A multi‐country analysis of a decrease in government spending: The case of the European Union
Oscar Bajo‐Rubio (Universidad de Castilla‐La Mancha) Antonio G. Gómez‐Plana (Universidad Pública de Navarra)
December 2015 Very preliminary version Abstract In this paper, we analyse the global effects, i.e., the effects on the world economy, from a decrease in the level of public spending in the EU. Specifically, we will simulate the effects of a decrease in the level of public spending in the EU as a whole, and examine its effects on the main macroeconomic variables of seven regions of the world economy (namely, the EU, the US, Japan, China, Asia‐Pacific, Latin America and rest of the world). The empirical methodology will make use of a computable general equilibrium (CGE) model, which allows obtaining the consequences of changes in a particular variable on the whole economy under analysis, as well as the specific effects across the different productive sectors. Keywords: Computable general equilibrium, Government spending, Global economy, European Union JEL classification: C68, H62, H20, H50
1. Introduction One of the most relevant features of the global financial crisis that started in 2008 is the appearance of large fiscal imbalances in most advanced countries. As a consequence, a series of fiscal consolidation measures have been pursued in order to reduce the size of government deficits and the subsequent debt accumulation, so that the confidence of financial markets can be recovered, and the risk of sovereign default avoided. The economic effects of these fiscal consolidation policies have been the subject of intensive research since the beginning of the crisis. An influential line of research in the first stages of the crisis claimed that, unlike the traditional “Keynesian” effects of fiscal policy, contractionary fiscal policies could provoke an expansionary effect on output, due to the increased confidence of the private agents on government’s solvency, which would lead to lower expected taxes in the next future. This is the literature on the so called “non‐Keynesian” effects of fiscal policy, following the pioneering work of Giavazzi and Pagano (1990). The generality of these “non‐Keynesian” effects of fiscal policy, however, has been put recently into question. In particular, some recent empirical studies using a novel methodology (i.e., identifying changes in fiscal policy motivated by the desire to reduce the budget deficit from historical documents) find that fiscal consolidations have a contractionary effect on economic activity, as expected from standard Keynesian models; see Romer and Romer (2010) and Guajardo, Leigh and Pescatori (2014). In addition, as shown by Auerbach and Gorodnichenko (2013), fiscal policy multipliers seem to be larger in recessions, which can be explained from several features that characterize depressed economies, such as the absence of supply constraints in the short run, and a binding zero lower bound on interest rates (DeLong and Summers, 2012). As a result, contractionary fiscal policies implemented during the crisis would have led to a permanent decline in output levels, as well as being unable to reduce debt to GDP ratios (Fatás and Summers, 2015). Another relevant issue for the assessment of the effects of fiscal consolidations relates to their composition. Following previous contributions on this topic, Alesina and Ardagna (2010) concluded that, in the case of a fiscal consolidation, spending cuts are more effective than tax increases in order to stabilize the debt and avoiding a recession; whereas, for the case of a fiscal stimulus, the opposite result would hold, i.e., tax cuts are more expansionary than spending increases. Empirical support for these results has been provided by Alesina, Favero and Giavazzi (2015), who simulated the fiscal plans adopted by 16 OECD countries over a 30‐ year period (1978‐2009) and found that spending‐based fiscal consolidations were associated with minor and short‐lived recessions; unlike tax‐based consolidations, which led to deeper and longer recessions. The authors justified these results in terms of the confidence of investors, which recovers much sooner following a spending‐based adjustment than a tax‐ based one. However, using a completely different methodology, Bajo‐Rubio and Gómez‐Plana (2015) simulated by means of a computable general equilibrium (CGE) model the effects of several alternative policy measures intended to reduce the Spanish government deficits, finding that the strongest negative effects on GDP and employment appeared in the case of an increase in the income tax, followed by spending cuts (especially in public education and, at a smaller extent, public health and public administration); in contrast, for indirect tax increases the negative effects on GDP and employment were milder. 1
