Wine Economics and Policy 9(2): 113-126, 2020 Firenze University Press www.fupress.com/wep ISSN 2212-9774 (online) | ISSN 2213-3968 (print) | DOI: 10.36253/wep-7833 Wine Economics and Policy Citation: Maria Raimondo, Concetta Nazzaro, Annamaria Nifo, Giuseppe Marotta (2020) Does the Institutional Qual- ity Affect Labor Productivity in Italian Vineyard Farms?. Wine Economics and Policy 9(2): 113-126. doi: 10.36253/ wep-7833 Copyright: © 2020 Maria Raimondo, Concetta Nazzaro, Annamaria Nifo, Giuseppe Marotta. This is an open access, peer-reviewed article published by Firenze University Press (http:// www.fupress.com/wep) and distributed under the terms of the Creative Com- mons Attribution License, which per- mits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Does the Institutional Quality Affect Labor Productivity in Italian Vineyard Farms? Maria Raimondo1,*, Concetta Nazzaro4, Annamaria Nifo3, Giuseppe Marotta2 1 Università degli Studi del Sannio, Department of Law, Economics, Management and Quantitative Methods, Benevento, Italy, Email: raimondo@unisannio.it 2 Università degli Studi del Sannio, Department of Law, Economics, Management and Quantitative Methods, Benevento, Italy, Email: marotta@unisannio.it 3 Università degli Studi del Sannio, Department of Law, Economics, Management and Quantitative Methods, Benevento, Italy, Email: nifo@unisannio.it 4 Università degli Studi del Sannio, Department of Law, Economics, Management and Quantitative Methods, Benevento, Italy, Email: cnazzaro@unisannio.it *Corresponding author Abstract. The paper aims at analyzing the effect of institutional quality on labor pro- ductivity in the agricultural sector. To meet this aim, a Gaussian log-linear model was applied to 773 vineyard farms, located in 71 Italian provinces. The applied methodol- ogy enabled to quantify the overall impact of the institutional quality on labor produc- tivity by discriminating with respect to the Italian regions and macro-areas (i.e. North, South or Central Italy). The findings of the investigation show a positive effect of the institutional quality on labor productivity, with an overall impact of 39%. Moreover, huge differences among Italian regions and macro-areas were detected. The study find- ings provide recommendations for academics and policy-makers to improve both theo- retical and practical aspects. Keywords: IQI, labor productivity, vineyard farm. 1. INTRODUCTION In the last decades, institutional factors have attracted great interest as one of the main determinants of economic performance of countries and regions [1, 2, 3, 4]. A large literature has emphasized the role of institutions in influencing both inputs (physical and human capital) and productivity, thus focusing on the existence of an additional effect of institutions on the per capita Gross Domestic Products (through productivity changes). Previ- ous studies have also emphasized the role of institutions into influencing the ability of firms to combine inputs more efficiently [5, 6, 7]. Often, a positive and important of context factor is also recognized in the good institutional quality of the geographical area where the firm is located. Such a quality may be defined as a fruitful combination of formal institutions, good rules and 114 Maria Raimondo, Concetta Nazzaro, Annamaria Nifo, Giuseppe Marotta practices, cooperation among firms, researchers and pol- icy makers [8, 9, 7, 10]. In this vein, institutions shape the key incentives of individuals and firms, influencing investments in physi- cal capital, human capital, technology and the ability to organize production, determining not only the potential for aggregate economic growth, but also the distribu- tion of resources [11, 12, 13]. As for the agricultural sec- tor, some authors have theoretically analyzed the effect of institutional context on economic performances of farms [14]. However, few researches have empirically investigated the effect of institutional quality on farm’s economic performances [15, 13, 16]. Accordingly, the general goal of this study is to empirically investigate the effect of institutional quality on economic performances of Italian farms. In particular, since better institutions create a legal structure which increases: i) the adoption of technological innovation [17], ii) the likelihood that a firm conducts and transfer R&D activities [18] and iii) the human development [19], the research hypothesis is that the institutional quality positively affects labor pro- ductivity in Italian vineyard farms. Precisely, by taking Italian farms specialized in viticulture (wine of excel- lence) as a case study, the specific goals of the present study are to: i) investigate the effect of the institutional quality on labor productivity, ii) quantify the effect of institutional quality on labor productivity, and finally, iii) assess the effect of institutional quality on labor pro- ductivity among Italian regions and macro-areas (North, Center and South). Italian vineyard farms have been chosen for the fol- lowing reasons: i) Italy is one of the main wine produc- ing and wine-exporting country in the world [20, 21, 22]. In fact, in 2016, Italy has produced more than 50 million hL of wine, the highest in the world. [23] Cur- rently it counts more than 600.000 hectares of vineyards and around 350 autochthonous grape varieties, 470 pro- tected designation of origin (PDO) wines and 120 pro- tected geographical