is feasible, but not guaranteed. Over one-third of countries reaching a similar stage of development as Latvia in 2014 outperformed the United States’ per capita real GDP growth by more than 2 percent. A decomposition of growth drivers shows that Latvia faces particular challenges from adverse demographic trends; to counter these, efforts will be needed to increase the employment ratio by reducing still high structural unemployment. On the other hand, Latvia has much scope for improving its convergence prospects by generating higher investment ratios, which are well below the levels achieved by good performers in the sample.

25. At a sectoral level, convergence in Latvia has had a mixed record since the early 2000s. We show that some sectors exhibited absolute divergence over 2002–12, while others converged at different speeds. We also find that sectors which lacked convergence hired an increasingly larger fraction of the total employment over time, which suggests that if the situation is not improved, workers in these sectors would lag behind in sharing prosperity. The evidence suggests that reallocation across sectors became worse after 2008, which hampered aggregate labor productivity growth.

26. While some sectors lack “intrinsic” convergence, their productivity growth is quite sensitive to investment. A prominent example is the manufacturing sector. The current level of investment in the manufacturing sector in Latvia falls short of the level needed to achieve historical average labor productivity growth in this sector. One reason for the low level of investment could be the weak credit environment, with the stock of bank credit shrinking for many years. Reversing this trend could play an important role in supporting investment. On the other hand, some sectors have underperformed in terms of convergence despite being characterized by intrinsic convergence and attracting robust investment; further investigation will be needed to uncover the structural impediments to growth in these areas.

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Prepared by Weicheng Lian


We estimate the output gap using a production function approach using a method similar to that used by the Congressional Budget Office of the United States (Congressional Budget Office (2001)). See chapter 1 of the 2012 Article IV of Latvia special issue papers for methodological details.


42 percent in 2012


One caveat is that some fast growing economies are dropped from the Table 1 as their DTF in available years was always smaller than 75 percent


Latvia’s population growth is projected to be close to zero over the next five years, so that real GDP growth and real GDP growth per capita are very similar.


A ten year time period is chosen to reflect medium-term trends, but the results are very similar if a five year period is chosen instead


For some countries, we cannot compute their speed of convergence, as their real GDPs are missing in the period of interest in the WEO dataset. Among countries for which we can compute the speed of convergence, about half outperformed the United States’ per capita real GDP growth by 2 percent or more.


The normalization captures the idea that the speed of convergence tends to be faster when the DTF is higher.


Sectors failing to converge all lie above the fitted line of sectors successful in convergence.


Weighted by employment.


In results not shown here, we found that sectors that failed to converge expanded more relative to those that converged successfully in 2007–12


They include all the European economies with sectoral information in the Eurostat. Some countries without data, such as Malta, are left out.


We define investment to GVA ratio as the average in the previous three years, and the productivity growth as the average in the next three years.


Our approach is in the same spirit of Mankiw, Weil and Romer (1992), by looking at conditional convergence.


We treat the transportation sector and the information and communication sector as one, and put several sectors together as the “other services” sector.


This value is different from the DTF defined using per capita PPPGDP relative to that of the United States.


One explanation for the lack of convergence in these sectors could be that there has been limited technology progress in them and the improvement in productivity relies investment. For example, Davis and Heathcote, (2005) shows that the productivity growth in the construction sector is negative in the United States.


This is in contrast with the small coefficient of investment to gross value added ratio when we estimate the regression among all the industries. The latter is consistent with what we see in Table 4, as in many industries, higher investment does not lead to an increase in the labor productivity. This however does not go against the idea that a higher capital to labor ratio should imply higher labor productivity, and the reason is that we did not control employment growth and other factors in the regression. A full exploration of this issue is beyond the scope of the current paper.

Annex I. Remittances in Latvia

Although migrants’ remittances to Latvia have grown considerably over the last two decades, reaching 2.5 percent of GDP, the country’s dependence on remittance inflows remains low by international standards. Remittances strengthen Latvia’s balance of payments by providing stable and countercyclical inflows of private capital. They may also have supported households’ living standards, in particular during the financial crisis.

Stylized facts

Migrants’ remittances to Latvia have considerably grown over the last two decades, partly fueled by emigration. From a negligible level in 2000, remittances have steadily grown to more than half a billion euros. Over the period, remittances have generally increased faster than GDP and private consumption, including in the aftermath of the financial crisis (Figure 1). The ratio of remittance inflows to GDP has exceeded 2.5 percent of GDP since 2010.

Figure 1.
Figure 1.

Republic of Latvia: Remittances Inflows, 2000–14

Citation: IMF Staff Country Reports 2015, 111; 10.5089/9781475535525.002.A001

Sources: Latvian authorities, The World Bank, and IMF staff calculations.