On the other hand, especially in the member countries of the European Union (EU), the preferred way of implementing consolidation plans has been by reducing government spending, rather than increasing revenues. Leaving aside its ideological implications, this fact can be related to the standard result of the literature on fiscal policy and growth, which can be traced back to Barro (1991), of a negative and significant effect of the level of public consumption as a percentage of GDP (which would proxy government size) on the growth rate of a cross section of countries. This is justified on the grounds that a greater government intervention would distort the incentives systems, so that a higher government size would be associated with a lower productivity, and hence a lower growth. However, this effect did not appear robust to changes in the conditioning variables in the influential study of Levine and Renelt (1992). In addition, and even more importantly, it is not very clear the use of government consumption as a proxy of the whole public expenditure. In particular, a model intended to analyse the effects of fiscal policy on growth should consider instead some other components of public spending more directly linked to growth, such as the government capital stock (directly, as an additional productive factor in the aggregate production function, and through its favourable effects on private capital’s productivity), as well as public transfers that encourage accumulation and growth (as an externality in the aggregate production function); see Bajo‐Rubio (2000). In fact, over a long‐term viewpoint, consolidation strategies based on cutting public expenditure items such as education, health care, R&D or public investments might harm future growth prospects (European Commission, 2012). For all these reasons, and even more in the current context of credit supply restrictions, fiscal adjustments should be gradual, and rely also on increases in government revenues in addition to spending cuts, in order to not dampen future growth (Baldacci, Gupta and Mulas‐Granados, 2015). And all this would be particularly relevant since, as emphasized by Reinhart and Rogoff (2009), the decrease in public revenues due to the subsequent recession is the main reason behind the higher government deficits associated with financial crises. Our aim in this paper will be to analyse the global effects, i.e., the effects on the world economy, from a decrease in the level of public spending in the EU. As is well known, faced to the increase in government deficits in most EU countries following the financial crisis that started in 2008, the EU authorities have endorsed the implementation of fiscal consolidation strategies, known as austerity policies. While only partially successful in reducing government deficits, such austerity policies have resulted in deepening the recession in most EU countries (De Grauwe and Ji, 2013). Specifically, we will simulate the effects of a decrease in the level of public spending in the EU as a whole, and examine its effects on the main macroeconomic variables of seven regions of the world economy (namely, the EU, the US, Japan, China, Asia‐ Pacific, Latin America and rest of the world). The empirical methodology will make use of a CGE model, through a version of the Global Trade Analysis Project (GTAP) model. This methodology allows obtaining the consequences of changes in a particular variable on the whole economy under analysis, as well as the specific effects across the different productive sectors. Thus, the potential of CGE models lies in their ability to integrate micro and macro elements (Devarajan and Robinson, 2005). 2
The rest of the paper is organized as follows. A brief description of the model is provided in Section 2. The data and calibration process are discussed in Section 3. The results from the simulations are presented in Section 4. Section 5 concludes.
2. The model The model is an extension of Rutherford (2015), based on GTAP9inGAMS, and is a static, multi‐ region computable general equilibrium model. This paper presents a version describing seven open economies (see Table 1 and Appendix), disaggregated in fifteen productive sectors (see Table 1 and Appendix), a private representative consumer and a public sector for each region and three primary factors (i.e., labour, capital and natural resources). Table 1. Sectors and countries/regions AGR CRP MVH OTN IND OME ELE CNS TRD TCM OFI OBS ROS OSG SER
Sectors Agricultural products Chemical industry Motor vehicles Other transport equipment Other industry Machinery and equipment Electronic equipment Construction Trade Transport and communications Other financial intermediation Other business services Recreational services Government services Other services
EU USA JPN CHI LAT PAC ROW
Regions European Union United States of America Japan China Latin America Asia Pacific Rest of the World
The extension of the model is twofold: (1) The original version of GTAP9inGAMS has one representative agent for each country or region. The model developed here splits the representative agent into public and private agents, extending the equations, and using National Accounts and other data sources to assign the corresponding micro and macro variables (2) This version includes unemployment at a regional level under a wage curve setting. It must be noted that due to the high unemployment rate in some regions, instead of using the common assumption of full employment in labour markets, the model includes unemployment in a way derived from the wage curve models. Next, we will present a brief description of the model. The full set of equations is available from the authors, and the core equations can be found in Rutherford (2011). 3