indication (PGI) wines [24]; ii) viti- culture is widely spread in all Italian regions; iii) during the last decade, the labor productivity in Italian viticul- ture is gaining attention by strengthening the mechani- zation along the production process [25, 22]. For the purposes of the present paper, we refer to the Farm Accountancy Data Network (FADN), a data- set which records information about statistically repre- sentative aspects of farms and farmers, referred to 2012. As for the institutional quality, we have accounted for the Institutional Quality Index (IQI) developed by Nifo and Vecchione [9]), which regards institutional qual- ity in Italian provinces as a composite indicator derived by 24 elementary indexes grouped into five institutional dimensions (namely corruption, government effective- ness, regulatory quality, rule of law, voice and account- ability). The investigation is conducted on a sample of 773 Italian vineyard farms, located in 71 of the overall 107 Italian provinces. Given the nature of the data, a Gauss- ian log-linear model is performed. The paper is organized as follows: paragraph 2 out- lines the theoretical framework; paragraph 3 illustrates the statistical model once described the materials of the study. Then, the study findings are exploited in para- graph 4, and discussed in paragraph 5. Conclusions and implications are drawn in paragraph 6. 2. THEORETICAL BACKGROUND The decisive impact that institutions may have on economic growth, on the environment, on service level- of-quality, and on overall efficiency of an area has been examined by a broad strand of the economics literature that, in recent years, has paid growing attention to the role of political and administrative contexts as well as social, historical and cultural factors in conditioning and steering development processes. Starting from the work of Douglass North [1, p. 3], according to whom “institu- tions are the rules of the game in a society”, institutions contribute to forming the set of incentives underly- ing behavior and individual choices. As a consequence, several studies have been concerned with measuring the quality of political and administrative institutions (in terms, for example, of well-defined property rights, respect for regulations, degree of corruption, and barriers to entry on markets) both for cross-country [26, 27, 28, 29, 30, 31, 32, 33] and inter-regional comparisons [34, 35, 36, 37]. Several researches [6, 38, 39, 40] have specifically focused on the importance of institutional quality as the basic determinant of economic growth and total produc- tivity of factors in the long term. The institutional differ- ences as a key factor of growth and stagnation as well as disparities in productivity and accumulation of physical and human capital is also investigated [11]. Some authors have focused on the role of sub-national institutions, par- ticularly the regional ones, in fostering economic growth. Porter [41, 42] has argued that economic development is pursued by favoring not isolated companies but industri- al clusters, which include firms, suppliers and also local institutions and research centers. Additional contribu- tions have extended the notion of institutional quality to social capital endowment [43, 44, 45] and institutional thickness [46]. Empirical evidence has pointed out that social cohesion [47] as well as the spread of collaborative 115Does the Institutional Quality Affect Labor Productivity in Italian Vineyard Farms? and associative practices [43, 48, 49] are drivers of eco- nomic development. Notwithstanding the institutional quality has been investigated from decades to come, the role of institu- tional context on value creation in agricultural sector has gained attention only in the last few years [16, 50, 13, 51, 14]. Through disparate analytical perspectives, sev- eral theoretical and empirical studies have shown differ- ent relations between institutional quality and economic performance in agricultural sector ([6, 14, 51]. Lin et al. [16], by using structural gravity models to measure how institutions affect the trade performance of some coco- nut producing countries, have shown that government effectiveness increases trade flows of high value coconut products. Conversely, Nadarajah and Flaaten, [13] by investigating the relationship between annual growth in aquaculture production and the quality of institutions, emphasized the insignificant correlation between aqua- culture growth and the quality of institutions in ana- lyzed countries. The institutional context has been also analyzed as determinant of voluntary traceability stand- ards in the Italian wine sector (50). A previous study, from Marotta and Nazzaro [14], theoretically analyzed the role of institutional context in new business models for value creation in agriculture sector. More deeply, according to the “value portfolio” (VP) model, macroeconomic factors such as territorial assets, the quality of institutions and policies play a stra- tegic role on value creation in agricultural sector. In other words, the VP of a farm is composed by organizational schemes in which internal resources of a farm (i.e. entrepreneurship and human resources; physi- cal and financial resources; technological resources and networking) are combined with the external ones, such as social capital, fixed social capital and institutional context [52, 53, 14]. Based on what has been discussed so far, it is crucial to investigate also empirically the effect of institutional quality on labor productivity in agricul- tural sector. 