Cross Border Data Flows: The impact of data localisation on IoT

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CROSS-BORDER DATA FLOWS: THE IMPACT OF DATA LOCALISATION ON IOT How the scenarios are defined? A two-step approach to assess <strong>data</strong> <strong>localisation</strong> effects on IoT. <strong>The</strong> GTAP model in this study builds on two scenarios. In the first, IoT increases the productivity <strong>of</strong> the global economy. In the second, DLRs reduce the productivity gains achieved from IoT for the countries surveyed as part <strong>of</strong> this study. Figure 1 Schematic illustration <strong>of</strong> the scenarios in this study. Scenario 1 introduces IoT gains, while Scenario 2 illustrates the losses from DLRs. Scenario 2: losses <strong>Data</strong> <strong>localisation</strong> affecting IoT reducing productivity (by increasing costs) Scenario 1: gains IoT increasing productivity (by boosting efficiency) 12
CROSS-BORDER DATA FLOWS: THE IMPACT OF DATA LOCALISATION ON IOT Scenario 1: IoT generates new gains compared to today. <strong>The</strong>re are two types <strong>of</strong> efficiency gains from IoT deployment in our model: the productivity gains within the economy; and the trade-enhancing effects when the economy trades with other economies. In the first instance, we shock the model by using the productivity gains from GSMA Intelligence (2019) to Brazil, Indonesia, South Africa and the rest <strong>of</strong> the world that agglomerates all other economies in the model. <strong>The</strong> productivity shocks are introduced as TFP increases that apply to the domestic economy. In other words, we assume that the global economy deploys IoT applications according to the GSMA Intelligence (2019) calculations for the year 2025. Obviously, this baseline assumes ceteris paribus for all other technologies, policy measures or economic developments aside from the extrapolation described in the previous chapter. <strong>The</strong> TFP shocks from the GSMA Intelligence study are: Table 1 Domestic productivity increase due to IoT. Percentage change in the respective country/region from IoT technology. Total Factor Productivity (TFP) change (%) Brazil 0.27 Indonesia 0.24 South Africa 0.17 Rest <strong>of</strong> the world 0.34 In addition to the domestic shocks, we also introduce a productivity effect ‘at the border’ for imports <strong>of</strong> trade and services from overseas. This trade efficiency <strong>impact</strong> <strong>of</strong> IoT applies concurrently with the domestic productivity increase identified by GSMA Intelligence (2019). <strong>The</strong> trade efficiency-enhancing effect from IoT is based on the <strong>impact</strong> <strong>of</strong> connectivity on trade which was identified in the WTO study cited above. How IoT reduces the import prices <strong>of</strong> goods and services and increase product variety is estimated for a similar point in time, i.e. after approximately seven years .25 Table 2 Import efficiency due to IoT. Percentage change in the respective country/region from IoT technology. Trade cost change: AMS (%) Brazil -0.68 Indonesia -1.88 South Africa -4.82 Rest <strong>of</strong> the world -0.62 25. This trade efficiency shock is denoted Aggregate Measurement <strong>of</strong> Support (AMS) shock in the GTAP model 13
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Cross Border Data Flows: The impact of data localisation on IoT

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