Latvia: Remittance Inflows and Emigration, 2000–2014

Citation: IMF Staff Country Reports 2015, 111; 10.5089/9781475535525.002.A001

Sources: Latvian authorrities and The World Bank1 Latvians who stayed abroad for less than a year

Although growing fast, Latvia’s dependence on remittance inflows remains low by international standards (Figure 1). Latvia’s ratio of remittance inflows to GDP is low compared to the largest recipients of remittances in the world, including European countries such as Moldova (25 percent of GDP in 2013), Kosovo (16 percent of GDP), and Georgia (12 percent of GDP). Latvia is the second largest recipient of remittances among Baltic countries, after Lithuania.

Remittances to Latvia come almost entirely from individuals who stayed abroad for a short period (Figure 1). A large part of remittances to Latvia comes from compensation of employees, which comprises mostly remuneration of residents working abroad (Latvians who stayed abroad for less than one year). Workers’ remittances and migrants’ transfers represent less than 7 percent of total remittances, suggesting that individuals who stayed outside Latvia for one year or longer do not remit much or use informal channels.1

The bulk of remittance inflows are from Latvians staying in large European countries and North America (Figure 1). Inflows from the United Kingdom are the largest, representing 52 percent of compensation of residents working abroad, followed by inflows from Ireland, Germany, and Norway. Inflows from the United States represent 14 percent of compensation of residents working abroad. These countries also host large Latvian communities. Remittances from the other two Baltic countries are small. Various factors may have influenced the magnitude and geographical distribution of remittance inflows, including immigration policies and economic conditions in host countries (e.g., GDP growth and growth differential between host countries and Latvia) and the profile of migrants (e.g., skill level, marriage, age, and ties with the home country).2 In the case of Latvia, it is hard to find analytical evidence of these effects because of data scarcity.

Macroeconomic effects

Remittance inflows strengthen Latvia’s balance of payments stability by providing stable and countercyclical inflows of private capital, which has partly mitigated the volatility of other capital flows in particular since the financial crisis (Figure 1). Inflows of remittances have grown steadily since 2000, with double digit growth rates for most of the period and low volatility. Remittances are also less volatile than other private capital inflows, including FDI, and portfolio and other investment inflows. Remittance inflows remained stable during the financial crisis, decelerating only slightly also reflecting an increase in emigration flows out of the country. The countercyclical inflows compensated partly for the reversal in non-resident deposits and Nordic parent bank funding. As a result, remittances have represented a large share of private capital inflows since the crisis, even higher than foreign direct investment in some years.


Latvia: Remittances and other capital inflows, 2000–14

(Millions of Euros)

Citation: IMF Staff Country Reports 2015, 111; 10.5089/9781475535525.002.A001

Sources: The World Bank and IMF staff calculations.1 Remittances are defined as compensation of employees.

Remittance inflows can also support economic growth and increase households’ living standards. First, remittances increase financing available for consumption and investment, and reduce dependence on foreign capital including foreign direct investment. Second, in many countries including Latvia, remittances are stable and countercyclical, which can increase their beneficial impact on growth. Third, remittances are directly targeted at households, helping raise standards of living and reduce poverty. However, these beneficial effects could be (at least partially) offset by the negative impact of remittances on recipients’ incentives to work, possible Dutch disease effects (Mansoor and Quillin, 2006), and unattractive investment environment in recipient countries (De Haas, 2005). These effects could explain why some studies did not find a positive impact of remittances on economic growth (e.g., Spatafora, 2005).

How to increase emigrants’ economic contribution?

Initiatives to strengthen remittance inflows through formal channels, including by strengthening ties with the diaspora, could help increase emigrants’ economic contribution to Latvia. Latvia has recently adopted an action plan prepared by the Ministry of Foreign Affairs to strengthen ties with the diaspora and encourage migrants’ return. Implementation of this action plan could help boost remittance inflows and serve as a framework to attract more investment by the diaspora (e.g., increasing support to migrant associations and encouraging pension schemes targeted at migrants). Similarly, in light of the little evidence of transfers by long-term migrants found in official data, it might be worth assessing whether there are any impediments to using formal channels for remittance transfers that could deter long-term migrants from using them (e.g., fees or taxes on financial services). Banks could also explore the possibility of lending to emigrants who would like to invest in Latvia (e.g., mortgage lending).

Initiatives to develop innovative ways of tapping the diaspora as an alternative funding source for the government and banks could also increase emigrants’ economic contribution to Latvia, and provide a cushion against future headwinds. In the current environment of near-zero interest rates and abundant liquidity, banks and the government do not face any immediate financing constraints. However, funding needs could emerge in the future, at a different point of the cycle. To prepare for such possibility, policymakers could consider tapping the diaspora as an alternative funding source using innovative instruments including diaspora bonds or loans. Such instruments have been used by many countries to access funding at lower interest rate and longer maturity than sovereign bond terms. Israel, the pioneer in this type of instrument, issued its first diaspora bond in 1951 and has been issuing diaspora bonds for US$1 billion every year for a decade. Similarly, India has issued several diaspora bonds to finance infrastructure projects in 1991, 1998, and 2000.

Annex II. Econometric Analysis

Data and methodology

The home countries in our sample are East European countries, which include the Czech Republic, Estonia, Latvia, Lithuania, Hungary, Poland, Slovakia, and Slovenia. The sample period is 2004–12. Most economic variables including emigration flows and wages come from Eurostat, the output gap from the World Economic Outlook Database, geographic information from Geodist, and the stock of emigration from United Nation Population Statistics.