Equilibrium conditions The equilibrium of the model is a set of prices and an allocation of goods and factors. It involves the simultaneous solution of three sets of equations: Zero‐profit conditions. Market clearing in goods, natural resources and capital markets. Constraints on income balance (total revenue must equal total expenditure), labour market (that includes unemployment), and macroeconomic closure of the model. Production Production is based on a constant returns‐to‐scale technology characterized by a nested CES‐ Leontief structure of intermediate inputs and factors. The firms’ decision problem is to maximise profits subject to the technology constraints, obtaining the unit cost functions, which are further used in the zero‐profit conditions. In turn, the demands for factors and intermediate inputs are obtained from Shephard’s lemma on cost functions, and then used in the market‐clearing equations. Firms show constant returns to scale in their technologies and follow a competitive pricing rule, with free entry and exit of firms. Consumption Each country or region has two consumers: a representative private household behaving as a rational consumer, and a public consumer (see next section on the public sector). The level of consumer’s welfare is determined by the budget constraint that includes the rents from endowments of factors and exogenous savings. The fixed endowment of labour should be interpreted as a maximum supply of labour since unemployment is assumed to be endogenous. Hence, labour supply would be elastic up to the endowment constraint. The household’s decision problem consists of choosing an optimal consumption bundle, by maximizing a nested CES utility function subject to the budget constraint. Preferences are represented by a nested utility function on consumption of goods. Demand functions for goods are derived from the first‐order conditions, and are included in the goods and factor markets equations, as well as in the macroeconomic closure for savings. Public sector The public sector has been included in the core GTAP9inGAMS model. GTAP includes a single representative agent, so it has been necessary to split it into a private representative household and the public sector. For this purpose, we have made use of the National Accounts, the GTAP database (Narayanan et al., 2015), as well as other sources, namely, United Nations (2014), European Commission (2015) and International Monetary Fund (2015). The procedure has involved adding to the multi‐country model the level of public savings, as well as the public gross capital formation at national/region level. The role of the public sector in the model is twofold, i.e., it is an owner of resources and a purchaser of certain goods. As an owner of resources, its income includes net tax revenues, where net taxes consist of tax rates on primary factors and commodities, domestic tax rates on firms, tariff rates, subsidy rates on output and subsidy rates on exports. The public sector also enters the model as a purchaser of goods; the most relevant in quantitative terms 4
being those included in the sector Government services (i.e., public administration, defence, education, health). Foreign sector The model represents the world divided in seven regions, so there is trade balance at a global level, although trade imbalances are allowed at a national or regional level. These trade imbalances are endogenous. We assume that goods are differentiated according to their origin (i.e., domestic or foreign), following Armington’s assumption (Armington, 1969), which allows for the possibility of intra‐industry trade. Consumers (both private and public) perceive domestic and imported goods as differentiated. Factor markets The representative private household owns fixed endowments of natural resources, capital and labour, which are internationally immobile. The natural resources’ and capital rents adjust to clear the domestic markets. Natural resources are sector‐specific. Labour employment (i.e., the labour endowment less unemployment) is elastic up to the fixed amount of labour. The unemployment rate is determined through a wage equation, which postulates a negative relationship between the real wage rate and the rate of unemployment: 1
u θ w u0 P where w is the nominal wage, P is the consumer price index, u is the unemployment rate, and u0 is the unemployment rate in the benchmark (see Table 2). Notice that, as long as θ → ∞, the wage equation approaches a downward‐rigid real wage. Such a wage equation can be derived from trade union models, or from efficiency wage models, and has been extensively used in CGE models; see, e.g., Rutherford and Light (2001) for an explanation. Figure 1 illustrates the wage curve in a traditional labour market diagram, where the real wage rate is measured in the vertical axis and the amount of labour in the horizontal axis. Full employment occurs with a real wage rate (w/P)0 at the intersection of the labour demand function L and the formal labour supply function LS. If we replace the labour supply curve with the real wage curve, the equilibrium wage rate (w/P)1 lies above the market clearing wage rate, which causes unemployment equal to (LS)1 – (L)1. 5
Figure 1. The labour market
Macroeconomic closure Total investment is split into sectoral gross capital formation using a fixed‐coefficients Leontief structure (Dervis et al., 1981). Notice that, in our static framework, total gross capital formation affects the economy as a component of final demand. The model embodies a macroeconomic closure equation stating that investment and savings (private, public, and foreign) are equal. Finally, the model is solved as explained in Rutherford (1999), with the general equilibrium model defined as a mixed complementarity problem (see Mathiesen, 1985). The software used in the empirical application is GAMS/MPSGE.
