Diferencias en la ejecución en la provincia de Arequipa e Ica

For this study, we use an extremely well balanced panel data set covering 19 Italian regions10, which accounts for the period 1996-2007. Choosing this period has been determined by the availability of the data11.

The fiscal variables (namely the economic and functional components of government expenditure) are the key variables of the model. They have all been derived from the data issued by Department for the development and economic cohesion (DPS – Dipartimento per lo

Sviluppo e la Coesione Economica). They are better explained in the next section.

8

The 30 sectors under scrutiny and included in the functional classification are: General administration, Defence, Public order, Justice, Education, Training, Research and development, Culture and Recreational Services, Residential Building and Urban Development, Health, other social affairs (Support and Charity), Water, Sewers and Water Treatment, Environment, Waste Disposal, other health and sanitation services, Labour, Pensions and Wage Supplementation, Roads, other Transport, Telecommunications, Agriculture, Marine Fishing and Aquaculture, Tourism, Wholesale and Retail Distribution, Industry and Artisans, Energy, other public works, other economic sectors, unclassified expenditure.

9 _{COFOG is the official classification of expenditures incurred by Public Administrations according to the}

purposes, set by the ONU and adopted by international institutions.

10

In the empirical analysis, we exclude Valle d’Aosta because it is an outlier.

11

49

The economic and functional component of public expending are also expressed as a share of total government expenditure, while the total government spending is expressed as a share of GDP at constant prices (year 2005). An important feature of the present analysis is that this data set is strongly balanced. In the literature, many empirical works on the relationship between growth and components of government expenditure exist. However, most of them use an unbalanced data set. The dependent variable is chosen here as the per capita real GDP growth rate (natural log difference of GDP per capita in millions of euro, constant prices 2005). Another important determinant of growth rate is the ratio of private stock and public capital (𝑝𝑟𝑖𝑣𝑎𝑡𝑒_𝑘), which is derived from Ghosh and Gregoriou’s (2008) theoretical model, as illustrated in Section 2. The data about capital stock have been kindly provided by Montanaro

et al. (2012b). This variable is also expressed as a ratio of total government spending.

Our dataset also contains few macroeconomic variables, included as control variables. They seek to capture the factors affecting economic growth and have been obtained from the Italian National Institute of Statistics (ISTAT). One key control variable incorporates the percentage of population aged 24 to 35 having completed tertiary education. It is used in our reference regressions in order to take into account the growth effects of human capital in the researched regions. Thus, the estimated coefficients of the fiscal variables measure the growth impact of policies beyond their effect on physical and human capital accumulation. In addition, we also make use of the percentage of total population aged 65 and over as control. Another control used in our robustness checks includes the employment growth variable (𝑒𝑚𝑝𝑙_𝑔𝑟𝑜𝑤𝑡𝑕) so as to control for business cycles effects on growth12. Most empirical panel data studies on growth existing in literature have been carried out for periods of approximately 30 years, with five- year averaged observations that help isolating business cycles influences on growth (Devarajan

et al., 1996, Kneller et al., 1999, Ghosh and Gregoriou, 2008). However, this firstly this implies

loss of information and efficiency of estimates. Secondly, the lack of synchronicity in country business cycles does not filter five-year averages from cyclical effects (Bassanini et al., 2001). We estimate the following equations, including the economic classification of public expenditures:

𝐺𝑖𝑡 = 𝑎𝑖+ 𝑏𝑡+ 𝛽1𝑡𝑜𝑡_𝑒𝑥𝑝𝑖𝑡+ 𝛽2𝑐𝑎𝑝_𝑒𝑥𝑝𝑖𝑡 + 𝛽3𝑝𝑟𝑖𝑣𝑎𝑡𝑒_𝑘𝑖𝑡+ 𝛽4𝑕𝑢𝑚𝑎𝑛_𝑐𝑎𝑝𝑖𝑡 +

𝛽5𝑝𝑜𝑝_65𝑖𝑡+ 𝑒𝑖𝑡 (22)

12

Benos (2009) also used this variable to determine the relationship between fiscal policy and economic growth.

50 𝐺𝑖𝑡 = 𝑎𝑖+ 𝑏𝑡+ 𝛽1𝑡𝑜𝑡_𝑒𝑥𝑝𝑖𝑡+ 𝛽2𝑐𝑢𝑟𝑟_𝑒𝑥𝑝𝑖𝑡 + 𝛽3𝑝𝑟𝑖𝑣𝑎𝑡𝑒_𝑘𝑖𝑡 + 𝛽4𝑕𝑢𝑚𝑎𝑛_𝑐𝑎𝑝𝑖𝑡+ 𝛽5𝑝𝑜𝑝_65𝑖𝑡+ 𝑒𝑖𝑡 (23) 𝐺𝑖𝑡 = 𝑎𝑖+ 𝑏𝑡+ 𝛽1𝑡𝑜𝑡_𝑒𝑥𝑝𝑖𝑡+ 𝛽2𝑐𝑎𝑝_𝑡𝑟𝑎𝑛𝑠𝑓𝑖𝑡+ 𝛽3 𝑓𝑖𝑛_𝑎𝑠𝑠𝑒𝑡𝑠𝑖𝑡+ 𝛽4𝑛_𝑓𝑖𝑛_𝑎𝑠𝑠𝑒𝑡𝑠𝑖𝑡 + 𝛽5𝑝𝑟𝑖𝑣𝑎𝑡𝑒_𝑘𝑖𝑡 + 𝛽6𝑕𝑢𝑚𝑎𝑛_𝑐𝑎𝑝𝑖𝑡+ 𝛽7𝑝𝑜𝑝_65𝑖𝑡 + 𝑒𝑖𝑡 (24) 𝐺𝑖𝑡 = 𝑎𝑖+ 𝑏𝑡+ 𝛽1𝑡𝑜𝑡_𝑒𝑥𝑝𝑖𝑡 + 𝛽2 𝑐𝑢𝑟𝑟_𝑡𝑟𝑎𝑛𝑠𝑓𝑖𝑡+ 𝛽3𝑜𝑡𝑕𝑒𝑟_𝑐𝑢𝑟𝑟𝑖𝑡+ 𝛽4𝑝𝑟𝑖𝑣𝑎𝑡𝑒_𝑘𝑖𝑡+ 𝛽5𝑕𝑢𝑚𝑎𝑛_𝑐𝑎𝑝𝑖𝑡 + 𝛽6𝑝𝑜𝑝_65𝑖𝑡 + 𝑒𝑖𝑡 (25)

