Putting Precarity Back to Production: A Case Study of Didi Kuaiche Drivers in the City of Nanjing, China

Qi, Hao; Li, Zhongjin

doi:10.1177/0486613419859030

Cited by 12 publications

(12 citation statements)

References 31 publications

(32 reference statements)

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“…When considering the kinds of (algorithmic) control and precarity enacted by food delivery platforms, more critical attention should be focused on the everyday conditions under which a wage can be earned and the loss of grip on these conditions, as well as on a controllable future more generally (cf. Alberti et al, 2018;Qi & Li, 2019).…”

Section: Reconfiguring the Courier's Wagementioning

confidence: 99%

At what price? Labour politics and calculative power struggles in on-demand food delivery

Doorn¹

2020

Work Organisation, Labour &Amp; Globalisation

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This article asks what can be gained by making calculability a pivotal demand for wage politics in gig economies characterised by dynamic fee structures. It examines how a small group of Berlin-based food delivery workers attempted to challenge Deliveroo's market power by building their own makeshift calculative equipment to help them ‘reverse engineer’ the company's fee pricing algorithm. It then documents the difficulties these ‘riders’ experienced when attempting to translate their fight for calculable earnings into a more comprehensive labour politics. Finally, the article addresses the limits of market-based struggles over calculative power, against the background of a still pervasive ‘platform exceptionalism’: a socio-legal imaginary that treats platform companies as unique business entities, enabling them to engage in regulatory arbitrage while contractually enforcing the subordination and rightlessness of gig workers.

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Section: Reconfiguring the Courier's Wagementioning

confidence: 99%

At what price? Labour politics and calculative power struggles in on-demand food delivery

Doorn¹

2020

Work Organisation, Labour &Amp; Globalisation

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“…Although the empirical analysis above presents the weak profitability of labor platforms—ubiquitous across geographical locations and persistent over years—it seems to contradict many academic findings and public concerns that gig workers on these labor platforms are largely undercompensated and overworked (Montalban, Frigant, and Jullien 2019; Qi and Li 2020). In order to reconcile and make sense of the contradiction between the extractive labor practice and the low actual profitability of labor platform companies, we argue in the following section that the profitability puzzle can be explained by the modes of production and competition, two critical components underlying digital labor platforms.…”

Section: Stylized Factsmentioning

confidence: 89%

The Profitability Puzzle of Digital Labor Platforms

2023

Review of Radical Political Economics

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By examining digital labor platforms that connect customers with gig workers, we attempt to solve the puzzle concerning why these labor platforms, such as ride-hailing companies and food delivery companies, continue to suffer weak profitability even though they promise a more efficient way to mobilize and utilize the platform’s labor force. Stylized facts on the profitability of the top ten global labor platforms and a descriptive analysis of their cost structures are presented. We further argue that the modes of production and competition inherent in digital labor platforms are critical to this profitability puzzle because they determine the upper limit of surplus value extraction in production and the heavy financial burden of profit generation for labor platforms. JEL Classification: P12, E24, D40

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“…• Multilayer Precipitation (MLP): In the four fully connected neural networks, the layers are connected with neurons with 128, 128, 64 and 75, respectively. By considering the corresponding channels of demand matrices of previous n time intervals as input, the MLP predicts each channel of X in the time interval t (Qi & Li, 2020).…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

“…Due to the convenience it provides for our daily journeys, online cab riding services such as Didi Uber, NYCTaxi, and others have seen a remarkable development in recent years. This large business, on the other hand, it is all affected by certain unsustainable operations (Harrison et al, 2020;Qi & Li, 2020), that is, long…”

mentioning

confidence: 99%

Regional‐based multi‐module spatial–temporal networks predicting city‐wide taxi pickup/dropoff demand from origin to destination

et al. 2021

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Taxi demand forecasting from origin to destination (OD) is an important component in managing public transportation needs on a city-wide scale. Accurate taxi demand forecasting may provide several benefits, including economic and traffic flow optimization. However, due to complicated spatial-temporal connections and irregular distant locations, predicting taxi demand becomes difficult. To address these issues, we proposed a novel architecture of multi-module spatial-temporal networks to collectively predict city-wide OD taxi demand. In our work to deal with adjacent areas in a city, we employed the 3D convolutional neural networks to extract the spatialtemporal dependencies and learn the OD taxi demand pattern. To handle remote areas, we created an attention-based auto encoder-decoder, in which the input of the set of convolutional layers creates a feature matrix. The feature matrix embeds the spatial-temporal correlation jointly and passing through the encoder layer. We encode the spatial-temporal features with the help of pooling layer, then flatten layer used the back-propagation method to decode the weight matrix. We apply the normalization function to determine the demand pattern influences. The influence vector we compute with the Euclidean distance formula to determine the similarity of all distant regions. Finally, we use the attention mechanism to calculate the attention weight score for each region that its neighbour impacted. Then we used long shortterm memory, we captured the significant relationship of spatial-temporal dependencies with external factors. We train our model simultaneously to forecast city-wide taxi services OD. We have performed comprehensive experiments and large-scale dataset comparisons that reveal the taxi demand prediction problem.attention-based auto encoder-decoder, multi-module, origin-destination | INTRODUCTIONTaxis, being one of the most common modes of urban transportation, have had an influence on people's daily life. Due to the convenience it provides for our daily journeys, online cab riding services such as Didi Uber, NYCTaxi, and others have seen a remarkable development in recent years. This large business, on the other hand, it is all affected by certain unsustainable operations (Harrison et al., 2020;Qi & Li, 2020), that is, long

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Putting Precarity Back to Production: A Case Study of Didi Kuaiche Drivers in the City of Nanjing, China

Cited by 12 publications

References 31 publications

At what price? Labour politics and calculative power struggles in on-demand food delivery

At what price? Labour politics and calculative power struggles in on-demand food delivery

The Profitability Puzzle of Digital Labor Platforms

Regional‐based multi‐module spatial–temporal networks predicting city‐wide taxi pickup/dropoff demand from origin to destination

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