Natural Decrease in the Countries and Counties of Europe: A Multi-level Analysis

Dudley L. Poston, Jr., Texas A&M University

Kenneth M. Johnson, University of New Hampshire

Layton M. Field, Mount St. Mary's University (Emmitsburg, Maryland)

[email protected] [email protected] [email protected]

Introduction In Europe today there is virtually no population growth. According to recent data from the Population Reference Bureau (2015), the crude birth and death rates in Europe are both

11/1,000, and in the European Union they are both 10/1,000, resulting in percentage rates of natural increase (RNI) in Europe and the EU of 0.0. All of Europe’s 45 countries1 have RNIs less than 1.0, the highest being both Kosovo and Ireland at 0.9%. Fifteen of Europe’s countries have negative RNIs, and seven have RNIs of 0.0%. The countries with the highest negative RNIs are

Bulgaria at -0.6%, Serbia at -0.5%, and Hungary, Ukraine, and Romania, all at -0.4%. Russia, the largest country in Europe with a population of 144.3 million, has an RNI of 0.0%; the next two largest countries have negative RNIs, Germany at 81.1 million with an RNI of -0.3%, and

Italy at 62.5 million with an RNI or -0.2%.

Negative rates of natural increase result from an excess of deaths over births in the population. That is, in a particular year or over several years the population has more deaths than births. This phenomenon of excess mortality is known in demography as natural decrease. A long-term continuation of natural decrease will result in the continual diminution of the population, and eventually lead to its disappearance, unless the excess of deaths over births is not offset by population gains due to increased fertility and/or to net in-migration.

To gain an appreciation of the implications of a negative rate of growth over an extended period of time, demographers use the notion of “halving time,” or the number of years it would take a population to become half as large if its negative RNI remains unchanged (Poston and

Bouvier, 2010: 274-275). One divides the natural log of 2 by the RNI, and multiplies the result by 100. In the case of Bulgaria with an RNI of -0.6, if its RNI remains unchanged into the future, its population of 7.2 million would drop to 3.6 million in 116 years. If its negative RNI of -0.6 continued indefinitely, in less than 1,000 years, there would no longer be a country of Bulgaria.

Demographers have been analyzing the phenomenon of natural decrease since the 1960s when Calvin Beale published his classic article in 1969 in the journal Demography on “Natural

Decrease of Population: The Current and Prospective Status of an Emergent American

Phenomenon.” Indeed, most demographic analyses of subnational natural decrease have been conducted among the counties of the United States (Beale, 1969; Fuguitt, Brown and Beale

1989; Johnson 1993, 2011a, 2011b; Johnson and Beale 1992; Johnson and Purdy 1980; Morrill

1995; Adamchak 1981; Chang 1974; Koebernick and Markides 1975; Poston, Bradshaw and

DeAre 1972). Demographers know far less about the incidence and extent of natural decrease in the subnational areas of Europe.

There has been some attention to natural decrease at the national level in Europe (e.g.,

Heilig et al. 1990; van de Kaa 1987). Research has shown that numerous European nations, for instance, Germany, Italy, Hungary and Portugal, are currently experiencing natural decrease

(Doteuchi 2006; Muenz 2006; Nikitina 2000; Population Reference Bureau 2015). But, by far the bulk of demographic analyses of natural decrease worldwide has been conducted among the counties and subareas of the United States. Very few analyses of natural decrease have been undertaken among the subareas of the countries of Europe. Yet it is in Europe where there is a far greater amount of natural decrease, and there is far less in the United States.

Indeed, in the circa 2000-10 time period, 774 (or 59%) of all 1,315 counties in Europe experienced natural decrease. If we restrict the comparison to only those European countries with at least 7 natural decrease counties, then 763 (or 62%) of 1,222 counties experienced natural decrease. These European percentages of 59% and 62% are more than twice the magnitude of the percentage of all U.S. counties, 27%, experiencing natural decrease in the 2000-09 period.

Clearly, it is among the subareas of most European countries where there is the greatest amount of natural decrease in the world. Yet, demographers know very little about natural decrease among the subareas of the European countries.

In this paper we endeavor to address this void. Using data from EUROSTAT (2011) for the subareas of the countries of Europe for the circa 2000-10 time period, we ascertain the degree of natural decrease in the respective county-level areas. We describe in detail the prevalence of natural decrease in the subareas of the countries of Europe.

Next, to gain a better understanding of the major demographic factors producing natural decrease and increase, we estimate multivariate regression equations predicting for each

European county its birth/death ratio, i.e., the number of births in the county per 1,000 deaths.

This rate is a more nuanced measure than a dummy variable indicating whether or not a county has experienced more deaths than births. Prior research of natural decrease in U.S. counties suggests that in its initial phase, natural decrease is intermittent in a county with periods of minimal natural decrease interspersed with periods of minimal natural increase (Johnson 2011a).

Statistically this introduces considerable “noise” resulting in some counties with very similar situations experiencing natural increase (because they have a few more births than deaths), while others experience natural decrease (because they have a few more deaths than births). The birth/death ratio allows us to identify these two sets of counties as very similar, in contrast to a simple dichotomy of natural decrease or natural increase.

