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Determining economic factors for sex trafficking in the United States using count time series regression

Author

Listed:
  • Yuhyeong Jang

    (Southern Methodist University)

  • Raanju R. Sundararajan

    (Southern Methodist University)

  • Wagner Barreto-Souza

    (University College Dublin)

  • Elizabeth Wheaton-Paramo

    (Southern Methodist University)

Abstract

The article presents a robust quantitative approach for determining significant economic factors for sex trafficking in the United States. The aim is to study monthly counts of sex trafficking-related convictions, and use a wide range of economic variables as covariates to investigate their effect on conviction counts. A count time series model is considered along with a regression setup to include economic time series as covariates (economic factors) to explain the counts on sex trafficking-related convictions. The statistical significance of these economic factors is investigated and the significant factors are ranked based on appropriate model selection methods. The inclusion of time-lagged versions of the economic factor time series in the regression model is also explored. Our findings indicate that economic factors relating to immigration policy, consumer price index and labor market regulations are the most significant in explaining sex trafficking convictions.

Suggested Citation

  • Yuhyeong Jang & Raanju R. Sundararajan & Wagner Barreto-Souza & Elizabeth Wheaton-Paramo, 2024. "Determining economic factors for sex trafficking in the United States using count time series regression," Empirical Economics, Springer, vol. 67(1), pages 337-354, July.
    • Handle: RePEc:spr:empeco:v:67:y:2024:i:1:d:10.1007_s00181-023-02549-w
    DOI: 10.1007/s00181-023-02549-w
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    References listed on IDEAS

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    More about this item

    Keywords

    Count times series; INGARCH model; Human trafficking; Sex trafficking; Economic factors;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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