where 𝑖 and 𝑡 denote the cross-sectional and time series dimensions respectively; 𝑎𝑖 captures

the time-invariant unobserved country-specific fixed effects, and 𝑏𝑡 captures the unobservable

individual-invariant time effects. 𝐺𝑖𝑡 is the per capita real GDP growth rate, 𝑡𝑜𝑡_𝑒𝑥𝑝𝑖𝑡 is total

government expenditure, 𝑐𝑎𝑝_𝑒𝑥𝑝𝑖𝑡 and 𝑐𝑢𝑟𝑟_𝑒𝑥𝑝𝑖𝑡 are public the capital and current

expenditure shares, 𝑝𝑟𝑖𝑣𝑎𝑡𝑒_𝑘𝑖𝑡 is the private stock and 𝑕𝑢𝑚𝑎𝑛_𝑐𝑎𝑝𝑖𝑡 and 𝑝𝑜𝑝_65𝑖𝑡 are the

control variables, as already explained above.

Regarding functional classification, we estimate the following equations:

𝐺𝑖𝑡 = 𝑎𝑖+ 𝑏𝑡+ 𝛽1𝑡𝑜𝑡_𝑒𝑥𝑝𝑖𝑡+ 𝛽2𝑐𝑎𝑝_𝑔𝑒𝑛_𝑝_𝑠𝑒𝑟𝑣𝑖𝑡+ 𝛽3𝑐𝑎𝑝_𝑑𝑒𝑓𝑒𝑛𝑖𝑡+ 𝛽4𝑐𝑎𝑝_𝑒𝑐_𝑎𝑓𝑓𝑖𝑡+ 𝛽5𝑐𝑎𝑝_𝑕𝑒𝑎𝑙𝑡𝑕𝑖𝑡+ 𝛽6𝑐𝑎𝑝_𝑒𝑑𝑢𝑐𝑖𝑡+ 𝛽7𝑐𝑎𝑝_𝑠𝑜_𝑝𝑟𝑜𝑡𝑖𝑡+ 𝛽8𝑝𝑟𝑖𝑣𝑎𝑡𝑒_𝑘𝑖𝑡+ 𝛽9𝑕𝑢𝑚𝑎𝑛_𝑐𝑎𝑝𝑖𝑡+ 𝛽10𝑝𝑜𝑝_65𝑖𝑡+ 𝜀𝑖𝑡 (26) 𝐺𝑖𝑡 = 𝑎𝑖+ 𝑏𝑡+ 𝛽1𝑡𝑜𝑡_𝑒𝑥𝑝𝑖𝑡+ 𝛽2𝑐𝑢𝑟𝑟_𝑔𝑒𝑛_𝑝_𝑠𝑒𝑟𝑣𝑖𝑡+ 𝛽3𝑐𝑢𝑟𝑟_𝑑𝑒𝑓𝑒𝑛𝑖𝑡+ 𝛽4𝑐𝑢𝑟𝑟_𝑒𝑐_𝑎𝑓𝑓𝑖𝑡+ 𝛽5𝑐𝑢𝑟𝑟_𝑕𝑒𝑎𝑙𝑡𝑕𝑖𝑡+ 𝛽6𝑐𝑢𝑟𝑟_𝑒𝑑𝑢𝑐𝑖𝑡+ 𝛽7𝑐𝑢𝑟𝑟_𝑠𝑜_𝑝𝑟𝑜𝑡𝑖𝑡+ 𝛽8𝑝𝑟𝑖𝑣𝑎𝑡𝑒_𝑘𝑖𝑡 + 𝛽9𝑕𝑢𝑚𝑎𝑛_𝑐𝑎𝑝𝑖𝑡+ 𝛽10𝑝𝑜𝑝_65𝑖𝑡+ 𝜀𝑖𝑡 (27)

where 𝑔𝑒𝑛_𝑝_𝑠𝑒𝑟𝑣, 𝑑𝑒𝑓𝑒𝑛, 𝑒𝑐_𝑎𝑓𝑓, 𝑕𝑒𝑎𝑙𝑡𝑕, 𝑒𝑑𝑢𝑐 and 𝑠𝑜𝑐_𝑝𝑟𝑜𝑡 are the capital and current shares of general public spending, defence, economic affairs, health, education and social protection expenditures. The remaining variables are as defined in the previous set.