We will estimate multiple regression equations and will show that the birth to death ratio is the lowest for counties with a large percentage of older adults and with a low level of fertility. Finally, in an analysis not yet undertaken but to be completed prior to the PAA conference in March of 2016, we will undertake a multi-level analysis, in which the counties will be the level-1 units and the European countries will be the level-2 units. We will model the birth/death ratio, the dependent variable, with the fertility rate and the aging index at the county level, i.e., level-1, and with three country-level, i.e., level-2, variables, namely, GNI per capita, urbanization level, and religious affiliation.

Past Research

In the United States, births have always exceeded deaths by a substantial margin. For example, in 2015 the U.S. rate of natural increase was 0.5%. By comparison, only five of the 45 countries of Europe have RNIs of 0.5% or higher (Kosovo, Iceland, Ireland, Albania, Andorra).

Since natural decrease has never occurred at the national level in the U.S., the topic has received little if any demographic attention at the level only if the U.S. However, U.S. demographers have investigated numerous instances of sub-national natural decrease in the U.S. (we cited some of this literature in the previous section). Dorn (1939) conducted the very first analysis of natural decrease and documented its brief emergence in a few places in the U.S. during the Great

Depression of the 1930s. He concluded that these early instances of natural decrease were due mainly to low fertility rates. The natural decrease was short-lived and disappeared when fertility rose in the 1940s and during the “baby boom” that began at the end of World War II. Beale reported the growing incidence of sub-national natural decrease in the 1950s and 1960s at the peak of the Baby Boom years in rural America, when most demographers were focusing on the substantial contribution of natural increase to the rapid population growth in the U.S. (Beale

1964, 1969). By 2005, half of all U.S. counties had experienced at least one year of natural decrease. And, the number of counties experiencing natural decrease was accelerating from 483 counties in 1990 to a record 985 in 2002 (Johnson 2011a). Subnational natural decrease was fairly unusual in the U.S. prior to the latter part of the 20th century, but it became increasingly common by the late 20th century.

There is no companion body of literature of subnational analyses of natural decrease in the counties of Europe. At the country level, low fertility is often cited as the reason for the growing incidence of natural decrease in Europe (Coleman and Rowthorn, 2011). We noted above that Dorn (1939) offered a similar argument for the early incidence of natural decrease in the U.S. during the Great Depression. Low fertility has been mentioned as one of the major reasons for natural decrease both in the countries of Europe and in the U.S.

However, there is a competing hypothesis emerging from the literature on natural decrease in the U.S. Beale has argued that the main cause of sub-regional natural decrease in the

U.S was changes in local age structures resulting from the protracted age-selective out-migration of young adults (Beale 1969). His finding has since been substantiated in numerous subsequent

U.S. studies (Johnson 2011a). Beale’s conclusion was not consistent with the conventional wisdom of the time that attributed natural decrease to low fertility. We estimate below multivariate regression equations allowing us to assess the relative importance of age structure and fertility levels in the emergence of sub-national natural decrease in the counties of Europe.

Data and Methods

In the analyses for the European countries that we undertake in this paper, we use data from the EUROSTAT Yearbook, 2011 (EUROSTAT, 2011) and examine the numbers of deaths and births in the county-level units of every country of Europe with at least eight counties. The periods of time covered vary somewhat for each country, ranging from 2000-09 to 2003-10. To be included in our analysis, we required that a country must have at least eight counties and must have birth and death data available for the counties for at least 8 consecutive years in the 2000-

2010 period. If a country had fewer than eight counties, or had data for its counties for less than eight consecutive years, it was excluded. Turkey is a good example. Turkey has 84 counties, but only provided birth and death data for the four consecutive years of 2007-2010. So we did not include Turkey. We excluded Finland, Denmark, and Slovakia for a similar reason. All our included countries provided the required demographic data through EUROSTAT for at least the consecutive years of 2003-2010.The twenty-two European countries included in our paper and their respective time periods are listed in Table 1.

** Table 1 About Here **

What is a “county” in a European country? How is a European county defined spatially and demographically? Sub-national regions, i.e., counties, in each European country vary from one country to the next. However, “Regulation (EC) No 1059/2003 of the European Parliament and of the Council adopted in May 2003” uses the Nomenclature of Territorial Units for

Statistics (NUTS), a classification system we also use in this paper. (See Council of the

European Communities [2003] and EUROSTAT [2007] for more detail.)

The purpose of NUTS is to create geographical divisions in each country that enable meaningful comparisons over time from one country to the next (Council of the European

Communities, 2003). Each country is divided into three levels (NUTS 1, NUTS 2, and NUTS 3); the third level most closely resembles counties in the United States, although the median geographic size of the NUTS 3 regions is slightly smaller than the average size of counties in the

U.S. (Ciccone,2002).