3. MATERIALS AND METHOD 3.1 Data In order to achieve the specific aims of the study a cross-section dataset from the FADN have been used. The dataset records information about statistically repre- sentative farms and farmers aspects. The FADN is com- posed by an annual survey carried out by the member states of the European Union. It is the unique source of microeconomic data based on the same principles in all European countries that aims to provide representative data along three dimensions: the economic size, type of farming and the region. More deeply, the aim of the net- work is to collect accounting data from farms in order to know incomes and to conduct business analyses of agri- cultural holdings with the aim of evaluating, ex-ante and ex-post, the impacts of the Common Agricultural Policy (CAP). Our analysis includes data on overall 773 Italian farms specialized in viticulture producing grapevines for quality wine (with certification of origin PDO/PGI or variety indication as regulated by EU Reg. 1308/2013 and Reg. 607/2011) and located in 71 Italian provinces of all Italian regions (Appendix A). A summary statistics of the variables included in the model is given in section 3.2. In order to know information about the quality of institutions in Italian provinces, we referred to the insti- tutional quality index. Major attention should be devot- ed to the IQI description. This is achieved in the follow- ing subsection. 3.1.1 The IQI index The aim of this subsection is to describe the IQI that is getting momentum in recent scientific stud- ies [7, 54, 55, 10]. It is a composite indicator that meas- ures the quality of Italian institutions at province level through the analytic hierarchy process [56] for the peri- od 2004-2012. The following five dimensions: “Voice and Accountability”, “Government Effectiveness”, “Regula- tory Quality”, “Rule of Law” and “Control and Corrup- tion” are the main components of the IQI. The first one concerns the degree of freedom of press and association, the second one is related to the quality of public services as well as the definition and the implementation of poli- cies by the local government. The third refers to the abil- ity of government to promote and formulate effective regulatory interventions, while the fourth accounts for the perception of the law application in terms of con- tract fulfilment, property rights, police forces, activities of the magistracy as well as crime levels. Lastly, the fifth dimension takes into account the degree of corruption of public employees. The IQI index is prompted by the World Governance Indicator (WGI) proposed by Kraay et al. [57] in the context of the Knowledge for Change Programme promoted by the World Bank. However, it considers only five of the six dimensions of the WGI. Indeed, the so-called “Political stability and absence of violence and terrorism” dimension is omitted in the IQI since it is related to the frequency of terrorist attacks and to the presence of military in politics, that are not relevant in Italy [9]. Each dimension is composed, in turn, by the aggregation of elementary indexes (see Figure 1) evaluated by data from institutional sources, 116 Maria Raimondo, Concetta Nazzaro, Annamaria Nifo, Giuseppe Marotta research institutes and professional registers. Appendix B reports the list of all elementary indexes employed and sources. As for the methodological approach, three steps have been implemented to obtain the IQI index from elementary indices, such as: normalization, attribution of weights to each index and aggregation. First of all, the elementary indices were normalized, then measured in the interval [0, 1], determining the distance of each of them from the maximum value found at the prov- ince level. Thus, through the analytical hierarchy process (AHP) [56], a weight was assigned to each normalized elementary index. Finally, once normalized and weighed, the elementary indices were aggregated to obtain the institution’s quality index for 107 provinces – from 2004 to 2012 – which, by construction, takes values in the interval [0,1] [9]. Appendix B reports values of IQI of each Italian province and region included in the study. 3.2 Method description The effect of institutional quality on labor produc- tivity in Italian vineyard farms is assessed by designing the following Gaussian log-linear model: ln_LPi=α+β youngi+γ farmi+δ IQIi+ εi i=1,2…773. (1) where ln_LP is the logarithmic of the labor productivity for each i-farm. More specifically, the LP is the depend- ent variable of the model obtained by the ratio between the gross marketable output (GMO) and the work units employed in each farm (euro/worker). Some control variables were chosen, including farm- ers and farms aspects, based on what the scientific litera- ture considers as crucial elements for labor productivity in agricultural sector [58, 59, 60, 61, 62, 63, 64, 65, 66]. Young is a dummy variable, meaning the youth of the farmer that assumes value 1 if the farmer is 40 years old and 0 otherwise. In our model we called farm the vector of farms’ variables. The vector includes five control vari- ables, i.e. machines capital, land-labor ratio, circulating agricultural capital, irrigation and second pillar found- ing. The variable machines capital is the ratio between the