Place-to-place migration in Israel: estimates of a logistic model

"A multinomial logit model focusing on economic and other locational factors is formulated and applied to data on place-to-place migration in Israel. Results indicate the effects of expected industrial wage differentials, in accordance with the hypothesis of Harris and Todaro (1970), and of disparities in the structure of industrial employment, suggesting that perceived risk as well as expected return enter into the decision to migrate, as Stark and Levhari (1982) have argued. Other effects include those associated with regional differentials in amenities and agglomeration associated with urbanization, population mobility by age group, center-periphery migration trends, border security hazards, and the like. Implications of the analysis for the Israeli policy of population dispersion are discussed."     

Stochastic demographic forecasting

"This paper describes a particular approach to stochastic population forecasting, which is implemented for the U.S.A. through 2065. Statistical time series methods are combined with demographic models to produce plausible long run forecasts of vital rates, with probability distributions. The resulting mortality forecasts imply gains in future life expectancy that are roughly twice as large as those forecast by the Office of the Social Security Actuary....Resulting stochastic forecasts of the elderly population, elderly dependency ratios, and payroll tax rates for health, education and pensions are presented."     

Modeling and forecasting U.S. sex differentials in mortality

"This paper examines differentials in observed and forecasted sex-specific life expectancies and longevity in the United States from 1900 to 2065. Mortality models are developed and used to generate long-run forecasts, with confidence intervals that extend recent work by Lee and Carter (1992). These results are compared for forecast accuracy with univariate naive forecasts of life expectancies and those prepared by the Actuary of the Social Security Administration."     

Determinants of the Choice of Migration Destination*

This paper examines migrants’ choice of destination conditional on migration. The study uses data from two rounds of Nepal Living Standard Surveys and a Population Census and examine how the choice of a migration destination is influenced by various covariates, including income differentials across possible destinations. We find that migrants move primarily to nearby, high population density areas where many people share their language and ethnic background. Better access to amenities is significant as well. Differentials in average income across destination districts are significant in univariate comparisons but not once we control for other covariates. Differentials in consumption expenditures are statistically significant but smaller in magnitude than other determinants. It is differentials in absolute, not relative, consumption between destination districts that are correlated with the destination of work migrants. Except for the latter, results are robust to different specifications and datasets.     

Developing Official Stochastic Population Forecasts at the US Census Bureau

The U.S. Census Bureau is approaching a critical decision regarding a major facet of its methodology for forecasting the United States population. In the past, it has relied on alternative scenarios, low, medium, and high, to reflect varying interpretations of current trends in fertility, mortality, and international migration to forecast population. This approach has limitations that have been noted in the recent literature on population forecasting. Census Bureau researchers are embarking on an attempt to incorporate probabilistic reasoning to forecast prediction intervals around point forecasts to future dates. The current literature offers a choice of approaches to this problem. We are opting to employ formal time series modeling of parameters of fertility, mortality, and international migration, with stochastic renewal processes. The endeavor is complicated by the administrative necessity to produce a large amount of racial and Hispanic origin detail in the population, as well as the ubiquitous cross-categories of age and sex. As official population forecasts must meet user demand, we are faced with the added challenge of presenting and supporting the resulting product in a way that is comprehensible to users, many of whom have little or no comprehension of the technical forecasting literature, and are accustomed to simple, deterministic answers. We may well find a need to modify our strategy, depending on the realities that may emerge from the limitations of data, the administrative requirements of the product, and the diverse needs of our user community.     

MIGRATION THEORIES AND EVIDENCE: AN ASSESSMENT

Abstract. This paper presents a critical survey of theories of migration, their welfare and policy implications and their empirical relevance. We also develop some extensions to the theory beginning with the Harris and Todaro (HT) model. In particular, the HT model is extended to examine risk averse behaviour within families where the migration of members of families serves to diversify risk. The welfare implications of the individual migration decision and government intervention in the form of employment subsidies are examined. Recent evidence on international migration is presented. It is shown that migration does not flow automatically in response to wage differentials. Characteristics of migrants and the process of self-selection are found to be important determinants of the rate of migration.     