In implementing the NUTS system throughout Europe, EUROSTAT sometimes combined contiguous geographic areas into single NUTS 3 areas so to make the NUTS 3 areas more similar geographically and demographically throughout Europe. To illustrate, Scotland has

33 territorial units referred to in the UK as “counties.” Many are small in population. Thus,

EUROSTAT combined several of the smaller ones, as long as they were contiguous, into single

NUTS 3 units so that Scotland now has 23 NUTS 3 units. One of Scotland’s NUTS 3 units is

“Caithness and Sutherland and Ross & Cromarty,” an area consisting of the three counties of

Caithness, Sutherland, and Ross & Cromarty. This NUTS 3 unit has a population size in 2010 of just over 91,000, which has it almost reaching the preferred minimum size of NUTS 3 units throughout Europe of 150,000. But had these three counties not been combined into a single

NUTS 3 unit, the smallest of them, Sutherland, at just over 13,000 people, would have been substantially below the preferred minimum size of 150,000.

There are a total of 1,303 NUTS 3 regions in Europe, each consisting of a population ranging in size from under 100,000 to no higher than 800,000. The NUTS designation takes into account existing geographic and political divisions in each country but provides a standard that allows for cross national comparisons. The NUTS 3 units refer to Départements in France, to

Kreise in Germany, to Provincie in Italy, to Provincias in Spain, and to Counties in the United

Kingdom. In our paper we refer to the NUTS 3 regions in all the European countries as

“counties.”

Natural Decrease in the Counties and Countries of Europe: Description

We showed above in Table 1 for each of the 22 European countries included in the multivariate analyses its number of natural decrease counties and its percentage of all counties that experienced natural decrease in the circa 2000-10 period. Of the 22 countries, Germany by far has the most counties, 429, with the United Kingdom and Italy following with 133 and 117 counties, respectively.

In eight of the 22 European countries, more than one-half of their counties experienced natural decrease in the time period being analyzed (recall that the time period in each country for determining the existence of natural decrease in its counties varies slightly from country to country). All of Lithuania’s 10 counties experienced natural decrease, and almost 91 percent of

Croatia’s 21 counties experienced natural decrease; these are followed by 82 percent of

Germany’s 429 counties, and 81 percent of Romania’s 42 counties; other countries with more than half of its counties experiencing natural decrease are Greece (71%), Italy (64%), Portugal

(63%), Sweden (62%), and Slovenia (58%). Almost 60 percent of all the counties in Europe experienced natural decrease in the circa 2000-10 period.

To get an idea of the prevalence of natural decrease throughout Europe, and not only in the 22 European countries shown in Table 1, we prepared a map of all of Europe, distinguishing among the counties according to whether they experienced natural decrease (colored red) or natural increase (colored blue) (see Figure 1). More than 58 percent of the European counties shown in the map experienced overall natural decrease during the study period. But as we show in the map, the spatial impact of natural decrease was not only pervasive but was broad as well.

Deaths exceeded births in most of the counties of Germany, Hungary, Croatia, Romania,

Bulgaria and the Czech Republic, as well as in Sweden and the Baltic States. Further south, natural decrease is also found occurring in the majority of the counties of Greece, Portugal and

Italy. Though natural decrease was common in much of Europe, it was far from universal.

Natural increase is widespread in Ireland, Cyprus, Iceland, Lichtenstein and Luxembourg.

Natural increase is also evident in broad regions of France, Belgium, the Netherlands,

Switzerland, United Kingdom and Norway. In only two countries of Europe, namely, Ireland and

Iceland, was there natural increase in all the counties (Johnson, Field, and Poston, 2016).

*** Figure 1 About Here ***

Our attention thus far has focused on whether a county had overall natural decrease for the entire study period. Research in the U.S. has shown that initially counties have intermittent natural decrease, which eventually becomes more consistent (Johnson 2011a). We find in our data that the trend is similar in Europe (Johnson, Field, and Poston, 2016). Some 41 percent of the European counties had natural decrease in every year for which data are available. An additional 17 counties had natural decrease in at least half of the years, and in 13 percent of the counties there were instances of natural decrease, but it occurred in fewer than half the years. In all, only 29 percent of the European counties had consistent natural increase in each year of the study period.

Factors Influencing Natural Decrease in Europe: Multivariate Analyses

In this section we first estimate several multivariate regression equations for the counties of Europe to ascertain the relative effects of the above two factors, age structure and fertility, on natural decrease. Our dependent variable is the birth/death ratio, which reflects the number of births per 1,000 deaths. We first estimate a single regression equation for all 1,315 counties of the 22 countries of Europe shown in Table 1 with the two independent variables of age structure and fertility.

We next divide the counties into four categories based on their spatial location along the rural-urban continuum. We expect the spatial location of a county on the continuum to be relevant. Location reflects the cumulative impact of numerous economic, social and organizational forces that drive population redistribution. Together these contextual forces are expected to influence the age structure and fertility patterns of counties and in so doing contribute to the occurrence of natural decrease. Research in the U.S. has consistently shown that the incidence of natural decrease tends to be the highest in remote rural counties and the lowest in large urban agglomerations (Johnson 2011a). It is our expectation that a similar pattern will be evident in Europe (Johnson, Field, and Poston, 2016).

We thus divided the counties in Europe into four groups: counties in large metropolitan agglomerations, counties in smaller metropolitan areas, counties that are outside metropolitan areas but immediately adjacent to them, and counties that are not adjacent to metropolitan regions. It is in the latter group of European counties that we expect to find the highest levels of natural decrease. Moreover, when estimating multivariate regression equation for all four groups, we expect the effects of age structure and fertility to vary in the equations, with the greatest effects found in the equation for counties not adjacent to metro areas.