economic value of machines and the used agricul- tural area (UAA), attached to the level of farm’s invest- ments in mechanization. The land-labor ratio variable, obtained by dividing the UAA per worker, giving infor- mation on the number of hectares per worker is a meas- ure of the labor intensity. The circulating agricultural capital, defined as the ratio between the circulating agri- cultural capital and the (UAA), is an indicator that sug- gest the availability of euros per hectare. The irrigation variable is a dummy variable that assumes value 1 if the farm has irrigated land and 0 if the farm has not irri- gated land. As for the second pillar founding variable, it is a dummy variable that means whether or not the farm received subsidies from the second pillar founding of the CAP. In other words, the variable assumes value 1 if the farm has received some payments for measures of Axis 2 from the Rural Development Plan and 0 otherwise. The IQI is an explanatory variable of our model that measures, in the interval from 0 to 1, the institutional quality of the province in which the farm is located. Finally, ε is the error term. A descriptive statistics of the variables included in the model is given in Table 1. The average LP is around 50 thousand euros. As for the age of farmers, only 13% is younger than 40 years. The average value of the machines capital is roughly 3 thousand euros per hectare, about 1 thousand euros lower than the average circulating agricultural capital per hectare (3985.73 euros/ha). As for the land-labor ratio, each worker has, on average, less than 10 hectares (9.22). The 38% is the percentage of the irrigated land, while the 47% is the percentage of farms that have received found- ing from the second pillar founding. Last, the average value of the IQI is 0.69, with the lowest equal to 0.04 and the highest value equal to 1 (meaning the maximum of the IQI). Figure 1. Dimensions and elementary indexes of IQI. Source: Structure of the Institutional Quality Index (IQI) from Lasagni et al, [7]. 117Does the Institutional Quality Affect Labor Productivity in Italian Vineyard Farms? 4. RESULTS 4.1 The Gaussian log-linear model estimates Results from the designed statistical model are sum- marized in Table 2. At a first glance, the coefficient of IQI has a significant and positive effect on LP, meaning that the institutional quality positively affects the labor productivity thus corroborating our research hypoth- esis. As for the impact of the institutional quality on the dependent variable, we followed Benoit [67] for inter- preting coefficients with logarithmic transformation. In the log-linear model, the interpretation of estimated coefficient β (see the second column of the Table 2) is that a one-unit increase in X will produce an expected increase in log Y of β units. In terms of Y, this means that the expected value of Y is multiplied by eβ. Brief- ly, in terms of effects of changes in X on Y (unlogged), each 1-unit increases in X multiplies the expected value of Y by eβ. Accordingly, the impact of the IQI on LP is quantified in 39% (the third column of the Table 2). This means that going from the lowest level of the IQI (equal to 0) to the maximum one (equal to 1), the labor productivity will increase by 39% in Italian vineyard farms. Except for young, all control variables are statisti- cally significant. More deeply, all of them have a positive effect on LP. 4.2 The sensitivity analysis of the IQI index The sensitivity analysis allows to determine and to quantify the impact of small input perturbations on the model output [68]. Thus, we have carried out sev- eral perturbations to the IQI index. More deeply, we have assigned several different values to the institutional Table 1. Descriptive statistics of the variables included in the statistical model. Variable name Variable Description Mean Std. Dev. Min Max Dependent variable LP Labor Productivity (euro/worker). The ratio between the GMO and the units of labor 48262.17 43514.6 1148.39 360860.8 Independent variables Young 1 = under 40 years old; 0 = otherwise .13 N.A. 0 1 Machines capital Ratio between machines capital and UAA (euro/ha) 2949.81 7482.68 0 105383.8 Land-labor ratio Available UAA per worker (ha/worker) 9.22 9.54 .64 107.25 Circulating Agricultural Capital Ratio between circulating agricultural capital and used agricultural area (euro/ha) 3985.73 17223.03 0 333915.2 Irrigation 1=yes; 0=no .38 N.A. 0 1 Second Pillar Founding 1=yes; 0=no .47 N.A. 0 1 IQI Institutional Quality Index .69 .14 .04 1 N.A.: Not Applicable*. Table 2. Effect of IQI (Institutional Quality Index) on value creation in vineyard farms. Gaussian log-linear model estimates. Parameters β Coef. (eβ) Std. Err. t p-Value IQI 0.330 1.39 0.17 1.88 0.060 * Young 0.104 1.11 0.07 1.43 0.155 Machines_capital 0.006 1.01 0.00 1.65 0.099* Circulating Agricultural Capital 0.013 1.01 0.00 9.29 0.000*** Irrigation 0.202 1.22 0.05 3.95 0.000*** Second pillar founding 0.115 1.12 0.05 2.33 0.020 ** Land-labor ratio 0.035 1.03 0.00 13.44 0.000*** Cons 9.719 16.63 0.13 72.80 0.000*** Note: N=773; * p-value < 0.1; ** p-value < 0.05; ***p-value < 0.01. R2 =0.26. 