3. Calibration and data The model has been calibrated using GTAP9 data (Narayanan et al., 2015) with data for 2011. The calibration method is based on a benchmark equilibrium corresponding to the National Accounts and a set of exogenous parameters. A detailed explanation for the calibration method can be found in Dawkins et al. (2001). Elasticities play a key role in the model. The benchmark values for those elasticities are taken from Narayanan and Walmsley (2008). Unemployment rates have been estimated for each of the seven regions using the labour force and the total unemployment for each country or region (see Table 2); the data comes from World Bank (2015). The shares of public gross capital formation on total gross capital formation have been estimated with data from European Commission (2015) and United Nations (2014), together 6
with the exchange rates taken from International Monetary Fund (2015) (at 30 December 2011). The figures for the EU, United States and Japan have been taken from European Commission (2015), and those for the rest of the regions from United Nations (2014). Latin America has been proxied using data from Brazil (2009) and Mexico, the Republic of Korea is the proxy for the region Asia Pacific, and the Rest of the World has been estimated as the average of the six regions. Table 2. Additional variables EU USA JPN CHI LAT PAC ROW
Public savings −1244.0234 73.8966210 115.9641381 344.0529696 37.8348033 94.9887281 317.9953664
Unemployment rate (%) 9.58 9.00 4.50 4.30 6.71 4.42 4.92
Public gross capital formation (share of total GCF)
0.151 0.209 0.155 0.105 0.121 0.162 0.150
4. Simulation results The simulation performed consists of a decrease in public expenditure in the EU in order to get a fall of one‐percentage point in the government deficit to GDP ratio. The results on the main macroeconomic variables appear in Table 3 as percentage changes from benchmark, except when indicated. The contraction in the EU public expenditure (−5.508%) and public revenue (−0.107%) to reach the target in the government deficit to GDP ratio has a negligible effect on the deficit to GDP ratio for the rest of the regions, although there is an increase across regions in both public expenditure and public revenue. USA has the smaller quantitative effect on both variables (0.023% increase in public expenditure and 0.028% increase in public revenue), and China has the highest increase (0.058% in public expenditure and 0.068% in public revenue). The cut in public expenditure has a contractionary effect on the European economy, coupled with a small expansion at the world level. The negative effect on the EU’s GDP (−0.194%) contrasts with the slightly positive effect on the GDP of the rest of regions and countries: GDP growth rates move from an increase of 0.053% in China to 0.029% in the USA. The fall in GDP in the EU is due to the lower employment level and higher unemployment rate following the policy simulation (−0.265% and 2.506%. respectively), together with the decrease in real wages (−0.321%). The latter effect more than offset the positive effect on GDP that might come from the increase in the other factors real rents (capital and land’s real rents are 0.103% and 1.319% higher, respectively). Hence, this policy has clear redistributive effects across factor owners in the EU. 7
Table 3. Simulation results: Effect on macroeconomic variables (% change from benchmark equilibrium) EU USA JPN CHI LAT PAC
Public deficit/GDP (p.p.) Public expenditure Public revenue GDP Employment Unemployment (%) Unemployment (p.p.) Real wage rate Real capital rent Real land rent Compensation of employees Gross operating surplus Exports Imports
−1.000 −5.508 −0.107 −0.194 −0.265 2.506 0.240 −0.321 0.103 1.319 −0.577 0.102 0.879 0.845
0.002 0.023 0.028 0.029 0.002 −0.024 −0.002 0.026 0.028 0.056 0.029 0.028 0.070 0.052