In each multivariate regression equation, the independent variable dealing with age structure is the proportion of the population comprised of older adults. Populations with greater numbers of older adults are at a greater risk of natural decrease than populations with fewer older adults owing to the increased likelihood of substantial mortality loses. How a local population’s proportion of older adults grows varies from place to place, but it is certainly due to a combination of aging in place and age-specific net migration. Age selective migration will manifest itself differently from county to county. For example, in the U.S., the net inflow of older migrants to retirement destination counties, or the net loss of a significant proportion of the young adult population from agricultural counties, would both be typical migration signatures that would impact county age structure (Johnson and Fuguitt 2000; Johnson, et.al 2005; Johnson,

Winkler and Rogers 2013). The concentration of older adults is measured by the number of adults aged 65+ per 1,000 residents. Our expectation is that the higher the proportion elderly in a county, the lower the birth-death ratio. This means that as the number of elderly persons in a county increases, the number of births per 1,000 deaths decreases.

Our second independent variable is fertility. Low fertility has been widely mentioned as an important factor in the widespread occurrence of natural decrease in developed nations

(Doteuchi 2006; Muenz 2006; Nikitina 2000). For example, one major explanation offered for the slowdown in natural increase in Europe is that inhabitants are having fewer children. It was noted that the aggregate total fertility rate of the 27 countries that form the European Union dropped from 2.5 births per woman in the early 1960s to 1.6 for the 2006 to 2008 period

(Eurostat 2011). Concerns about below replacement level fertility have also been recognized as having significant implications for overall population decline in developed countries (Morgan and Taylor 2006). In the U.S., however, the literature is less clear on the impact of local fertility levels on natural decrease. Johnson (2011a) has suggested that although temporal declines in fertility have contributed to the increasing incidence of county level natural decrease, there is little evidence that fertility rates in natural decrease areas are inordinately low compared to those in other counties. Here fertility is measured using the child-women ratio (children under 5/ women 15-44). We expect the fertility rates to be positively associated with the birth/death ratio; the higher the fertility rate, the higher the birth/death ratio.

Of course, these two explanations (low fertility and age structure shifts) are not mutually exclusive. In fact, low fertility in a population with a large number of older adults will maximize the likelihood of natural decrease. However, the relative contributions of these factors with respect to the emergence of natural decrease have not been examined among the counties of

Europe. In the U.S. experience, natural decrease has occurred because mainly of an aging population. An inordinately low fertility rate among these women appears to have been less of a factor (Johnson 2011a). It is not clear whether a similar pattern exists in Europe, where there is widespread concern about the impact of low fertility (Coleman and Rowthorn 2011).

We report in Table 2 the results of the multivariate regression equations estimated for the

European counties. In the first column are the results for all 1,315 counties of the 22 countries listed in Table 1. In the next four columns are the results for the counties after subdividing them into the following four spatial categories: 1) nonmetropolitan counties that are not adjacent to metropolitan areas; 2) nonmetropolitan counties that are adjacent to metropolitan areas; 3) counties comprising or located in small metropolitan areas; and 4) counties comprising or located in large metropolitan areas.

The metric coefficients and the standardized coefficients are shown in the table. The two independent variables are statistically significant in all five equations. And in all five equations, it is the age structure variable that has the greatest relative effect on the birth/death ratio. The variable measuring the percentage of the population of age 65+ is in every equation far more important in predicting the birth/death ratio than is the fertility variable. These results are consistent with results from previous analyses of natural decrease in the counties of the U.S. And these results for the counties of Europe are consistent with the late Calvin Beale (1969) who long argued that the most important and major cause of natural decrease in U.S. counties was changes in local age structures resulting from the protracted age-selective out-migration of young adults, a view that was inconsistent with the conventional wisdom at the time in the U.S. attributing natural decrease mainly to low fertility.

** Table 2 About Here **

We now mention the next stage of this paper we have not yet conducted, but will do so prior to the PAA convention in late March of 2016. We will undertake a multi-level analysis of natural decrease in the counties of Europe. We will ask whether there are any particular characteristics of the European countries themselves that influence the birth/death ratios of their counties. We hypothesize that several characteristics of the countries may be influencing the levels of the birth/death ratios of their counties, namely, urbanization level, GNI per capita, and religious affiliation. We expect that among the countries the higher the GNI per capita and the greater its level of urbanization, the higher the birth/death ratio of its counties.

This reasoning is grounded in human ecological theory positing a relationship between economic opportunities and urbanization and the greater likelihood of net in-migration than net- outmigration, thus minimizing the likelihood in the counties of having more deaths than births

(Poston and Frisbie, 1998, 2005). Our other level-2 independent variable is religious affiliation.

Here we hypothesize that the greater the religious beliefs and affiliation of a country, the greater the birth/death ratio of its counties. The case of Ireland, a predominantly Catholic country, is striking. We showed above in Figure 1 that of all 22 of the countries of Europe in our analysis,

Ireland is the only country in which none of its counties are natural decrease counties (Iceland is also shown in the map as having no natural decrease counties, but it is not one of the 22 countries in our analysis).