118 Maria Raimondo, Concetta Nazzaro, Annamaria Nifo, Giuseppe Marotta quality index in the range from 0 to 1 (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1) where 0 corresponds to the minimum level of the institution quality while 1 is the maximum value. Afterwards, we have quantified the average labor productivity, at each level of IQI (Table 3). In the Figure 2 we have plotted the LP (y–axis) versus the perturbations of the IQI(x–axis). The graph reveals the linear effect of institutional quality index on the LP. Specifically, the sensitivity analysis indicated that the institutional context has a positive and constant impact on labor productivity in vineyard farms. The slope of the line in Figure 2 is the sensitivity of the LP with respect to the IQI (by taking fixed the other vari- ables).In particular, as shown by the statistical model, if the IQI index is equal to 1 the average LP is 39% higher than that obtained under the IQI index equal to 0. In Table 4, the difference between the average LP at region level by considering the current IQI and that obtained by giving to all provinces the maximum IQI value (i.e. equal to 1) suggests the economic loss, in terms of labor productivity, due to low institutional quality. The developed analysis shows that the LP in the Italian regions and macro-areas (Northern, Southern and Central) is not homogeneous. More specifically, it is possible to state that in Calabria the average economic loss caused by the low quality of institutions is more than 37%. Conversely, in Trentino Alto Adige the average economic loss is rough- ly 5%. Moreover, the economic loss increases by passing from the North to the Southern regions, as shown in the last column of the Table 4. Accordingly, investments for improving the institutional quality in the Southern regions would enhance the labor productivity in vine- yard farms, thus improving the agricultural sector in underdeveloped areas. 5. DISCUSSIONS The present paper had three specific goals. First, it developed, for the first time, an empirical study to ana- lyze the relation between the institutional quality of the Italian provinces and labor productivity in Italian vine- yard farms. Second, once answered to the first aim, the Table 3. Assumptions tested in the sensitivity analysis. Assumptions Average labor productivity (euro/worker) The IQI index is equal to 0 42901.02 The IQI index is equal to 0.1 44340.35 The IQI index is equal to 0.2 45827.97 The IQI index is equal to 0.3 47365.49 The IQI index is equal to 0.4 48954.60 The IQI index is equal to 0.5 50597.03 The IQI index is equal to 0.6 52294.56 The IQI index is equal to 0.7 54049.04 The IQI index is equal to 0.8 55862.39 The IQI index is equal to 0.9 57736.57 The IQI index is equal to 1 59673.63 40000 45000 50000 55000 60000 65000 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 A ve ra ge l ab or p ro d u ct iv it y (L P ) (e u ro /w or k er s) IQI index Figure 2. The sensitivity analysis changing IQI index. Table 4. The average labor productivity (LP) in Italian regions at current IQI and at maximum value of IQI (equal to 1) in all Italian provinces.   Average LP (tEur/w) at current IQI Average LP (tEur/w) at IQI equal to 1 in Italian provinces Economic loss (%) due to low institutional quality Trentino Alto Adige 139633 146329 4.80 Tuscany 44225 47043 6.37 Abruzzo 37970 40589 6.90 Emilia Romagna 110657 119892 8.35 Valle D’Aosta 57200 62182 8.71 Veneto 46239 50291 8.76 Umbria 72550 79120 9.05 Friuli Venezia Giulia 49611 54512 9.88 Piedmont 46027 50775 10.32 Lombardy 45512 50218 10.34 Marche 43628 48158 10.38 Lazio 46242 52960 14.53 Liguria 45628 53438 17.12 Campania 35125 41444 17.99 Puglia 44793 54109 20.80 Sardinia 76916 93006 20.92 Basilicata 45715 55770 21.99 Molise 48154 62701 30.21 Sicily 60323 78563 30.24 Calabria 31245 42894 37.28 119Does the Institutional Quality Affect Labor Productivity in Italian Vineyard Farms? study quantified the effect size of the institutional qual- ity on the economic value created per worker and finally, it measured the impact of the institutional quality on labor productivity in vineyard farms located in all Ital- ian regions and macro-areas (North, South and Central Italy). To this end, we developed a Gaussian log-linear model, which considers the ratio between the gross mar- ketable output and the number of workers employed in each farm as the dependent variable of the statistical model. Further, the IQI is one of the independent vari- ables together with the farms and farmers’ aspects. The model output highlighted a significant and positive effect of the institutional quality on labor productivity in Italian vineyard farms. Although there are no previous empirical studies about the effect of institutional quality of Italian prov- inces on labor productivity in agricultural sector, our findings are consistent with previous theoretical and empirical studies developed in non-agricultural sec- tor [5, 14, 51, 69, 9, 7, 70]. Based on the study findings, one can state that vineyard farms operating in a good institutional context consistently increase the labor pro- ductivity. Several reasons may explain this result. First, getting the “right” price from the market and reducing the transaction costs is helpful in increasing the gross marketable output. Several authors, from decades to come, have indeed highlighted the role of both formal and informal institutions in improving the level and quality of entrepreneurship [71] as well as in removing the market imperfections and the transaction costs [1, 32]. Furthermore, a favorable institutional context (in terms of bureaucracy efficiency and economic facilities) encourages farms to invest in technology and