0.000 0.041 0.041 0.042 0.002 −0.039 −0.002 0.042 0.042 0.019 0.044 0.042 0.061 0.059
0.000 0.058 0.068 0.053 0.004 −0.086 −0.004 0.051 0.051 0.024 0.054 0.051 0.097 0.103
0.001 0.026 0.030 0.030 0.001 −0.016 −0.001 0.027 0.030 0.066 0.028 0.030 0.045 0.048
0.001 0.051 0.050 0.052 0.005 −0.102 −0.005 0.049 0.055 0.022 0.054 0.055 0.067 0.065
ROW 0.001 0.042 0.045 0.041 0.005 −0.087 −0.004 0.035 0.039 0.053 0.040 0.039 0.066 0.070
There is a slight increase in labour employment in the rest of the regions (from 0.005% in Asia‐Pacific and ROW to 0.001% in Latin America) and a small decrease in the unemployment rates (from −0.102% in China to −0.016% in Latin America). However, the main reason for the increase in GDP is the rise in the real rents of all factors in the rest of countries: real wages increase from 0.051% in China to 0.026% in USA, the real capital rent from 0.055% in Latin America to 0.028% in USA, and the real land rate from 0.066% in Latin America to 0.019% in Japan. There is also a redistributive effect across factors, but smaller than in the EU. The redistributive effect in this case is slightly favourable to capital. There also appear some effects on international trade flows: exports increase in the EU by 0.879% and imports by 0.845%, leaving the trade balance roughly unchanged. The impact in the rest of the regions is much lower, being the second highest effect in China (with a 0.097% increase in exports and 0.103% in imports) and the lowest in Latin America (0.045% in exports and 0.048% in imports). On the other hand, the above macroeconomic effects are non‐homogenously reproduced at microeconomic level. Table 4 shows the effects on output at the sectoral level. The asymmetric effect is noticeable, not only at the sectoral level, but also across countries and regions. In all the regions several sectors appear negatively affected, although the largest effects, as expected, occur within the EU. Electronic equipment and Construction are the only sectors with output growth for all the regions. Electronic equipment has also the highest quantitative changes (positive or negative) except for the EU and USA. Although the effects are relatively small, in Asia Pacific nine sectors (out of fifteen) are negatively affected. In the case of the EU, the higher decrease in output occurs in Government services (−4.067%), the sector more linked to the government. There are only two additional sectors that experience a negative effect, namely, Recreational services (−0.232%) and Other services (−0.175%). 8
AGR CRP MVH OTN IND OME ELE CNS TRD TCM OFI OBS ROS OSG SER
Table 4. Simulation results: effects on sectoral output (% change from benchmark) EU USA JPN CHI LAT Agricultural products Chemical industry Motor vehicles Other transport equipment Other industry Machinery and equipment Electronic equipment Construction Trade Transport and communications Other financial intermediation Other business services Recreational services Government services Other services
0.488 0.442 1.343 1.674 0.827 2.390 2.401 4.832 0.080 0.131 0.257 1.012 −0.232 −4.067 −0.175
0.016 −0.061 −0.019 0.138 0.008 0.044 0.131 0.008 0.006 −0.006 −0.004 0.011 −0.001 −0.012 0.002
−0.005 −0.061 −0.053 −0.011 −0.008 0.011 0.081 0.028 −0.001 0.007 −0.005 0.000 0.001 0.000 0.003
−0.007 −0.050 −0.042 −0.027 −0.013 0.005 0.247 0.007 −0.008 0.003 0.006 0.002 0.003 0.009 0.008
0.019 −0.040 −0.012 0.038 0.007 −0.040 0.076 0.009 0.001 −0.006 −0.003 0.001 −0.008 −0.005 0.002
PAC
ROW
−0.004 −0.104 −0.045 0.054 −0.023 −0.004 0.223 0.039 0.004 0.001 −0.013 −0.001 −0.020 −0.007 0.006
0.012 −0.111 0.006 0.021 0.003 0.076 0.119 0.012 0.004 −0.007 −0.007 0.007 −0.019 −0.005 0.005