Before estimating the hierarchical regression equations, however, we first need to conduct an analysis of variance (ANOVA) that will enable us to determine the total amount of variability in the dependent variable of the birth/death ratio within and among the countries. If the country-level variance does not equal zero, this means that there is variance in the birth/death ratio across the countries, and is an indication that multilevel modeling is appropriate.

th The level-1 or county-level equation predicting the birth/death ratio, Yij , for the i county in the jth country of Europe, with no independent variables, is the following (Raudenbush and Bryk 2002: 69-70):

Yij = 0j + rij (1)

This equation models the birth/death ratio in each county with just an intercept, 0j, which in this case is the mean level of the birth/death ratio for the country; rij is the error for the ith county in the jth country.

In the level-2, or the country equation, each country's mean birth/death ratio, 0j, is modeled as a function of the grand mean of the birth/death ratio, plus a random error, and is the following:

0j = 00 + u0j (2)

These two equations (1) and (2) are combined into one individual-level equation, and estimated, as follows:

Yij = 00 + u0j + rij (3) Maximum likelihood estimates are also produced for the variance components at level-1

(among counties) and at level-2 (among countries). The variance estimate, σ2, is produced for level-1; and the variance estimate at level-2, that is, between the countries, τ00, is also produced.

If the level-2 variance estimate, τ00, is significantly different from zero, this means that there is a statistically significant amount of variance in the birth/death ratios that occurs between the countries, warranting a multilevel analysis of the birth/death ratios. We also calculate an intraclass correlation, which represents the actual proportion of variance in the birth death ratios between countries (Raudenbush and Bryk 2002: 71), using this formula:

2  = 00 / (00 +  ) (4)

The value of the intraclass correlation tells us precisely the proportion of all the variance in the birth/death ratio that occurs among countries, and that, therefore, by subtraction from 1.0, the amount that occurs among the counties.

It is expected that the ANOVA model just discussed will indicate that there is a statistically significant amount of variance in the birth death ratios across the 22 European countries, thus requiring a multi-level analysis. We will now discuss the hierarchical equations we will use to model these variances.

In our models of the birth/death ratios at the county level, we have shown above that we will use two independent variables, age structure and fertility. The relationship between these two county-level independent variables and the dependent variable of the birth/death ratio is represented in the following regression equation:

Yi = 0 + 1(age-structure)i + 2(fertility)i + ri (5) The above equation (5) is a typical microlevel equation, just like the ones we estimated above and whose results reported in Table 2. It specifies that the effects of the two independent variables on the birth/death ratio (1 and 2) to be the same for all countries. In other words, the intercept and slope effects in the model of equation (5) are equal for all counties irrespective of the countries in which they are located; the effects are fixed; this is a fixed effects model.

Let us move beyond equation (5) to an equation in which the three effects (the intercept and the two slopes) are random, and not fixed. Our EUROSTAT data refer to 1,315 counties located in 22 European countries. Thus we will estimate the micro-level equation (5) above for each of the 22 European countries, as follows:

Yij = 0j + 1j(age-structure)ij + 2j(fertility)ij + rij (6)

Note that the only difference between equation (5) and equation (6) is that in the latter equation the intercept and the two slopes have been subscripted with j. Thus the three effects, 0j and 1j through 2j, are now permitted to vary across all the counties of the countries. They are being treated as random.

We now turn to the level-2 or country-based equations in which we use three country level characteristics to predict each of these three effects. As already noted, the three country- level characteristics are GNI per capita, urbanization level, and religious affiliation. We, of course, will need to first determine if we can use all these level-1 and level-2 independent variables at the same time owing to issues of multicollinearity. Assuming there is no multicollinearity among the independent variables at level-1 and level-2, for which we will test, here then is the set of three level-2 or country level equations that will be used to estimate the above three effects shown in equation (6): 0j = 00 + 01 (GNI)j + 02 (urbanization)j + 01 (religion)j + u0j (7)

1j = 00 + 01 (GNI)j + 02 (urbanization)j + 01 (religion)j + u0j (8)

2j = 00 + 01 (GNI)j + 02 (urbanization)j + 01 (religion)j + u0j (9)

Equations (7) through (9) are then combined and integrated with equation (6) to yield one combined hierarchical equation. This will be a true multi-level random effects model. This combined hierarchical equation is then solved. It provides us with the following results:

1. the direct effects on the birth/death ratios of each of the three country-level characteristics of GNI per capita, urbanization, and religious affiliation.

2. the direct effects on the birth/death ratios of each of the two county-level characteristics of age structure and fertility.

3. and, importantly, the cross-level effects of each of the three country-level variables on the slope of each of the two county-level variables with the birth/death ratio.