mechani- zation [18, 7], thus increasing the economic value cre- ated through the intensification of output produced per worker. The availability of economic facilities is also helpful for improving crop productivity and techni- cal efficiency by the increase of financial services [72]. Further, associations and social cooperatives are help- ful tools for labor productivity by overcoming market imperfections and constraints [73, 74, 75, 76]. Indeed, according to Fischer and Qaim [77] social cooperatives increase farm income and profit. Moreover, being part of social cooperatives and associations may improve labor productivity by sharing knowledge and information among workers. As for the measure of the effect of the institutional quality on the average labor productivity in vineyard farms located in the North, South and in the Central Italy, the finding showed the lowest LP in farms located in the Southern regions. This is in agreement with the work of Lasagni and co-authors [7]) who showed that the total factor productivity in manufacturing firms is lower in industries located in the Southern Italian regions than those located in the Northern and in the Central ones. Differences in LP among Italian vineyard farms may be attributed to differences in transport and infrastructures [78] as well as to institutional factors [79]. More deeply, as for the transport field, according to Carlucci et al. [78] the Southern Italy suffers from an infrastructure and logistic gap compared to Northern Italian regions and, in the same regions, bureaucracy is less efficient in terms of costs and time required [80]. Moreover, widespread differences among Italian macro- areas are also shown in terms of corruption. Indeed, 6 of the 7 Southern regions have the number of reported crimes higher than the national average, meaning a high index of corruption that is a relevant issue in transport infrastructure financing and service provision [81, 82, 78]. To summarize, the main result of this study not only confirms the well known differences in endow- ments of institutional quality among Italian provinces, but it pointed out, for the first time, that these differenc- es also affect economic performances, specifically the LP in the Italian vineyard farms. The impacts of control variables assessed in this research, except for the “young” one, are also significant and they are in line with scientific evidences. First, the higher capital endowment, both in terms of machines and financial capital, increases the LP. These results are consistent with previous studies in which the mechani- zation at farm level is a very critical tool for enhancing economic productivity [58, 66]. Mechanization improves value created per workers in two ways: i) reducing the hard labor (and, consequently, drudgery) and ii) improving gross marketable output through the time- less of agricultural operations [59, 63]. Conversely, the un-mechanized agriculture reveals much more negative economic performances [60, 64]. On the other hand, the availability of financial capital is helpful in purchase inputs of production, such as fertilizers and pesticides. Indeed, a good amount of economic capital allows a huge consistency of fertilizers and pesticides increasing crop yield and, once again, the gross marketable output per workers [62]. Likewise, the endowment of irrigated hectares may enhance value created reducing the risk of yield loss in vineyard farms located in the Mediterrane- an area, where a deficit of irrigation reduces the yield of grape [61]. As for the second pillar founding, the model output has shown a positive impact on LP. It is a natural result since several measures of the second pillar of the CAP providing physical investments1 could enhance the 1 http://www.europarl.europa.eu/factsheets/en/sheet/110/second-pillar- of-the-cap-rural-development-policy 120 Maria Raimondo, Concetta Nazzaro, Annamaria Nifo, Giuseppe Marotta output per workers. A positive role on value creation is also played by the land-labor ratio variable, in agreement with Urgessa [62] and Fuglie [65]. The latter highlighted that the growth of population in rural areas-through the decline of the ratio between land and labor - can reduce the average output per workers [65]. 6. CONCLUSIONS AND POLICY IMPLICATIONS The present study analyses, for the first time, the effect of macroeconomic aspects, e.g. the quality of insti- tution, on labor productivity in Italian farms. To this end, we built a cross-section dataset of overall 773 Ital- ian farms specialized in viticulture and located in 71 Italian provinces, where both micro and macroeconom- ic aspects are considered. Then, data were analyzed by means of a Gaussian log-linear model in order to grasp the effect of the institutional quality on LP. Despite some limitations, among the others the specificity of the farms (vineyard farms) considered for the research and the type of the dataset used (cross-section), results assign a critical role to the business environment and institutional quality into determining labor productiv- ity differentials in Italian vineyard farms, in accordance with previous conceptualizations and empirical studies. This means that the economic performance of vineyard farms does not depend on internal resources of farms solely, but it is also affected by the quality of institutions in which farms operate. However, the variables (which we have shown to have a significant and