# **4. Methodology**

# **4.1. Data Sources**

# _Micro Data_

The main database for our analysis is the second Employment and Innovation Dynamics National Survey (ENDEI 2). The use of this data set is completely novel. Access was granted to us by the National Directorate of Scientific Information (Dirección Nacional de Información Científica, DNIC) and the Under-secretariat of Studies and Prospective (Subsecretaría de Estudios y Prospectiva), specifically for the needs of this project.<sup>14</sup> This survey covers the 2014–16 period and was carried out jointly by the Labor and Employment Secretariat (Secretaría de Trabajo y Empleo) and the Science, Technology and Productive Innovation Secretariat<sup>15</sup> (Secretaría de Ciencia, Tecnología e Innovación Productiva). The sample was drawn to be representative of manufacturing firms with at least 10 employees, in terms of size (small, medium, and large firms), region (five geographical areas), and sector (mostly at the 2- digit ISIC level).<sup>16</sup> The sample includes 3,945 firms. For some exercises, we used the previous ENDEI (hereafter ENDEI 1), comprising 3,691 firms, which covers the period 2010–12 and which is representative at the size and sectoral level. We could not use panel data because both waves were not matched at the firm level.<sup>17</sup> The ENDEI has two structured questionnaires, one self-administered and one that requires a face-to-face interview. The former contains questions that require inputs from different areas of the firm: income; expenses (such as wages and salaries, intermediate consumption, and investment in fixed assets);<sup>18</sup> employment (according to hierarchies and qualification); and remuneration and spending in innovation activities (such as R&D, consultancy, and acquisition of machinery and equipment). The latter contains mainly qualitative information on several issues regarding innovation and employment dynamics: organizational capability and business 14 We were allowed access to this information with an agreement in 2019 between the World Bank and the Ministry of Production and Labor (Ministerio de Producción y Trabajo). The database was completely anonymized and prepared specifically for the needs of this paper. In order to respect the confidentiality agreement with the firms surveyed, we worked with a computer in the offices of the DNIC, under constant supervision of the team.

15 The former Ministry of Science and Technology, which was transformed into a Secretariat in September 2018. At the end of 2019, the institution returned to a Ministry structure again.

16 ISIC, the International Standard Industrial Classification of All Economic Activities, is the international reference classification of productive activities. The following sectors are included in this study <mark>: Food, beverages and tobacco; Chemicals and petrochemicals; Pharmaceutical; Basic metals; Motor vehicles, ships and other transport equipment; Paper and publishing; Rubber and plastic; Machinery and equipment; Textiles and wearing apparel; Electrical machinery and apparatus, TV and radio equipment; Wood and products of wood; Leather and footwear; and Other i</mark> ndustries. For some sectors of special interest, information was disaggregated at 4 digits (Food and beverages; Chemicals; Machinery and equipment and Motor vehicles). The five regions were: Patagonia (including the provinces of Chubut, Neuquén, Rio Negro, Santa Cruz and Tierra del Fuego); Cuyo (including the provinces of Mendoza, San Juan and San Luis); the Northern region (including <mark>Chaco, Corrientes, Formosa, Misiones, Catamarca Jujuy, La Rioja, Salta, Santiago del Estero and Tucuman); Pampeana (including Buenos Aires, Cordoba, Entre Rios, Santa Fe, and La Pampa); and the region of the Capital city and suburbs.</mark> 17 However, it is worth highlighting that sampling methods changed between both waves. In this respect, only ENDEI 2 is relevant for our exercise of assessing the role of regional/sectoral contextual factors because ENDEI 1 is not representative at the regional level.

18 Unfortunately, neither information for energy consumption nor investment in physical assets was correctly measured in ENDEI 2. Although those questions were included in the questionnaire, data were not made available because they include severe measurement errors. We needed to develop strategies to overcome these data constraints to estimate capital stock in the production functions, as discussed later in the paper.

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