This third group of effects allows us to determine if and whether the value of the slope of a county-level variable on the birth/death ratio increases or decreases according to one of the three characteristics of the country. To illustrate, suppose we obtain a positive value of 3.53 for the county-level slope of fertility on the birth/death ratio (as we did above in Table 2). This would mean that among all the European counties, the higher the fertility of the county the higher the birth/death ratio. Specifically, for every one unit increase in the child/woman ratio, there is an increase of 3.53 in the number of births in the county per 1,000 deaths. But in a hierarchical equation, this coefficient of 3.53 for the fertility variable is not treated as a fixed effect, that is, it is not the same for all the counties in the sample. Instead it is treated as random and allowed to vary among all 22 European countries in our analysis.

Thus we will interact the level-2 country characteristics with the level-1 county-level slopes. Suppose the interaction of the country characteristic of GNI per capita with the county- level fertility-birth/death ratio slope is .20. This would signify that the slope effect of fertility on the birth/death ratio increases for a country by .20 with every one unit increase in the country’s

GNI. In other words, in richer countries, there is a stronger effect of fertility on the birth/death ratio than in poorer countries.

These cross-level interactions should be an important addition to our analysis of natural decrease via our equations modelling the birth/death ratios, over and above the information obtained in coefficients about the direct effects of the county (level-1) and country (level-2) independent variables on the birth/death ratios. Hopefully, these illustrative paragraphs show satisfactorily how we will use multilevel models in the next stage of our paper, how they will be estimated, and how their results will be interpreted.

We now make one final comment regarding the possible effects of country-level characteristics on county-level natural decrease. We have been following closely the recent news about the varying degrees of reception in many European countries regarding the acceptance of immigrants from Syria and other countries in the Middle East. This provides an interesting example of how the European countries are not all responding in a similar way with regard to accepting the migrants. Germany, Europe’s second largest country with a population of 81 million, is a natural decrease country. Currently, for every 8 births in Germany, there are 11 deaths. If these fertility and mortality patterns were to remain unchanged in future years, in just over 230 years, Germany’s population of 81 million would drop to 40 million. It is not a surprise, therefore, that Germany has been one of the more welcoming of the European countries to these non-European migrants. The migrants are young in average age; moreover since many are

Muslims, they have a higher fertility rate than Germany’s TFR of 1.5.

In contrast, Hungary, another natural decrease country, with currently 13 deaths for every

9 births, a depopulation more severe than Germany’s, is at best reluctant about accepting the new migrants, if not outright disdainful of them. Many Hungarians fear their Christian (largely

Catholic) heritage will be imperiled by the heavily Muslim migrants.

Finally, one of the few natural increase countries of Europe is Ireland, with 15 births for every 6 deaths. One reason why Ireland is not experiencing depopulation like most of Europe is its positive response to international migrants. With its booming economy ten years ago, Ireland opened its borders to migrants from Eastern Europe, mainly Poland, Lithuania and Latvia. As of

2011, one in eight people residing in Ireland were foreign-born, more than double the number in

2002. These migrants were mainly young Catholic adults with children, and they often sought jobs in construction. Although many of them suffered in the recent economic downturn in

Ireland, their youth and higher than average fertility have helped Ireland avoid depopulation.

We thus ask what is about Germany and Hungary and Ireland per se that has resulted in their becoming more or less accepting of international migrants. Are their certain characteristics of the countries, such as their wealth or perhaps their religious or cultural mileu, that make them more or less accepting of migrants. The more accepting they are of the migrants, the more likely it is that their counties will have higher birth/death ratios. Our multi-level analyses described above will enable us to test out some hypotheses about the effects of country-level characteristics on natural decrease/increase at the county level.

Discussion and Conclusion

We end this proposed paper with a few remarks about natural decrease among the

European counties. These remarks are general ones, and some of them may need to be rewritten after completing the multi-level analyses just discussed.

In Europe there is a far greater amount of natural decrease than in the U.S. In the

European countries in our analysis, 62% of their counties experienced natural decrease. In eight of the European countries, more than half of their counties experienced natural decrease:

Lithuania (100%), Croatia (91%), Germany (82%), and Romania (81%). In contrast, 27% of the

3,147 U.S. counties (which is less than half that of the 62% in Europe), experienced natural decrease. The U.S. has a good amount of natural decrease in its counties, but nowhere as substantial and dramatic as in the counties of Europe.

Fertility is much lower in most of Europe than in the U.S. Demographic research indicates continuing sub-replacement level fertility in most European countries. Much research in Europe now focuses on identifying policies encouraging higher fertility that could mitigate the adverse effects of natural decrease.

In our mind, this may not be the best strategy, esp. given the “low-fertility trap” (Lutz et al., 2006; Billari, 2008). We mentioned above that the late Calvin Beale (1969) long argued that the most proximate cause of county-level natural decrease in the U.S was changes in local age structures resulting from the out-migration of young adults. What might the future portend for Europe regarding natural decrease? We first note that natural decrease will not go away. The empirical literature in the U.S. is consistent in showing that once natural decrease occurs for a number of years, it is likely to reoccur in later periods.

We have shown here that the major cause of natural decrease in Europe is a distorted county age structure with many older adults and few young adults. Other research cited earlier in this paper reports pretty much the same results in the U.S.