positive impact on LP) that were used in the present study to describe the institutional quality, are not managed by farmers neither by the CAP instruments. As a consequence, the findings of the present study have theoretical and politi- cal implications. As for the former, a wide discussion can be found in pervious publications where the role of institutional context on economic performances of farms is discussed [83, 84, 69, 51]. As for the political implica- tions, it should be emphasized that critical aspects for the agricultural development, such as infrastructure facilities, bureaucracy efficiency and business environ- ment, are not influenced by the CAP. However, in the last decades, the policy makers have considered the sec- ond pillar of the CAP the only available tool to enhance the rural development, without considering the general EU development strategies. These latter, meaning the European Regional Development Found (ERDF) and the European Social Found (ESF), were indeed never inte- grated within the European Agricultural Fund for Rural Development (EAFRD), since they are almost exclusively implemented in urban areas. 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Province Number of farms Province Number of farms Province Number of farms Province Number of farms Agrigento 3 Firenze 22 Palermo 1 Salerno 1 Alessandria 55 Foggia 1 Pavia 4 Sassari 4 Ancona 17 Forlì-Cesena 1 Perugia 26 Siena 3 Aosta 51 Genova 3 Pesaro e Urbino 4 Sondrio 6 Arezzo 5 Gorizia 20 Pescara 10 Taranto 7 Ascoli Piceno 20 Grosseto 15 Piacenza 1 Teramo 9 Asti 57 Imperia 4 Pisa 1 Terni 17 Avellino 1 Isernia 9 Pistoia 1 Torino 1 Benevento 15 La Spezia 6 Pordenone 42 Trapani 3 Bergamo 5 Latina 1 Potenza 2 Trento 21 Bologna 3 Lecce 3 Prato 1 Treviso 15 Bolzano/Bozen 18 Lucca 1 Ragusa 2 Trieste 1 Brescia 12 Macerata 1 Ravenna 5 Udine 41 Brindisi 38 Mantova 7 Reggio di Calabria 2 Venezia 9 Cagliari 8 Modena 4 Reggio nell’Emilia 3 Verona 19 Caserta 3 Novara 1 Rieti 1 Vicenza 7 Chieti 36 Nuoro 1 Rimini 1 Viterbo 5 Cuneo 41 Padova 6 Roma 3     Source: FADN dataset. APPENDIX B Table 2A. Structure of elementary IQI indexes Index Value Source (details in notes) Year Voice and accountability Social cooperatives Absolute Value1 ISTAT 2001 Associations Absolute Value1 ISTAT 2004 Election participation Turnout %2 Interior Ministry 2001 Books published Absolute Value3 ISTAT 2007 Purchased in bookshops Index4 Sole24Ore 2004 Government effectiveness Endowment of social facilities Index5 Tagliacarne 2001 Endowment of econ. facilities Index6 Tagliacarne 2001 Regional health deficit Absolute Value7 MEF and MH 1997-2004 Separate waste collection Separate/total8 Tagliacarne 2007 Urban environment index Index9 Legambiente 2004 Regulatory quality Economy openness Index10 Tagliacarne 2001 Local government employees Absolute Value11 ISTAT 2003 Business density Index12 Tagliacarne 2008 Business start-ups/mortality Registration/cessation13 Tagliacarne 2003-2004 Business environment Index14 Confartigianato 2009 125Does the Institutional Quality Affect Labor Productivity in Italian Vineyard Farms? Rule of law Crimes against property Absolute Value15 ISTAT 2003 Crimes reported Absolute Value16 ISTAT 2003 Trial times Trial lengths I, II, III17 Crenos 1999 Magistrate productivity MagistrateTrials18 Ministry of Justice 2004-2008 Submerged economy Tax evasion Index19 Index20 ISTAT Revenue Agency 2003 1998-2002 Corruption Crimes against PA Index21 Interior Ministry & ISTAT 2004 Golden-Picci Index Index22 Golden and Picci (2005) 1997 Special Commissioners Municipalities overruled23 Interior Ministry 1991-2005 Notes:1Social cooperatives per 100,000 residents, provincial level. ISTAT: “Le cooperative sociali in Italia” (2006) and “Le organizzazioni di volontariato in Italia” (2005); 22001 general election, provincial level. Interior Ministry: “Archivio storico delle elezioni” http://elezionis- torico.interno.it/ ; 3Books published, in absolute value, provincial level. ISTAT: “La produzione libraia” (2007); 4Purchased books over resi- dent population, provincial level. Il Sole24Ore “Dossier sulla qualità della vita” (2004); 5Includes education, healthcare and leisure facilities, provincial level.Tagliacarne Institute “Atlante di competitività delle province italiane” (2001); 6Includes the following networks: roads, rail- roads, ports, airports, energy, ICT, banking, provincial level. Tagliacarne Institute “Atlante di competitività delle provincie italiane” (2001); 7Regional health deficit per capita 1997-2004, regional level. Elaboration on Ministry of Economy and Finance and Ministry of Health data from “Relazione generale sulla situazione economica del Paese” (1997-2004); 8Share of separate waste collection on total waste collection, provincial level. Tagliacarne Institute “Atlante di competitività delle province italiane” (2001); 9Includes 25 indexes relative to: air quality, water quality, purification plants, waste management, public transportation, energy consumption, Public parks, Eco management, provincial level. Legambiente “Ecosistema Urbano 2004” (2004); 10Import + Export on the gross domestic product, provincial level. Tagliacarne Insti- tute “Atlante di competitività delle provincie italiane” (2001); 11Public servants over resident population, regional level. ISTAT: “Indicatori statistici sulle amministrazioni centrali e locali” (2003) http://dati.statistiche-pa.it/ ; 12Number of firms for 100 residents, provincial