Table 5 shows the change in sectoral employment, following a similar pattern than output even though the negative effects are smaller. There is a small effect on total employment for the rest of the regions as the “total” and “average” rows show, but there are some asymmetric sectoral effects (see “variance” and its relationship with the “total” in quantitative terms). Four sectors expand their employment in all the regions: Other business services, Electronic equipment, Construction and Other services, while some sectors are specially suffering a negative shock in most of the regions: Chemical industry, Motor vehicles, Other financial intermediation and Government services. Within the EU, the most negative effect is concentrated in the sector most related to government, i.e., Government services (with a decrease of −3.957%), while the remaining sectors expand their employment levels, in particular Electronic equipment (2.629%), Machinery and equipment (2.552%), Other transport equipment (1.809%), Motor vehicles (1.506%), Other business services (1.282%) and Other industry (1.003%). Regarding the effect on international trade flows, in aggregate terms both exports and imports expand (see Table 3), but the effect is very different at the sectoral level. While on the exports side (Table 6) there is a number of sectors in all the regions where exports fall, on the imports side (Table 7) only in the EU some sectors experience a decline in imports, namely, Recreational services, Government services, Trade and Other services. In the EU, exports increase in Chemical industry, Motor vehicles, Other financial intermediation and Transport and Communications. 9
AGR CRP MVH OTN IND OME ELE CNS TRD TCM OFI OBS ROS OSG SER
Table 5. Simulation results: effects on sectoral employment (% change from benchmark) EU USA JPN CHI LAT Agricultural products 0.596 Chemical industry 0.678 Motor vehicles 1.506 Other transport equipment 1.809 Other industry 1.003 Machinery and equipment 2.552 Electronic equipment 2.629 Construction 5.102 Trade 0.348 Transport and communications 0.442 Other financial intermediation 0.491 Other business services 1.282 Recreational services 0.014 Government services −3.957 Other services 0.283 Total −0.265 Average (not weighted) 0.968 Variance 3.585
0.020 −0.060 −0.018 0.138 0.009 0.044 0.132 0.008 0.007 −0.005 −0.004 0.011 −0.001 −0.011 0.004 0.002 0.018 0.003
−0.006 −0.061 −0.053 −0.011 −0.008 0.011 0.082 0.028 −0.001 0.007 −0.004 0.000 0.001 0.000 0.004 0.002 −0.001 0.001
−0.009 −0.050 −0.042 −0.026 −0.014 0.006 0.247 0.007 −0.007 0.003 0.007 0.002 0.003 0.009 0.008 0.004 0.010 0.005
0.023 −0.038 −0.010 0.039 0.009 −0.039 0.078 0.011 0.004 −0.003 −0.001 0.003 −0.006 −0.005 0.005 0.001 0.005 0.001
PAC
ROW
−0.007 −0.100 −0.041 0.058 −0.022 0.000 0.228 0.042 0.009 0.006 −0.008 0.002 −0.017 −0.005 0.013 0.005 0.011 0.005
0.015 −0.108 0.009 0.023 0.007 0.078 0.122 0.014 0.008 −0.003 −0.004 0.009 −0.017 −0.004 0.009 0.005 0.011 0.002
AGR CRP MVH OTN IND OME ELE CNS TRD TCM OFI OBS ROS OSG SER
Table 6. Simulation results: effects on exports (% change from benchmark) EU USA JPN CHI Agricultural products Chemical industry Motor vehicles Other transport equipment Other industry Machinery and equipment Electronic equipment Construction Trade Transport and communications Other financial intermediation Other business services Recreational services Government services Other services Total exports
0.289 0.410 1.118 1.677 0.871 1.887 2.290 2.341 0.167 0.175 0.268 0.613 −0.030 −0.562 0.022 0.879
10
0.088 −0.118 −0.016 0.484 0.073 0.252 0.508 1.213 −0.140 −0.031 −0.113 0.227 −0.238 −0.750 −0.020 0.070
0.062 −0.104 −0.043 0.057 0.002 0.083 0.329 1.693 −0.191 0.105 −0.156 0.081 −0.184 −0.303 −0.037 0.061
0.078 −0.209 −0.030 0.087 −0.018 0.177 0.438 1.819 −0.160 0.039 −0.175 0.083 −0.247 −0.621 −0.053 0.097
LAT
PAC
ROW
0.125 −0.107 0.016 0.289 0.059 0.015 0.206 2.026 −0.156 −0.044 −0.114 0.152 −0.279 −0.736 −0.012 0.045
0.084 −0.127 −0.036 0.168 −0.013 0.059 0.328 1.450 −0.200 0.046 −0.175 0.096 −0.327 −0.655 −0.087 0.067