Forecasting with Bayesian Vector Autoregressions Estimated Using Professional Forecasts

We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short‐term survey forecasts as an additional source of information about model parameters. In particular, we augment the vector of dependent variables by their survey nowcasts, and claim that each variable modelled in the VAR and its nowcast are likely to depend in a similar way on the lagged dependent variables. In an application to macroeconomic data, we find that the forecasts obtained from a VAR fitted by our new shrinkage approach typically yield smaller mean squared forecast errors than the forecasts obtained from a range of benchmark methods. Copyright © 2015 John Wiley & Sons, Ltd.     

A fuzzy bi-level method for modeling age-specific migration

Modeling and forecasting international migration are significant research areas since migration forecasts are vital in decision making and policy design regarding economy, security, society, and resource allocation. The methods for modeling and forecasting migration rely on strict subjective or statistical assumptions which may not always be met. In addition, lack of a universally accepted definition of the term “migrant” and the ambiguities in data due to recording and collection systems result in inconsistencies and vagueness in migration modeling. Considering these, in this paper, a fuzzy bi-level age-specific migration modeling method is proposed. The bi-level structure embedded in the model makes use of the well-known Lee-Carter method as well as fuzzy regression, singular value decomposition technique, and hierarchical clustering to reflect the general characteristics of the country of concern together with the distinct emigration and immigration behaviors of the age groups. Bayesian time series models are fitted to the time-variant fuzzy parameters obtained through the proposed method to forecast future migration values. The proposed method is applied on female and male age-specific emigration and immigration counts of Finland for 1990–2010 period and Germany for 1995–2012 period, and the future values are forecasted for 2011–2025 and 2013–2025 respectively. The method is compared with an existing Bayesian approach and the numerical findings display that the proposed fuzzy method is superior to the existing one in modeling and forecasting age-specific migration values within significantly narrower prediction intervals.     

Population forecasting theory,methods and assessments of accuracy. The magnitude of error due to different vital processes in population forecasts

The accuracy of population forecasts depends in part upon the method chosen for forecasting the vital rates of fertility, mortality, and migration. Methods for handling the stochastic propagation of error calculations in demographic forecasting are hard to do precisely. This paper discusses this obstacle in stochastic cohort-component population forecasts. The uncertainty of forecasts is due to uncertain estimates of the jump-off population and to errors in the forecasts of the vital rates. Empirically based of each source are presented and propagated through a simplified analytical model of population growth that allows assessment of the role of each component in the total error. Numerical estimates based on the errors of an actual vector ARIMA forecast of the US female population. These results broadly agree with those of the analytical model. This work especially uncertainty in the fertility forecasts to be so much higher than that in the other sources that the latter can be ignored in the propagation of error calculations for those cohorts that are born after the jump-off year of the forecast. A methodology is therefore presented which far simplifies the propagation of error calculations. It is noted, however, that the uncertainty of the jump-off population, migration, and mortality in the propagation of error for those alive at the jump-off time of the forecast must still be considered.     

One model and various experts: Evaluating Dutch macroeconomic forecasts

The Netherlands Bureau for Economic Policy Analysis (CPB) uses a large macroeconomic model to create forecasts of various important macroeconomic variables. The outcomes of this model are usually filtered by experts, and it is the expert forecasts that are made available to the general public. In this paper we re-create the model forecasts for the period 1997-2008 and compare the expert forecasts with the pure model forecasts. Our key findings from the first time that this unique database has been analyzed are that (i) experts adjust upwards more often; (ii) expert adjustments are not autocorrelated, but their sizes do depend on the value of the model forecast; (iii) the CPB model forecasts are biased for a range of variables, but (iv) at the same time, the associated expert forecasts are more often unbiased; and that (v) expert forecasts are far more accurate than the model forecasts, particularly when the forecast horizon is short. In summary, the final CPB forecasts de-bias the model forecasts and lead to higher accuracies than the initial model forecasts.     