The future for natural decrease counties in the U.S. is not entirely a bleak one. The influx of Hispanic migrants to many of the “new destination” counties in the U.S. has diminished their risk of natural decrease. Hispanic migrants include many young adults of child-bearing age and this coupled with the higher fertility rates of Hispanics results in a disproportionately greater number of births reducing the ratio of deaths over births. Johnson and Lichter (2008) have shown that this influx of Hispanics is having a profound impact on natural increase and the age structure of the U.S. population.

A similar mildly positive prognosis is not the case for most of Europe. Europe’s fertility levels are much lower than in the United States. The total fertility rate in 2015 in Europe was 1.4 versus 1.9 in the U.S. Only one European country has a fertility rate in 2015 at or above the replacement level of 2.1, namely Kosovo at 2.3. In addition, only Ireland and France have TFRs above 1.9. There is also little migration to most European countries that would result in the sizeable fertility advantages associated with Hispanic in-migration that many U.S. counties are now experiencing. As a consequence, population growth is minimal in most of Europe.

The European population is also considerably older than the U.S. population is now or will be in the near future. Less than 15 percent of Europe’s female population is under age 15 compared to nearly 20 percent in the U.S. Thus, with continued low fertility rates, dwindling cohorts of young women, and growing older populations, the likelihood of continued natural decrease in Europe is extremely high. Indeed, 18 of Europe’s 45 countries (including Germany,

Poland, Hungary, Russia, Greece, Portugal and Spain) are currently experiencing population loss and/or are projected to experience population loss between 2015 and 2030. This is occurring and/or is projected to occur in the next decade or so because mortality and out-migration are exceeding fertility and in-migration.

The ebb and flow of natural decrease deserve additional attention from scholars both in

Europe and in the United States. County-level natural decrease is widespread in Europe, much more so than it is in the U.S., and needs more attention. Natural decrease is the ultimate demographic consequence of population aging and low fertility rates that now characterize much of Europe and a growing part of the United States. With the population continuing to age and low fertility rates expected to persist, the widespread incidence of natural decrease in Europe is likely to continue and spread. Given the pronounced spatial clustering of natural decrease (see our earlier map in Figure 1) and its likely significance for policy-making and planning, demographers need to continue monitoring this emergent demographic phenomenon and considering its implications.

Endnote

1. The Population Reference Bureau (PRB) provides data on its 2015 World Population Data

Sheet for all the countries of the world. A geopolitical entity is defined by the PRB as a country if it has a population of at least 150,000 or more persons and/or if it is a member of the United

Nations. Thus the PRB countries “include sovereign states, dependencies, overseas departments, and some territories whose status or boundaries may be undetermined or in dispute” (Population

Reference Bureau, 2015).

References

Adamchak, Donald J. 1981. "Population Decrease and Change in Nonmetropolitan Kansas."

Transactions of the Kansas Academy of Science (1903-) 84:15-31.

Beale, Calvin L. 1964. "Rural Depopulation in the United States: Some Demographic

Consequences of Agricultural Adjustments." Demography 1:264-272.

Beale, Calvin L. 1969. "Natural Decrease of Population: The Current and Prospective Status of

an Emergent American Phenomenon." Demography 6:91-99.

Billari, Francesco C. 2008. "Lowesl-low Fertility in Europe: Exploring the Causes and Finding

Some Surprises." The Japanese Journal of Population 6: 2-18.

Chang, H. C. 1974. "Natural Population Decrease in Iowa Counties." Demography 11:657-672.

Ciccone, Antonio. 2002. "Agglomeration effects in Europe." European Economic Review

46:213.

Coleman, David and Robert Rowthorn. 2011. "Who's Afraid of Population Decline? A Critical

Examination of Its Consequences." Population and Development Review 37:217-248.

Council of the European Communities. 2003. "Regulation (EC) No 1059/2003 of the European

Parliament and of the Council of 26 May 2003 on the establishment of a common

classification of territorial units for statistics (NUTS)." Official Journal of the European

Union.

Dorn, Harold F. 1939. "The Natural Decrease of Population in Certain American Communities."

Journal of the American Statistical Association 34:106-109. Doteuchi, A. 2006. “Three Initiatives to Invigorate Society in an Era of Population Decrease.

NLI Research 5(2):1–6

EUROSTAT. 2007. Regions in the European Union. Luxembourg: EUROSTAT.

EUROSTAT. 2011. EUROSTAT Regional Yearbook, 2011. Luxembourg: EUROSTAT.

Fuguitt, Glenn V., David L. Brown, and Calvin L. Beale. 1989. Rural and Small Town America.

Russel Sage Foundation.

Heilig, Gerhard, Thomas Buttner, and Wolfgang Lutz. 1990. "Germany's Population: Turbulent

Past, Uncertain Future." Population Bulletin 45:1-1.

Johnson, Kenneth M. 1993. "When Deaths Exceed Births: Natural Decrease in the United

States." International Regional Science Review 15:179-198.

Johnson, Kenneth M. 2011a. "Natural Decrease in America: More Coffins than Cradles." Issue

Brief 30: 1–5. Carsey Institute, University of New Hampshire.

Johnson, Kenneth M. 2011b. "The Continuing Incidence of Natural Decrease in American

Counties." Rural Sociology 76:74-100.

Johnson, Kenneth M. and Calvin L. Beale. 1992. Natural Population Decrease in the United

States.” Rural Development Perspectives 8:8-15.