level. Tagliacarne Institute “Atlante di competitività delle province italiane” (2008); 13Firms registration/mortality, provincial level. Tagliacarne Institute “Atlante di competitività delle province italiane” (2003-2004); 14Includes 39 indexes relative to: entrepreneurship, job Market, tax system, market competition, banking, bureaucracy; public services to firms, firms’ cooperation, provincial level. Confartigianato: “L’indice Confartigianato – Qualità della vita dell’impresa” (2009); 15Number of crimes against property over resident population, provincial level. ISTAT: “Indicatori territoriali per le politiche di sviluppo” (2003); 16Number of crimes reported over resident population, provincial level. ISTAT: “Indicatori territoriali per le politiche di sviluppo” (2003); 17Average length of judicial process, regional level.CRENOS “Data-base on crime and deterrence in the Italian regions (1970-1999)”; 18Number of completed civil and criminal trials for magistrate, regional courts level. Ministry of Justice, statistics: “Graduatoria rispetto agli esauriti per magistrato presente” (2004-2008); 19ISTAT estimation, provincial level.ISTAT: “Le misure dell’economia sommersa secondo le statistiche ufficiali” (2003); 20Based on the difference between the estimated added value by national accounts and tax system (IRAP and individual income tax returns), provincial level. Agenzia delle entrate: “Analisi dell’evasione fondata su dati IRAP, Anni 1998-2002” (2006); 21Number of crimes against the public administration over number of public servants, regional level. ISTAT: “Indicatori territoriali per le politiche di sviluppo” (2004); 22Difference between the amounts of physically existing public infrastructure and the amounts of money cumulatively allocated by government to create these public works, provincial level. Golden and Picci (2005); 23Absolute value of the overruled municipalities on total municipalities, regional level. Interior Ministry: “Relazione sull’attività svolta dalla gestione straordinaria dei Comuni commissariati” (1991-2005). Table 3A. The Institutional Quality Index of considered provinces in 2012. Province IQI Province IQI Province IQI Province IQI Agrigento 0.2135 Firenze 1 Palermo 0.1998 Salerno 0.5378 Alessandria 0.6651 Foggia 0.3511 Pavia 0.6229 Sassari 0.4713 Ancona 0.7505 Forlì-Cesena 0.7719 Perugia 0.7572 Siena 0.877 Aosta 0.7469 Genova 0.5228 Pesaro e Urbino 0.7524 Sondrio 0.6969 Arezzo 0.8635 Gorizia 0.775 Pescara 0.6235 Taranto 0.3795 Ascoli Piceno 0.6794 Grosseto 0.7928 Piacenza 0.7435 Teramo 0.7788 Asti 0.6614 Imperia 0.4221 Pisa 0.8757 Terni 0.7312 Avellino 0.4538 Isernia 0.2001 Pistoia 0.7705 Torino 0.6823 Benevento 0.5197 La Spezia 0.6083 Pordenone 0.703 Trapani 0.147 Bergamo 0.7405 Latina 0.5209 Potenza 0.3976 Trento 0.873 Bologna 0.695 Lecce 0.4937 Prato 0.8179 Treviso 0.7935 Bolzano/Bozen 0.8553 Lucca 0.8504 Ragusa 0.2887 Trieste 0.7984 126 Maria Raimondo, Concetta Nazzaro, Annamaria Nifo, Giuseppe Marotta Brescia 0.7029 Macerata 0.7209 Ravenna 0.8135 Udine 0.698 Brindisi 0.4459 Mantova 0.729 Reggio di Calabria 0.0398 Venezia 0.7247 Cagliari 0.3927 Modena 0.7035 Reggio nell’Emilia 0.7126 Verona 0.7312 Caserta 0.411 Novara 0.7585 Rieti 0.5958 Vicenza 0.7186 Chieti 0.8574 Nuoro 0.4515 Rimini 0.7645 Viterbo 0.5397 Cuneo 0.8075 Padova 0.7308 Roma 0.7297     Source: 9. Table 4A. The average IQI at region level in 2012. Italian regions Italian macro- area Average IQI Trentino Alto Adige Northern 0.8642 Tuscany Central 0.8109 Abruzzo Southern 0.8020 Valle D’Aosta Northern 0.7469 Veneto Northern 0.7452 Emilia Romagna Northern 0.7436 Umbria Central 0.7396 Friuli Venzia Giulia Northern 0.7158 Lombardy Northern 0.7033 Piedmont Northern 0.7021 Marche Central 0.6955 Lazio Central 0.5831 Liguria Northern 0.5313 Campania Southern 0.5010 Apulia Southern 0.4374 Sardinia Southern 0.4214 Basilicata Southern 0.3976 Sicily Southern 0.2065 Molise Southern 0.2001 Calabria Southern 0.0398 Total 0.6898 Source: our elaborations on data by Nifo and Vecchione (2014). Wine Economics and Policy Volume 9, Issue 2 - 2020 Firenze University Press The Influence of Alcohol Warning Labels on Consumers’ Choices of Wine and Beer Azzurra Annunziata1,*, Lara Agnoli2, Riccardo Vecchio3, Steve Charters4, Angela Mariani5 A Bad Year? Climate Variability and the Wine Industry in Chile Eduardo Haddad1,*, Patricio Aroca2, Pilar Jano3, Ademir Rocha4, Bruno Pimenta5 Sparkling Wine International Market Structure and Competitiveness Karim Marini Thome*, Vitoria A. Leal Paiva The Role of Context Definition in Choice Experiments: a Methodological Proposal Based on Customized Scenarios Fabio Boncinelli*, Caterina Contini, Francesca Gerini, Caterina Romano, Gabriele Scozzafava, Leonardo Casini The Impact of Country of Origin on Brand Equity: An Analysis of The Wine Sector Nádia Passagem1, Cátia Fernandes Crespo2,*, Nuno Almeida3 Competitive Strategies for Wine Cooperatives in the German Wine Industry Barbara Richter1,*, Jon Hanf2 Valuation of Viticultural Adaptation to Climate Change in Vineyards: A Discrete Choice Experiment to Prioritize Trade-Offs Perceived by Citizens Verónica Farreras1,2, Laura Abraham3,* Does the Institutional Quality Affect Labor Productivity in Italian Vineyard Farms? Maria Raimondo1,*, Concetta Nazzaro4, Annamaria Nifo3, Giuseppe Marotta2 The Role, Scope and Management of R&D and Innovation in the Wine Sector: an Interview with Antonio Graca Peter Hayes AM