A framework for stabilizing the euro/yen/dollar triplet

The current debate on exchange rate regimes is shaped by the so-called inconsistency triangle. In this paper I show that the discussion has overlooked a “third way” which combines capital mobility with monetary policy autonomy, and an exchange rate path determined by interest rate differentials. My scheme relies on interventions that are always carried out by the central bank with the strong currency, and a full sterilization of interventions with the instrument of a deposit facility. Thus the G3 central banks could set a band for their bilateral exchange rates with the floor and the ceiling defended by the respective strong central bank. As exchange rates bands are adjusted according to short-term interest rate differentials, there are no sterilization costs for the intervening central bank. Over the medium and long run interest rates are mainly determined by inflation differentials. Thus, the exchange rate band would follow a PPP path.     

EVALUATING MACROECONOMIC FORECASTS: A CONCISE REVIEW OF SOME RECENT DEVELOPMENTS

Macroeconomic forecasts are frequently produced, widely published, intensively discussed, and comprehensively used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyze some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macroeconomic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC), and the ECB, are typically based on econometric model forecasts jointly with human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes nonstandard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econometric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model and intuition; and (iii) the two forecasts are generated from two distinct (but unknown) combinations of different models and intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the (econometric) Staff of the Federal Reserve Board and the FOMC on inflation, unemployment, and real GDP growth. It is shown that the FOMC does not forecast significantly better than the Staff, and that the intuition of the FOMC does not add significantly in forecasting the actual values of the economic fundamentals. This would seem to belie the purported expertise of the FOMC.     

Efficient forecast tests for conditional policy forecasts

Central Banks regularly make forecasts, such as the Fed’s Greenbook forecast, that are conditioned on hypothetical paths for the policy interest rate. While there are good public policy reasons to evaluate the quality of such forecasts, up until now, the most common approach has been to ignore their conditional nature and apply standard forecast efficiency tests. In this paper we derive tests for the efficiency of conditional forecasts. Intuitively, these tests involve implicit estimates of the degree to which the conditioning path is counterfactual and the magnitude of the policy feedback over the forecast horizon. We apply the tests to the Greenbook forecast and the Bank of England’s inflation report forecast, finding some evidence of forecast inefficiency. Nonetheless, we argue that the conditional nature of the forecasts made by central banks represents a substantial impediment to the analysis of their quality—stronger assumptions are needed and forecast inefficiency may go undetected for longer than would be the case if central banks were instead to report unconditional forecasts.     

Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)

While combining forecasts is well-known to reduce error, the question of how to best combine forecasts remains. Prior research suggests that combining is most beneficial when relying on diverse forecasts that incorporate different information. Here, I provide evidence in support of this hypothesis by analyzing data from the PollyVote project, which has published combined forecasts of the popular vote in U.S. presidential elections since 2004. Prior to the 2020 election, the PollyVote revised its original method of combining forecasts by, first, restructuring individual forecasts based on their underlying information and, second, adding naïve forecasts as a new component method. On average across the last 100 days prior to the five elections from 2004 to 2020, the revised PollyVote reduced the error of the original specification by eight percent and, with a mean absolute error (MAE) of 0.8 percentage points, was more accurate than any of its component forecasts. The results suggest that, when deciding about which forecasts to include in the combination, forecasters should be more concerned about the component forecasts’ diversity than their historical accuracy.     

Do inter-city differences in intra-city wage differentials have any interesting implications?

Recent research has related characteristics of cities to differences in the distribution of wages across workers with different skill levels. We demonstrate that these differences in wage differentials arise naturally as a compensating variation in Rosen’s theoretical model of inter-city wages. For example, if the income elasticity of demand for housing services is less than unity, cities with higher house prices will have smaller money wage differentials between low and high skill workers. This result has no implications for differences in either absolute or relative real productivity or welfare of unskilled workers. Similarly, changes in the amenity of an urban area may result in changes in relative wages of skilled and unskilled workers with no implications for real productivity or welfare differentials.Empirical tests in which housing cost differentials are added as a determinant of inter-city differences in an intra-urban wage differential model provide empirical confirmation of the theoretical expectations. It appears that intra-urban money wage differentials, differences in the quality of life, and variation in the cost of living in each city are jointly determined variables just as Rosen’s model of inter-city wage differentials predicts.     