Johnson, Kenneth M., Layton Field, and Dudley L. Poston, Jr. 2016 forthcoming. "More Deaths

than Births: Subnational Natural Decrease in Europe and the United States. Population

and Development Review: forthcoming.

Johnson, Kenneth M. and Glenn V. Fuguitt. 2000. “Continuity and Change in Rural Migration

Patterns, 1950–1995. Rural Sociology 65(1):27–49.

Johnson, Kenneth M.and Daniel T. Lichter. 2008. "Natural Increase: A New Source of

Population Growth in Emerging Hispanic Destinations in the United States." Population and Development Review 34:327-346.

Johnson, Kenneth M.and Ross L. Purdy. 1980. "Recent Nonmetropolitan Population Change in

Fifty-Year Perspective." Demography 17:57-70.

Johnson, Kenneth M., Paul R. Voss, Glenn V. Fuguitt, R. Hammer, and S.C. McNiven. 2005.

“Temporal and Spatial Variation in Age-Specific Net Migration in the United States.

Demography 42:791–812.

Johnson, Kenneth M., Richelle Winkler, and Luke T. Rogers, "Age and lifecycle patterns driving

U.S. migration shifts" (2013). The Carsey School of Public Policy at the Scholars'

Repository. Paper 192.

Koebernick, T.E. and K.S. Markides. 1975. “Natural Decrease in Texas Counties.” Presented at

the annual meeting of the Rural Sociological Society, San Francisco, CA, August 15.

Lutz, W., V. Skirbekk, and M.R. Testa. 2006. “The Low Fertility Trap Hypothesis. Forces that

May Lead to Further Postponement and Fewer Births in Europe.” Vienna Yearbook of

Population Research 2006:115-151.

Morgan, S. Philip, and Miles G. Taylor. 2006. “Low Fertility at the Turn of the Twenty-First

Century.” Annual Review of Sociology 32:375-399.

Morrill, Richard L. 1995. "Aging in place, age specific migration and natural decrease." Annals

of Regional Science 29:41.

Muenz, R. 2006. Europe: Population and Migration in 2005. Washington, DC: Migration Policy

Institute.

Nikitina, S.V. 2000. “Population Decline and Population Ageing in the Russian Federation.”

Presented at the Expert Group Meeting on Policy Responses to Population Ageing and

Decline, United Nations, Population Division, New York, October 6. Population Reference Bureau. 2015. 2015 World Population Data Sheet. Washington, DC:

Population Reference Bureau.

Poston, Dudley L., Jr., Benjamin S. Bradshaw, and Diana DeAre. 1972. "Texas Population in

1970: Trends in Natural Decrease, 1950-1970." Texas Business Review 46:239-247.

Poston, Dudley L., Jr.and Leon F. Bouvier. 2010. Population and Society: An Introduction to

Demography. New York, NY: Cambridge University Press.

Poston, Dudley L., Jr.and W. Parker Frisbie. 1998. "Human Ecology, Sociology, and

Demography." Pp. 27-50 in Continuities in Sociological Human Ecology, edited by

Michael Micklin and Dudley L. Poston, Jr. New York: Plenum Press, 1998.

Poston, Dudley L., Jr.and W. Parker Frisbie. 2005. "Ecological Demography." in Handbook of

Population, edited by Dudley L. Poston, Jr. and Michael Micklin. New York: Springer

Publishers.

Raudenbush, Stephen W. and Anthony S. Bryk. 2002. Hierarchical Linear Models:

Applications and Data Analysis Methods. Second Edition. Thousand Oaks, CA: Sage

Publications.

Van De Kaa, D. J. 1987. "Europe's second demographic transition." Population Bulletin 42:1-59.

Table 1. Number of Counties and Number of Natural Decrease Counties: Countries of Europe, Circa 2000-10 Time Total Natural Decrease Country Period Counties Counties Percent of Number all Counties Austria 2002-2010 35 17 48.57 Belgium 2000-2009 44 14 31.82 Bulgaria 2000-2009 28 28 100 Croatia 2002-2010 21 19 90.48 Czech Republic 2002-2010 14 7 50 France 2000-2009 100 26 26 Germany 2000-2009 429 353 82.28 Greece 2001-2010 51 36 70.59 Hungary 2003-2010 20 20 100 Ireland 2002-2010 8 0 0 Italy 2002-2010 107 69 64.49 Lithuania 2000-2009 10 10 100 Netherlands 2003-2010 40 3 7.5 Norway 2001-2010 19 3 15.79 Poland 2001-2010 66 26 39.39 Portugal 2000-2009 30 19 63.33 Romania 2000-2009 42 34 80.95 Slovenia 2003-2010 12 7 58.33 Spain 2001-2010 59 22 37.29 Sweden 2000-2009 21 13 61.90 Switzerland 2000-2009 26 5 19.23 United Kingdom 2001-2009 133 43 32.33

TOTAL 1,315 774 58.8

Figure 1 Natural Decrease (colored red) and Natural Increase (colored blue): Counties of Europe, 2000-2010

(Blue: natural increase. Red: natural decrease. Gray: no data)