0.106 −0.149 0.156 0.263 0.063 0.531 0.744 1.828 −0.183 −0.019 −0.139 0.157 −0.302 −0.739 −0.050 0.066
AGR CRP MVH OTN IND OME ELE CNS TRD TCM OFI OBS ROS OSG SER
Table 7. Simulation results: effects on imports (% change from benchmark) EU USA JPN CHI Agricultural products Chemical industry Motor vehicles Other transport equipment Other industry Machinery and equipment Electronic equipment Construction Trade Transport and communications Other financial intermediation Other business services Recreational services Government services Other services Total imports
0.316 0.147 1.332 2.055 0.564 2.521 2.454 4.840 −0.197 0.548 0.021 0.651 −0.467 −2.209 −0.152 0.845
0.007 0.035 0.018 0.168 0.008 0.060 0.022 0.021 0.020 0.034 0.066 0.023 0.041 0.017 0.015 0.052
0.005 0.035 0.115 0.069 0.016 0.062 0.046 0.056 0.044 0.052 0.044 0.052 0.049 0.031 0.035 0.059
0.019 0.050 0.101 0.240 0.046 0.123 0.166 0.068 0.071 0.088 0.062 0.076 0.053 0.063 0.050 0.103
LAT 0.020 0.013 0.003 0.029 0.017 0.010 0.017 0.042 0.044 0.048 0.050 0.043 0.040 0.045 0.010 0.048
PAC ROW 0.001 0.008 0.023 0.041 0.015 0.029 0.164 0.065 0.068 0.047 0.086 0.068 0.063 0.070 0.057 0.065
0.027 0.014 0.022 0.034 0.046 0.054 0.043 0.054 0.063 0.062 0.062 0.054 0.052 0.063 0.026 0.070
Finally, the change in prices (see Table 8) shows that the simulated policy has a deflationary effect on real prices in the EU with respect to other regions. This would make the EU goods more competitive at international level and can explain the increase in EU exports shown in Table 6. Regarding the rest of regions, China and Latin America are those experiencing a higher increase in real prices. Table 8. Simulation results: effects on real prices (% change) EU USA JPN CHI LAT PAC ROW AGR Agricultural products 0.028 CRP Chemical industry −0.090 MVH Motor vehicles −0.109 OTN Other Transport equipment −0.120 IND Other industry −0.078 OME Machinery and equipment −0.119 ELE Electronic equipment −0.089 CNS Construction −0.106 TRD Trade −0.117 TCM Transport and Communications −0.091 OFI Other Financial Intermediation −0.115 OBS Other Business Services −0.100 ROS Recreational services −0.110 OSG Government services −0.188 SER Other services 0.001 11
0.031 0.021 0.022 0.019 0.025 0.024 0.024 0.025 0.025 0.024 0.026 0.025 0.022 0.025 0.027
0.036 0.034 0.036 0.036 0.036 0.037 0.038 0.039 0.040 0.039 0.039 0.040 0.040 0.040 0.040
0.042 0.041 0.036 0.040 0.041 0.041 0.041 0.043 0.046 0.045 0.049 0.046 0.046 0.047 0.046
0.035 0.023 0.021 0.020 0.027 0.023 0.025 0.026 0.027 0.025 0.027 0.027 0.026 0.026 0.028
0.037 0.037 0.035 0.037 0.038 0.037 0.037 0.041 0.046 0.040 0.047 0.044 0.044 0.045 0.048
0.038 0.023 0.018 0.023 0.031 0.024 0.025 0.029 0.033 0.029 0.034 0.032 0.030 0.032 0.033
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APPENDIX A.1.Regional aggregation The correspondence with Database GTAP9 (Narayanan et al., 2015) is: European Union (EU) AUT ! Austria BEL ! Belgium DNK ! Denmark FIN ! Finland FRA ! France DEU ! Germany ITA ! Italy GBR ! United Kingdom GRC ! Greece IRL ! Ireland LUX ! Luxembourg NLD ! Netherlands PRT ! Portugal ESP ! Spain SWE ! Sweden CZE ! Czech Republic HUN ! Hungary MLT ! Malta POL ! Poland ROU ! Romania SVK ! Slovakia SVN ! Slovenia EST ! Estonia LVA ! Latvia LTU ! Lithuania BGR ! Bulgaria CYP ! Cyprus HRV ! Croatia United States (USA) Japan (JPN) China (CHI) CHN ! China HKG ! Hong Kong Latin America (LAT) MEX ! Mexico BRA ! Brazil ARG ! Argentina BOL ! Bolivia CHL ! Chile COL ! Colombia ECU ! Ecuador 15
PRY ! PER ! URY ! VEN ! XSM ! CRI ! GTM ! NIC ! PAN ! HND ! SLV ! XCA ! DOM ! JAM ! PRI ! TTO ! XCB ! Asia Pacific (PAC) KHM ! LAO ! MYS ! TWN ! PHL ! SGP ! THA ! VNM ! XSE ! KOR ! IDN ! BRN ! Rest of the World (ROW) IND ! BGD ! XSA ! XEA ! PAK ! LKA ! NPL ! MNG ! KGZ ! XWF ! XCF ! XAC ! ETH ! KEN ! MDG ! MWI ! MOZ ! TZA !