Assumptions on Fertility in Stochastic Population Forecasts*

In recent years Statistics Netherlands has published several stochastic population forecasts. The degree of uncertainty of the future population is assessed on the basis of assumptions about the probability distribution of future fertility, mortality and migration. The assumptions on fertility are based on an analysis of historic forecasts of the total fertility rate (TFR), on time‐series models of observations of the TFR, and on expert knowledge. This latter argument‐based approach refers to the TFR distinguished by birth order. In the most recent Dutch forecast the 95% forecast interval of the total fertility rate in 2050 is assumed to range from 1.2 to 2.3 children per woman.     

Do inter-city differences in intra-city wage differentials have any interesting implications?

Recent research has related characteristics of cities to differences in the distribution of wages across workers with different skill levels. We demonstrate that these differences in wage differentials arise naturally as a compensating variation in Rosen’s theoretical model of inter-city wages. For example, if the income elasticity of demand for housing services is less than unity, cities with higher house prices will have smaller money wage differentials between low and high skill workers. This result has no implications for differences in either absolute or relative real productivity or welfare of unskilled workers. Similarly, changes in the amenity of an urban area may result in changes in relative wages of skilled and unskilled workers with no implications for real productivity or welfare differentials.Empirical tests in which housing cost differentials are added as a determinant of inter-city differences in an intra-urban wage differential model provide empirical confirmation of the theoretical expectations. It appears that intra-urban money wage differentials, differences in the quality of life, and variation in the cost of living in each city are jointly determined variables just as Rosen’s model of inter-city wage differentials predicts.     

LOCAL PAY DETERMINATION

Abstract. Theoretical models of local pay are reviewed, ranging from neoclassical perspectives, unemployment/wage trade-off models, and segmented labour market approaches. The empirical work on U.S. North-South differentials is discussed. For the U.K., studies of the extent of and trends in geographical pay differentials are considered, as well as behavioural explanations focusing on the role of excess demand. The issue of adjustment processes is then discussed, both in theory and in relation to empirical work for the U.S. and U.K. This discussion focuses largely on geographical spillover effects, in terms of origins, evidence, and issues of identification. Finally, the impact of certain impediments in the adjustment process are considered, specifically in relation to the U.K. These concern the role and influence of national wage bargaining, as well as factors inhibiting labour migration, stemming from the housing market and the climate of the national labour market.     

Demographically based global income forecasts up to the year 2050

Demographic projections of age structure provide the best information available on long-term future human resources and demand. Fairly robust correlations between age structure, GDP and GDP growth have been discovered in current data. In this paper we use these two facts to study the forecasting properties of demographically based models. Extending the forecasts to 2050 suggests that, due to projected fertility decreases, the poor countries of today will start to catch up with developed economies, in which the growth process will stagnate due to the growth of the elderly population. This remains the case whether or not indications of positive longevity effects are taken into account.     

Analysts' utilization of historical earnings information

This study examines whether security analysts (in)efficiently utilize the information contained in past series of annual and quarterly earnings in producing earnings forecasts. To do so, it investigates whether equal-weighted combinations of security analysts' forecasts with forecasts from statistical models based on historical earnings are superior, both in terms of being a better surrogate for the market's expectations of earnings and of accuracy, to forecasts from either one of these two sources. The empirical findings indicate that, although analysts' forecasts are superior to forecasts from statistical models, performance can be improved—both in terms of accuracy and also of being a better surrogate for market earnings expectations—by combining analysts' forecasts with forecasts from statistical models based on past quarterly earnings. Improvements in proxying for market earnings expectations were obtained even when analysts' forecasts made in June of the forecast year were used in the combinations. An implication of these findings is that investors can improve their investment decisions by using an average of the mean analysts' forecasts and the forecast produced by a time-series model of quarterly earnings in their investment decisions.