The basic regression specification takes the form:


where the dependent variable is gross out flow from country i to country j at time t divided by the population size of country i at time t, and the explanatory variables include the mean wage of country j at time t divided by the mean wage of country i at time t, the output gap of country i at time t, and a vector of time-invariant measures of country pair i-j, Zij, which includes the distance between the capital city of country i and that of country j, a dummy indicating whether country i and country j share a border, and a dummy indicating whether in country i and j, more than 9 percent of their populations speak the same language. We do not control for country pair fixed effects or home country fixed effects, because the ratio of the mean wage of the host country to that of the home country is slow moving for a given home country and similar across major destination countries (elaborated below). Including these fixed effects would largely absorb the effects of the ratio of mean wages. We do not include host country fixed effects, as they are not likely to play a significant role if we restrict our sample to major destination countries (elaborated below).

We use a two-step analysis to forecast aggregate emigration flows. First, for each of the home countries in our sample, we only study emigration flow to the top five destinations in terms of the size of emigration flow. Second, we multiply the estimated elasticity of emigration flow to economic conditions by five, which is further divided by an adjustment factor to get the elasticity of the aggregate emigration flow to economic conditions. We choose the adjustment factor to be the average of the share of emigration flows from a home country to its corresponding top five destinations in the total emigration flow from the home country.

Alternative assumptions regarding expectation formations

As noted in the main text, expectations are likely to play an important role in the relationship between the wage gap and emigration, but are not accounted for in the main forecasts. Here we describe an alternative method for taking expectations into account, corresponding to the last text chart in Section IV of the paper. Since permanent income is not observable, we rely on the following three assumptions to analyze how Latvia’s aggregate emigration outflow responds to different wage growth expectations.

Assumption 1. Emigration is a linear function of the ratio of the permanent income in the host country to that of the home country. Thus:


where Xijt are other factors influencing emigration from country i to country j.

Assumption 2. Latvia’s elasticity of migration with respect to wage difference in 2014 is the same as the average in Eastern European economies in 2004–14:


Since the coefficient of the wage ratio in equation (1),β^, is estimated from eastern European countries’ emigration history between 2004 and 2014, which is a mix of boom and bust cycles, we think assumption 2 is not implausible.

Assumption 3. Latvia public’s perception of permanent wage growth was 1.5 percent in 2014. This can be justified by assuming that people use adaptive rules to form their forecasts of future wage growth. To put this number into context, note that the average wage growth between 2009 and 2013 was only 0.9 percent, and the level of wage in Latvia in 2013 was still 3.5 percent lower than its peak in 2008.

Then, we can use the following formula to compute the emigration flow as a share of Latvia population in year t >= 2014 if the expected wage growth rate in year t is g%:


Some remarks are as follows:


Fourth, in 2015, even if WageLatvia, 2015/Wage Latvia, 2014 is not going to be much different from Wage j,2015/Wage j,2014, Δ(g,t) can be quite different from zero if g is different from 1.5. This captures the idea that the perception of future wage growth can strongly affect the emigration decision. Then, for each scenario with a certain wage growth assumption, we can compute the corresponding Δ(g,t), based on which we can forecast emigration outflow using equation (2).

First, f(x) is the ratio of permanent income divided by the current wage from the perspective of a worker aged 25 and who is expected to retire in age 65;

Second, we use the year 2014 as a starting point, and change in wage growth can lead to a change in emigration flow relative to what is observed in 2014. It is easy to check in the special case where t = 2014 and g = 1.5, Δ(g,t) = 0;

Third, when g > 1.5 and WageLatvia, t/WageLatvia, 2014 > Wagej,t/Wagej,2014, Δ(g,t)> 0.

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Prepared by Astou Diouf, Weicheng Lian, and Gabriel Srour.


There were large outflows as well as inflows of population prior to 1990, but the latter predominated over that period.


See for instance Asch (1994) and Ratha et al. (2011).


Although in principle a fraction of these positions, for instance related to self-employment, could be lost permanently.


Data on the prior employment status and education/skill level of Latvian emigrants is very limited. The analysis is further complicated by the simultaneity effects between unemployment, wages, and emigration.


See IOM (2004) for practices across European countries.


Formal inward remittances are the sum of workers’ remittances, compensation of employees, and migrants’ transfers (The International Transactions in Remittances, Guide for Compilers and Users, IMF, 2009). Workers’ remittances refer to transfers in cash or in kind from migrants, i.e. workers staying abroad for one year or more. Compensation of employees refers to remuneration, in cash or in kind, paid to individuals who work in a country where they have stayed for less than one year. It also includes wages and salaries earned by the local staff of foreign institutions, such as embassies and international organizations, and companies based abroad but operating locally. Migrants’ transfers include flows of goods and financial assets linked to the migrants’ cross-border movements.

Republic of Latvia: Selected Issues
Author: International Monetary Fund. European Dept.