Paraguay Peru Uruguay Venezuela Rest of South America Costa Rica Guatemala Nicaragua Panama Honduras El Salvador Rest of Central America Dominican Republic Jamaica Puerto Rico Trinidad and Tobago Caribbean
Cambodia Lao People’s Democratic Republic Malaysia Taiwan Philippines Singapore Thailand Vietnam Rest of Southeast Asia Korea Indonesia Brunei Darussalam
India Bangladesh Rest of South Asia Rest of East Asia Pakistan Sri Lanka Nepal Mongolia Kyrgyzstan Rest of Western Africa Rest of Central Africa Rest of South Central Africa Ethiopia Kenya Madagascar Malawi Mozambique Tanzania 16
RWA ! UGA ! ZMB ! ZWE ! XEC ! EGY ! MAR ! TUN ! XNF ! BEN ! BFA ! CMR ! CIV ! GHA ! GIN ! NGA ! SEN ! TGO ! MUS ! BWA ! ZAF ! NAM ! XSC !
Rwanda Uganda Zambia Zimbabwe Rest of Eastern Africa Egypt Morocco Tunisia Rest of North Africa Benin Burkina Faso Cameroon Cote d'Ivoire Ghana Guinea Nigeria Senegal Togo Mauritius Botswana South Africa Namibia Rest of South African Customs Union
AUS NZL XOC
Australia New Zealand Rest of Oceania
CAN ! Canada XNA ! Rest of North America
ALB ! RUS ! BLR ! UKR ! XEE ! KAZ ! XSU ! ARM ! AZE ! GEO ! CHE ! NOR ! XEF ! XER !
Albania Russia Belarus Ukraine Rest of Eastern Europe Kazakhstan Rest of Former Soviet Union Armenia Azerbaijan Georgia Switzerland Norway Rest of EFTA Rest of Europe
IRN BHR ISR JOR KWT OMN
Iran, Islamic Republic of Bahrain Israel Jordan Kuwait Oman
! ! !
! ! ! ! ! !
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QAT ! Qatar SAU ! Saudi Arabia ARE ! United Arab Emirates TUR ! Turkey XWS ! Rest of Western Asia XTW ! Rest of the World A.2. Sectoral aggregation The correspondence of sectors included in Table 1 with Database GTAP9 sector listing (Narayanan et al., 2015) is: Sector
Code
Description
AGR
PDR
Paddy rice
AGR
WHT
Wheat
AGR
GRO
Cereal grains nec
AGR
V_F
Vegetables, fruit, nuts
AGR
OSD
Oil seeds
AGR
C_B
Sugar cane, sugar beet
AGR
PFB
Plant-based fibres
AGR
OCR
Crops nec
AGR
CTL
Bovine cattle, sheep and goats, horses
AGR
OAP
Animal products nec
AGR
RMK
Raw milk
AGR
WOL
Wool, silk-worm cocoons
AGR
FRS
Forestry
AGR
FSH
Fishing
IND
COA
Coal
IND
OIL
Oil
IND
GAS
Gas
IND
OMN
Minerals nec
IND
CMT
Bovine meat products
IND
OMT
Meat products nec
IND
VOL
Vegetable oils and fats
IND
MIL
Dairy products
IND
PCR
Processed rice
IND
SGR
Sugar
IND
OFD
Food products nec
IND
B_T
Beverages and tobacco products
IND
TEX
Textiles
IND
WAP
Wearing apparel
IND
LEA
Leather products
18
IND
LUM
Wood products
IND
PPP
Paper products, publishing
IND
P_C
Petroleum, coal products
CRP
CRP
Chemical, rubber, plastic products
IND
NMM
Mineral products nec
IND
I_S
Ferrous metals
IND
NFM
Metals nec
IND
FMP
Metal products
MVH
MVH
Motor vehicles and parts
OTN
OTN
Transport equipment nec
ELE
ELE
Electronic equipment
OME
OME
Machinery and equipment nec
IND
OMF
Manufactures nec
SER
ELY
Electricity
SER
GDT
Gas manufacture, distribution
SER
WTR
Water
CNS
CNS
Construction
TRD
TRD
Trade
TCM
OTP
Transport nec
TCM
WTP
Water transport
TCM
ATP
Air transport
TCM
CMN
Communication
OFI
OFI
Financial services nec
SER
ISR
Insurance
OBS
OBS
Business services nec
ROS
ROS
Recreational and other services
OSG
OSG
Public Administration, Defence, Education, Health
SER
DWE
Dwellings
19